Ben Bolstad
| Snapshot Date: 2018-10-15 16:45:08 -0400 (Mon, 15 Oct 2018) |
| URL: https://git.bioconductor.org/packages/BufferedMatrix |
| Branch: RELEASE_3_7 |
| Last Commit: 6087ec6 |
| Last Changed Date: 2018-04-30 10:35:06 -0400 (Mon, 30 Apr 2018) |
| Back to Multiple platform build/check report for BioC 3.7 |
|
This page was generated on 2018-10-17 08:33:06 -0400 (Wed, 17 Oct 2018).
| Package 172/1561 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| BufferedMatrix 1.44.0 Ben Bolstad
| malbec2 | Linux (Ubuntu 16.04.1 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ OK ] | OK | | ||||||
| merida2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | |
| Package: BufferedMatrix |
| Version: 1.44.0 |
| Command: C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:BufferedMatrix.install-out.txt --library=C:\Users\biocbuild\bbs-3.7-bioc\R\library --no-vignettes --timings BufferedMatrix_1.44.0.tar.gz |
| StartedAt: 2018-10-17 00:51:18 -0400 (Wed, 17 Oct 2018) |
| EndedAt: 2018-10-17 00:52:22 -0400 (Wed, 17 Oct 2018) |
| EllapsedTime: 63.4 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: BufferedMatrix.Rcheck |
| Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:BufferedMatrix.install-out.txt --library=C:\Users\biocbuild\bbs-3.7-bioc\R\library --no-vignettes --timings BufferedMatrix_1.44.0.tar.gz ### ############################################################################## ############################################################################## * using log directory 'C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck' * using R version 3.5.1 Patched (2018-07-24 r75005) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * using option '--no-vignettes' * checking for file 'BufferedMatrix/DESCRIPTION' ... OK * this is package 'BufferedMatrix' version '1.44.0' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'BufferedMatrix' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... NOTE prepare_Rd: createBufferedMatrix.Rd:26: Dropping empty section \keyword prepare_Rd: createBufferedMatrix.Rd:17-18: Dropping empty section \details prepare_Rd: createBufferedMatrix.Rd:15-16: Dropping empty section \value prepare_Rd: createBufferedMatrix.Rd:19-20: Dropping empty section \references prepare_Rd: createBufferedMatrix.Rd:21-22: Dropping empty section \seealso prepare_Rd: createBufferedMatrix.Rd:23-24: Dropping empty section \examples * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking compiled code ... NOTE Note: information on .o files for i386 is not available Note: information on .o files for x64 is not available File 'C:/Users/biocbuild/bbs-3.7-bioc/R/library/BufferedMatrix/libs/i386/BufferedMatrix.dll': Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran) Compiled code should not call entry points which might terminate R nor write to stdout/stderr instead of to the console, nor use Fortran I/O nor system RNGs. The detected symbols are linked into the code but might come from libraries and not actually be called. See 'Writing portable packages' in the 'Writing R Extensions' manual. * checking files in 'vignettes' ... OK * checking examples ... NONE * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... Running 'Rcodetesting.R' Running 'c_code_level_tests.R' Running 'objectTesting.R' Running 'rawCalltesting.R' OK ** running tests for arch 'x64' ... Running 'Rcodetesting.R' Running 'c_code_level_tests.R' Running 'objectTesting.R' Running 'rawCalltesting.R' OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 2 NOTEs See 'C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/00check.log' for details.
BufferedMatrix.Rcheck/00install.out
##############################################################################
##############################################################################
###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.7/bioc/src/contrib/BufferedMatrix_1.44.0.tar.gz && rm -rf BufferedMatrix.buildbin-libdir && mkdir BufferedMatrix.buildbin-libdir && C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=BufferedMatrix.buildbin-libdir BufferedMatrix_1.44.0.tar.gz && C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD INSTALL BufferedMatrix_1.44.0.zip && rm BufferedMatrix_1.44.0.tar.gz BufferedMatrix_1.44.0.zip
###
##############################################################################
##############################################################################
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 201k 100 201k 0 0 3118k 0 --:--:-- --:--:-- --:--:-- 3532k
install for i386
* installing *source* package 'BufferedMatrix' ...
** libs
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c RBufferedMatrix.c -o RBufferedMatrix.o
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c doubleBufferedMatrix.c -o doubleBufferedMatrix.o
doubleBufferedMatrix.c: In function 'dbm_ReadOnlyMode':
doubleBufferedMatrix.c:1580:7: warning: suggest parentheses around operand of '!' or change '&' to '&&' or '!' to '˜' [-Wparentheses]
if (!(Matrix->readonly) & setting){
^
doubleBufferedMatrix.c: At top level:
doubleBufferedMatrix.c:3327:12: warning: 'sort_double' defined but not used [-Wunused-function]
static int sort_double(const double *a1,const double *a2){
^
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c doubleBufferedMatrix_C_tests.c -o doubleBufferedMatrix_C_tests.o
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c init_package.c -o init_package.o
C:/Rtools/mingw_32/bin/gcc -shared -s -static-libgcc -o BufferedMatrix.dll tmp.def RBufferedMatrix.o doubleBufferedMatrix.o doubleBufferedMatrix_C_tests.o init_package.o -LC:/extsoft/lib/i386 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.buildbin-libdir/BufferedMatrix/libs/i386
** R
** inst
** byte-compile and prepare package for lazy loading
Creating a new generic function for 'rowMeans' in package 'BufferedMatrix'
Creating a new generic function for 'rowSums' in package 'BufferedMatrix'
Creating a new generic function for 'colMeans' in package 'BufferedMatrix'
Creating a new generic function for 'colSums' in package 'BufferedMatrix'
Creating a generic function for 'ncol' from package 'base' in package 'BufferedMatrix'
Creating a generic function for 'nrow' from package 'base' in package 'BufferedMatrix'
** help
*** installing help indices
converting help for package 'BufferedMatrix'
finding HTML links ... done
BufferedMatrix-class html
as.BufferedMatrix html
createBufferedMatrix html
** building package indices
** installing vignettes
** testing if installed package can be loaded
In R CMD INSTALL
install for x64
* installing *source* package 'BufferedMatrix' ...
** libs
C:/Rtools/mingw_64/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mtune=generic -c RBufferedMatrix.c -o RBufferedMatrix.o
C:/Rtools/mingw_64/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mtune=generic -c doubleBufferedMatrix.c -o doubleBufferedMatrix.o
doubleBufferedMatrix.c: In function 'dbm_ReadOnlyMode':
doubleBufferedMatrix.c:1580:7: warning: suggest parentheses around operand of '!' or change '&' to '&&' or '!' to '˜' [-Wparentheses]
if (!(Matrix->readonly) & setting){
^
doubleBufferedMatrix.c: At top level:
doubleBufferedMatrix.c:3327:12: warning: 'sort_double' defined but not used [-Wunused-function]
static int sort_double(const double *a1,const double *a2){
^
C:/Rtools/mingw_64/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mtune=generic -c doubleBufferedMatrix_C_tests.c -o doubleBufferedMatrix_C_tests.o
C:/Rtools/mingw_64/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mtune=generic -c init_package.c -o init_package.o
C:/Rtools/mingw_64/bin/gcc -shared -s -static-libgcc -o BufferedMatrix.dll tmp.def RBufferedMatrix.o doubleBufferedMatrix.o doubleBufferedMatrix_C_tests.o init_package.o -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.7-B/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.buildbin-libdir/BufferedMatrix/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'BufferedMatrix' as BufferedMatrix_1.44.0.zip
* DONE (BufferedMatrix)
In R CMD INSTALL
In R CMD INSTALL
* installing to library 'C:/Users/biocbuild/bbs-3.7-bioc/R/library'
package 'BufferedMatrix' successfully unpacked and MD5 sums checked
In R CMD INSTALL
|
BufferedMatrix.Rcheck/tests_i386/c_code_level_tests.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix", "BufferedMatrix", .libPaths());.C("dbm_c_tester",integer(1))
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
Adding Additional Column
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
0.000000 1.000000 2.000000 3.000000 4.000000 0.000000
1.000000 2.000000 3.000000 4.000000 5.000000 0.000000
2.000000 3.000000 4.000000 5.000000 6.000000 0.000000
3.000000 4.000000 5.000000 6.000000 7.000000 0.000000
4.000000 5.000000 6.000000 7.000000 8.000000 0.000000
Reassigning values
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Resizing Buffers
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 3
Buffer Cols: 3
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Activating Row Buffer
In row mode: 1
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Squaring Last Column
1.000000 6.000000 11.000000 16.000000 21.000000 676.000000
2.000000 7.000000 12.000000 17.000000 22.000000 729.000000
3.000000 8.000000 13.000000 18.000000 23.000000 784.000000
4.000000 9.000000 14.000000 19.000000 24.000000 841.000000
5.000000 10.000000 15.000000 20.000000 25.000000 900.000000
Square rooting Last Row, then turing off Row Buffer
In row mode: 0
Checking on value that should be not be in column buffer2.236068
1.000000 6.000000 11.000000 16.000000 21.000000 676.000000
2.000000 7.000000 12.000000 17.000000 22.000000 729.000000
3.000000 8.000000 13.000000 18.000000 23.000000 784.000000
4.000000 9.000000 14.000000 19.000000 24.000000 841.000000
2.236068 3.162278 3.872983 4.472136 5.000000 30.000000
Single Indexing. Assign each value its square
1.000000 36.000000 121.000000 256.000000 441.000000 676.000000
4.000000 49.000000 144.000000 289.000000 484.000000 729.000000
9.000000 64.000000 169.000000 324.000000 529.000000 784.000000
16.000000 81.000000 196.000000 361.000000 576.000000 841.000000
25.000000 100.000000 225.000000 400.000000 625.000000 900.000000
Resizing Buffers Smaller
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
1.000000 36.000000 121.000000 256.000000 441.000000 676.000000
4.000000 49.000000 144.000000 289.000000 484.000000 729.000000
9.000000 64.000000 169.000000 324.000000 529.000000 784.000000
16.000000 81.000000 196.000000 361.000000 576.000000 841.000000
25.000000 100.000000 225.000000 400.000000 625.000000 900.000000
Activating Row Mode.
Resizing Buffers
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
Activating ReadOnly Mode.
The results of assignment is: 0
Printing matrix reversed.
900.000000 625.000000 400.000000 225.000000 100.000000 25.000000
841.000000 576.000000 361.000000 196.000000 81.000000 16.000000
784.000000 529.000000 324.000000 169.000000 64.000000 9.000000
729.000000 484.000000 289.000000 144.000000 49.000000 -30.000000
676.000000 441.000000 256.000000 121.000000 -20.000000 -10.000000
[[1]]
[1] 0
>
> proc.time()
user system elapsed
0.45 0.03 0.46
|
BufferedMatrix.Rcheck/tests_x64/c_code_level_tests.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix", "BufferedMatrix", .libPaths());.C("dbm_c_tester",integer(1))
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
Adding Additional Column
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
0.000000 1.000000 2.000000 3.000000 4.000000 0.000000
1.000000 2.000000 3.000000 4.000000 5.000000 0.000000
2.000000 3.000000 4.000000 5.000000 6.000000 0.000000
3.000000 4.000000 5.000000 6.000000 7.000000 0.000000
4.000000 5.000000 6.000000 7.000000 8.000000 0.000000
Reassigning values
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Resizing Buffers
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 3
Buffer Cols: 3
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Activating Row Buffer
In row mode: 1
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Squaring Last Column
1.000000 6.000000 11.000000 16.000000 21.000000 676.000000
2.000000 7.000000 12.000000 17.000000 22.000000 729.000000
3.000000 8.000000 13.000000 18.000000 23.000000 784.000000
4.000000 9.000000 14.000000 19.000000 24.000000 841.000000
5.000000 10.000000 15.000000 20.000000 25.000000 900.000000
Square rooting Last Row, then turing off Row Buffer
In row mode: 0
Checking on value that should be not be in column buffer2.236068
1.000000 6.000000 11.000000 16.000000 21.000000 676.000000
2.000000 7.000000 12.000000 17.000000 22.000000 729.000000
3.000000 8.000000 13.000000 18.000000 23.000000 784.000000
4.000000 9.000000 14.000000 19.000000 24.000000 841.000000
2.236068 3.162278 3.872983 4.472136 5.000000 30.000000
Single Indexing. Assign each value its square
1.000000 36.000000 121.000000 256.000000 441.000000 676.000000
4.000000 49.000000 144.000000 289.000000 484.000000 729.000000
9.000000 64.000000 169.000000 324.000000 529.000000 784.000000
16.000000 81.000000 196.000000 361.000000 576.000000 841.000000
25.000000 100.000000 225.000000 400.000000 625.000000 900.000000
Resizing Buffers Smaller
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
1.000000 36.000000 121.000000 256.000000 441.000000 676.000000
4.000000 49.000000 144.000000 289.000000 484.000000 729.000000
9.000000 64.000000 169.000000 324.000000 529.000000 784.000000
16.000000 81.000000 196.000000 361.000000 576.000000 841.000000
25.000000 100.000000 225.000000 400.000000 625.000000 900.000000
Activating Row Mode.
Resizing Buffers
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
Activating ReadOnly Mode.
The results of assignment is: 0
Printing matrix reversed.
900.000000 625.000000 400.000000 225.000000 100.000000 25.000000
841.000000 576.000000 361.000000 196.000000 81.000000 16.000000
784.000000 529.000000 324.000000 169.000000 64.000000 9.000000
729.000000 484.000000 289.000000 144.000000 49.000000 -30.000000
676.000000 441.000000 256.000000 121.000000 -20.000000 -10.000000
[[1]]
[1] 0
>
> proc.time()
user system elapsed
0.48 0.09 0.56
|
|
BufferedMatrix.Rcheck/tests_i386/objectTesting.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
>
> ### this is used to control how many repetitions in something below
> ### higher values result in more checks.
> nreps <-100 ##20000
>
>
> ## test creation and some simple assignments and subsetting operations
>
> ## first on single elements
> tmp <- createBufferedMatrix(1000,10)
>
> tmp[10,5]
[1] 0
> tmp[10,5] <- 10
> tmp[10,5]
[1] 10
> tmp[10,5] <- 12.445
> tmp[10,5]
[1] 12.445
>
>
>
> ## now testing accessing multiple elements
> tmp2 <- createBufferedMatrix(10,20)
>
>
> tmp2[3,1] <- 51.34
> tmp2[9,2] <- 9.87654
> tmp2[,1:2]
[,1] [,2]
[1,] 0.00 0.00000
[2,] 0.00 0.00000
[3,] 51.34 0.00000
[4,] 0.00 0.00000
[5,] 0.00 0.00000
[6,] 0.00 0.00000
[7,] 0.00 0.00000
[8,] 0.00 0.00000
[9,] 0.00 9.87654
[10,] 0.00 0.00000
> tmp2[,-(3:20)]
[,1] [,2]
[1,] 0.00 0.00000
[2,] 0.00 0.00000
[3,] 51.34 0.00000
[4,] 0.00 0.00000
[5,] 0.00 0.00000
[6,] 0.00 0.00000
[7,] 0.00 0.00000
[8,] 0.00 0.00000
[9,] 0.00 9.87654
[10,] 0.00 0.00000
> tmp2[3,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 51.34 0 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19] [,20]
[1,] 0 0 0 0 0 0 0
> tmp2[-3,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 9.87654 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19] [,20]
[1,] 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0
> tmp2[2,1:3]
[,1] [,2] [,3]
[1,] 0 0 0
> tmp2[3:9,1:3]
[,1] [,2] [,3]
[1,] 51.34 0.00000 0
[2,] 0.00 0.00000 0
[3,] 0.00 0.00000 0
[4,] 0.00 0.00000 0
[5,] 0.00 0.00000 0
[6,] 0.00 0.00000 0
[7,] 0.00 9.87654 0
> tmp2[-4,-4]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[2,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[3,] 51.34 0.00000 0 0 0 0 0 0 0 0 0 0 0
[4,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[5,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[6,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[7,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[8,] 0.00 9.87654 0 0 0 0 0 0 0 0 0 0 0
[9,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19]
[1,] 0 0 0 0 0 0
[2,] 0 0 0 0 0 0
[3,] 0 0 0 0 0 0
[4,] 0 0 0 0 0 0
[5,] 0 0 0 0 0 0
[6,] 0 0 0 0 0 0
[7,] 0 0 0 0 0 0
[8,] 0 0 0 0 0 0
[9,] 0 0 0 0 0 0
>
> ## now testing accessing/assigning multiple elements
> tmp3 <- createBufferedMatrix(10,10)
>
> for (i in 1:10){
+ for (j in 1:10){
+ tmp3[i,j] <- (j-1)*10 + i
+ }
+ }
>
> tmp3[2:4,2:4]
[,1] [,2] [,3]
[1,] 12 22 32
[2,] 13 23 33
[3,] 14 24 34
> tmp3[c(-10),c(2:4,2:4,10,1,2,1:10,10:1)]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 11 21 31 11 21 31 91 1 11 1 11 21 31
[2,] 12 22 32 12 22 32 92 2 12 2 12 22 32
[3,] 13 23 33 13 23 33 93 3 13 3 13 23 33
[4,] 14 24 34 14 24 34 94 4 14 4 14 24 34
[5,] 15 25 35 15 25 35 95 5 15 5 15 25 35
[6,] 16 26 36 16 26 36 96 6 16 6 16 26 36
[7,] 17 27 37 17 27 37 97 7 17 7 17 27 37
[8,] 18 28 38 18 28 38 98 8 18 8 18 28 38
[9,] 19 29 39 19 29 39 99 9 19 9 19 29 39
[,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
[1,] 41 51 61 71 81 91 91 81 71 61 51 41
[2,] 42 52 62 72 82 92 92 82 72 62 52 42
[3,] 43 53 63 73 83 93 93 83 73 63 53 43
[4,] 44 54 64 74 84 94 94 84 74 64 54 44
[5,] 45 55 65 75 85 95 95 85 75 65 55 45
[6,] 46 56 66 76 86 96 96 86 76 66 56 46
[7,] 47 57 67 77 87 97 97 87 77 67 57 47
[8,] 48 58 68 78 88 98 98 88 78 68 58 48
[9,] 49 59 69 79 89 99 99 89 79 69 59 49
[,26] [,27] [,28] [,29]
[1,] 31 21 11 1
[2,] 32 22 12 2
[3,] 33 23 13 3
[4,] 34 24 14 4
[5,] 35 25 15 5
[6,] 36 26 16 6
[7,] 37 27 17 7
[8,] 38 28 18 8
[9,] 39 29 19 9
> tmp3[-c(1:5),-c(6:10)]
[,1] [,2] [,3] [,4] [,5]
[1,] 6 16 26 36 46
[2,] 7 17 27 37 47
[3,] 8 18 28 38 48
[4,] 9 19 29 39 49
[5,] 10 20 30 40 50
>
> ## assignment of whole columns
> tmp3[,1] <- c(1:10*100.0)
> tmp3[,1:2] <- tmp3[,1:2]*100
> tmp3[,1:2] <- tmp3[,2:1]
> tmp3[,1:2]
[,1] [,2]
[1,] 1100 1e+04
[2,] 1200 2e+04
[3,] 1300 3e+04
[4,] 1400 4e+04
[5,] 1500 5e+04
[6,] 1600 6e+04
[7,] 1700 7e+04
[8,] 1800 8e+04
[9,] 1900 9e+04
[10,] 2000 1e+05
>
>
> tmp3[,-1] <- tmp3[,1:9]
> tmp3[,1:10]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1100 1100 1e+04 21 31 41 51 61 71 81
[2,] 1200 1200 2e+04 22 32 42 52 62 72 82
[3,] 1300 1300 3e+04 23 33 43 53 63 73 83
[4,] 1400 1400 4e+04 24 34 44 54 64 74 84
[5,] 1500 1500 5e+04 25 35 45 55 65 75 85
[6,] 1600 1600 6e+04 26 36 46 56 66 76 86
[7,] 1700 1700 7e+04 27 37 47 57 67 77 87
[8,] 1800 1800 8e+04 28 38 48 58 68 78 88
[9,] 1900 1900 9e+04 29 39 49 59 69 79 89
[10,] 2000 2000 1e+05 30 40 50 60 70 80 90
>
> tmp3[,1:2] <- rep(1,10)
> tmp3[,1:2] <- rep(1,20)
> tmp3[,1:2] <- matrix(c(1:5),1,5)
>
> tmp3[,-c(1:8)] <- matrix(c(1:5),1,5)
>
> tmp3[1,] <- 1:10
> tmp3[1,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
> tmp3[-1,] <- c(1,2)
> tmp3[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
[2,] 1 2 1 2 1 2 1 2 1 2
[3,] 2 1 2 1 2 1 2 1 2 1
[4,] 1 2 1 2 1 2 1 2 1 2
[5,] 2 1 2 1 2 1 2 1 2 1
[6,] 1 2 1 2 1 2 1 2 1 2
[7,] 2 1 2 1 2 1 2 1 2 1
[8,] 1 2 1 2 1 2 1 2 1 2
[9,] 2 1 2 1 2 1 2 1 2 1
[10,] 1 2 1 2 1 2 1 2 1 2
> tmp3[-c(1:8),] <- matrix(c(1:5),1,5)
> tmp3[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
[2,] 1 2 1 2 1 2 1 2 1 2
[3,] 2 1 2 1 2 1 2 1 2 1
[4,] 1 2 1 2 1 2 1 2 1 2
[5,] 2 1 2 1 2 1 2 1 2 1
[6,] 1 2 1 2 1 2 1 2 1 2
[7,] 2 1 2 1 2 1 2 1 2 1
[8,] 1 2 1 2 1 2 1 2 1 2
[9,] 1 3 5 2 4 1 3 5 2 4
[10,] 2 4 1 3 5 2 4 1 3 5
>
>
> tmp3[1:2,1:2] <- 5555.04
> tmp3[-(1:2),1:2] <- 1234.56789
>
>
>
> ## testing accessors for the directory and prefix
> directory(tmp3)
[1] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386"
> prefix(tmp3)
[1] "BM"
>
> ## testing if we can remove these objects
> rm(tmp, tmp2, tmp3)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 403329 12.4 838562 25.6 627611 19.2
Vcells 471075 3.6 8388608 64.0 1468487 11.3
>
>
>
>
> ##
> ## checking reads
> ##
>
> tmp2 <- createBufferedMatrix(10,20)
>
> test.sample <- rnorm(10*20)
>
> tmp2[1:10,1:20] <- test.sample
>
> test.matrix <- matrix(test.sample,10,20)
>
> ## testing reads
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ which.col <- sample(1:20,1)
+ if (tmp2[which.row,which.col] != test.matrix[which.row,which.col]){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> date()
[1] "Wed Oct 17 00:51:49 2018"
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
> date()
[1] "Wed Oct 17 00:51:49 2018"
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,which.col] == test.matrix[which.row,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
>
>
> RowMode(tmp2)
<pointer: 0x01f29de0>
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ which.col <- sample(1:20,1)
+ if (tmp2[which.row,which.col] != test.matrix[which.row,which.col]){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,5,replace=TRUE)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> date()
[1] "Wed Oct 17 00:51:52 2018"
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:20,5,replace=TRUE)
+ if (!all(tmp2[which.row,which.col] == test.matrix[which.row,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
> date()
[1] "Wed Oct 17 00:51:53 2018"
>
> ColMode(tmp2)
<pointer: 0x01f29de0>
>
>
>
> ### Now testing assignments
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+
+ new.data <- rnorm(20)
+ tmp2[which.row,] <- new.data
+ test.matrix[which.row,] <- new.data
+ if (rep > 1){
+ if (!all(tmp2[prev.row,] == test.matrix[prev.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ new.data <- rnorm(10)
+ tmp2[,which.col] <- new.data
+ test.matrix[,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[,prev.col] == test.matrix[,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.col <- which.col
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,5,replace=TRUE)
+ new.data <- matrix(rnorm(50),5,10)
+ tmp2[,which.col] <- new.data
+ test.matrix[,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[,prev.col] == test.matrix[,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.col <- which.col
+ }
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ new.data <- matrix(rnorm(50),5,10)
+ tmp2[which.row,] <- new.data
+ test.matrix[which.row,]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[prev.row,] == test.matrix[prev.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:20,5,replace=TRUE)
+ new.data <- matrix(rnorm(25),5,5)
+ tmp2[which.row,which.col] <- new.data
+ test.matrix[which.row,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[prev.row,prev.col] == test.matrix[prev.row,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+ prev.col <- which.col
+ }
>
>
>
>
> ###
> ###
> ### testing some more functions
> ###
>
>
>
> ## duplication function
> tmp5 <- duplicate(tmp2)
>
> # making sure really did copy everything.
> tmp5[1,1] <- tmp5[1,1] +100.00
>
> if (tmp5[1,1] == tmp2[1,1]){
+ stop("Problem with duplication")
+ }
>
>
>
>
> ### testing elementwise applying of functions
>
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 99.3212433 -0.9294247 -0.04442830 -1.10080604
[2,] -0.1218589 -0.7369502 0.77398322 1.22773030
[3,] -0.1584110 -1.0441870 -0.07337497 1.23024343
[4,] -0.7133699 0.6121352 -1.97777693 0.08039276
> ewApply(tmp5,abs)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 1.9 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 99.3212433 0.9294247 0.04442830 1.10080604
[2,] 0.1218589 0.7369502 0.77398322 1.22773030
[3,] 0.1584110 1.0441870 0.07337497 1.23024343
[4,] 0.7133699 0.6121352 1.97777693 0.08039276
> ewApply(tmp5,sqrt)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 1.9 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 9.9660044 0.9640667 0.2107802 1.0491930
[2,] 0.3490830 0.8584580 0.8797632 1.1080299
[3,] 0.3980088 1.0218547 0.2708781 1.1091634
[4,] 0.8446123 0.7823907 1.4063346 0.2835362
>
> my.function <- function(x,power){
+ (x+5)^power
+ }
>
> ewApply(tmp5,my.function,power=2)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 1.9 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 223.98129 35.57009 27.15223 36.59274
[2,] 28.61269 34.32153 34.57161 37.30803
[3,] 29.13850 36.26273 27.78216 37.32188
[4,] 34.15949 33.43604 41.04112 27.91575
>
>
>
> ## testing functions that elementwise transform the matrix
> sqrt(tmp5)
<pointer: 0x01d64fa0>
> exp(tmp5)
<pointer: 0x01d64fa0>
> log(tmp5,2)
<pointer: 0x01d64fa0>
> pow(tmp5,2)
>
>
>
>
>
> ## testing functions that apply to entire matrix
> Max(tmp5)
[1] 466.1877
> Min(tmp5)
[1] 52.76734
> mean(tmp5)
[1] 72.40593
> Sum(tmp5)
[1] 14481.19
> Var(tmp5)
[1] 860.5852
>
>
> ## testing functions applied to rows or columns
>
> rowMeans(tmp5)
[1] 90.28360 67.98415 69.24860 69.49812 71.32456 72.30861 67.84502 70.21572
[9] 72.38345 72.96743
> rowSums(tmp5)
[1] 1805.672 1359.683 1384.972 1389.962 1426.491 1446.172 1356.900 1404.314
[9] 1447.669 1459.349
> rowVars(tmp5)
[1] 7890.48644 79.33938 96.45416 97.12676 82.64398 104.74041
[7] 87.07227 33.37892 75.97483 61.35284
> rowSd(tmp5)
[1] 88.828410 8.907266 9.821108 9.855291 9.090874 10.234276 9.331252
[8] 5.777449 8.716354 7.832806
> rowMax(tmp5)
[1] 466.18770 81.06753 89.95332 90.25491 87.18877 91.90168 90.95210
[8] 83.45919 89.19361 87.36779
> rowMin(tmp5)
[1] 55.51212 54.08749 55.94784 55.86291 53.61578 52.76734 54.92777 58.07572
[9] 59.37264 59.55858
>
> colMeans(tmp5)
[1] 106.95023 73.07067 69.14720 70.84946 69.86382 72.75770 77.40108
[8] 68.18576 70.05351 70.21106 69.77828 67.97265 73.43476 67.09859
[15] 71.21470 75.95630 68.59027 67.92778 69.63398 68.02072
> colSums(tmp5)
[1] 1069.5023 730.7067 691.4720 708.4946 698.6382 727.5770 774.0108
[8] 681.8576 700.5351 702.1106 697.7828 679.7265 734.3476 670.9859
[15] 712.1470 759.5630 685.9027 679.2778 696.3398 680.2072
> colVars(tmp5)
[1] 16005.53823 22.74365 121.34886 90.52160 72.48644 53.03687
[7] 82.97730 48.38804 112.92280 138.76157 97.89527 37.14739
[13] 113.35375 48.06404 85.05136 93.72477 72.67952 71.44277
[19] 38.27958 68.98936
> colSd(tmp5)
[1] 126.512996 4.769030 11.015846 9.514284 8.513897 7.282642
[7] 9.109188 6.956151 10.626514 11.779710 9.894204 6.094866
[13] 10.646772 6.932823 9.222329 9.681156 8.525228 8.452382
[19] 6.187049 8.305983
> colMax(tmp5)
[1] 466.18770 81.15784 86.85578 85.92946 84.45802 87.28191 90.95210
[8] 78.51361 89.95332 86.85857 91.90168 78.17422 89.19361 81.62457
[15] 83.45919 90.25491 79.75511 78.50957 77.90727 78.89263
> colMin(tmp5)
[1] 53.61578 64.19254 56.51381 55.41755 58.68040 63.77083 66.45106 58.33993
[9] 54.91193 55.33593 55.51212 59.40224 59.03789 57.40097 56.16864 59.55858
[17] 52.76734 54.92777 55.86291 54.08749
>
>
> ### setting a random element to NA and then testing with na.rm=TRUE or na.rm=FALSE (The default)
>
>
> which.row <- sample(1:10,1,replace=TRUE)
> which.col <- sample(1:20,1,replace=TRUE)
>
> tmp5[which.row,which.col] <- NA
>
> Max(tmp5)
[1] NA
> Min(tmp5)
[1] NA
> mean(tmp5)
[1] NA
> Sum(tmp5)
[1] NA
> Var(tmp5)
[1] NA
>
> rowMeans(tmp5)
[1] 90.28360 67.98415 69.24860 69.49812 NA 72.30861 67.84502 70.21572
[9] 72.38345 72.96743
> rowSums(tmp5)
[1] 1805.672 1359.683 1384.972 1389.962 NA 1446.172 1356.900 1404.314
[9] 1447.669 1459.349
> rowVars(tmp5)
[1] 7890.48644 79.33938 96.45416 97.12676 72.43806 104.74041
[7] 87.07227 33.37892 75.97483 61.35284
> rowSd(tmp5)
[1] 88.828410 8.907266 9.821108 9.855291 8.511055 10.234276 9.331252
[8] 5.777449 8.716354 7.832806
> rowMax(tmp5)
[1] 466.18770 81.06753 89.95332 90.25491 NA 91.90168 90.95210
[8] 83.45919 89.19361 87.36779
> rowMin(tmp5)
[1] 55.51212 54.08749 55.94784 55.86291 NA 52.76734 54.92777 58.07572
[9] 59.37264 59.55858
>
> colMeans(tmp5)
[1] 106.95023 73.07067 69.14720 NA 69.86382 72.75770 77.40108
[8] 68.18576 70.05351 70.21106 69.77828 67.97265 73.43476 67.09859
[15] 71.21470 75.95630 68.59027 67.92778 69.63398 68.02072
> colSums(tmp5)
[1] 1069.5023 730.7067 691.4720 NA 698.6382 727.5770 774.0108
[8] 681.8576 700.5351 702.1106 697.7828 679.7265 734.3476 670.9859
[15] 712.1470 759.5630 685.9027 679.2778 696.3398 680.2072
> colVars(tmp5)
[1] 16005.53823 22.74365 121.34886 NA 72.48644 53.03687
[7] 82.97730 48.38804 112.92280 138.76157 97.89527 37.14739
[13] 113.35375 48.06404 85.05136 93.72477 72.67952 71.44277
[19] 38.27958 68.98936
> colSd(tmp5)
[1] 126.512996 4.769030 11.015846 NA 8.513897 7.282642
[7] 9.109188 6.956151 10.626514 11.779710 9.894204 6.094866
[13] 10.646772 6.932823 9.222329 9.681156 8.525228 8.452382
[19] 6.187049 8.305983
> colMax(tmp5)
[1] 466.18770 81.15784 86.85578 NA 84.45802 87.28191 90.95210
[8] 78.51361 89.95332 86.85857 91.90168 78.17422 89.19361 81.62457
[15] 83.45919 90.25491 79.75511 78.50957 77.90727 78.89263
> colMin(tmp5)
[1] 53.61578 64.19254 56.51381 NA 58.68040 63.77083 66.45106 58.33993
[9] 54.91193 55.33593 55.51212 59.40224 59.03789 57.40097 56.16864 59.55858
[17] 52.76734 54.92777 55.86291 54.08749
>
> Max(tmp5,na.rm=TRUE)
[1] 466.1877
> Min(tmp5,na.rm=TRUE)
[1] 52.76734
> mean(tmp5,na.rm=TRUE)
[1] 72.49129
> Sum(tmp5,na.rm=TRUE)
[1] 14425.77
> Var(tmp5,na.rm=TRUE)
[1] 863.4666
>
> rowMeans(tmp5,na.rm=TRUE)
[1] 90.28360 67.98415 69.24860 69.49812 72.16177 72.30861 67.84502 70.21572
[9] 72.38345 72.96743
> rowSums(tmp5,na.rm=TRUE)
[1] 1805.672 1359.683 1384.972 1389.962 1371.074 1446.172 1356.900 1404.314
[9] 1447.669 1459.349
> rowVars(tmp5,na.rm=TRUE)
[1] 7890.48644 79.33938 96.45416 97.12676 72.43806 104.74041
[7] 87.07227 33.37892 75.97483 61.35284
> rowSd(tmp5,na.rm=TRUE)
[1] 88.828410 8.907266 9.821108 9.855291 8.511055 10.234276 9.331252
[8] 5.777449 8.716354 7.832806
> rowMax(tmp5,na.rm=TRUE)
[1] 466.18770 81.06753 89.95332 90.25491 87.18877 91.90168 90.95210
[8] 83.45919 89.19361 87.36779
> rowMin(tmp5,na.rm=TRUE)
[1] 55.51212 54.08749 55.94784 55.86291 53.61578 52.76734 54.92777 58.07572
[9] 59.37264 59.55858
>
> colMeans(tmp5,na.rm=TRUE)
[1] 106.95023 73.07067 69.14720 72.56412 69.86382 72.75770 77.40108
[8] 68.18576 70.05351 70.21106 69.77828 67.97265 73.43476 67.09859
[15] 71.21470 75.95630 68.59027 67.92778 69.63398 68.02072
> colSums(tmp5,na.rm=TRUE)
[1] 1069.5023 730.7067 691.4720 653.0770 698.6382 727.5770 774.0108
[8] 681.8576 700.5351 702.1106 697.7828 679.7265 734.3476 670.9859
[15] 712.1470 759.5630 685.9027 679.2778 696.3398 680.2072
> colVars(tmp5,na.rm=TRUE)
[1] 16005.53823 22.74365 121.34886 68.76127 72.48644 53.03687
[7] 82.97730 48.38804 112.92280 138.76157 97.89527 37.14739
[13] 113.35375 48.06404 85.05136 93.72477 72.67952 71.44277
[19] 38.27958 68.98936
> colSd(tmp5,na.rm=TRUE)
[1] 126.512996 4.769030 11.015846 8.292242 8.513897 7.282642
[7] 9.109188 6.956151 10.626514 11.779710 9.894204 6.094866
[13] 10.646772 6.932823 9.222329 9.681156 8.525228 8.452382
[19] 6.187049 8.305983
> colMax(tmp5,na.rm=TRUE)
[1] 466.18770 81.15784 86.85578 85.92946 84.45802 87.28191 90.95210
[8] 78.51361 89.95332 86.85857 91.90168 78.17422 89.19361 81.62457
[15] 83.45919 90.25491 79.75511 78.50957 77.90727 78.89263
> colMin(tmp5,na.rm=TRUE)
[1] 53.61578 64.19254 56.51381 58.10299 58.68040 63.77083 66.45106 58.33993
[9] 54.91193 55.33593 55.51212 59.40224 59.03789 57.40097 56.16864 59.55858
[17] 52.76734 54.92777 55.86291 54.08749
>
> # now set an entire row to NA
>
> tmp5[which.row,] <- NA
> rowMeans(tmp5,na.rm=TRUE)
[1] 90.28360 67.98415 69.24860 69.49812 NaN 72.30861 67.84502 70.21572
[9] 72.38345 72.96743
> rowSums(tmp5,na.rm=TRUE)
[1] 1805.672 1359.683 1384.972 1389.962 0.000 1446.172 1356.900 1404.314
[9] 1447.669 1459.349
> rowVars(tmp5,na.rm=TRUE)
[1] 7890.48644 79.33938 96.45416 97.12676 NA 104.74041
[7] 87.07227 33.37892 75.97483 61.35284
> rowSd(tmp5,na.rm=TRUE)
[1] 88.828410 8.907266 9.821108 9.855291 NA 10.234276 9.331252
[8] 5.777449 8.716354 7.832806
> rowMax(tmp5,na.rm=TRUE)
[1] 466.18770 81.06753 89.95332 90.25491 NA 91.90168 90.95210
[8] 83.45919 89.19361 87.36779
> rowMin(tmp5,na.rm=TRUE)
[1] 55.51212 54.08749 55.94784 55.86291 NA 52.76734 54.92777 58.07572
[9] 59.37264 59.55858
>
>
> # now set an entire col to NA
>
>
> tmp5[,which.col] <- NA
> colMeans(tmp5,na.rm=TRUE)
[1] 112.87628 73.30436 68.92439 NaN 70.54832 73.29377 77.84397
[8] 67.03822 69.11270 68.36134 70.28567 67.79106 71.90653 67.59209
[15] 70.71505 76.08983 67.34974 67.04760 69.17433 68.70218
> colSums(tmp5,na.rm=TRUE)
[1] 1015.8865 659.7392 620.3195 0.0000 634.9349 659.6439 700.5958
[8] 603.3440 622.0143 615.2520 632.5710 610.1196 647.1588 608.3288
[15] 636.4355 684.8085 606.1476 603.4284 622.5690 618.3197
> colVars(tmp5,na.rm=TRUE)
[1] 17611.15219 24.97225 135.95896 NA 76.27613 56.43357
[7] 91.14271 39.62203 117.08039 117.61515 107.23596 41.41988
[13] 101.24896 51.33222 92.87425 105.23977 64.45146 71.65749
[19] 40.68766 72.38860
> colSd(tmp5,na.rm=TRUE)
[1] 132.707016 4.997224 11.660144 NA 8.733620 7.512228
[7] 9.546869 6.294604 10.820369 10.845052 10.355480 6.435828
[13] 10.062254 7.164651 9.637129 10.258644 8.028167 8.465075
[19] 6.378688 8.508149
> colMax(tmp5,na.rm=TRUE)
[1] 466.18770 81.15784 86.85578 -Inf 84.45802 87.28191 90.95210
[8] 75.88926 89.95332 81.06753 91.90168 78.17422 89.19361 81.62457
[15] 83.45919 90.25491 77.72789 78.50957 77.90727 78.89263
> colMin(tmp5,na.rm=TRUE)
[1] 59.55356 64.19254 56.51381 Inf 58.68040 63.77083 66.45106 58.33993
[9] 54.91193 55.33593 55.51212 59.40224 59.03789 57.40097 56.16864 59.55858
[17] 52.76734 54.92777 55.86291 54.08749
>
>
>
>
> copymatrix <- matrix(rnorm(200,150,15),10,20)
>
> tmp5[1:10,1:20] <- copymatrix
> which.row <- 3
> which.col <- 1
> cat(which.row," ",which.col,"\n")
3 1
> tmp5[which.row,which.col] <- NA
> copymatrix[which.row,which.col] <- NA
>
> rowVars(tmp5,na.rm=TRUE)
[1] 251.93772 237.45427 289.40240 199.61628 127.24428 246.54406 437.22960
[8] 184.71120 98.15813 269.87924
> apply(copymatrix,1,var,na.rm=TRUE)
[1] 251.93772 237.45427 289.40240 199.61628 127.24428 246.54406 437.22960
[8] 184.71120 98.15813 269.87924
>
>
>
> copymatrix <- matrix(rnorm(200,150,15),10,20)
>
> tmp5[1:10,1:20] <- copymatrix
> which.row <- 1
> which.col <- 3
> cat(which.row," ",which.col,"\n")
1 3
> tmp5[which.row,which.col] <- NA
> copymatrix[which.row,which.col] <- NA
>
> colVars(tmp5,na.rm=TRUE)-apply(copymatrix,2,var,na.rm=TRUE)
[1] -2.842171e-14 -5.684342e-14 -8.526513e-14 0.000000e+00 5.684342e-14
[6] 8.526513e-14 5.684342e-14 0.000000e+00 8.526513e-14 0.000000e+00
[11] 1.136868e-13 -1.136868e-13 2.842171e-14 0.000000e+00 -1.705303e-13
[16] 1.989520e-13 -2.842171e-14 0.000000e+00 -1.705303e-13 5.684342e-14
>
>
>
>
>
>
>
>
>
>
> ## making sure these things agree
> ##
> ## first when there is no NA
>
>
>
> agree.checks <- function(buff.matrix,r.matrix,err.tol=1e-10){
+
+ if (Max(buff.matrix,na.rm=TRUE) != max(r.matrix,na.rm=TRUE)){
+ stop("No agreement in Max")
+ }
+
+
+ if (Min(buff.matrix,na.rm=TRUE) != min(r.matrix,na.rm=TRUE)){
+ stop("No agreement in Min")
+ }
+
+
+ if (abs(Sum(buff.matrix,na.rm=TRUE)- sum(r.matrix,na.rm=TRUE)) > err.tol){
+
+ cat(Sum(buff.matrix,na.rm=TRUE),"\n")
+ cat(sum(r.matrix,na.rm=TRUE),"\n")
+ cat(Sum(buff.matrix,na.rm=TRUE) - sum(r.matrix,na.rm=TRUE),"\n")
+
+ stop("No agreement in Sum")
+ }
+
+ if (abs(mean(buff.matrix,na.rm=TRUE) - mean(r.matrix,na.rm=TRUE)) > err.tol){
+ stop("No agreement in mean")
+ }
+
+
+ if(abs(Var(buff.matrix,na.rm=TRUE) - var(as.vector(r.matrix),na.rm=TRUE)) > err.tol){
+ stop("No agreement in Var")
+ }
+
+
+
+ if(any(abs(rowMeans(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,mean,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowMeans")
+ }
+
+
+ if(any(abs(colMeans(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,mean,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in colMeans")
+ }
+
+
+ if(any(abs(rowSums(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,sum,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in rowSums")
+ }
+
+
+ if(any(abs(colSums(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,sum,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in colSums")
+ }
+
+ ### this is to get around the fact that R doesn't like to compute NA on an entire vector of NA when
+ ### computing variance
+ my.Var <- function(x,na.rm=FALSE){
+ if (all(is.na(x))){
+ return(NA)
+ } else {
+ var(x,na.rm=na.rm)
+ }
+
+ }
+
+ if(any(abs(rowVars(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,my.Var,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowVars")
+ }
+
+
+ if(any(abs(colVars(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,my.Var,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowVars")
+ }
+
+
+ if(any(abs(rowMax(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,max,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMax")
+ }
+
+
+ if(any(abs(colMax(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,max,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMax")
+ }
+
+
+
+ if(any(abs(rowMin(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,min,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMin")
+ }
+
+
+ if(any(abs(colMin(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,min,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMin")
+ }
+
+ if(any(abs(colMedians(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,median,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMedian")
+ }
+
+ if(any(abs(colRanges(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,range,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colRanges")
+ }
+
+
+
+ }
>
>
>
>
>
>
>
>
>
> for (rep in 1:20){
+ copymatrix <- matrix(rnorm(200,150,15),10,20)
+
+ tmp5[1:10,1:20] <- copymatrix
+
+
+ agree.checks(tmp5,copymatrix)
+
+ ## now lets assign some NA values and check agreement
+
+ which.row <- sample(1:10,1,replace=TRUE)
+ which.col <- sample(1:20,1,replace=TRUE)
+
+ cat(which.row," ",which.col,"\n")
+
+ tmp5[which.row,which.col] <- NA
+ copymatrix[which.row,which.col] <- NA
+
+ agree.checks(tmp5,copymatrix)
+
+ ## make an entire row NA
+ tmp5[which.row,] <- NA
+ copymatrix[which.row,] <- NA
+
+
+ agree.checks(tmp5,copymatrix)
+
+ ### also make an entire col NA
+ tmp5[,which.col] <- NA
+ copymatrix[,which.col] <- NA
+
+ agree.checks(tmp5,copymatrix)
+
+ ### now make 1 element non NA with NA in the rest of row and column
+
+ tmp5[which.row,which.col] <- rnorm(1,150,15)
+ copymatrix[which.row,which.col] <- tmp5[which.row,which.col]
+
+ agree.checks(tmp5,copymatrix)
+ }
3 9
2 15
8 18
8 8
10 5
2 9
1 20
6 5
6 4
6 17
7 4
2 8
6 8
10 7
9 20
3 20
6 18
8 17
2 9
3 18
There were 50 or more warnings (use warnings() to see the first 50)
>
>
> ### now test 1 by n and n by 1 matrix
>
>
> err.tol <- 1e-12
>
> rm(tmp5)
>
> dataset1 <- rnorm(100)
> dataset2 <- rnorm(100)
>
> tmp <- createBufferedMatrix(1,100)
> tmp[1,] <- dataset1
>
> tmp2 <- createBufferedMatrix(100,1)
> tmp2[,1] <- dataset2
>
>
>
>
>
> Max(tmp)
[1] 1.801464
> Min(tmp)
[1] -2.732013
> mean(tmp)
[1] 0.0450221
> Sum(tmp)
[1] 4.50221
> Var(tmp)
[1] 1.021054
>
> rowMeans(tmp)
[1] 0.0450221
> rowSums(tmp)
[1] 4.50221
> rowVars(tmp)
[1] 1.021054
> rowSd(tmp)
[1] 1.010472
> rowMax(tmp)
[1] 1.801464
> rowMin(tmp)
[1] -2.732013
>
> colMeans(tmp)
[1] -1.00368837 0.70556766 0.62505451 1.53445481 -0.34989947 -0.02318150
[7] 1.11832711 -0.28883067 -0.79740761 0.07974269 -0.23705882 1.46457320
[13] 0.25415435 1.80146408 1.46694571 0.10636131 -0.50472465 1.09485991
[19] -0.53097343 1.74053057 0.32325848 1.07963390 0.72409198 0.89498937
[25] 0.18574441 0.43250329 -0.38391515 0.92075269 -1.69054420 0.51805918
[31] 1.02928187 0.27710687 0.75208038 -0.94054858 -0.39513363 0.90331954
[37] 0.59889775 -0.79586145 -0.35813955 0.14119770 -0.49071813 1.78692503
[43] 1.16461365 0.04972128 0.29275440 -1.12554830 -0.53548474 -0.62821839
[49] 1.33325807 -0.73461308 0.05197468 0.36583274 -0.82239966 -1.26428129
[55] 0.99504894 0.33251397 1.77387845 -1.14188978 -0.48230961 0.20015182
[61] 1.13940027 -0.33867887 -0.30135623 -0.87919928 -0.83713170 -0.59735034
[67] -2.22665747 1.13820815 -0.68023011 0.83783557 1.21969603 -1.13714609
[73] -1.08476587 1.01683435 -2.20009876 -0.83974463 -0.52799651 1.00851224
[79] 0.28080876 1.10351800 1.52062955 0.68663560 1.14836243 -0.40010634
[85] 0.31428971 -2.57548287 -0.96996597 0.40423105 -0.63537911 -0.06364243
[91] 1.44259519 -0.28487397 1.50336552 -1.30585253 -0.03249566 -1.32485998
[97] -0.40725715 -1.23339482 -2.73201335 -0.24128888
> colSums(tmp)
[1] -1.00368837 0.70556766 0.62505451 1.53445481 -0.34989947 -0.02318150
[7] 1.11832711 -0.28883067 -0.79740761 0.07974269 -0.23705882 1.46457320
[13] 0.25415435 1.80146408 1.46694571 0.10636131 -0.50472465 1.09485991
[19] -0.53097343 1.74053057 0.32325848 1.07963390 0.72409198 0.89498937
[25] 0.18574441 0.43250329 -0.38391515 0.92075269 -1.69054420 0.51805918
[31] 1.02928187 0.27710687 0.75208038 -0.94054858 -0.39513363 0.90331954
[37] 0.59889775 -0.79586145 -0.35813955 0.14119770 -0.49071813 1.78692503
[43] 1.16461365 0.04972128 0.29275440 -1.12554830 -0.53548474 -0.62821839
[49] 1.33325807 -0.73461308 0.05197468 0.36583274 -0.82239966 -1.26428129
[55] 0.99504894 0.33251397 1.77387845 -1.14188978 -0.48230961 0.20015182
[61] 1.13940027 -0.33867887 -0.30135623 -0.87919928 -0.83713170 -0.59735034
[67] -2.22665747 1.13820815 -0.68023011 0.83783557 1.21969603 -1.13714609
[73] -1.08476587 1.01683435 -2.20009876 -0.83974463 -0.52799651 1.00851224
[79] 0.28080876 1.10351800 1.52062955 0.68663560 1.14836243 -0.40010634
[85] 0.31428971 -2.57548287 -0.96996597 0.40423105 -0.63537911 -0.06364243
[91] 1.44259519 -0.28487397 1.50336552 -1.30585253 -0.03249566 -1.32485998
[97] -0.40725715 -1.23339482 -2.73201335 -0.24128888
> colVars(tmp)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> colSd(tmp)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> colMax(tmp)
[1] -1.00368837 0.70556766 0.62505451 1.53445481 -0.34989947 -0.02318150
[7] 1.11832711 -0.28883067 -0.79740761 0.07974269 -0.23705882 1.46457320
[13] 0.25415435 1.80146408 1.46694571 0.10636131 -0.50472465 1.09485991
[19] -0.53097343 1.74053057 0.32325848 1.07963390 0.72409198 0.89498937
[25] 0.18574441 0.43250329 -0.38391515 0.92075269 -1.69054420 0.51805918
[31] 1.02928187 0.27710687 0.75208038 -0.94054858 -0.39513363 0.90331954
[37] 0.59889775 -0.79586145 -0.35813955 0.14119770 -0.49071813 1.78692503
[43] 1.16461365 0.04972128 0.29275440 -1.12554830 -0.53548474 -0.62821839
[49] 1.33325807 -0.73461308 0.05197468 0.36583274 -0.82239966 -1.26428129
[55] 0.99504894 0.33251397 1.77387845 -1.14188978 -0.48230961 0.20015182
[61] 1.13940027 -0.33867887 -0.30135623 -0.87919928 -0.83713170 -0.59735034
[67] -2.22665747 1.13820815 -0.68023011 0.83783557 1.21969603 -1.13714609
[73] -1.08476587 1.01683435 -2.20009876 -0.83974463 -0.52799651 1.00851224
[79] 0.28080876 1.10351800 1.52062955 0.68663560 1.14836243 -0.40010634
[85] 0.31428971 -2.57548287 -0.96996597 0.40423105 -0.63537911 -0.06364243
[91] 1.44259519 -0.28487397 1.50336552 -1.30585253 -0.03249566 -1.32485998
[97] -0.40725715 -1.23339482 -2.73201335 -0.24128888
> colMin(tmp)
[1] -1.00368837 0.70556766 0.62505451 1.53445481 -0.34989947 -0.02318150
[7] 1.11832711 -0.28883067 -0.79740761 0.07974269 -0.23705882 1.46457320
[13] 0.25415435 1.80146408 1.46694571 0.10636131 -0.50472465 1.09485991
[19] -0.53097343 1.74053057 0.32325848 1.07963390 0.72409198 0.89498937
[25] 0.18574441 0.43250329 -0.38391515 0.92075269 -1.69054420 0.51805918
[31] 1.02928187 0.27710687 0.75208038 -0.94054858 -0.39513363 0.90331954
[37] 0.59889775 -0.79586145 -0.35813955 0.14119770 -0.49071813 1.78692503
[43] 1.16461365 0.04972128 0.29275440 -1.12554830 -0.53548474 -0.62821839
[49] 1.33325807 -0.73461308 0.05197468 0.36583274 -0.82239966 -1.26428129
[55] 0.99504894 0.33251397 1.77387845 -1.14188978 -0.48230961 0.20015182
[61] 1.13940027 -0.33867887 -0.30135623 -0.87919928 -0.83713170 -0.59735034
[67] -2.22665747 1.13820815 -0.68023011 0.83783557 1.21969603 -1.13714609
[73] -1.08476587 1.01683435 -2.20009876 -0.83974463 -0.52799651 1.00851224
[79] 0.28080876 1.10351800 1.52062955 0.68663560 1.14836243 -0.40010634
[85] 0.31428971 -2.57548287 -0.96996597 0.40423105 -0.63537911 -0.06364243
[91] 1.44259519 -0.28487397 1.50336552 -1.30585253 -0.03249566 -1.32485998
[97] -0.40725715 -1.23339482 -2.73201335 -0.24128888
> colMedians(tmp)
[1] -1.00368837 0.70556766 0.62505451 1.53445481 -0.34989947 -0.02318150
[7] 1.11832711 -0.28883067 -0.79740761 0.07974269 -0.23705882 1.46457320
[13] 0.25415435 1.80146408 1.46694571 0.10636131 -0.50472465 1.09485991
[19] -0.53097343 1.74053057 0.32325848 1.07963390 0.72409198 0.89498937
[25] 0.18574441 0.43250329 -0.38391515 0.92075269 -1.69054420 0.51805918
[31] 1.02928187 0.27710687 0.75208038 -0.94054858 -0.39513363 0.90331954
[37] 0.59889775 -0.79586145 -0.35813955 0.14119770 -0.49071813 1.78692503
[43] 1.16461365 0.04972128 0.29275440 -1.12554830 -0.53548474 -0.62821839
[49] 1.33325807 -0.73461308 0.05197468 0.36583274 -0.82239966 -1.26428129
[55] 0.99504894 0.33251397 1.77387845 -1.14188978 -0.48230961 0.20015182
[61] 1.13940027 -0.33867887 -0.30135623 -0.87919928 -0.83713170 -0.59735034
[67] -2.22665747 1.13820815 -0.68023011 0.83783557 1.21969603 -1.13714609
[73] -1.08476587 1.01683435 -2.20009876 -0.83974463 -0.52799651 1.00851224
[79] 0.28080876 1.10351800 1.52062955 0.68663560 1.14836243 -0.40010634
[85] 0.31428971 -2.57548287 -0.96996597 0.40423105 -0.63537911 -0.06364243
[91] 1.44259519 -0.28487397 1.50336552 -1.30585253 -0.03249566 -1.32485998
[97] -0.40725715 -1.23339482 -2.73201335 -0.24128888
> colRanges(tmp)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] -1.003688 0.7055677 0.6250545 1.534455 -0.3498995 -0.0231815 1.118327
[2,] -1.003688 0.7055677 0.6250545 1.534455 -0.3498995 -0.0231815 1.118327
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] -0.2888307 -0.7974076 0.07974269 -0.2370588 1.464573 0.2541544 1.801464
[2,] -0.2888307 -0.7974076 0.07974269 -0.2370588 1.464573 0.2541544 1.801464
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 1.466946 0.1063613 -0.5047247 1.09486 -0.5309734 1.740531 0.3232585
[2,] 1.466946 0.1063613 -0.5047247 1.09486 -0.5309734 1.740531 0.3232585
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
[1,] 1.079634 0.724092 0.8949894 0.1857444 0.4325033 -0.3839152 0.9207527
[2,] 1.079634 0.724092 0.8949894 0.1857444 0.4325033 -0.3839152 0.9207527
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
[1,] -1.690544 0.5180592 1.029282 0.2771069 0.7520804 -0.9405486 -0.3951336
[2,] -1.690544 0.5180592 1.029282 0.2771069 0.7520804 -0.9405486 -0.3951336
[,36] [,37] [,38] [,39] [,40] [,41] [,42]
[1,] 0.9033195 0.5988977 -0.7958614 -0.3581395 0.1411977 -0.4907181 1.786925
[2,] 0.9033195 0.5988977 -0.7958614 -0.3581395 0.1411977 -0.4907181 1.786925
[,43] [,44] [,45] [,46] [,47] [,48] [,49]
[1,] 1.164614 0.04972128 0.2927544 -1.125548 -0.5354847 -0.6282184 1.333258
[2,] 1.164614 0.04972128 0.2927544 -1.125548 -0.5354847 -0.6282184 1.333258
[,50] [,51] [,52] [,53] [,54] [,55] [,56]
[1,] -0.7346131 0.05197468 0.3658327 -0.8223997 -1.264281 0.9950489 0.332514
[2,] -0.7346131 0.05197468 0.3658327 -0.8223997 -1.264281 0.9950489 0.332514
[,57] [,58] [,59] [,60] [,61] [,62] [,63]
[1,] 1.773878 -1.14189 -0.4823096 0.2001518 1.1394 -0.3386789 -0.3013562
[2,] 1.773878 -1.14189 -0.4823096 0.2001518 1.1394 -0.3386789 -0.3013562
[,64] [,65] [,66] [,67] [,68] [,69] [,70]
[1,] -0.8791993 -0.8371317 -0.5973503 -2.226657 1.138208 -0.6802301 0.8378356
[2,] -0.8791993 -0.8371317 -0.5973503 -2.226657 1.138208 -0.6802301 0.8378356
[,71] [,72] [,73] [,74] [,75] [,76] [,77]
[1,] 1.219696 -1.137146 -1.084766 1.016834 -2.200099 -0.8397446 -0.5279965
[2,] 1.219696 -1.137146 -1.084766 1.016834 -2.200099 -0.8397446 -0.5279965
[,78] [,79] [,80] [,81] [,82] [,83] [,84]
[1,] 1.008512 0.2808088 1.103518 1.52063 0.6866356 1.148362 -0.4001063
[2,] 1.008512 0.2808088 1.103518 1.52063 0.6866356 1.148362 -0.4001063
[,85] [,86] [,87] [,88] [,89] [,90] [,91]
[1,] 0.3142897 -2.575483 -0.969966 0.404231 -0.6353791 -0.06364243 1.442595
[2,] 0.3142897 -2.575483 -0.969966 0.404231 -0.6353791 -0.06364243 1.442595
[,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] -0.284874 1.503366 -1.305853 -0.03249566 -1.32486 -0.4072571 -1.233395
[2,] -0.284874 1.503366 -1.305853 -0.03249566 -1.32486 -0.4072571 -1.233395
[,99] [,100]
[1,] -2.732013 -0.2412889
[2,] -2.732013 -0.2412889
>
>
> Max(tmp2)
[1] 2.913395
> Min(tmp2)
[1] -2.262053
> mean(tmp2)
[1] -0.01497635
> Sum(tmp2)
[1] -1.497635
> Var(tmp2)
[1] 1.135478
>
> rowMeans(tmp2)
[1] 0.94211693 0.46182869 -0.27662976 0.96609472 0.02961423 -0.45849352
[7] -0.94240993 0.70576981 -0.03093382 -0.50293001 -1.31996460 -0.14220529
[13] -1.51538023 -0.36789614 -1.09085015 0.22339940 -0.07345427 -0.16543297
[19] -2.26205292 -0.61152410 -0.58415364 -1.72129570 -1.00266933 -0.28763962
[25] -0.24859088 0.34788488 -0.97474578 -1.47026067 2.87255609 0.11893414
[31] 0.15157754 -0.03113702 -0.35912155 0.20144704 -0.21709413 2.89694253
[37] 2.55370260 -0.24006788 0.09247356 0.37794418 1.92633826 0.44229801
[43] -0.58615196 -0.89326402 1.32065818 0.27222966 0.17602401 0.48856838
[49] 0.32556642 0.96082365 -1.66593504 -0.36348145 1.10091009 0.49505882
[55] -0.34516428 0.06282730 0.16089617 1.12834371 -0.81205945 -2.16963088
[61] -1.21114290 1.10428471 0.26790873 0.18137640 -0.66930492 0.03299028
[67] -0.71690240 -0.71546954 0.43521638 -0.34549391 -0.89093188 -1.42825510
[73] 2.91339495 1.04684218 1.95333155 -0.72999242 2.22453530 -0.96098290
[79] -0.68038240 1.33710252 0.24983245 0.63198420 -0.52007648 0.24380308
[85] -0.55220468 0.21902234 1.58897019 -1.38851904 -0.52859191 0.41868989
[91] -0.05799096 0.02675843 -0.10316845 -0.22398181 -0.92754430 -1.89326444
[97] -2.06192680 -0.10150430 1.00630738 1.25743789
> rowSums(tmp2)
[1] 0.94211693 0.46182869 -0.27662976 0.96609472 0.02961423 -0.45849352
[7] -0.94240993 0.70576981 -0.03093382 -0.50293001 -1.31996460 -0.14220529
[13] -1.51538023 -0.36789614 -1.09085015 0.22339940 -0.07345427 -0.16543297
[19] -2.26205292 -0.61152410 -0.58415364 -1.72129570 -1.00266933 -0.28763962
[25] -0.24859088 0.34788488 -0.97474578 -1.47026067 2.87255609 0.11893414
[31] 0.15157754 -0.03113702 -0.35912155 0.20144704 -0.21709413 2.89694253
[37] 2.55370260 -0.24006788 0.09247356 0.37794418 1.92633826 0.44229801
[43] -0.58615196 -0.89326402 1.32065818 0.27222966 0.17602401 0.48856838
[49] 0.32556642 0.96082365 -1.66593504 -0.36348145 1.10091009 0.49505882
[55] -0.34516428 0.06282730 0.16089617 1.12834371 -0.81205945 -2.16963088
[61] -1.21114290 1.10428471 0.26790873 0.18137640 -0.66930492 0.03299028
[67] -0.71690240 -0.71546954 0.43521638 -0.34549391 -0.89093188 -1.42825510
[73] 2.91339495 1.04684218 1.95333155 -0.72999242 2.22453530 -0.96098290
[79] -0.68038240 1.33710252 0.24983245 0.63198420 -0.52007648 0.24380308
[85] -0.55220468 0.21902234 1.58897019 -1.38851904 -0.52859191 0.41868989
[91] -0.05799096 0.02675843 -0.10316845 -0.22398181 -0.92754430 -1.89326444
[97] -2.06192680 -0.10150430 1.00630738 1.25743789
> rowVars(tmp2)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> rowSd(tmp2)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> rowMax(tmp2)
[1] 0.94211693 0.46182869 -0.27662976 0.96609472 0.02961423 -0.45849352
[7] -0.94240993 0.70576981 -0.03093382 -0.50293001 -1.31996460 -0.14220529
[13] -1.51538023 -0.36789614 -1.09085015 0.22339940 -0.07345427 -0.16543297
[19] -2.26205292 -0.61152410 -0.58415364 -1.72129570 -1.00266933 -0.28763962
[25] -0.24859088 0.34788488 -0.97474578 -1.47026067 2.87255609 0.11893414
[31] 0.15157754 -0.03113702 -0.35912155 0.20144704 -0.21709413 2.89694253
[37] 2.55370260 -0.24006788 0.09247356 0.37794418 1.92633826 0.44229801
[43] -0.58615196 -0.89326402 1.32065818 0.27222966 0.17602401 0.48856838
[49] 0.32556642 0.96082365 -1.66593504 -0.36348145 1.10091009 0.49505882
[55] -0.34516428 0.06282730 0.16089617 1.12834371 -0.81205945 -2.16963088
[61] -1.21114290 1.10428471 0.26790873 0.18137640 -0.66930492 0.03299028
[67] -0.71690240 -0.71546954 0.43521638 -0.34549391 -0.89093188 -1.42825510
[73] 2.91339495 1.04684218 1.95333155 -0.72999242 2.22453530 -0.96098290
[79] -0.68038240 1.33710252 0.24983245 0.63198420 -0.52007648 0.24380308
[85] -0.55220468 0.21902234 1.58897019 -1.38851904 -0.52859191 0.41868989
[91] -0.05799096 0.02675843 -0.10316845 -0.22398181 -0.92754430 -1.89326444
[97] -2.06192680 -0.10150430 1.00630738 1.25743789
> rowMin(tmp2)
[1] 0.94211693 0.46182869 -0.27662976 0.96609472 0.02961423 -0.45849352
[7] -0.94240993 0.70576981 -0.03093382 -0.50293001 -1.31996460 -0.14220529
[13] -1.51538023 -0.36789614 -1.09085015 0.22339940 -0.07345427 -0.16543297
[19] -2.26205292 -0.61152410 -0.58415364 -1.72129570 -1.00266933 -0.28763962
[25] -0.24859088 0.34788488 -0.97474578 -1.47026067 2.87255609 0.11893414
[31] 0.15157754 -0.03113702 -0.35912155 0.20144704 -0.21709413 2.89694253
[37] 2.55370260 -0.24006788 0.09247356 0.37794418 1.92633826 0.44229801
[43] -0.58615196 -0.89326402 1.32065818 0.27222966 0.17602401 0.48856838
[49] 0.32556642 0.96082365 -1.66593504 -0.36348145 1.10091009 0.49505882
[55] -0.34516428 0.06282730 0.16089617 1.12834371 -0.81205945 -2.16963088
[61] -1.21114290 1.10428471 0.26790873 0.18137640 -0.66930492 0.03299028
[67] -0.71690240 -0.71546954 0.43521638 -0.34549391 -0.89093188 -1.42825510
[73] 2.91339495 1.04684218 1.95333155 -0.72999242 2.22453530 -0.96098290
[79] -0.68038240 1.33710252 0.24983245 0.63198420 -0.52007648 0.24380308
[85] -0.55220468 0.21902234 1.58897019 -1.38851904 -0.52859191 0.41868989
[91] -0.05799096 0.02675843 -0.10316845 -0.22398181 -0.92754430 -1.89326444
[97] -2.06192680 -0.10150430 1.00630738 1.25743789
>
> colMeans(tmp2)
[1] -0.01497635
> colSums(tmp2)
[1] -1.497635
> colVars(tmp2)
[1] 1.135478
> colSd(tmp2)
[1] 1.065588
> colMax(tmp2)
[1] 2.913395
> colMin(tmp2)
[1] -2.262053
> colMedians(tmp2)
[1] -0.06572261
> colRanges(tmp2)
[,1]
[1,] -2.262053
[2,] 2.913395
>
> dataset1 <- matrix(dataset1,1,100)
>
> agree.checks(tmp,dataset1)
>
> dataset2 <- matrix(dataset2,100,1)
> agree.checks(tmp2,dataset2)
>
>
> tmp <- createBufferedMatrix(10,10)
>
> tmp[1:10,1:10] <- rnorm(100)
> colApply(tmp,sum)
[1] -2.0308009 -0.9103742 -0.7758046 2.8153088 4.2205314 2.9256068
[7] -0.5606895 -3.4133601 4.7736783 3.4116259
> colApply(tmp,quantile)[,1]
[,1]
[1,] -1.9468490
[2,] -1.0521891
[3,] -0.1576915
[4,] 0.4819692
[5,] 1.5299883
>
> rowApply(tmp,sum)
[1] 1.7209099 0.4251605 -2.4519008 3.1720747 2.4994320 -2.2081219
[7] 1.2264910 3.5175763 3.2625166 -0.7084163
> rowApply(tmp,rank)[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 8 9 10 5 9 6 2 1 1 1
[2,] 2 1 2 6 4 1 5 9 3 10
[3,] 5 6 3 4 2 2 7 7 8 7
[4,] 6 10 6 8 10 4 4 3 4 3
[5,] 3 3 5 9 8 9 6 6 6 8
[6,] 7 8 4 10 6 7 1 10 7 2
[7,] 1 4 8 7 3 10 8 2 2 9
[8,] 4 5 9 3 1 3 3 4 5 6
[9,] 9 7 7 1 5 8 10 8 9 4
[10,] 10 2 1 2 7 5 9 5 10 5
>
> tmp <- createBufferedMatrix(5,20)
>
> tmp[1:5,1:20] <- rnorm(100)
> colApply(tmp,sum)
[1] 1.0832340 0.4083505 0.1810614 -0.2565906 3.0667902 1.0942879
[7] -0.1302828 -1.8700469 0.3636345 1.7019901 -1.7361811 3.4751718
[13] -0.0554423 2.7733427 1.0645418 0.9336157 -1.8584923 0.4902663
[19] 1.4354354 -0.6110727
> colApply(tmp,quantile)[,1]
[,1]
[1,] -0.8094290
[2,] -0.4865238
[3,] 0.6050725
[4,] 0.7702089
[5,] 1.0039055
>
> rowApply(tmp,sum)
[1] 2.2670462 6.8699468 0.4795777 3.5669342 -1.6298916
> rowApply(tmp,rank)[1:5,]
[,1] [,2] [,3] [,4] [,5]
[1,] 18 13 5 16 6
[2,] 8 12 2 6 19
[3,] 12 11 18 8 1
[4,] 19 16 4 2 3
[5,] 10 8 20 13 15
>
>
> as.matrix(tmp)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1.0039055 -0.1639954 0.2071708 1.1226058 0.10040182 0.1392559
[2,] 0.6050725 0.5289394 0.3135065 1.3992066 -0.04224973 -0.2254339
[3,] -0.4865238 -1.0828322 1.4376059 -0.5764219 1.72368877 -0.3247937
[4,] 0.7702089 -0.3107158 -0.0482800 -1.1025642 0.69031706 1.0104415
[5,] -0.8094290 1.4369545 -1.7289418 -1.0994169 0.59463225 0.4948182
[,7] [,8] [,9] [,10] [,11] [,12]
[1,] -0.8437795 -1.5781787 -0.4623171 0.71407341 -0.5960683 0.6339820
[2,] -0.2656356 -0.8591694 1.4553773 -0.78805731 0.7200986 1.8800671
[3,] 1.4838241 -0.4317804 -0.4220307 0.05250235 0.1457353 -0.3135376
[4,] 0.3017203 0.8682094 0.7671118 1.01616950 -1.7607946 -0.5838909
[5,] -0.8064120 0.1308722 -0.9745068 0.70730217 -0.2451522 1.8585510
[,13] [,14] [,15] [,16] [,17] [,18]
[1,] 0.6723494 -0.05254175 0.32204922 -0.2378205 -0.3857094 1.4819721
[2,] 0.1304314 0.88349117 0.09692688 1.5468424 -0.7935470 -0.4940974
[3,] -0.9846199 0.80038759 -1.88285228 -0.1386003 0.7263188 0.3609935
[4,] -0.1552298 0.75637599 1.78518628 0.3128310 -0.5446771 -0.5448282
[5,] 0.2816266 0.38562974 0.74323170 -0.5496368 -0.8608777 -0.3137738
[,19] [,20]
[1,] 0.7091912 -0.51950024
[2,] -0.6470625 1.42523985
[3,] 0.4257848 -0.03327043
[4,] 0.1931878 0.14615521
[5,] 0.7543341 -1.62969710
>
>
> is.BufferedMatrix(tmp)
[1] TRUE
>
> as.BufferedMatrix(as.matrix(tmp))
BufferedMatrix object
Matrix size: 5 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 1.9 Kilobytes.
Disk usage : 800 bytes.
>
>
>
> subBufferedMatrix(tmp,1:5,1:5)
BufferedMatrix object
Matrix size: 5 5
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 629 bytes.
Disk usage : 200 bytes.
> subBufferedMatrix(tmp,,5:8)
BufferedMatrix object
Matrix size: 5 4
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 541 bytes.
Disk usage : 160 bytes.
> subBufferedMatrix(tmp,1:3,)
BufferedMatrix object
Matrix size: 3 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 1.9 Kilobytes.
Disk usage : 480 bytes.
>
>
> rm(tmp)
>
>
> ###
> ### Testing colnames and rownames
> ###
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
>
>
> colnames(tmp)
NULL
> rownames(tmp)
NULL
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> colnames(tmp)
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
> rownames(tmp)
[1] "row1" "row2" "row3" "row4" "row5"
>
>
> tmp["row1",]
col1 col2 col3 col4 col5 col6 col7
row1 -0.2850879 -0.6166518 0.01724934 0.595072 -1.034552 2.004017 0.2514941
col8 col9 col10 col11 col12 col13 col14
row1 -0.7048524 0.8063529 0.8998846 -0.3576413 -0.8996102 -0.6586478 -0.5739105
col15 col16 col17 col18 col19 col20
row1 0.9959259 -0.6397202 0.7704281 0.6826987 -0.9416243 -1.916301
> tmp[,"col10"]
col10
row1 0.8998846
row2 0.3311127
row3 1.7627290
row4 0.5763505
row5 0.6558482
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6
row1 -0.2850879 -0.6166518 0.01724934 0.59507198 -1.0345517 2.0040168
row5 0.9458809 -0.4585103 0.19634924 0.04691651 0.3770209 0.1750349
col7 col8 col9 col10 col11 col12
row1 0.25149407 -0.7048524 0.8063529 0.8998846 -0.35764128 -0.8996102
row5 -0.08855676 -0.8944066 -1.1039134 0.6558482 -0.05004362 0.6434634
col13 col14 col15 col16 col17 col18
row1 -0.6586478 -0.5739105 0.9959259 -0.6397202 0.7704281 0.6826987
row5 1.7107673 0.8994958 -0.5641079 0.1634213 0.1507780 -0.4974813
col19 col20
row1 -0.9416243 -1.9163007
row5 -1.0257774 0.3346197
> tmp[,c("col6","col20")]
col6 col20
row1 2.0040168 -1.9163007
row2 0.4720957 -1.8356929
row3 0.9936958 1.0762071
row4 0.2189460 -0.2375070
row5 0.1750349 0.3346197
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 2.0040168 -1.9163007
row5 0.1750349 0.3346197
>
>
>
>
> tmp["row1",] <- rnorm(20,mean=10)
> tmp[,"col10"] <- rnorm(5,mean=30)
> tmp[c("row1","row5"),] <- rnorm(40,mean=50)
> tmp[,c("col6","col20")] <- rnorm(10,mean=75)
> tmp[c("row1","row5"),c("col6","col20")] <- rnorm(4,mean=105)
>
> tmp["row1",]
col1 col2 col3 col4 col5 col6 col7 col8
row1 50.84529 48.33802 48.44782 49.59428 50.44169 106.576 50.44489 50.1701
col9 col10 col11 col12 col13 col14 col15 col16
row1 48.77274 50.41374 50.00417 50.85268 50.98088 50.16238 49.76864 50.35969
col17 col18 col19 col20
row1 51.71035 49.63928 50.34057 103.2169
> tmp[,"col10"]
col10
row1 50.41374
row2 29.09821
row3 29.07903
row4 31.54187
row5 49.29568
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6 col7 col8
row1 50.84529 48.33802 48.44782 49.59428 50.44169 106.5760 50.44489 50.17010
row5 49.80350 50.32101 49.56573 52.63363 48.21136 106.0057 49.65572 50.35725
col9 col10 col11 col12 col13 col14 col15 col16
row1 48.77274 50.41374 50.00417 50.85268 50.98088 50.16238 49.76864 50.35969
row5 49.45678 49.29568 48.19572 48.82009 48.81609 48.54410 49.61458 49.71330
col17 col18 col19 col20
row1 51.71035 49.63928 50.34057 103.2169
row5 50.41041 49.70069 49.88415 106.6470
> tmp[,c("col6","col20")]
col6 col20
row1 106.57600 103.21685
row2 74.22624 77.12191
row3 74.28217 73.99898
row4 72.66297 75.42438
row5 106.00573 106.64702
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 106.5760 103.2169
row5 106.0057 106.6470
>
>
> subBufferedMatrix(tmp,c("row1","row5"),c("col6","col20"))[1:2,1:2]
col6 col20
row1 106.5760 103.2169
row5 106.0057 106.6470
>
>
>
>
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
>
> tmp[,"col13"]
col13
[1,] -0.4076673
[2,] -0.8170153
[3,] 1.0219611
[4,] -0.1301806
[5,] -0.6517425
> tmp[,c("col17","col7")]
col17 col7
[1,] 0.57884511 -2.16598976
[2,] -0.03430508 0.07031875
[3,] 0.04753974 -1.58350430
[4,] -0.63526060 -0.21467800
[5,] 1.12990717 -0.73346373
>
> subBufferedMatrix(tmp,,c("col6","col20"))[,1:2]
col6 col20
[1,] 1.36046500 -1.7489383
[2,] -0.20415196 -0.0705798
[3,] -0.84896638 -0.3436134
[4,] -0.09142581 -1.6620963
[5,] -0.68458051 -1.4557686
> subBufferedMatrix(tmp,1,c("col6"))[,1]
col1
[1,] 1.360465
> subBufferedMatrix(tmp,1:2,c("col6"))[,1]
col6
[1,] 1.360465
[2,] -0.204152
>
>
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
>
>
>
> subBufferedMatrix(tmp,c("row3","row1"),)[,1:20]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row3 0.3993195 0.914657 -1.3435261 1.669533 1.018866 0.7576878 1.076517
row1 -0.5816809 0.436192 0.1630572 -1.578244 1.860678 -0.2500150 -2.107826
[,8] [,9] [,10] [,11] [,12] [,13]
row3 -2.333668 0.4920228 -0.9570082 -1.160541 -1.6124968 0.1468259
row1 1.853161 0.4934560 0.7556481 -0.434197 -0.2493374 0.4357992
[,14] [,15] [,16] [,17] [,18] [,19]
row3 -0.2314478192 -0.4673641 -0.0629807 -0.2626823 1.760858 0.9836591
row1 0.0008139638 -0.9758742 0.2332606 0.5055155 -1.261252 -0.3712828
[,20]
row3 -0.9821492
row1 -0.3188987
> subBufferedMatrix(tmp,c("row2"),1:10)[,1:10]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row2 -0.1924263 -0.3870562 -1.497337 1.083512 0.7636734 0.2838089 -2.318966
[,8] [,9] [,10]
row2 -0.5986179 0.3764702 -0.303888
> subBufferedMatrix(tmp,c("row5"),1:20)[,1:20]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row5 2.651281 0.4402686 0.3073411 -0.9141612 -0.3723133 -1.23232 -1.255126
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
row5 -1.09067 0.8065425 2.004007 -0.9066228 -0.8110925 0.8555542 -0.2808537
[,15] [,16] [,17] [,18] [,19] [,20]
row5 -0.2017128 -0.2103269 0.1355358 -1.33466 -0.1399158 -1.057243
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> colnames(tmp)
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
> rownames(tmp)
[1] "row1" "row2" "row3" "row4" "row5"
>
>
> colnames(tmp) <- NULL
> rownames(tmp) <- NULL
>
> colnames(tmp)
NULL
> rownames(tmp)
NULL
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> dimnames(tmp)
[[1]]
[1] "row1" "row2" "row3" "row4" "row5"
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
> dimnames(tmp) <- NULL
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> dimnames(tmp)
[[1]]
NULL
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
>
> dimnames(tmp) <- NULL
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
> dimnames(tmp)
[[1]]
[1] "row1" "row2" "row3" "row4" "row5"
[[2]]
NULL
>
> dimnames(tmp) <- list(NULL,c(colnames(tmp,do.NULL=FALSE)))
> dimnames(tmp)
[[1]]
NULL
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
>
>
> ###
> ### Testing logical indexing
> ###
> ###
>
> tmp <- createBufferedMatrix(230,15)
> tmp[1:230,1:15] <- rnorm(230*15)
> x <-tmp[1:230,1:15]
>
> for (rep in 1:10){
+ which.cols <- sample(c(TRUE,FALSE),15,replace=T)
+ which.rows <- sample(c(TRUE,FALSE),230,replace=T)
+
+ if (!all(tmp[which.rows,which.cols] == x[which.rows,which.cols])){
+ stop("No agreement when logical indexing\n")
+ }
+
+ if (!all(subBufferedMatrix(tmp,,which.cols)[,1:sum(which.cols)] == x[,which.cols])){
+ stop("No agreement when logical indexing in subBufferedMatrix cols\n")
+ }
+ if (!all(subBufferedMatrix(tmp,which.rows,)[1:sum(which.rows),] == x[which.rows,])){
+ stop("No agreement when logical indexing in subBufferedMatrix rows\n")
+ }
+
+
+ if (!all(subBufferedMatrix(tmp,which.rows,which.cols)[1:sum(which.rows),1:sum(which.cols)]== x[which.rows,which.cols])){
+ stop("No agreement when logical indexing in subBufferedMatrix rows and columns\n")
+ }
+ }
>
>
> ##
> ## Test the ReadOnlyMode
> ##
>
> ReadOnlyMode(tmp)
<pointer: 0x01d92a90>
> is.ReadOnlyMode(tmp)
[1] TRUE
>
> filenames(tmp)
[1] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c15642470"
[2] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c54c57f23"
[3] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c24012c"
[4] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c7b591a0"
[5] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c140a678c"
[6] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c3bda3bb"
[7] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c59019d8"
[8] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c71942b8a"
[9] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c3aff2d76"
[10] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c2a554d4d"
[11] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c344d4952"
[12] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c21da1cf6"
[13] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c46bc6383"
[14] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c118818e9"
[15] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_i386\\BM17c68f1722b"
>
>
> ### testing coercion functions
> ###
>
> tmp <- as(tmp,"matrix")
> tmp <- as(tmp,"BufferedMatrix")
>
>
>
> ### testing whether can move storage from one location to another
>
> MoveStorageDirectory(tmp,"NewDirectory",full.path=FALSE)
<pointer: 0x0355d588>
> MoveStorageDirectory(tmp,getwd(),full.path=TRUE)
<pointer: 0x0355d588>
Warning message:
In dir.create(new.directory) :
'C:\Users\biocbuild\bbs-3.7-bioc\meat\BufferedMatrix.Rcheck\tests_i386' already exists
>
>
> RowMode(tmp)
<pointer: 0x0355d588>
> rowMedians(tmp)
[1] -0.070610783 -0.076501168 -0.272381777 -0.064181004 0.052891375
[6] 0.273263520 0.128709731 -0.569709250 -0.247850766 -0.001210824
[11] 0.142045067 -0.479936163 -0.201072598 -0.036704396 -0.221943471
[16] 0.013805101 0.387561069 -0.339804562 0.644988708 0.083087501
[21] 0.378447148 0.482114192 0.163769412 -0.508275126 0.410329809
[26] -0.091759588 -0.235034738 0.155259686 0.062792851 -0.063267016
[31] -0.674369807 -0.162740763 -0.359610418 0.542739585 0.085722391
[36] 0.318982571 -0.118216614 -0.575236435 -0.222342995 -0.098828214
[41] -0.608556089 -0.188926160 0.344114014 0.131205968 -0.533302806
[46] 0.495702264 0.052613017 -0.162569819 0.209571336 0.572716121
[51] 0.522058727 -0.096125293 -0.338363404 -0.027878342 -0.138420863
[56] -0.117512728 -0.608589246 0.012959239 0.333532537 -0.076960112
[61] -0.221573442 0.080350704 -0.295772240 -0.214329078 -0.111635795
[66] 0.016401986 0.232401069 0.278004596 -0.150727094 0.037505076
[71] -0.089486945 -0.500222265 -0.162354205 -0.119306201 -0.054272257
[76] -0.523314220 -0.249144751 0.613429764 -0.013712178 -0.500383143
[81] -0.426173743 -0.125067769 0.220286011 0.008016561 0.314997256
[86] -0.658148653 -0.072571963 0.396114493 0.239345921 -0.306028833
[91] -0.015589270 -0.520368657 0.700161639 0.240294409 0.488154885
[96] -0.015813180 -0.313975255 0.426866260 0.349305361 -0.435470716
[101] -0.134295405 0.095958157 0.351241906 0.116568748 0.214486318
[106] 0.154298767 -0.299428559 0.018790644 -0.181337329 -0.178885991
[111] -0.096602171 0.104631867 0.405382058 -0.085114646 0.123681775
[116] -0.035519801 -0.132663927 -0.429296069 -0.139799582 -0.123793430
[121] 0.131310266 -0.194395495 -0.486530968 -0.020539414 -0.504586483
[126] 0.289660426 -0.057039300 0.708082249 -0.238765442 0.270183934
[131] -0.620391960 0.172153696 0.043970278 -0.069086313 -0.218379021
[136] -0.785205767 -0.124920300 0.117808520 -0.278205317 -0.235698377
[141] 0.324290405 0.375831674 0.804707262 -0.719928126 0.198490554
[146] -0.539183440 0.064647001 -0.105697890 0.033954319 -0.348401919
[151] 0.270613490 -0.387766415 0.449357921 -0.258294408 0.194482169
[156] 0.296603992 0.592073481 0.235365609 -0.388661531 -0.100372285
[161] 0.370155764 0.285760149 0.130941193 0.142423800 -0.067098646
[166] -0.351261814 -0.619346303 -0.490863949 0.285876061 0.312618018
[171] 0.620026121 0.158840209 0.231827579 -0.619639326 0.112077934
[176] -0.138570635 0.477206626 -0.105001494 -0.477500904 0.475392239
[181] 0.372992588 0.364275981 0.053873204 -0.246193978 -0.003587581
[186] 0.109392990 0.291572298 0.425940966 0.542081003 -0.039726047
[191] -0.142165734 0.085962011 -0.026460016 -0.242672261 0.171355818
[196] -0.287352481 0.328064693 -0.185482925 0.255297376 -0.359727434
[201] -0.139697161 0.455891231 -0.286156306 0.060191852 0.020299442
[206] 0.068075009 0.081746560 -0.156882099 -0.515309845 -0.185243507
[211] 0.483435114 -0.029006273 -0.418043534 -0.004565803 0.130407752
[216] -0.118726163 0.017213437 -0.057944323 -0.085981737 0.281449381
[221] 0.122594294 0.016758907 -0.479834706 -0.430402525 -0.380385897
[226] 0.143600063 0.068684974 0.112763904 0.012774128 0.224135060
>
> proc.time()
user system elapsed
3.64 9.12 13.75
|
BufferedMatrix.Rcheck/tests_x64/objectTesting.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
>
> ### this is used to control how many repetitions in something below
> ### higher values result in more checks.
> nreps <-100 ##20000
>
>
> ## test creation and some simple assignments and subsetting operations
>
> ## first on single elements
> tmp <- createBufferedMatrix(1000,10)
>
> tmp[10,5]
[1] 0
> tmp[10,5] <- 10
> tmp[10,5]
[1] 10
> tmp[10,5] <- 12.445
> tmp[10,5]
[1] 12.445
>
>
>
> ## now testing accessing multiple elements
> tmp2 <- createBufferedMatrix(10,20)
>
>
> tmp2[3,1] <- 51.34
> tmp2[9,2] <- 9.87654
> tmp2[,1:2]
[,1] [,2]
[1,] 0.00 0.00000
[2,] 0.00 0.00000
[3,] 51.34 0.00000
[4,] 0.00 0.00000
[5,] 0.00 0.00000
[6,] 0.00 0.00000
[7,] 0.00 0.00000
[8,] 0.00 0.00000
[9,] 0.00 9.87654
[10,] 0.00 0.00000
> tmp2[,-(3:20)]
[,1] [,2]
[1,] 0.00 0.00000
[2,] 0.00 0.00000
[3,] 51.34 0.00000
[4,] 0.00 0.00000
[5,] 0.00 0.00000
[6,] 0.00 0.00000
[7,] 0.00 0.00000
[8,] 0.00 0.00000
[9,] 0.00 9.87654
[10,] 0.00 0.00000
> tmp2[3,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 51.34 0 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19] [,20]
[1,] 0 0 0 0 0 0 0
> tmp2[-3,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 9.87654 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19] [,20]
[1,] 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0
> tmp2[2,1:3]
[,1] [,2] [,3]
[1,] 0 0 0
> tmp2[3:9,1:3]
[,1] [,2] [,3]
[1,] 51.34 0.00000 0
[2,] 0.00 0.00000 0
[3,] 0.00 0.00000 0
[4,] 0.00 0.00000 0
[5,] 0.00 0.00000 0
[6,] 0.00 0.00000 0
[7,] 0.00 9.87654 0
> tmp2[-4,-4]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[2,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[3,] 51.34 0.00000 0 0 0 0 0 0 0 0 0 0 0
[4,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[5,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[6,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[7,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[8,] 0.00 9.87654 0 0 0 0 0 0 0 0 0 0 0
[9,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19]
[1,] 0 0 0 0 0 0
[2,] 0 0 0 0 0 0
[3,] 0 0 0 0 0 0
[4,] 0 0 0 0 0 0
[5,] 0 0 0 0 0 0
[6,] 0 0 0 0 0 0
[7,] 0 0 0 0 0 0
[8,] 0 0 0 0 0 0
[9,] 0 0 0 0 0 0
>
> ## now testing accessing/assigning multiple elements
> tmp3 <- createBufferedMatrix(10,10)
>
> for (i in 1:10){
+ for (j in 1:10){
+ tmp3[i,j] <- (j-1)*10 + i
+ }
+ }
>
> tmp3[2:4,2:4]
[,1] [,2] [,3]
[1,] 12 22 32
[2,] 13 23 33
[3,] 14 24 34
> tmp3[c(-10),c(2:4,2:4,10,1,2,1:10,10:1)]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 11 21 31 11 21 31 91 1 11 1 11 21 31
[2,] 12 22 32 12 22 32 92 2 12 2 12 22 32
[3,] 13 23 33 13 23 33 93 3 13 3 13 23 33
[4,] 14 24 34 14 24 34 94 4 14 4 14 24 34
[5,] 15 25 35 15 25 35 95 5 15 5 15 25 35
[6,] 16 26 36 16 26 36 96 6 16 6 16 26 36
[7,] 17 27 37 17 27 37 97 7 17 7 17 27 37
[8,] 18 28 38 18 28 38 98 8 18 8 18 28 38
[9,] 19 29 39 19 29 39 99 9 19 9 19 29 39
[,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
[1,] 41 51 61 71 81 91 91 81 71 61 51 41
[2,] 42 52 62 72 82 92 92 82 72 62 52 42
[3,] 43 53 63 73 83 93 93 83 73 63 53 43
[4,] 44 54 64 74 84 94 94 84 74 64 54 44
[5,] 45 55 65 75 85 95 95 85 75 65 55 45
[6,] 46 56 66 76 86 96 96 86 76 66 56 46
[7,] 47 57 67 77 87 97 97 87 77 67 57 47
[8,] 48 58 68 78 88 98 98 88 78 68 58 48
[9,] 49 59 69 79 89 99 99 89 79 69 59 49
[,26] [,27] [,28] [,29]
[1,] 31 21 11 1
[2,] 32 22 12 2
[3,] 33 23 13 3
[4,] 34 24 14 4
[5,] 35 25 15 5
[6,] 36 26 16 6
[7,] 37 27 17 7
[8,] 38 28 18 8
[9,] 39 29 19 9
> tmp3[-c(1:5),-c(6:10)]
[,1] [,2] [,3] [,4] [,5]
[1,] 6 16 26 36 46
[2,] 7 17 27 37 47
[3,] 8 18 28 38 48
[4,] 9 19 29 39 49
[5,] 10 20 30 40 50
>
> ## assignment of whole columns
> tmp3[,1] <- c(1:10*100.0)
> tmp3[,1:2] <- tmp3[,1:2]*100
> tmp3[,1:2] <- tmp3[,2:1]
> tmp3[,1:2]
[,1] [,2]
[1,] 1100 1e+04
[2,] 1200 2e+04
[3,] 1300 3e+04
[4,] 1400 4e+04
[5,] 1500 5e+04
[6,] 1600 6e+04
[7,] 1700 7e+04
[8,] 1800 8e+04
[9,] 1900 9e+04
[10,] 2000 1e+05
>
>
> tmp3[,-1] <- tmp3[,1:9]
> tmp3[,1:10]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1100 1100 1e+04 21 31 41 51 61 71 81
[2,] 1200 1200 2e+04 22 32 42 52 62 72 82
[3,] 1300 1300 3e+04 23 33 43 53 63 73 83
[4,] 1400 1400 4e+04 24 34 44 54 64 74 84
[5,] 1500 1500 5e+04 25 35 45 55 65 75 85
[6,] 1600 1600 6e+04 26 36 46 56 66 76 86
[7,] 1700 1700 7e+04 27 37 47 57 67 77 87
[8,] 1800 1800 8e+04 28 38 48 58 68 78 88
[9,] 1900 1900 9e+04 29 39 49 59 69 79 89
[10,] 2000 2000 1e+05 30 40 50 60 70 80 90
>
> tmp3[,1:2] <- rep(1,10)
> tmp3[,1:2] <- rep(1,20)
> tmp3[,1:2] <- matrix(c(1:5),1,5)
>
> tmp3[,-c(1:8)] <- matrix(c(1:5),1,5)
>
> tmp3[1,] <- 1:10
> tmp3[1,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
> tmp3[-1,] <- c(1,2)
> tmp3[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
[2,] 1 2 1 2 1 2 1 2 1 2
[3,] 2 1 2 1 2 1 2 1 2 1
[4,] 1 2 1 2 1 2 1 2 1 2
[5,] 2 1 2 1 2 1 2 1 2 1
[6,] 1 2 1 2 1 2 1 2 1 2
[7,] 2 1 2 1 2 1 2 1 2 1
[8,] 1 2 1 2 1 2 1 2 1 2
[9,] 2 1 2 1 2 1 2 1 2 1
[10,] 1 2 1 2 1 2 1 2 1 2
> tmp3[-c(1:8),] <- matrix(c(1:5),1,5)
> tmp3[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
[2,] 1 2 1 2 1 2 1 2 1 2
[3,] 2 1 2 1 2 1 2 1 2 1
[4,] 1 2 1 2 1 2 1 2 1 2
[5,] 2 1 2 1 2 1 2 1 2 1
[6,] 1 2 1 2 1 2 1 2 1 2
[7,] 2 1 2 1 2 1 2 1 2 1
[8,] 1 2 1 2 1 2 1 2 1 2
[9,] 1 3 5 2 4 1 3 5 2 4
[10,] 2 4 1 3 5 2 4 1 3 5
>
>
> tmp3[1:2,1:2] <- 5555.04
> tmp3[-(1:2),1:2] <- 1234.56789
>
>
>
> ## testing accessors for the directory and prefix
> directory(tmp3)
[1] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64"
> prefix(tmp3)
[1] "BM"
>
> ## testing if we can remove these objects
> rm(tmp, tmp2, tmp3)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 403330 21.6 838565 44.8 627611 33.6
Vcells 709884 5.5 8388608 64.0 1668139 12.8
>
>
>
>
> ##
> ## checking reads
> ##
>
> tmp2 <- createBufferedMatrix(10,20)
>
> test.sample <- rnorm(10*20)
>
> tmp2[1:10,1:20] <- test.sample
>
> test.matrix <- matrix(test.sample,10,20)
>
> ## testing reads
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ which.col <- sample(1:20,1)
+ if (tmp2[which.row,which.col] != test.matrix[which.row,which.col]){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> date()
[1] "Wed Oct 17 00:52:04 2018"
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
> date()
[1] "Wed Oct 17 00:52:05 2018"
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,which.col] == test.matrix[which.row,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
>
>
> RowMode(tmp2)
<pointer: 0x0000000005eecfd0>
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ which.col <- sample(1:20,1)
+ if (tmp2[which.row,which.col] != test.matrix[which.row,which.col]){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,5,replace=TRUE)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> date()
[1] "Wed Oct 17 00:52:07 2018"
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:20,5,replace=TRUE)
+ if (!all(tmp2[which.row,which.col] == test.matrix[which.row,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
> date()
[1] "Wed Oct 17 00:52:08 2018"
>
> ColMode(tmp2)
<pointer: 0x0000000005eecfd0>
>
>
>
> ### Now testing assignments
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+
+ new.data <- rnorm(20)
+ tmp2[which.row,] <- new.data
+ test.matrix[which.row,] <- new.data
+ if (rep > 1){
+ if (!all(tmp2[prev.row,] == test.matrix[prev.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ new.data <- rnorm(10)
+ tmp2[,which.col] <- new.data
+ test.matrix[,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[,prev.col] == test.matrix[,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.col <- which.col
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,5,replace=TRUE)
+ new.data <- matrix(rnorm(50),5,10)
+ tmp2[,which.col] <- new.data
+ test.matrix[,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[,prev.col] == test.matrix[,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.col <- which.col
+ }
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ new.data <- matrix(rnorm(50),5,10)
+ tmp2[which.row,] <- new.data
+ test.matrix[which.row,]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[prev.row,] == test.matrix[prev.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:20,5,replace=TRUE)
+ new.data <- matrix(rnorm(25),5,5)
+ tmp2[which.row,which.col] <- new.data
+ test.matrix[which.row,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[prev.row,prev.col] == test.matrix[prev.row,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+ prev.col <- which.col
+ }
>
>
>
>
> ###
> ###
> ### testing some more functions
> ###
>
>
>
> ## duplication function
> tmp5 <- duplicate(tmp2)
>
> # making sure really did copy everything.
> tmp5[1,1] <- tmp5[1,1] +100.00
>
> if (tmp5[1,1] == tmp2[1,1]){
+ stop("Problem with duplication")
+ }
>
>
>
>
> ### testing elementwise applying of functions
>
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 100.7931875 -2.1499987 -0.4416072 0.6314798
[2,] 0.3764198 0.6854907 -0.9126349 1.1903530
[3,] -2.0554418 1.3538851 -0.3126795 -0.4150676
[4,] 1.1617579 0.8897976 1.1407717 -2.0917428
> ewApply(tmp5,abs)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 100.7931875 2.1499987 0.4416072 0.6314798
[2,] 0.3764198 0.6854907 0.9126349 1.1903530
[3,] 2.0554418 1.3538851 0.3126795 0.4150676
[4,] 1.1617579 0.8897976 1.1407717 2.0917428
> ewApply(tmp5,sqrt)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 10.0395810 1.4662874 0.6645353 0.7946570
[2,] 0.6135306 0.8279437 0.9553193 1.0910330
[3,] 1.4336812 1.1635657 0.5591776 0.6442574
[4,] 1.0778487 0.9432909 1.0680691 1.4462859
>
> my.function <- function(x,power){
+ (x+5)^power
+ }
>
> ewApply(tmp5,my.function,power=2)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 226.18900 41.81287 32.08696 33.57805
[2,] 31.51173 33.96493 35.46583 37.10068
[3,] 41.39225 37.98954 30.90446 31.85764
[4,] 36.94024 35.32271 36.82146 41.55460
>
>
>
> ## testing functions that elementwise transform the matrix
> sqrt(tmp5)
<pointer: 0x0000000005755fe0>
> exp(tmp5)
<pointer: 0x0000000005755fe0>
> log(tmp5,2)
<pointer: 0x0000000005755fe0>
> pow(tmp5,2)
>
>
>
>
>
> ## testing functions that apply to entire matrix
> Max(tmp5)
[1] 470.7828
> Min(tmp5)
[1] 53.32176
> mean(tmp5)
[1] 72.251
> Sum(tmp5)
[1] 14450.2
> Var(tmp5)
[1] 869.2813
>
>
> ## testing functions applied to rows or columns
>
> rowMeans(tmp5)
[1] 90.37930 72.66589 68.66068 72.52007 68.76694 70.28515 71.62882 69.44625
[9] 68.45077 69.70612
> rowSums(tmp5)
[1] 1807.586 1453.318 1373.214 1450.401 1375.339 1405.703 1432.576 1388.925
[9] 1369.015 1394.122
> rowVars(tmp5)
[1] 8071.62310 80.06444 83.15409 62.89010 62.87041 47.12915
[7] 108.08575 44.03313 70.07550 67.34138
> rowSd(tmp5)
[1] 89.842212 8.947874 9.118887 7.930328 7.929086 6.865068 10.396430
[8] 6.635746 8.371111 8.206179
> rowMax(tmp5)
[1] 470.78276 89.75034 86.15255 87.87864 88.92176 83.29299 87.38895
[8] 84.48967 82.83177 86.23337
> rowMin(tmp5)
[1] 58.21757 56.79416 54.15604 59.30006 55.47125 56.14880 54.72657 53.32176
[9] 55.28051 56.43904
>
> colMeans(tmp5)
[1] 110.99394 74.41081 68.07508 71.71721 68.78662 66.62816 72.55256
[8] 69.02008 70.37073 70.73979 69.67655 69.21377 73.32194 69.21696
[15] 71.21166 64.24813 73.18834 73.24632 71.04196 67.35936
> colSums(tmp5)
[1] 1109.9394 744.1081 680.7508 717.1721 687.8662 666.2816 725.5256
[8] 690.2008 703.7073 707.3979 696.7655 692.1377 733.2194 692.1696
[15] 712.1166 642.4813 731.8834 732.4632 710.4196 673.5936
> colVars(tmp5)
[1] 16026.42594 58.64844 33.48510 72.28461 48.14326 64.79722
[7] 102.05998 88.84794 38.32945 49.27473 57.28673 56.67930
[13] 119.81191 71.78338 49.37200 36.24888 106.53765 72.53677
[19] 98.08397 79.07084
> colSd(tmp5)
[1] 126.595521 7.658227 5.786632 8.502036 6.938534 8.049672
[7] 10.102474 9.425919 6.191078 7.019596 7.568800 7.528566
[13] 10.945863 8.472507 7.026521 6.020705 10.321707 8.516852
[19] 9.903735 8.892178
> colMax(tmp5)
[1] 470.78276 87.02802 76.63905 86.49046 82.77850 78.19441 88.92176
[8] 82.83177 80.64121 84.35148 81.33952 81.66647 89.75034 80.58134
[15] 79.35792 75.38316 86.23337 84.70655 87.87864 86.45557
> colMin(tmp5)
[1] 64.24588 63.33953 56.89893 55.47125 57.86861 54.72657 58.21757 56.14880
[9] 57.52540 58.77664 59.79968 56.20540 56.43904 54.15604 55.89213 56.79416
[17] 59.31862 59.03011 57.15400 53.32176
>
>
> ### setting a random element to NA and then testing with na.rm=TRUE or na.rm=FALSE (The default)
>
>
> which.row <- sample(1:10,1,replace=TRUE)
> which.col <- sample(1:20,1,replace=TRUE)
>
> tmp5[which.row,which.col] <- NA
>
> Max(tmp5)
[1] NA
> Min(tmp5)
[1] NA
> mean(tmp5)
[1] NA
> Sum(tmp5)
[1] NA
> Var(tmp5)
[1] NA
>
> rowMeans(tmp5)
[1] NA 72.66589 68.66068 72.52007 68.76694 70.28515 71.62882 69.44625
[9] 68.45077 69.70612
> rowSums(tmp5)
[1] NA 1453.318 1373.214 1450.401 1375.339 1405.703 1432.576 1388.925
[9] 1369.015 1394.122
> rowVars(tmp5)
[1] 8503.64815 80.06444 83.15409 62.89010 62.87041 47.12915
[7] 108.08575 44.03313 70.07550 67.34138
> rowSd(tmp5)
[1] 92.215227 8.947874 9.118887 7.930328 7.929086 6.865068 10.396430
[8] 6.635746 8.371111 8.206179
> rowMax(tmp5)
[1] NA 89.75034 86.15255 87.87864 88.92176 83.29299 87.38895 84.48967
[9] 82.83177 86.23337
> rowMin(tmp5)
[1] NA 56.79416 54.15604 59.30006 55.47125 56.14880 54.72657 53.32176
[9] 55.28051 56.43904
>
> colMeans(tmp5)
[1] 110.99394 74.41081 68.07508 71.71721 68.78662 66.62816 72.55256
[8] 69.02008 70.37073 70.73979 69.67655 NA 73.32194 69.21696
[15] 71.21166 64.24813 73.18834 73.24632 71.04196 67.35936
> colSums(tmp5)
[1] 1109.9394 744.1081 680.7508 717.1721 687.8662 666.2816 725.5256
[8] 690.2008 703.7073 707.3979 696.7655 NA 733.2194 692.1696
[15] 712.1166 642.4813 731.8834 732.4632 710.4196 673.5936
> colVars(tmp5)
[1] 16026.42594 58.64844 33.48510 72.28461 48.14326 64.79722
[7] 102.05998 88.84794 38.32945 49.27473 57.28673 NA
[13] 119.81191 71.78338 49.37200 36.24888 106.53765 72.53677
[19] 98.08397 79.07084
> colSd(tmp5)
[1] 126.595521 7.658227 5.786632 8.502036 6.938534 8.049672
[7] 10.102474 9.425919 6.191078 7.019596 7.568800 NA
[13] 10.945863 8.472507 7.026521 6.020705 10.321707 8.516852
[19] 9.903735 8.892178
> colMax(tmp5)
[1] 470.78276 87.02802 76.63905 86.49046 82.77850 78.19441 88.92176
[8] 82.83177 80.64121 84.35148 81.33952 NA 89.75034 80.58134
[15] 79.35792 75.38316 86.23337 84.70655 87.87864 86.45557
> colMin(tmp5)
[1] 64.24588 63.33953 56.89893 55.47125 57.86861 54.72657 58.21757 56.14880
[9] 57.52540 58.77664 59.79968 NA 56.43904 54.15604 55.89213 56.79416
[17] 59.31862 59.03011 57.15400 53.32176
>
> Max(tmp5,na.rm=TRUE)
[1] 470.7828
> Min(tmp5,na.rm=TRUE)
[1] 53.32176
> mean(tmp5,na.rm=TRUE)
[1] 72.24405
> Sum(tmp5,na.rm=TRUE)
[1] 14376.57
> Var(tmp5,na.rm=TRUE)
[1] 873.6619
>
> rowMeans(tmp5,na.rm=TRUE)
[1] 91.26064 72.66589 68.66068 72.52007 68.76694 70.28515 71.62882 69.44625
[9] 68.45077 69.70612
> rowSums(tmp5,na.rm=TRUE)
[1] 1733.952 1453.318 1373.214 1450.401 1375.339 1405.703 1432.576 1388.925
[9] 1369.015 1394.122
> rowVars(tmp5,na.rm=TRUE)
[1] 8503.64815 80.06444 83.15409 62.89010 62.87041 47.12915
[7] 108.08575 44.03313 70.07550 67.34138
> rowSd(tmp5,na.rm=TRUE)
[1] 92.215227 8.947874 9.118887 7.930328 7.929086 6.865068 10.396430
[8] 6.635746 8.371111 8.206179
> rowMax(tmp5,na.rm=TRUE)
[1] 470.78276 89.75034 86.15255 87.87864 88.92176 83.29299 87.38895
[8] 84.48967 82.83177 86.23337
> rowMin(tmp5,na.rm=TRUE)
[1] 58.21757 56.79416 54.15604 59.30006 55.47125 56.14880 54.72657 53.32176
[9] 55.28051 56.43904
>
> colMeans(tmp5,na.rm=TRUE)
[1] 110.99394 74.41081 68.07508 71.71721 68.78662 66.62816 72.55256
[8] 69.02008 70.37073 70.73979 69.67655 68.72267 73.32194 69.21696
[15] 71.21166 64.24813 73.18834 73.24632 71.04196 67.35936
> colSums(tmp5,na.rm=TRUE)
[1] 1109.9394 744.1081 680.7508 717.1721 687.8662 666.2816 725.5256
[8] 690.2008 703.7073 707.3979 696.7655 618.5040 733.2194 692.1696
[15] 712.1166 642.4813 731.8834 732.4632 710.4196 673.5936
> colVars(tmp5,na.rm=TRUE)
[1] 16026.42594 58.64844 33.48510 72.28461 48.14326 64.79722
[7] 102.05998 88.84794 38.32945 49.27473 57.28673 61.05086
[13] 119.81191 71.78338 49.37200 36.24888 106.53765 72.53677
[19] 98.08397 79.07084
> colSd(tmp5,na.rm=TRUE)
[1] 126.595521 7.658227 5.786632 8.502036 6.938534 8.049672
[7] 10.102474 9.425919 6.191078 7.019596 7.568800 7.813505
[13] 10.945863 8.472507 7.026521 6.020705 10.321707 8.516852
[19] 9.903735 8.892178
> colMax(tmp5,na.rm=TRUE)
[1] 470.78276 87.02802 76.63905 86.49046 82.77850 78.19441 88.92176
[8] 82.83177 80.64121 84.35148 81.33952 81.66647 89.75034 80.58134
[15] 79.35792 75.38316 86.23337 84.70655 87.87864 86.45557
> colMin(tmp5,na.rm=TRUE)
[1] 64.24588 63.33953 56.89893 55.47125 57.86861 54.72657 58.21757 56.14880
[9] 57.52540 58.77664 59.79968 56.20540 56.43904 54.15604 55.89213 56.79416
[17] 59.31862 59.03011 57.15400 53.32176
>
> # now set an entire row to NA
>
> tmp5[which.row,] <- NA
> rowMeans(tmp5,na.rm=TRUE)
[1] NaN 72.66589 68.66068 72.52007 68.76694 70.28515 71.62882 69.44625
[9] 68.45077 69.70612
> rowSums(tmp5,na.rm=TRUE)
[1] 0.000 1453.318 1373.214 1450.401 1375.339 1405.703 1432.576 1388.925
[9] 1369.015 1394.122
> rowVars(tmp5,na.rm=TRUE)
[1] NA 80.06444 83.15409 62.89010 62.87041 47.12915 108.08575
[8] 44.03313 70.07550 67.34138
> rowSd(tmp5,na.rm=TRUE)
[1] NA 8.947874 9.118887 7.930328 7.929086 6.865068 10.396430
[8] 6.635746 8.371111 8.206179
> rowMax(tmp5,na.rm=TRUE)
[1] NA 89.75034 86.15255 87.87864 88.92176 83.29299 87.38895 84.48967
[9] 82.83177 86.23337
> rowMin(tmp5,na.rm=TRUE)
[1] NA 56.79416 54.15604 59.30006 55.47125 56.14880 54.72657 53.32176
[9] 55.28051 56.43904
>
>
> # now set an entire col to NA
>
>
> tmp5[,which.col] <- NA
> colMeans(tmp5,na.rm=TRUE)
[1] 71.01740 73.00890 68.21844 71.92042 69.07759 65.34302 74.14534 69.78847
[9] 69.22956 71.08223 68.38066 NaN 74.96694 68.95180 71.06444 64.15256
[17] 73.90347 73.62941 70.75341 67.37812
> colSums(tmp5,na.rm=TRUE)
[1] 639.1566 657.0801 613.9660 647.2838 621.6983 588.0872 667.3081 628.0962
[9] 623.0661 639.7401 615.4260 0.0000 674.7025 620.5662 639.5799 577.3731
[17] 665.1312 662.6647 636.7807 606.4031
> colVars(tmp5,na.rm=TRUE)
[1] 50.84014 43.86923 37.43952 80.85562 53.20870 54.31657 86.27689
[8] 93.31167 28.47025 54.11484 45.55523 NA 104.34565 79.96532
[15] 55.29964 40.67726 114.10142 79.95290 109.40776 88.95073
> colSd(tmp5,na.rm=TRUE)
[1] 7.130227 6.623385 6.118784 8.991976 7.294430 7.369978 9.288536
[8] 9.659797 5.335752 7.356279 6.749462 NA 10.214972 8.942333
[15] 7.436373 6.377873 10.681827 8.941639 10.459816 9.431369
> colMax(tmp5,na.rm=TRUE)
[1] 86.15255 84.48967 76.63905 86.49046 82.77850 75.77204 88.92176 82.83177
[9] 75.69825 84.35148 80.67654 -Inf 89.75034 80.58134 79.35792 75.38316
[17] 86.23337 84.70655 87.87864 86.45557
> colMin(tmp5,na.rm=TRUE)
[1] 64.24588 63.33953 56.89893 55.47125 57.86861 54.72657 62.65081 56.14880
[9] 57.52540 58.77664 59.79968 Inf 56.43904 54.15604 55.89213 56.79416
[17] 59.31862 59.03011 57.15400 53.32176
>
>
>
>
> copymatrix <- matrix(rnorm(200,150,15),10,20)
>
> tmp5[1:10,1:20] <- copymatrix
> which.row <- 3
> which.col <- 1
> cat(which.row," ",which.col,"\n")
3 1
> tmp5[which.row,which.col] <- NA
> copymatrix[which.row,which.col] <- NA
>
> rowVars(tmp5,na.rm=TRUE)
[1] 338.86788 180.67092 143.88725 115.62999 174.19330 217.13365 322.44579
[8] 172.30848 313.85469 92.26242
> apply(copymatrix,1,var,na.rm=TRUE)
[1] 338.86788 180.67092 143.88725 115.62999 174.19330 217.13365 322.44579
[8] 172.30848 313.85469 92.26242
>
>
>
> copymatrix <- matrix(rnorm(200,150,15),10,20)
>
> tmp5[1:10,1:20] <- copymatrix
> which.row <- 1
> which.col <- 3
> cat(which.row," ",which.col,"\n")
1 3
> tmp5[which.row,which.col] <- NA
> copymatrix[which.row,which.col] <- NA
>
> colVars(tmp5,na.rm=TRUE)-apply(copymatrix,2,var,na.rm=TRUE)
[1] 2.842171e-14 -1.421085e-13 2.842171e-14 1.136868e-13 -4.263256e-14
[6] -5.684342e-14 0.000000e+00 8.526513e-14 -5.684342e-14 0.000000e+00
[11] 5.684342e-14 5.684342e-14 0.000000e+00 -2.842171e-14 5.684342e-14
[16] 8.526513e-14 2.842171e-14 0.000000e+00 -2.842171e-13 -4.263256e-14
>
>
>
>
>
>
>
>
>
>
> ## making sure these things agree
> ##
> ## first when there is no NA
>
>
>
> agree.checks <- function(buff.matrix,r.matrix,err.tol=1e-10){
+
+ if (Max(buff.matrix,na.rm=TRUE) != max(r.matrix,na.rm=TRUE)){
+ stop("No agreement in Max")
+ }
+
+
+ if (Min(buff.matrix,na.rm=TRUE) != min(r.matrix,na.rm=TRUE)){
+ stop("No agreement in Min")
+ }
+
+
+ if (abs(Sum(buff.matrix,na.rm=TRUE)- sum(r.matrix,na.rm=TRUE)) > err.tol){
+
+ cat(Sum(buff.matrix,na.rm=TRUE),"\n")
+ cat(sum(r.matrix,na.rm=TRUE),"\n")
+ cat(Sum(buff.matrix,na.rm=TRUE) - sum(r.matrix,na.rm=TRUE),"\n")
+
+ stop("No agreement in Sum")
+ }
+
+ if (abs(mean(buff.matrix,na.rm=TRUE) - mean(r.matrix,na.rm=TRUE)) > err.tol){
+ stop("No agreement in mean")
+ }
+
+
+ if(abs(Var(buff.matrix,na.rm=TRUE) - var(as.vector(r.matrix),na.rm=TRUE)) > err.tol){
+ stop("No agreement in Var")
+ }
+
+
+
+ if(any(abs(rowMeans(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,mean,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowMeans")
+ }
+
+
+ if(any(abs(colMeans(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,mean,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in colMeans")
+ }
+
+
+ if(any(abs(rowSums(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,sum,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in rowSums")
+ }
+
+
+ if(any(abs(colSums(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,sum,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in colSums")
+ }
+
+ ### this is to get around the fact that R doesn't like to compute NA on an entire vector of NA when
+ ### computing variance
+ my.Var <- function(x,na.rm=FALSE){
+ if (all(is.na(x))){
+ return(NA)
+ } else {
+ var(x,na.rm=na.rm)
+ }
+
+ }
+
+ if(any(abs(rowVars(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,my.Var,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowVars")
+ }
+
+
+ if(any(abs(colVars(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,my.Var,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowVars")
+ }
+
+
+ if(any(abs(rowMax(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,max,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMax")
+ }
+
+
+ if(any(abs(colMax(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,max,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMax")
+ }
+
+
+
+ if(any(abs(rowMin(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,min,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMin")
+ }
+
+
+ if(any(abs(colMin(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,min,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMin")
+ }
+
+ if(any(abs(colMedians(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,median,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMedian")
+ }
+
+ if(any(abs(colRanges(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,range,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colRanges")
+ }
+
+
+
+ }
>
>
>
>
>
>
>
>
>
> for (rep in 1:20){
+ copymatrix <- matrix(rnorm(200,150,15),10,20)
+
+ tmp5[1:10,1:20] <- copymatrix
+
+
+ agree.checks(tmp5,copymatrix)
+
+ ## now lets assign some NA values and check agreement
+
+ which.row <- sample(1:10,1,replace=TRUE)
+ which.col <- sample(1:20,1,replace=TRUE)
+
+ cat(which.row," ",which.col,"\n")
+
+ tmp5[which.row,which.col] <- NA
+ copymatrix[which.row,which.col] <- NA
+
+ agree.checks(tmp5,copymatrix)
+
+ ## make an entire row NA
+ tmp5[which.row,] <- NA
+ copymatrix[which.row,] <- NA
+
+
+ agree.checks(tmp5,copymatrix)
+
+ ### also make an entire col NA
+ tmp5[,which.col] <- NA
+ copymatrix[,which.col] <- NA
+
+ agree.checks(tmp5,copymatrix)
+
+ ### now make 1 element non NA with NA in the rest of row and column
+
+ tmp5[which.row,which.col] <- rnorm(1,150,15)
+ copymatrix[which.row,which.col] <- tmp5[which.row,which.col]
+
+ agree.checks(tmp5,copymatrix)
+ }
10 2
6 16
4 6
3 7
3 20
1 7
9 13
3 12
6 7
9 11
5 19
8 11
4 16
7 6
5 9
6 6
2 2
2 14
1 9
4 3
There were 50 or more warnings (use warnings() to see the first 50)
>
>
> ### now test 1 by n and n by 1 matrix
>
>
> err.tol <- 1e-12
>
> rm(tmp5)
>
> dataset1 <- rnorm(100)
> dataset2 <- rnorm(100)
>
> tmp <- createBufferedMatrix(1,100)
> tmp[1,] <- dataset1
>
> tmp2 <- createBufferedMatrix(100,1)
> tmp2[,1] <- dataset2
>
>
>
>
>
> Max(tmp)
[1] 2.810854
> Min(tmp)
[1] -2.371485
> mean(tmp)
[1] 0.1280285
> Sum(tmp)
[1] 12.80285
> Var(tmp)
[1] 1.152784
>
> rowMeans(tmp)
[1] 0.1280285
> rowSums(tmp)
[1] 12.80285
> rowVars(tmp)
[1] 1.152784
> rowSd(tmp)
[1] 1.073678
> rowMax(tmp)
[1] 2.810854
> rowMin(tmp)
[1] -2.371485
>
> colMeans(tmp)
[1] -1.81758735 -0.37631645 1.10107596 -0.80788793 -0.36184521 -0.81995991
[7] 0.44028968 0.81382620 -0.09385300 2.28202789 -0.66664589 0.22528281
[13] 0.67168953 0.44942433 1.52053732 0.91992127 1.68034107 -0.37287881
[19] 0.09147145 -0.24643296 0.82702440 0.64623192 2.76668135 0.85769170
[25] 0.72043752 -1.64952863 0.65846168 1.92123112 -0.23836755 2.81085416
[31] -0.68751229 0.92683864 0.02791746 -1.44132287 0.50474358 0.59426266
[37] -0.28083484 0.97468292 2.40117408 0.86448852 -0.59757913 0.95957426
[43] 0.08700159 0.97198444 -2.37148541 0.05629480 0.08186099 0.69827339
[49] -1.09459706 0.25220384 -1.76705618 -0.94740033 -1.47846820 0.04835885
[55] 1.96201666 1.00134478 1.20703709 -0.66638763 0.39872687 -1.61970387
[61] -0.22851478 0.47474696 -0.47515119 1.06515085 -0.09514412 -0.46352404
[67] -0.39487473 0.26056262 0.73462650 2.13369420 0.22112939 0.50940059
[73] 0.96975810 -1.03981790 1.47173976 -0.63366485 -1.14438359 -1.29905260
[79] 1.41879337 0.38047829 -0.96151552 -0.88690628 0.05525780 0.67466076
[85] -0.01443946 1.06523842 0.55766382 -1.48571668 -0.41000857 -0.04784583
[91] 0.89758602 -1.97197249 -0.82297018 -0.96244370 -1.68081083 -0.99910416
[97] 0.22574755 0.59904248 -0.56989865 0.65569286
> colSums(tmp)
[1] -1.81758735 -0.37631645 1.10107596 -0.80788793 -0.36184521 -0.81995991
[7] 0.44028968 0.81382620 -0.09385300 2.28202789 -0.66664589 0.22528281
[13] 0.67168953 0.44942433 1.52053732 0.91992127 1.68034107 -0.37287881
[19] 0.09147145 -0.24643296 0.82702440 0.64623192 2.76668135 0.85769170
[25] 0.72043752 -1.64952863 0.65846168 1.92123112 -0.23836755 2.81085416
[31] -0.68751229 0.92683864 0.02791746 -1.44132287 0.50474358 0.59426266
[37] -0.28083484 0.97468292 2.40117408 0.86448852 -0.59757913 0.95957426
[43] 0.08700159 0.97198444 -2.37148541 0.05629480 0.08186099 0.69827339
[49] -1.09459706 0.25220384 -1.76705618 -0.94740033 -1.47846820 0.04835885
[55] 1.96201666 1.00134478 1.20703709 -0.66638763 0.39872687 -1.61970387
[61] -0.22851478 0.47474696 -0.47515119 1.06515085 -0.09514412 -0.46352404
[67] -0.39487473 0.26056262 0.73462650 2.13369420 0.22112939 0.50940059
[73] 0.96975810 -1.03981790 1.47173976 -0.63366485 -1.14438359 -1.29905260
[79] 1.41879337 0.38047829 -0.96151552 -0.88690628 0.05525780 0.67466076
[85] -0.01443946 1.06523842 0.55766382 -1.48571668 -0.41000857 -0.04784583
[91] 0.89758602 -1.97197249 -0.82297018 -0.96244370 -1.68081083 -0.99910416
[97] 0.22574755 0.59904248 -0.56989865 0.65569286
> colVars(tmp)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> colSd(tmp)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> colMax(tmp)
[1] -1.81758735 -0.37631645 1.10107596 -0.80788793 -0.36184521 -0.81995991
[7] 0.44028968 0.81382620 -0.09385300 2.28202789 -0.66664589 0.22528281
[13] 0.67168953 0.44942433 1.52053732 0.91992127 1.68034107 -0.37287881
[19] 0.09147145 -0.24643296 0.82702440 0.64623192 2.76668135 0.85769170
[25] 0.72043752 -1.64952863 0.65846168 1.92123112 -0.23836755 2.81085416
[31] -0.68751229 0.92683864 0.02791746 -1.44132287 0.50474358 0.59426266
[37] -0.28083484 0.97468292 2.40117408 0.86448852 -0.59757913 0.95957426
[43] 0.08700159 0.97198444 -2.37148541 0.05629480 0.08186099 0.69827339
[49] -1.09459706 0.25220384 -1.76705618 -0.94740033 -1.47846820 0.04835885
[55] 1.96201666 1.00134478 1.20703709 -0.66638763 0.39872687 -1.61970387
[61] -0.22851478 0.47474696 -0.47515119 1.06515085 -0.09514412 -0.46352404
[67] -0.39487473 0.26056262 0.73462650 2.13369420 0.22112939 0.50940059
[73] 0.96975810 -1.03981790 1.47173976 -0.63366485 -1.14438359 -1.29905260
[79] 1.41879337 0.38047829 -0.96151552 -0.88690628 0.05525780 0.67466076
[85] -0.01443946 1.06523842 0.55766382 -1.48571668 -0.41000857 -0.04784583
[91] 0.89758602 -1.97197249 -0.82297018 -0.96244370 -1.68081083 -0.99910416
[97] 0.22574755 0.59904248 -0.56989865 0.65569286
> colMin(tmp)
[1] -1.81758735 -0.37631645 1.10107596 -0.80788793 -0.36184521 -0.81995991
[7] 0.44028968 0.81382620 -0.09385300 2.28202789 -0.66664589 0.22528281
[13] 0.67168953 0.44942433 1.52053732 0.91992127 1.68034107 -0.37287881
[19] 0.09147145 -0.24643296 0.82702440 0.64623192 2.76668135 0.85769170
[25] 0.72043752 -1.64952863 0.65846168 1.92123112 -0.23836755 2.81085416
[31] -0.68751229 0.92683864 0.02791746 -1.44132287 0.50474358 0.59426266
[37] -0.28083484 0.97468292 2.40117408 0.86448852 -0.59757913 0.95957426
[43] 0.08700159 0.97198444 -2.37148541 0.05629480 0.08186099 0.69827339
[49] -1.09459706 0.25220384 -1.76705618 -0.94740033 -1.47846820 0.04835885
[55] 1.96201666 1.00134478 1.20703709 -0.66638763 0.39872687 -1.61970387
[61] -0.22851478 0.47474696 -0.47515119 1.06515085 -0.09514412 -0.46352404
[67] -0.39487473 0.26056262 0.73462650 2.13369420 0.22112939 0.50940059
[73] 0.96975810 -1.03981790 1.47173976 -0.63366485 -1.14438359 -1.29905260
[79] 1.41879337 0.38047829 -0.96151552 -0.88690628 0.05525780 0.67466076
[85] -0.01443946 1.06523842 0.55766382 -1.48571668 -0.41000857 -0.04784583
[91] 0.89758602 -1.97197249 -0.82297018 -0.96244370 -1.68081083 -0.99910416
[97] 0.22574755 0.59904248 -0.56989865 0.65569286
> colMedians(tmp)
[1] -1.81758735 -0.37631645 1.10107596 -0.80788793 -0.36184521 -0.81995991
[7] 0.44028968 0.81382620 -0.09385300 2.28202789 -0.66664589 0.22528281
[13] 0.67168953 0.44942433 1.52053732 0.91992127 1.68034107 -0.37287881
[19] 0.09147145 -0.24643296 0.82702440 0.64623192 2.76668135 0.85769170
[25] 0.72043752 -1.64952863 0.65846168 1.92123112 -0.23836755 2.81085416
[31] -0.68751229 0.92683864 0.02791746 -1.44132287 0.50474358 0.59426266
[37] -0.28083484 0.97468292 2.40117408 0.86448852 -0.59757913 0.95957426
[43] 0.08700159 0.97198444 -2.37148541 0.05629480 0.08186099 0.69827339
[49] -1.09459706 0.25220384 -1.76705618 -0.94740033 -1.47846820 0.04835885
[55] 1.96201666 1.00134478 1.20703709 -0.66638763 0.39872687 -1.61970387
[61] -0.22851478 0.47474696 -0.47515119 1.06515085 -0.09514412 -0.46352404
[67] -0.39487473 0.26056262 0.73462650 2.13369420 0.22112939 0.50940059
[73] 0.96975810 -1.03981790 1.47173976 -0.63366485 -1.14438359 -1.29905260
[79] 1.41879337 0.38047829 -0.96151552 -0.88690628 0.05525780 0.67466076
[85] -0.01443946 1.06523842 0.55766382 -1.48571668 -0.41000857 -0.04784583
[91] 0.89758602 -1.97197249 -0.82297018 -0.96244370 -1.68081083 -0.99910416
[97] 0.22574755 0.59904248 -0.56989865 0.65569286
> colRanges(tmp)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] -1.817587 -0.3763164 1.101076 -0.8078879 -0.3618452 -0.8199599 0.4402897
[2,] -1.817587 -0.3763164 1.101076 -0.8078879 -0.3618452 -0.8199599 0.4402897
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 0.8138262 -0.093853 2.282028 -0.6666459 0.2252828 0.6716895 0.4494243
[2,] 0.8138262 -0.093853 2.282028 -0.6666459 0.2252828 0.6716895 0.4494243
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 1.520537 0.9199213 1.680341 -0.3728788 0.09147145 -0.246433 0.8270244
[2,] 1.520537 0.9199213 1.680341 -0.3728788 0.09147145 -0.246433 0.8270244
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
[1,] 0.6462319 2.766681 0.8576917 0.7204375 -1.649529 0.6584617 1.921231
[2,] 0.6462319 2.766681 0.8576917 0.7204375 -1.649529 0.6584617 1.921231
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
[1,] -0.2383676 2.810854 -0.6875123 0.9268386 0.02791746 -1.441323 0.5047436
[2,] -0.2383676 2.810854 -0.6875123 0.9268386 0.02791746 -1.441323 0.5047436
[,36] [,37] [,38] [,39] [,40] [,41] [,42]
[1,] 0.5942627 -0.2808348 0.9746829 2.401174 0.8644885 -0.5975791 0.9595743
[2,] 0.5942627 -0.2808348 0.9746829 2.401174 0.8644885 -0.5975791 0.9595743
[,43] [,44] [,45] [,46] [,47] [,48] [,49]
[1,] 0.08700159 0.9719844 -2.371485 0.0562948 0.08186099 0.6982734 -1.094597
[2,] 0.08700159 0.9719844 -2.371485 0.0562948 0.08186099 0.6982734 -1.094597
[,50] [,51] [,52] [,53] [,54] [,55] [,56]
[1,] 0.2522038 -1.767056 -0.9474003 -1.478468 0.04835885 1.962017 1.001345
[2,] 0.2522038 -1.767056 -0.9474003 -1.478468 0.04835885 1.962017 1.001345
[,57] [,58] [,59] [,60] [,61] [,62] [,63]
[1,] 1.207037 -0.6663876 0.3987269 -1.619704 -0.2285148 0.474747 -0.4751512
[2,] 1.207037 -0.6663876 0.3987269 -1.619704 -0.2285148 0.474747 -0.4751512
[,64] [,65] [,66] [,67] [,68] [,69] [,70]
[1,] 1.065151 -0.09514412 -0.463524 -0.3948747 0.2605626 0.7346265 2.133694
[2,] 1.065151 -0.09514412 -0.463524 -0.3948747 0.2605626 0.7346265 2.133694
[,71] [,72] [,73] [,74] [,75] [,76] [,77]
[1,] 0.2211294 0.5094006 0.9697581 -1.039818 1.47174 -0.6336648 -1.144384
[2,] 0.2211294 0.5094006 0.9697581 -1.039818 1.47174 -0.6336648 -1.144384
[,78] [,79] [,80] [,81] [,82] [,83] [,84]
[1,] -1.299053 1.418793 0.3804783 -0.9615155 -0.8869063 0.0552578 0.6746608
[2,] -1.299053 1.418793 0.3804783 -0.9615155 -0.8869063 0.0552578 0.6746608
[,85] [,86] [,87] [,88] [,89] [,90] [,91]
[1,] -0.01443946 1.065238 0.5576638 -1.485717 -0.4100086 -0.04784583 0.897586
[2,] -0.01443946 1.065238 0.5576638 -1.485717 -0.4100086 -0.04784583 0.897586
[,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] -1.971972 -0.8229702 -0.9624437 -1.680811 -0.9991042 0.2257476 0.5990425
[2,] -1.971972 -0.8229702 -0.9624437 -1.680811 -0.9991042 0.2257476 0.5990425
[,99] [,100]
[1,] -0.5698987 0.6556929
[2,] -0.5698987 0.6556929
>
>
> Max(tmp2)
[1] 2.393534
> Min(tmp2)
[1] -2.170584
> mean(tmp2)
[1] 0.04664353
> Sum(tmp2)
[1] 4.664353
> Var(tmp2)
[1] 1.052797
>
> rowMeans(tmp2)
[1] 0.09498011 -0.57201587 -0.46992287 -0.96368156 0.79554442 -1.27999092
[7] 1.07263270 -1.69274244 1.67176797 -0.34646283 -0.10361239 0.34464763
[13] -0.70088687 -0.21919248 -0.06408695 -0.35860443 0.52080356 0.73158310
[19] 0.42369736 -0.01613864 -0.94622843 -0.51260876 0.45533124 -1.03174092
[25] 1.21557349 -0.15240934 -0.32371662 0.08858951 -1.18639099 0.52943371
[31] -1.73280878 -0.01730358 0.22754554 -0.49884835 -0.42709759 -1.04350675
[37] -1.76488938 -0.06200558 0.42949553 0.95555534 -0.95593292 -0.11730765
[43] -0.09902903 -1.69341119 0.45836821 1.14689528 1.70182363 1.34805797
[49] -0.75578158 0.31671918 -0.38465290 2.01815412 -0.47248582 0.57543970
[55] 0.98036400 0.23870180 0.77630762 -0.14165312 -0.33501876 -1.78617639
[61] 2.00661856 0.95088266 2.25065667 0.41507188 0.99607056 -0.33807306
[67] 0.22674791 -0.84212276 1.74617191 -1.13887815 -1.47956896 0.42699769
[73] 0.33598710 1.56741773 0.48571355 0.24999473 0.58440630 -1.58718014
[79] -0.59585322 -1.94614480 -0.03920133 1.49718612 -2.17058378 1.67405980
[85] 2.39353418 1.03123971 0.94499162 -0.91306449 0.25214658 0.49008795
[91] -0.29035316 -1.50635889 1.18205893 0.49311308 0.06534970 1.09641596
[97] -0.24068444 -0.67010089 -1.78550545 0.95543606
> rowSums(tmp2)
[1] 0.09498011 -0.57201587 -0.46992287 -0.96368156 0.79554442 -1.27999092
[7] 1.07263270 -1.69274244 1.67176797 -0.34646283 -0.10361239 0.34464763
[13] -0.70088687 -0.21919248 -0.06408695 -0.35860443 0.52080356 0.73158310
[19] 0.42369736 -0.01613864 -0.94622843 -0.51260876 0.45533124 -1.03174092
[25] 1.21557349 -0.15240934 -0.32371662 0.08858951 -1.18639099 0.52943371
[31] -1.73280878 -0.01730358 0.22754554 -0.49884835 -0.42709759 -1.04350675
[37] -1.76488938 -0.06200558 0.42949553 0.95555534 -0.95593292 -0.11730765
[43] -0.09902903 -1.69341119 0.45836821 1.14689528 1.70182363 1.34805797
[49] -0.75578158 0.31671918 -0.38465290 2.01815412 -0.47248582 0.57543970
[55] 0.98036400 0.23870180 0.77630762 -0.14165312 -0.33501876 -1.78617639
[61] 2.00661856 0.95088266 2.25065667 0.41507188 0.99607056 -0.33807306
[67] 0.22674791 -0.84212276 1.74617191 -1.13887815 -1.47956896 0.42699769
[73] 0.33598710 1.56741773 0.48571355 0.24999473 0.58440630 -1.58718014
[79] -0.59585322 -1.94614480 -0.03920133 1.49718612 -2.17058378 1.67405980
[85] 2.39353418 1.03123971 0.94499162 -0.91306449 0.25214658 0.49008795
[91] -0.29035316 -1.50635889 1.18205893 0.49311308 0.06534970 1.09641596
[97] -0.24068444 -0.67010089 -1.78550545 0.95543606
> rowVars(tmp2)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> rowSd(tmp2)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> rowMax(tmp2)
[1] 0.09498011 -0.57201587 -0.46992287 -0.96368156 0.79554442 -1.27999092
[7] 1.07263270 -1.69274244 1.67176797 -0.34646283 -0.10361239 0.34464763
[13] -0.70088687 -0.21919248 -0.06408695 -0.35860443 0.52080356 0.73158310
[19] 0.42369736 -0.01613864 -0.94622843 -0.51260876 0.45533124 -1.03174092
[25] 1.21557349 -0.15240934 -0.32371662 0.08858951 -1.18639099 0.52943371
[31] -1.73280878 -0.01730358 0.22754554 -0.49884835 -0.42709759 -1.04350675
[37] -1.76488938 -0.06200558 0.42949553 0.95555534 -0.95593292 -0.11730765
[43] -0.09902903 -1.69341119 0.45836821 1.14689528 1.70182363 1.34805797
[49] -0.75578158 0.31671918 -0.38465290 2.01815412 -0.47248582 0.57543970
[55] 0.98036400 0.23870180 0.77630762 -0.14165312 -0.33501876 -1.78617639
[61] 2.00661856 0.95088266 2.25065667 0.41507188 0.99607056 -0.33807306
[67] 0.22674791 -0.84212276 1.74617191 -1.13887815 -1.47956896 0.42699769
[73] 0.33598710 1.56741773 0.48571355 0.24999473 0.58440630 -1.58718014
[79] -0.59585322 -1.94614480 -0.03920133 1.49718612 -2.17058378 1.67405980
[85] 2.39353418 1.03123971 0.94499162 -0.91306449 0.25214658 0.49008795
[91] -0.29035316 -1.50635889 1.18205893 0.49311308 0.06534970 1.09641596
[97] -0.24068444 -0.67010089 -1.78550545 0.95543606
> rowMin(tmp2)
[1] 0.09498011 -0.57201587 -0.46992287 -0.96368156 0.79554442 -1.27999092
[7] 1.07263270 -1.69274244 1.67176797 -0.34646283 -0.10361239 0.34464763
[13] -0.70088687 -0.21919248 -0.06408695 -0.35860443 0.52080356 0.73158310
[19] 0.42369736 -0.01613864 -0.94622843 -0.51260876 0.45533124 -1.03174092
[25] 1.21557349 -0.15240934 -0.32371662 0.08858951 -1.18639099 0.52943371
[31] -1.73280878 -0.01730358 0.22754554 -0.49884835 -0.42709759 -1.04350675
[37] -1.76488938 -0.06200558 0.42949553 0.95555534 -0.95593292 -0.11730765
[43] -0.09902903 -1.69341119 0.45836821 1.14689528 1.70182363 1.34805797
[49] -0.75578158 0.31671918 -0.38465290 2.01815412 -0.47248582 0.57543970
[55] 0.98036400 0.23870180 0.77630762 -0.14165312 -0.33501876 -1.78617639
[61] 2.00661856 0.95088266 2.25065667 0.41507188 0.99607056 -0.33807306
[67] 0.22674791 -0.84212276 1.74617191 -1.13887815 -1.47956896 0.42699769
[73] 0.33598710 1.56741773 0.48571355 0.24999473 0.58440630 -1.58718014
[79] -0.59585322 -1.94614480 -0.03920133 1.49718612 -2.17058378 1.67405980
[85] 2.39353418 1.03123971 0.94499162 -0.91306449 0.25214658 0.49008795
[91] -0.29035316 -1.50635889 1.18205893 0.49311308 0.06534970 1.09641596
[97] -0.24068444 -0.67010089 -1.78550545 0.95543606
>
> colMeans(tmp2)
[1] 0.04664353
> colSums(tmp2)
[1] 4.664353
> colVars(tmp2)
[1] 1.052797
> colSd(tmp2)
[1] 1.026059
> colMax(tmp2)
[1] 2.393534
> colMin(tmp2)
[1] -2.170584
> colMedians(tmp2)
[1] 0.02460553
> colRanges(tmp2)
[,1]
[1,] -2.170584
[2,] 2.393534
>
> dataset1 <- matrix(dataset1,1,100)
>
> agree.checks(tmp,dataset1)
>
> dataset2 <- matrix(dataset2,100,1)
> agree.checks(tmp2,dataset2)
>
>
> tmp <- createBufferedMatrix(10,10)
>
> tmp[1:10,1:10] <- rnorm(100)
> colApply(tmp,sum)
[1] -2.2161350 1.7315336 0.8001179 -7.1926757 2.0582897 -1.3959376
[7] 7.7868863 -2.5430608 2.3262693 2.0664249
> colApply(tmp,quantile)[,1]
[,1]
[1,] -1.65602038
[2,] -0.72669133
[3,] -0.06609442
[4,] 0.36475201
[5,] 0.63306898
>
> rowApply(tmp,sum)
[1] -2.318701642 -0.446064010 3.193700893 0.005309063 1.412736552
[6] 4.852287302 -2.423755899 -0.057961028 -2.361745295 1.565906479
> rowApply(tmp,rank)[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 8 2 8 6 3 4 3 5 6
[2,] 10 2 9 2 8 1 6 10 9 2
[3,] 9 9 4 9 1 4 2 4 3 10
[4,] 5 1 1 6 5 6 9 2 1 1
[5,] 2 6 5 5 7 7 10 9 2 4
[6,] 7 5 3 1 2 9 7 1 7 9
[7,] 4 10 10 3 10 2 8 8 10 8
[8,] 3 3 7 4 3 10 5 5 4 3
[9,] 8 7 8 7 4 8 3 6 8 7
[10,] 6 4 6 10 9 5 1 7 6 5
>
> tmp <- createBufferedMatrix(5,20)
>
> tmp[1:5,1:20] <- rnorm(100)
> colApply(tmp,sum)
[1] 2.4552034 -2.8693339 3.3236285 1.2279442 -3.5338661 -0.4186159
[7] -0.2550368 1.3866285 1.7799896 -3.0557540 -1.2099717 1.0774435
[13] -0.3137691 0.3758056 -3.2106619 -1.2339204 -1.4570157 -0.0916356
[19] -3.0185095 0.8367274
> colApply(tmp,quantile)[,1]
[,1]
[1,] -1.2362070
[2,] -0.3596972
[3,] 0.6525189
[4,] 1.2606374
[5,] 2.1379512
>
> rowApply(tmp,sum)
[1] 0.12872511 -3.74303232 -0.06262241 -4.29315560 -0.23463474
> rowApply(tmp,rank)[1:5,]
[,1] [,2] [,3] [,4] [,5]
[1,] 2 20 18 17 7
[2,] 10 14 5 8 2
[3,] 19 8 14 18 16
[4,] 13 16 13 11 6
[5,] 16 18 4 1 1
>
>
> as.matrix(tmp)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -1.2362070 0.09804684 1.2705026 0.39802054 0.5097001 -0.9916504
[2,] 2.1379512 0.31135007 -0.6002163 1.29791658 1.4626848 -0.2691237
[3,] 1.2606374 -0.80101551 0.6949494 0.35691503 -0.9463248 -0.1077221
[4,] 0.6525189 -0.60832243 1.2153777 -0.04959323 -2.4645253 2.4986805
[5,] -0.3596972 -1.86939291 0.7430151 -0.77531469 -2.0954008 -1.5488002
[,7] [,8] [,9] [,10] [,11] [,12]
[1,] -0.8845600 0.77911596 0.7481388 -1.533624 -0.09390434 -0.4589444
[2,] -0.3772220 -1.98058081 -1.0646744 1.319549 -0.95258557 1.6092093
[3,] 0.2850332 1.85625142 -0.7842375 -1.175497 0.77128389 -1.3763417
[4,] 0.5987714 0.05365856 -0.3018777 -2.223330 -0.64884899 0.1340215
[5,] 0.1229407 0.67818342 3.1826405 0.557148 -0.28591671 1.1694988
[,13] [,14] [,15] [,16] [,17] [,18]
[1,] -0.1548451 0.4668652 -1.07015269 0.4814281 1.6934263 0.2395147
[2,] -1.0941695 -1.9075250 0.39971660 -1.1403599 -0.5024990 0.2061945
[3,] 1.2664484 0.9338899 -0.67322063 0.1423131 -0.4789193 -0.1599596
[4,] -1.2094776 0.2726949 -1.83690852 0.2058654 -0.6594092 -1.3282994
[5,] 0.8782746 0.6098806 -0.03009671 -0.9231671 -1.5096145 0.9509142
[,19] [,20]
[1,] -0.42687029 0.2947240
[2,] -0.07062587 -2.5280225
[3,] -2.35603334 1.2289276
[4,] -0.14027402 1.5461222
[5,] -0.02470594 0.2949761
>
>
> is.BufferedMatrix(tmp)
[1] TRUE
>
> as.BufferedMatrix(as.matrix(tmp))
BufferedMatrix object
Matrix size: 5 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 800 bytes.
>
>
>
> subBufferedMatrix(tmp,1:5,1:5)
BufferedMatrix object
Matrix size: 5 5
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 677 bytes.
Disk usage : 200 bytes.
> subBufferedMatrix(tmp,,5:8)
BufferedMatrix object
Matrix size: 5 4
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 587 bytes.
Disk usage : 160 bytes.
> subBufferedMatrix(tmp,1:3,)
BufferedMatrix object
Matrix size: 3 20
Buffer size: 1 1
Directory: C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 480 bytes.
>
>
> rm(tmp)
>
>
> ###
> ### Testing colnames and rownames
> ###
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
>
>
> colnames(tmp)
NULL
> rownames(tmp)
NULL
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> colnames(tmp)
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
> rownames(tmp)
[1] "row1" "row2" "row3" "row4" "row5"
>
>
> tmp["row1",]
col1 col2 col3 col4 col5 col6 col7
row1 -0.0187329 -1.036899 1.152749 -0.6346298 1.135472 0.1622916 0.1746834
col8 col9 col10 col11 col12 col13 col14
row1 -1.103105 -0.5234235 1.808789 0.918559 0.4961412 1.011173 0.4892039
col15 col16 col17 col18 col19 col20
row1 -0.8559666 -0.1166146 -0.002119961 -0.1656059 -1.286833 -0.4926622
> tmp[,"col10"]
col10
row1 1.8087893
row2 1.0261120
row3 -1.0426298
row4 0.7617589
row5 0.1194737
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6 col7
row1 -0.0187329 -1.036899 1.1527494 -0.6346298 1.1354722 0.1622916 0.1746834
row5 -0.8477152 -1.320060 0.2148474 -0.8366659 0.1376391 -0.6376339 -0.4669184
col8 col9 col10 col11 col12 col13 col14
row1 -1.1031049 -0.5234235 1.8087893 0.918559 0.4961412 1.011173 0.4892039
row5 0.2278536 -1.3408211 0.1194737 2.609122 -1.5734497 1.121178 -0.7695913
col15 col16 col17 col18 col19 col20
row1 -0.8559666 -0.1166146 -0.002119961 -0.1656059 -1.286833 -0.4926622
row5 0.4457635 -1.5105325 0.126542024 0.4833603 1.604514 -0.8291992
> tmp[,c("col6","col20")]
col6 col20
row1 0.1622916 -0.4926622
row2 -0.4889578 0.8358506
row3 -1.8147627 0.2839421
row4 -1.2794788 1.7074985
row5 -0.6376339 -0.8291992
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 0.1622916 -0.4926622
row5 -0.6376339 -0.8291992
>
>
>
>
> tmp["row1",] <- rnorm(20,mean=10)
> tmp[,"col10"] <- rnorm(5,mean=30)
> tmp[c("row1","row5"),] <- rnorm(40,mean=50)
> tmp[,c("col6","col20")] <- rnorm(10,mean=75)
> tmp[c("row1","row5"),c("col6","col20")] <- rnorm(4,mean=105)
>
> tmp["row1",]
col1 col2 col3 col4 col5 col6 col7 col8
row1 50.92634 48.21523 50.53261 50.84441 50.63894 104.8595 50.19245 50.9468
col9 col10 col11 col12 col13 col14 col15 col16
row1 47.79223 49.7172 50.07684 49.79204 49.40815 50.51425 52.10877 49.21205
col17 col18 col19 col20
row1 48.5702 49.69182 49.13453 105.0675
> tmp[,"col10"]
col10
row1 49.71720
row2 28.67152
row3 29.69863
row4 28.87258
row5 51.49060
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6 col7 col8
row1 50.92634 48.21523 50.53261 50.84441 50.63894 104.8595 50.19245 50.94680
row5 49.08162 50.41284 49.39322 49.30375 50.00353 105.1232 51.22975 51.02031
col9 col10 col11 col12 col13 col14 col15 col16
row1 47.79223 49.7172 50.07684 49.79204 49.40815 50.51425 52.10877 49.21205
row5 50.98279 51.4906 49.41909 49.82251 51.23598 50.91017 50.98648 51.39189
col17 col18 col19 col20
row1 48.57020 49.69182 49.13453 105.0675
row5 50.77194 50.80797 50.94332 105.0406
> tmp[,c("col6","col20")]
col6 col20
row1 104.85952 105.06746
row2 75.40893 74.79412
row3 75.29094 74.02047
row4 77.14936 72.54429
row5 105.12324 105.04058
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 104.8595 105.0675
row5 105.1232 105.0406
>
>
> subBufferedMatrix(tmp,c("row1","row5"),c("col6","col20"))[1:2,1:2]
col6 col20
row1 104.8595 105.0675
row5 105.1232 105.0406
>
>
>
>
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
>
> tmp[,"col13"]
col13
[1,] -0.7416403
[2,] -0.7524128
[3,] -1.6825985
[4,] 0.3087556
[5,] -0.1300852
> tmp[,c("col17","col7")]
col17 col7
[1,] 0.1542997 -1.1112348
[2,] 1.9074967 0.7511658
[3,] 1.4037700 0.5650767
[4,] 0.6451973 0.4205331
[5,] -2.0964408 -0.9185816
>
> subBufferedMatrix(tmp,,c("col6","col20"))[,1:2]
col6 col20
[1,] 0.551742679 -0.47095970
[2,] -0.005313644 -0.02470127
[3,] 1.885619069 -0.39940281
[4,] 2.685063265 2.40222718
[5,] -0.237431273 1.51132421
> subBufferedMatrix(tmp,1,c("col6"))[,1]
col1
[1,] 0.5517427
> subBufferedMatrix(tmp,1:2,c("col6"))[,1]
col6
[1,] 0.551742679
[2,] -0.005313644
>
>
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
>
>
>
> subBufferedMatrix(tmp,c("row3","row1"),)[,1:20]
[,1] [,2] [,3] [,4] [,5] [,6]
row3 -0.1107887 -0.6941101 -0.009703601 -0.7489432 -0.6576432 -1.431203
row1 -1.6866174 0.5800103 0.600116377 1.7244722 -3.7150251 1.104416
[,7] [,8] [,9] [,10] [,11] [,12]
row3 0.1816296 -0.6485291 1.1662786 0.10271888 0.1450915 0.02988543
row1 -1.1812661 -0.3787254 -0.1841114 -0.05898536 -0.9154613 0.30671211
[,13] [,14] [,15] [,16] [,17] [,18] [,19]
row3 0.4329095 -1.411377 2.756101 -0.2634495 0.04796337 -1.969332 0.5896061
row1 -0.5144763 -1.085959 -1.781109 0.3527333 -0.58171353 -1.156552 0.8364067
[,20]
row3 -0.5569061
row1 0.3935459
> subBufferedMatrix(tmp,c("row2"),1:10)[,1:10]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row2 -0.2009128 -3.262179 0.08552322 0.5260572 -1.404156 0.8257341 0.1836876
[,8] [,9] [,10]
row2 0.2838725 2.375334 -0.08288451
> subBufferedMatrix(tmp,c("row5"),1:20)[,1:20]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
row5 -2.292824 0.216019 0.94166 -2.267814 -0.1049408 -1.64089 1.555276 1.110905
[,9] [,10] [,11] [,12] [,13] [,14] [,15]
row5 1.010135 -0.007347607 1.486459 -0.4705826 0.1902621 0.2108129 1.085138
[,16] [,17] [,18] [,19] [,20]
row5 1.803301 0.145334 0.07791255 0.7084501 -0.274696
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> colnames(tmp)
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
> rownames(tmp)
[1] "row1" "row2" "row3" "row4" "row5"
>
>
> colnames(tmp) <- NULL
> rownames(tmp) <- NULL
>
> colnames(tmp)
NULL
> rownames(tmp)
NULL
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> dimnames(tmp)
[[1]]
[1] "row1" "row2" "row3" "row4" "row5"
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
> dimnames(tmp) <- NULL
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> dimnames(tmp)
[[1]]
NULL
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
>
> dimnames(tmp) <- NULL
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
> dimnames(tmp)
[[1]]
[1] "row1" "row2" "row3" "row4" "row5"
[[2]]
NULL
>
> dimnames(tmp) <- list(NULL,c(colnames(tmp,do.NULL=FALSE)))
> dimnames(tmp)
[[1]]
NULL
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
>
>
> ###
> ### Testing logical indexing
> ###
> ###
>
> tmp <- createBufferedMatrix(230,15)
> tmp[1:230,1:15] <- rnorm(230*15)
> x <-tmp[1:230,1:15]
>
> for (rep in 1:10){
+ which.cols <- sample(c(TRUE,FALSE),15,replace=T)
+ which.rows <- sample(c(TRUE,FALSE),230,replace=T)
+
+ if (!all(tmp[which.rows,which.cols] == x[which.rows,which.cols])){
+ stop("No agreement when logical indexing\n")
+ }
+
+ if (!all(subBufferedMatrix(tmp,,which.cols)[,1:sum(which.cols)] == x[,which.cols])){
+ stop("No agreement when logical indexing in subBufferedMatrix cols\n")
+ }
+ if (!all(subBufferedMatrix(tmp,which.rows,)[1:sum(which.rows),] == x[which.rows,])){
+ stop("No agreement when logical indexing in subBufferedMatrix rows\n")
+ }
+
+
+ if (!all(subBufferedMatrix(tmp,which.rows,which.cols)[1:sum(which.rows),1:sum(which.cols)]== x[which.rows,which.cols])){
+ stop("No agreement when logical indexing in subBufferedMatrix rows and columns\n")
+ }
+ }
>
>
> ##
> ## Test the ReadOnlyMode
> ##
>
> ReadOnlyMode(tmp)
<pointer: 0x0000000005afa7e0>
> is.ReadOnlyMode(tmp)
[1] TRUE
>
> filenames(tmp)
[1] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e41a805a36"
[2] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e41964980"
[3] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e4effbfd"
[4] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e456b030c8"
[5] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e43b8a601f"
[6] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e43d4695"
[7] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e42c736adb"
[8] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e450464ac0"
[9] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e4403b4161"
[10] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e4766579b"
[11] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e4695f6938"
[12] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e470345c77"
[13] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e4427456dd"
[14] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e45e06516a"
[15] "C:/Users/biocbuild/bbs-3.7-bioc/meat/BufferedMatrix.Rcheck/tests_x64\\BM12e428763fad"
>
>
> ### testing coercion functions
> ###
>
> tmp <- as(tmp,"matrix")
> tmp <- as(tmp,"BufferedMatrix")
>
>
>
> ### testing whether can move storage from one location to another
>
> MoveStorageDirectory(tmp,"NewDirectory",full.path=FALSE)
<pointer: 0x0000000005230cd8>
> MoveStorageDirectory(tmp,getwd(),full.path=TRUE)
<pointer: 0x0000000005230cd8>
Warning message:
In dir.create(new.directory) :
'C:\Users\biocbuild\bbs-3.7-bioc\meat\BufferedMatrix.Rcheck\tests_x64' already exists
>
>
> RowMode(tmp)
<pointer: 0x0000000005230cd8>
> rowMedians(tmp)
[1] 0.068930305 -0.778446098 0.343767587 0.081687983 0.176667985
[6] -0.022318398 -0.285390247 -0.559524172 -0.091399567 0.334357059
[11] -0.015884221 -0.432441021 -0.123065842 -0.497208149 -0.518698595
[16] 0.042595476 0.285687874 0.309972822 -0.136398877 0.229838822
[21] 0.218477271 0.404428626 -0.189977821 0.032600771 0.199953795
[26] -0.164655389 -0.249025555 -0.149759591 0.337433756 0.111564318
[31] -0.281884518 0.416007710 -0.350031089 0.067445214 -0.356364172
[36] -0.576358069 0.372131576 0.064495103 -0.107946195 0.331737662
[41] 0.099418127 0.094720718 0.483521048 0.193197989 -0.225026316
[46] 0.464354588 -0.625835677 0.035860451 0.013404564 -0.400454463
[51] -0.149082824 -0.229310162 0.291231450 0.008312662 0.002540590
[56] -0.369287162 0.244778848 0.288597331 -0.395050857 -0.283395709
[61] 0.014081020 0.512777578 -0.656402409 -0.311677340 0.163476944
[66] 0.140219097 0.461402781 -0.066032559 0.213762632 -0.528555425
[71] -0.375149021 -0.073897481 0.091253413 0.565807706 0.379795600
[76] -0.203296066 0.426677597 0.451906955 0.071610970 0.611764358
[81] 0.523621090 0.363318552 -0.321070635 -0.247610642 0.516088930
[86] -0.364785094 -0.312322388 -0.159871021 -0.027678601 -0.676389286
[91] 0.354364270 -0.030727639 -0.215915421 -0.128060693 0.449517007
[96] -0.347790226 -0.456442115 0.727055942 -0.435831244 -0.342662622
[101] -0.406274839 0.088070166 0.180063322 0.381889705 0.195556923
[106] 0.005089098 0.188154390 0.089048402 0.061474152 -0.182434445
[111] -0.145351923 -0.367069820 0.032987547 -0.136215944 -0.780976717
[116] -0.003976084 0.158295073 0.329057541 -0.141845522 0.148195917
[121] 0.557221893 -0.213235126 0.003795588 -0.184459924 -0.497641892
[126] 0.060363392 -0.486882330 0.228911169 0.712802801 0.532532102
[131] -0.162134389 0.224002602 -0.061125513 0.363933152 0.147187515
[136] 0.566344977 0.122709311 -0.009150408 -0.046621998 0.478267904
[141] -0.442467827 -0.018311145 0.441572625 -0.250865632 -0.196583664
[146] -0.121830217 0.167549743 0.473399093 -0.264349297 0.193017037
[151] 0.142554382 -0.492985396 -0.127815751 0.202587700 0.333275768
[156] 0.005120470 0.081663698 -0.358458357 0.025576027 0.461453512
[161] -0.185239358 -0.170384196 0.043594651 -0.016767526 -0.345040555
[166] -0.268714418 0.081001650 0.273830053 0.322029440 0.068802342
[171] 0.157549537 -0.497777567 -0.007803039 -0.416649337 -0.006933287
[176] 0.131377180 -0.111564576 -0.155127652 -0.242095321 -0.072412828
[181] -0.508361349 -0.363396343 -0.487207598 -0.014331681 -0.027523345
[186] 0.515222456 0.397291509 0.294666868 -0.088644934 -0.216296194
[191] -0.373085661 -0.517295087 -0.215894464 -0.231414157 0.384908985
[196] -0.183020039 -0.383620597 0.058427692 0.313458635 -0.471267472
[201] 0.079918092 -0.107119367 -0.068153933 -0.032511744 0.023367592
[206] -0.183445522 0.363743032 -0.260718506 -0.408544022 -0.025336125
[211] 0.224585170 -0.193659412 -0.196699601 0.565604829 -0.160390061
[216] -0.010230737 0.041334939 -0.623328882 0.711795835 -0.200218220
[221] 0.188133551 -0.232213992 -0.258020896 -0.134315204 -0.012967182
[226] 0.036429593 0.126416237 -0.201540392 0.168234680 0.143762431
>
> proc.time()
user system elapsed
3.28 8.62 12.68
|
|
BufferedMatrix.Rcheck/tests_i386/rawCalltesting.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
> prefix <- "dbmtest"
> directory <- getwd()
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x035149f0>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x035149f0>
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 10
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x035149f0>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 10
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000 0.000000 0.000000 0.000000 0.000000 0.000000
1.000000 2.000000 3.000000 4.000000 5.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2.000000 3.000000 4.000000 5.000000 6.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3.000000 4.000000 5.000000 6.000000 7.000000 0.000000 0.000000 0.000000 0.000000 0.000000
4.000000 5.000000 6.000000 7.000000 8.000000 0.000000 0.000000 0.000000 0.000000 0.000000
<pointer: 0x035149f0>
> rm(P)
>
> #P <- .Call("R_bm_Destroy",P)
> #.Call("R_bm_Destroy",P)
> #.Call("R_bm_Test_C",P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,5)
[1] TRUE
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 0
Buffer Rows: 1
Buffer Cols: 1
Printing Values
<pointer: 0x0278cc30>
> .Call("R_bm_AddColumn",P)
<pointer: 0x0278cc30>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 1
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000
0.000000
0.000000
0.000000
0.000000
<pointer: 0x0278cc30>
> .Call("R_bm_AddColumn",P)
<pointer: 0x0278cc30>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0278cc30>
> rm(P)
>
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,5)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x0215fdd8>
> .Call("R_bm_AddColumn",P)
<pointer: 0x0215fdd8>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0215fdd8>
>
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x0215fdd8>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0215fdd8>
>
> .Call("R_bm_RowMode",P)
<pointer: 0x0215fdd8>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0215fdd8>
>
> .Call("R_bm_ColMode",P)
<pointer: 0x0215fdd8>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0215fdd8>
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x03b13a30>
> .Call("R_bm_SetPrefix",P,"BufferedMatrixFile")
<pointer: 0x03b13a30>
> .Call("R_bm_AddColumn",P)
<pointer: 0x03b13a30>
> .Call("R_bm_AddColumn",P)
<pointer: 0x03b13a30>
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFilee7014491eed" "BufferedMatrixFilee702b533a7"
> rm(P)
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFilee7014491eed" "BufferedMatrixFilee702b533a7"
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x01e611c0>
> .Call("R_bm_AddColumn",P)
<pointer: 0x01e611c0>
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x01e611c0>
> .Call("R_bm_isReadOnlyMode",P)
[1] TRUE
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x01e611c0>
> .Call("R_bm_isReadOnlyMode",P)
[1] FALSE
> .Call("R_bm_isRowMode",P)
[1] FALSE
> .Call("R_bm_RowMode",P)
<pointer: 0x01e611c0>
> .Call("R_bm_isRowMode",P)
[1] TRUE
> .Call("R_bm_ColMode",P)
<pointer: 0x01e611c0>
> .Call("R_bm_isRowMode",P)
[1] FALSE
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x02eaa500>
> .Call("R_bm_AddColumn",P)
<pointer: 0x02eaa500>
>
> .Call("R_bm_getSize",P)
[1] 10 2
> .Call("R_bm_getBufferSize",P)
[1] 1 1
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x02eaa500>
>
> .Call("R_bm_getBufferSize",P)
[1] 5 5
> .Call("R_bm_ResizeBuffer",P,-1,5)
<pointer: 0x02eaa500>
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x02ea6cf0>
> .Call("R_bm_getValue",P,3,3)
[1] 6
>
> .Call("R_bm_getValue",P,100000,10000)
[1] NA
> .Call("R_bm_setValue",P,3,3,12345.0)
[1] TRUE
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 12345.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x02ea6cf0>
> rm(P)
>
> proc.time()
user system elapsed
0.43 0.12 0.56
|
BufferedMatrix.Rcheck/tests_x64/rawCalltesting.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
> prefix <- "dbmtest"
> directory <- getwd()
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x00000000056ed358>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x00000000056ed358>
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 10
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x00000000056ed358>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 10
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000 0.000000 0.000000 0.000000 0.000000 0.000000
1.000000 2.000000 3.000000 4.000000 5.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2.000000 3.000000 4.000000 5.000000 6.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3.000000 4.000000 5.000000 6.000000 7.000000 0.000000 0.000000 0.000000 0.000000 0.000000
4.000000 5.000000 6.000000 7.000000 8.000000 0.000000 0.000000 0.000000 0.000000 0.000000
<pointer: 0x00000000056ed358>
> rm(P)
>
> #P <- .Call("R_bm_Destroy",P)
> #.Call("R_bm_Destroy",P)
> #.Call("R_bm_Test_C",P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,5)
[1] TRUE
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 0
Buffer Rows: 1
Buffer Cols: 1
Printing Values
<pointer: 0x0000000005aefa38>
> .Call("R_bm_AddColumn",P)
<pointer: 0x0000000005aefa38>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 1
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000
0.000000
0.000000
0.000000
0.000000
<pointer: 0x0000000005aefa38>
> .Call("R_bm_AddColumn",P)
<pointer: 0x0000000005aefa38>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0000000005aefa38>
> rm(P)
>
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,5)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x0000000005b89e78>
> .Call("R_bm_AddColumn",P)
<pointer: 0x0000000005b89e78>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0000000005b89e78>
>
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x0000000005b89e78>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0000000005b89e78>
>
> .Call("R_bm_RowMode",P)
<pointer: 0x0000000005b89e78>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0000000005b89e78>
>
> .Call("R_bm_ColMode",P)
<pointer: 0x0000000005b89e78>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x0000000005b89e78>
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x000000000692ff60>
> .Call("R_bm_SetPrefix",P,"BufferedMatrixFile")
<pointer: 0x000000000692ff60>
> .Call("R_bm_AddColumn",P)
<pointer: 0x000000000692ff60>
> .Call("R_bm_AddColumn",P)
<pointer: 0x000000000692ff60>
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFilee3c26d3378b" "BufferedMatrixFilee3c48ea3663"
> rm(P)
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFilee3c26d3378b" "BufferedMatrixFilee3c48ea3663"
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x0000000005c61e60>
> .Call("R_bm_AddColumn",P)
<pointer: 0x0000000005c61e60>
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x0000000005c61e60>
> .Call("R_bm_isReadOnlyMode",P)
[1] TRUE
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x0000000005c61e60>
> .Call("R_bm_isReadOnlyMode",P)
[1] FALSE
> .Call("R_bm_isRowMode",P)
[1] FALSE
> .Call("R_bm_RowMode",P)
<pointer: 0x0000000005c61e60>
> .Call("R_bm_isRowMode",P)
[1] TRUE
> .Call("R_bm_ColMode",P)
<pointer: 0x0000000005c61e60>
> .Call("R_bm_isRowMode",P)
[1] FALSE
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x00000000078031a0>
> .Call("R_bm_AddColumn",P)
<pointer: 0x00000000078031a0>
>
> .Call("R_bm_getSize",P)
[1] 10 2
> .Call("R_bm_getBufferSize",P)
[1] 1 1
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x00000000078031a0>
>
> .Call("R_bm_getBufferSize",P)
[1] 5 5
> .Call("R_bm_ResizeBuffer",P,-1,5)
<pointer: 0x00000000078031a0>
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x00000000077a0eb8>
> .Call("R_bm_getValue",P,3,3)
[1] 6
>
> .Call("R_bm_getValue",P,100000,10000)
[1] NA
> .Call("R_bm_setValue",P,3,3,12345.0)
[1] TRUE
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 12345.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x00000000077a0eb8>
> rm(P)
>
> proc.time()
user system elapsed
0.45 0.09 0.53
|
|
BufferedMatrix.Rcheck/tests_i386/Rcodetesting.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
> Temp <- createBufferedMatrix(100)
> dim(Temp)
[1] 100 0
> buffer.dim(Temp)
[1] 1 1
>
>
> proc.time()
user system elapsed
0.59 0.04 0.61
|
BufferedMatrix.Rcheck/tests_x64/Rcodetesting.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
> Temp <- createBufferedMatrix(100)
> dim(Temp)
[1] 100 0
> buffer.dim(Temp)
[1] 1 1
>
>
> proc.time()
user system elapsed
0.54 0.06 0.59
|