Matineh Rahmatbakhsh
| Snapshot Date: 2025-12-19 13:40 -0500 (Fri, 19 Dec 2025) |
| git_url: https://git.bioconductor.org/packages/HPiP |
| git_branch: devel |
| git_last_commit: e6c77ab |
| git_last_commit_date: 2025-11-23 15:13:33 -0500 (Sun, 23 Nov 2025) |
| Back to Multiple platform build/check report for BioC 3.23: simplified long |
|
This page was generated on 2025-12-20 11:35 -0500 (Sat, 20 Dec 2025).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | R Under development (unstable) (2025-10-20 r88955) -- "Unsuffered Consequences" | 4875 |
| kjohnson3 | macOS 13.7.7 Ventura | arm64 | R Under development (unstable) (2025-11-04 r88984) -- "Unsuffered Consequences" | 4593 |
| Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X | ||||
| Package 996/2332 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| HPiP 1.17.1 (landing page) Matineh Rahmatbakhsh
| nebbiolo1 | Linux (Ubuntu 24.04.3 LTS) / x86_64 | OK | OK | WARNINGS | | ||||||||
| kjohnson3 | macOS 13.7.7 Ventura / arm64 | OK | OK | WARNINGS | OK | | ||||||||
|
To the developers/maintainers of the HPiP package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/HPiP.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
| Package: HPiP |
| Version: 1.17.1 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HPiP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HPiP_1.17.1.tar.gz |
| StartedAt: 2025-12-19 20:11:59 -0500 (Fri, 19 Dec 2025) |
| EndedAt: 2025-12-19 20:15:27 -0500 (Fri, 19 Dec 2025) |
| EllapsedTime: 207.7 seconds |
| RetCode: 0 |
| Status: WARNINGS |
| CheckDir: HPiP.Rcheck |
| Warnings: 1 |
##############################################################################
##############################################################################
###
### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HPiP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HPiP_1.17.1.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck’
* using R Under development (unstable) (2025-11-04 r88984)
* using platform: aarch64-apple-darwin20
* R was compiled by
Apple clang version 16.0.0 (clang-1600.0.26.6)
GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.8
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.17.1’
* package encoding: UTF-8
* 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 for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* 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
checkRd: (-1) getHPI.Rd:29: Lost braces
29 | then the Kronecker product is the code{(pm × qn)} block matrix
| ^
* checking Rd metadata ... OK
* checking Rd cross-references ... INFO
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... WARNING
Codoc mismatches from Rd file 'pred_ensembel.Rd':
pred_ensembel
Code: function(features, gold_standard, classifier = c("avNNet",
"svmRadial", "ranger"), resampling.method = "cv",
ncross = 2, repeats = 2, verboseIter = TRUE, plots =
FALSE, filename = "plots.pdf")
Docs: function(features, gold_standard, classifier = c("avNNet",
"svmRadial", "ranger"), resampling.method = "cv",
ncross = 2, repeats = 2, verboseIter = TRUE, plots =
TRUE, filename = "plots.pdf")
Mismatches in argument default values:
Name: 'plots' Code: FALSE Docs: TRUE
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
FSmethod 19.119 0.986 21.123
corr_plot 18.980 0.987 20.952
var_imp 18.564 0.945 20.708
pred_ensembel 6.575 0.132 6.294
enrichfindP 0.195 0.037 12.885
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
Running ‘runTests.R’
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 1 WARNING, 2 NOTEs
See
‘/Users/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck/00check.log’
for details.
HPiP.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL HPiP ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.6-arm64/Resources/library’ * installing *source* package ‘HPiP’ ... ** this is package ‘HPiP’ version ‘1.17.1’ ** using staged installation ** R ** data ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (HPiP)
HPiP.Rcheck/tests/runTests.Rout
R Under development (unstable) (2025-11-04 r88984) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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.
> BiocGenerics:::testPackage('HPiP')
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
avNNet
Loading required package: ggplot2
Loading required package: lattice
Fitting Repeat 1
# weights: 103
initial value 95.259815
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 103.903063
final value 94.457914
converged
Fitting Repeat 3
# weights: 103
initial value 105.165531
final value 94.443244
converged
Fitting Repeat 4
# weights: 103
initial value 107.238478
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 96.936645
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 113.689700
final value 94.026542
converged
Fitting Repeat 2
# weights: 305
initial value 104.526061
iter 10 value 93.753917
iter 20 value 93.719176
iter 30 value 93.520596
iter 40 value 91.779700
final value 91.778224
converged
Fitting Repeat 3
# weights: 305
initial value 94.786715
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 109.489587
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 98.366446
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 94.715393
final value 94.484211
converged
Fitting Repeat 2
# weights: 507
initial value 111.707489
iter 10 value 94.649757
iter 20 value 94.271969
iter 30 value 93.389925
iter 40 value 92.829436
iter 50 value 91.829117
iter 60 value 91.825283
final value 91.825009
converged
Fitting Repeat 3
# weights: 507
initial value 98.707358
iter 10 value 94.232939
iter 20 value 87.107462
iter 30 value 86.724240
iter 40 value 86.721972
iter 50 value 85.933379
final value 85.924492
converged
Fitting Repeat 4
# weights: 507
initial value 110.115340
iter 10 value 89.809585
iter 20 value 86.657792
iter 30 value 86.657126
final value 86.657112
converged
Fitting Repeat 5
# weights: 507
initial value 101.328098
final value 94.484211
converged
Fitting Repeat 1
# weights: 103
initial value 96.421667
iter 10 value 89.248049
iter 20 value 85.778672
iter 30 value 83.842603
iter 40 value 83.602184
iter 50 value 83.541724
iter 60 value 82.189816
iter 70 value 81.417844
iter 80 value 81.180370
iter 90 value 81.170412
final value 81.169762
converged
Fitting Repeat 2
# weights: 103
initial value 95.411557
iter 10 value 90.771242
iter 20 value 89.208518
iter 30 value 84.036130
iter 40 value 83.146427
iter 50 value 82.775646
iter 60 value 82.390997
iter 70 value 81.615858
iter 80 value 81.201269
iter 90 value 81.107671
final value 81.107669
converged
Fitting Repeat 3
# weights: 103
initial value 107.102183
iter 10 value 94.487411
iter 20 value 94.486692
iter 30 value 92.764827
iter 40 value 88.698923
iter 50 value 87.992925
iter 60 value 85.885064
iter 70 value 81.797497
iter 80 value 81.615784
iter 90 value 81.175897
final value 81.169762
converged
Fitting Repeat 4
# weights: 103
initial value 100.178153
iter 10 value 94.315796
iter 20 value 87.877178
iter 30 value 86.768906
iter 40 value 86.134002
iter 50 value 85.735115
iter 60 value 85.603405
iter 70 value 85.523815
iter 80 value 85.512740
iter 90 value 85.511250
final value 85.511248
converged
Fitting Repeat 5
# weights: 103
initial value 96.171580
iter 10 value 91.299312
iter 20 value 88.202748
iter 30 value 87.924334
iter 40 value 87.196266
iter 50 value 85.321810
iter 60 value 85.185738
iter 70 value 85.148356
iter 80 value 85.146643
iter 80 value 85.146643
final value 85.146643
converged
Fitting Repeat 1
# weights: 305
initial value 102.648267
iter 10 value 94.530047
iter 20 value 92.896931
iter 30 value 85.968192
iter 40 value 85.362260
iter 50 value 85.123753
iter 60 value 84.905520
iter 70 value 84.032848
iter 80 value 80.985571
iter 90 value 80.330536
iter 100 value 80.019906
final value 80.019906
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 100.559949
iter 10 value 94.629659
iter 20 value 86.710698
iter 30 value 85.699735
iter 40 value 82.126929
iter 50 value 81.385148
iter 60 value 80.873127
iter 70 value 80.503664
iter 80 value 80.294213
iter 90 value 80.132065
iter 100 value 80.019761
final value 80.019761
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 113.622354
iter 10 value 94.399887
iter 20 value 91.038156
iter 30 value 90.410043
iter 40 value 89.776716
iter 50 value 89.617980
iter 60 value 83.869559
iter 70 value 81.899176
iter 80 value 81.000442
iter 90 value 80.720260
iter 100 value 80.643757
final value 80.643757
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 117.774531
iter 10 value 94.403199
iter 20 value 87.405113
iter 30 value 84.414882
iter 40 value 83.685493
iter 50 value 82.734018
iter 60 value 82.319860
iter 70 value 81.376399
iter 80 value 80.772240
iter 90 value 80.553837
iter 100 value 80.151313
final value 80.151313
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 102.196577
iter 10 value 94.634653
iter 20 value 87.602488
iter 30 value 84.652218
iter 40 value 82.448002
iter 50 value 81.048038
iter 60 value 80.618114
iter 70 value 80.503028
iter 80 value 80.404660
iter 90 value 80.237413
iter 100 value 80.099257
final value 80.099257
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 120.250674
iter 10 value 94.470196
iter 20 value 93.878606
iter 30 value 88.852823
iter 40 value 83.801486
iter 50 value 82.996378
iter 60 value 82.591800
iter 70 value 82.259582
iter 80 value 82.120863
iter 90 value 81.893525
iter 100 value 81.468118
final value 81.468118
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 122.967634
iter 10 value 94.458561
iter 20 value 86.226359
iter 30 value 85.462687
iter 40 value 84.585628
iter 50 value 83.217134
iter 60 value 82.952800
iter 70 value 82.746301
iter 80 value 81.813194
iter 90 value 81.264977
iter 100 value 80.970796
final value 80.970796
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 132.899206
iter 10 value 94.487101
iter 20 value 88.702638
iter 30 value 87.229768
iter 40 value 83.797142
iter 50 value 82.985850
iter 60 value 82.479301
iter 70 value 81.830929
iter 80 value 81.020861
iter 90 value 80.605867
iter 100 value 80.015864
final value 80.015864
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 112.716737
iter 10 value 94.494711
iter 20 value 88.567868
iter 30 value 87.540586
iter 40 value 87.273886
iter 50 value 85.770286
iter 60 value 83.636706
iter 70 value 82.362229
iter 80 value 81.209916
iter 90 value 80.000454
iter 100 value 79.810743
final value 79.810743
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 118.570709
iter 10 value 94.484003
iter 20 value 86.953247
iter 30 value 84.621190
iter 40 value 83.806173
iter 50 value 83.013952
iter 60 value 82.523578
iter 70 value 81.557620
iter 80 value 80.201651
iter 90 value 79.842944
iter 100 value 79.737954
final value 79.737954
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 96.345201
final value 94.485757
converged
Fitting Repeat 2
# weights: 103
initial value 97.683222
iter 10 value 94.445008
iter 20 value 94.443645
iter 30 value 94.043980
iter 40 value 93.625813
final value 93.624703
converged
Fitting Repeat 3
# weights: 103
initial value 97.306153
final value 94.485717
converged
Fitting Repeat 4
# weights: 103
initial value 96.953567
final value 94.445072
converged
Fitting Repeat 5
# weights: 103
initial value 98.112301
iter 10 value 94.445069
iter 20 value 94.443309
iter 30 value 92.666927
iter 40 value 86.660548
final value 86.659574
converged
Fitting Repeat 1
# weights: 305
initial value 103.632304
iter 10 value 94.481277
iter 20 value 90.102302
iter 30 value 89.956016
iter 40 value 89.943809
iter 50 value 89.918350
iter 60 value 89.868108
final value 89.867711
converged
Fitting Repeat 2
# weights: 305
initial value 96.269498
iter 10 value 94.488927
iter 20 value 92.799054
iter 30 value 87.712867
iter 40 value 86.138537
iter 50 value 86.031982
final value 86.031268
converged
Fitting Repeat 3
# weights: 305
initial value 98.020673
iter 10 value 94.488557
iter 20 value 94.484226
iter 20 value 94.484226
iter 20 value 94.484226
final value 94.484226
converged
Fitting Repeat 4
# weights: 305
initial value 103.097434
iter 10 value 94.449003
iter 20 value 94.446369
iter 30 value 91.948729
iter 40 value 84.541974
iter 50 value 81.056732
iter 60 value 79.600723
iter 70 value 79.348580
iter 80 value 79.342305
iter 90 value 79.336978
final value 79.324535
converged
Fitting Repeat 5
# weights: 305
initial value 103.133098
iter 10 value 94.488766
iter 20 value 94.459796
iter 30 value 86.775819
iter 40 value 86.741755
iter 50 value 86.361279
iter 60 value 86.347541
iter 70 value 86.281035
iter 80 value 86.275925
final value 86.275915
converged
Fitting Repeat 1
# weights: 507
initial value 97.387063
iter 10 value 93.705094
iter 20 value 93.443912
iter 30 value 93.442893
iter 40 value 93.438891
iter 50 value 93.434670
iter 60 value 93.429615
iter 70 value 93.425875
final value 93.425749
converged
Fitting Repeat 2
# weights: 507
initial value 101.414589
iter 10 value 94.451578
iter 20 value 93.785599
iter 30 value 87.084657
iter 40 value 86.141445
iter 50 value 84.009815
iter 60 value 83.120679
iter 70 value 82.613114
iter 80 value 81.663850
iter 90 value 81.651502
iter 100 value 81.648407
final value 81.648407
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 112.643457
iter 10 value 90.579978
iter 20 value 90.359932
iter 30 value 90.302493
iter 40 value 90.302004
final value 90.300461
converged
Fitting Repeat 4
# weights: 507
initial value 109.280543
iter 10 value 94.298294
iter 20 value 94.296105
iter 30 value 94.293263
iter 40 value 94.095361
iter 50 value 93.890696
iter 60 value 93.889233
iter 70 value 93.887527
iter 80 value 89.252708
iter 90 value 87.153216
iter 100 value 87.070357
final value 87.070357
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 110.703560
iter 10 value 94.491252
iter 20 value 94.482008
iter 30 value 88.243020
iter 40 value 87.669563
iter 50 value 87.622973
iter 60 value 87.622441
iter 70 value 87.621283
iter 80 value 87.621025
final value 87.621003
converged
Fitting Repeat 1
# weights: 103
initial value 95.837444
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 97.438157
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 108.257542
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 96.867318
final value 94.354396
converged
Fitting Repeat 5
# weights: 103
initial value 112.455299
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 97.509038
iter 10 value 93.125473
iter 20 value 89.259849
iter 30 value 89.173161
iter 40 value 89.027753
iter 50 value 84.755361
iter 60 value 83.568085
iter 70 value 82.834476
iter 80 value 82.426483
iter 90 value 82.170424
final value 82.166583
converged
Fitting Repeat 2
# weights: 305
initial value 120.667734
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 105.979496
final value 94.483810
converged
Fitting Repeat 4
# weights: 305
initial value 96.338308
final value 94.443243
converged
Fitting Repeat 5
# weights: 305
initial value 100.776553
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 93.037879
iter 10 value 85.844211
iter 20 value 85.841378
iter 30 value 85.594794
iter 40 value 84.265858
iter 50 value 84.226983
final value 84.226941
converged
Fitting Repeat 2
# weights: 507
initial value 105.406250
final value 94.484211
converged
Fitting Repeat 3
# weights: 507
initial value 107.257276
final value 94.484211
converged
Fitting Repeat 4
# weights: 507
initial value 100.649318
iter 10 value 93.262702
iter 20 value 93.212797
final value 93.212302
converged
Fitting Repeat 5
# weights: 507
initial value 105.730265
final value 94.354396
converged
Fitting Repeat 1
# weights: 103
initial value 104.370224
iter 10 value 93.417385
iter 20 value 85.255304
iter 30 value 85.148030
iter 40 value 85.115763
iter 50 value 84.142054
iter 60 value 84.024113
iter 70 value 83.561244
iter 80 value 83.488161
final value 83.487120
converged
Fitting Repeat 2
# weights: 103
initial value 98.821245
iter 10 value 94.439922
iter 20 value 91.374327
iter 30 value 87.559222
iter 40 value 87.297610
iter 50 value 87.205716
iter 60 value 85.575566
iter 70 value 82.852049
iter 80 value 81.652419
iter 90 value 81.576417
iter 100 value 81.428007
final value 81.428007
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 98.042603
iter 10 value 94.494430
final value 94.486432
converged
Fitting Repeat 4
# weights: 103
initial value 106.816642
iter 10 value 94.475777
iter 20 value 87.837759
iter 30 value 86.746615
iter 40 value 85.879137
iter 50 value 85.154261
final value 85.137748
converged
Fitting Repeat 5
# weights: 103
initial value 101.593969
iter 10 value 94.396957
iter 20 value 92.572076
iter 30 value 87.979011
iter 40 value 83.155640
iter 50 value 82.816940
iter 60 value 82.520201
iter 70 value 81.679283
iter 80 value 81.558849
iter 90 value 81.509996
iter 100 value 81.403892
final value 81.403892
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 105.323798
iter 10 value 92.693409
iter 20 value 86.226678
iter 30 value 83.867502
iter 40 value 82.708197
iter 50 value 81.745430
iter 60 value 81.341183
iter 70 value 80.853711
iter 80 value 80.438799
iter 90 value 80.254661
iter 100 value 80.096892
final value 80.096892
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 108.143056
iter 10 value 95.040038
iter 20 value 93.840454
iter 30 value 88.161824
iter 40 value 87.204475
iter 50 value 84.802009
iter 60 value 82.832994
iter 70 value 81.941699
iter 80 value 81.576885
iter 90 value 81.377564
iter 100 value 81.205090
final value 81.205090
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 107.744751
iter 10 value 94.481544
iter 20 value 90.820356
iter 30 value 85.676911
iter 40 value 83.509153
iter 50 value 82.432902
iter 60 value 81.285788
iter 70 value 80.361585
iter 80 value 80.155583
iter 90 value 80.132645
iter 100 value 79.914566
final value 79.914566
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 105.320560
iter 10 value 94.703429
iter 20 value 94.485092
iter 30 value 87.640017
iter 40 value 84.711202
iter 50 value 81.868032
iter 60 value 80.532320
iter 70 value 80.344098
iter 80 value 80.169413
iter 90 value 80.114019
iter 100 value 80.091153
final value 80.091153
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 109.776625
iter 10 value 94.468358
iter 20 value 93.019183
iter 30 value 89.689285
iter 40 value 86.638167
iter 50 value 84.705250
iter 60 value 82.665002
iter 70 value 81.597922
iter 80 value 80.920784
iter 90 value 80.698518
iter 100 value 80.409233
final value 80.409233
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 119.125749
iter 10 value 94.531907
iter 20 value 87.544210
iter 30 value 85.949857
iter 40 value 85.148864
iter 50 value 84.134890
iter 60 value 82.329090
iter 70 value 81.608535
iter 80 value 81.505895
iter 90 value 81.155260
iter 100 value 80.608243
final value 80.608243
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 108.983495
iter 10 value 94.480352
iter 20 value 87.525103
iter 30 value 86.508871
iter 40 value 84.979287
iter 50 value 83.002054
iter 60 value 81.079506
iter 70 value 80.420352
iter 80 value 79.913976
iter 90 value 79.785651
iter 100 value 79.762130
final value 79.762130
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 106.202930
iter 10 value 94.681300
iter 20 value 93.797792
iter 30 value 90.085093
iter 40 value 85.813133
iter 50 value 83.882584
iter 60 value 81.233527
iter 70 value 80.726136
iter 80 value 80.229207
iter 90 value 79.894766
iter 100 value 79.820498
final value 79.820498
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 114.343620
iter 10 value 95.417760
iter 20 value 93.244792
iter 30 value 84.889362
iter 40 value 82.622451
iter 50 value 82.351772
iter 60 value 80.425238
iter 70 value 80.226371
iter 80 value 80.187058
iter 90 value 80.184196
iter 100 value 80.145472
final value 80.145472
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 106.193216
iter 10 value 92.411666
iter 20 value 87.380629
iter 30 value 85.234899
iter 40 value 82.240837
iter 50 value 81.198318
iter 60 value 81.102727
iter 70 value 80.923653
iter 80 value 80.653490
iter 90 value 80.235076
iter 100 value 79.979036
final value 79.979036
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 101.734298
final value 94.485812
converged
Fitting Repeat 2
# weights: 103
initial value 101.158562
final value 94.486004
converged
Fitting Repeat 3
# weights: 103
initial value 102.651767
final value 94.485958
converged
Fitting Repeat 4
# weights: 103
initial value 94.722658
final value 94.485682
converged
Fitting Repeat 5
# weights: 103
initial value 98.809456
final value 94.485812
converged
Fitting Repeat 1
# weights: 305
initial value 96.875989
iter 10 value 94.489854
iter 20 value 94.484721
iter 30 value 94.139945
iter 40 value 91.958012
iter 50 value 91.957586
iter 60 value 88.986506
iter 70 value 83.141952
iter 80 value 83.117716
iter 90 value 82.310462
iter 100 value 81.574811
final value 81.574811
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 101.536797
iter 10 value 94.488352
iter 20 value 94.473596
iter 30 value 94.298856
iter 40 value 86.885942
iter 50 value 86.563659
final value 86.562110
converged
Fitting Repeat 3
# weights: 305
initial value 95.257922
iter 10 value 94.359426
iter 20 value 94.354904
iter 30 value 93.034294
iter 40 value 86.740693
iter 50 value 85.566456
final value 85.566354
converged
Fitting Repeat 4
# weights: 305
initial value 97.245209
iter 10 value 93.435024
iter 20 value 93.430515
iter 30 value 92.224195
iter 40 value 91.539660
iter 50 value 90.759706
iter 60 value 90.754857
iter 70 value 90.753836
final value 90.753831
converged
Fitting Repeat 5
# weights: 305
initial value 95.253097
iter 10 value 94.381036
iter 20 value 90.558336
iter 30 value 85.865993
iter 40 value 85.777988
iter 50 value 85.777546
iter 50 value 85.777545
final value 85.777545
converged
Fitting Repeat 1
# weights: 507
initial value 110.077251
iter 10 value 94.492486
iter 20 value 94.424035
iter 30 value 90.567477
iter 40 value 87.654193
iter 50 value 87.323392
iter 60 value 86.248425
iter 70 value 82.722803
iter 80 value 82.066320
iter 90 value 81.914679
iter 100 value 81.585969
final value 81.585969
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 96.185743
iter 10 value 94.362697
iter 20 value 94.340568
iter 30 value 93.696996
iter 40 value 85.543255
iter 50 value 84.474677
iter 60 value 84.446160
iter 70 value 84.404119
iter 80 value 83.884138
iter 90 value 83.755543
iter 100 value 83.562376
final value 83.562376
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 102.165958
iter 10 value 94.492164
iter 20 value 94.484367
iter 30 value 94.198555
iter 40 value 88.725086
iter 50 value 84.804180
iter 60 value 83.469344
iter 70 value 83.393306
iter 80 value 83.362410
iter 90 value 83.361291
iter 100 value 83.360812
final value 83.360812
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 94.741738
iter 10 value 93.713110
iter 20 value 93.119439
iter 30 value 92.908326
iter 40 value 92.904248
iter 50 value 92.850458
iter 60 value 92.820400
final value 92.819615
converged
Fitting Repeat 5
# weights: 507
initial value 99.629850
iter 10 value 94.490499
iter 20 value 94.328040
iter 30 value 89.656239
iter 40 value 81.923146
iter 50 value 80.146655
iter 60 value 80.142883
iter 70 value 79.971811
iter 80 value 79.461226
iter 90 value 79.392824
iter 100 value 79.363270
final value 79.363270
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 105.537457
final value 94.052910
converged
Fitting Repeat 2
# weights: 103
initial value 98.841715
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 94.630985
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 97.460846
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 94.535045
iter 10 value 93.877126
iter 20 value 93.328267
final value 93.328261
converged
Fitting Repeat 1
# weights: 305
initial value 104.221398
iter 10 value 93.328270
final value 93.328261
converged
Fitting Repeat 2
# weights: 305
initial value 94.583503
final value 94.052910
converged
Fitting Repeat 3
# weights: 305
initial value 106.607835
final value 92.523809
converged
Fitting Repeat 4
# weights: 305
initial value 101.472079
final value 93.473743
converged
Fitting Repeat 5
# weights: 305
initial value 98.039085
final value 94.052910
converged
Fitting Repeat 1
# weights: 507
initial value 95.464137
final value 94.052910
converged
Fitting Repeat 2
# weights: 507
initial value 95.443719
iter 10 value 90.409815
iter 20 value 84.269035
iter 30 value 84.216175
iter 40 value 84.207379
iter 50 value 84.153741
iter 60 value 83.999538
final value 83.976388
converged
Fitting Repeat 3
# weights: 507
initial value 99.166005
iter 10 value 87.730058
iter 20 value 80.999795
final value 80.998298
converged
Fitting Repeat 4
# weights: 507
initial value 99.478375
final value 93.328261
converged
Fitting Repeat 5
# weights: 507
initial value 100.788703
iter 10 value 93.338132
iter 20 value 93.316810
final value 93.316787
converged
Fitting Repeat 1
# weights: 103
initial value 99.376595
iter 10 value 93.957461
iter 20 value 92.875312
iter 30 value 90.165816
iter 40 value 84.327674
iter 50 value 83.451238
iter 60 value 82.537738
iter 70 value 79.910395
iter 80 value 78.786165
iter 90 value 78.494061
final value 78.493959
converged
Fitting Repeat 2
# weights: 103
initial value 102.622576
iter 10 value 94.045374
iter 20 value 93.295376
iter 30 value 92.798982
iter 40 value 92.785135
iter 50 value 86.808151
iter 60 value 82.454189
iter 70 value 81.934113
iter 80 value 80.490129
iter 90 value 79.706272
iter 100 value 79.079756
final value 79.079756
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 102.412590
iter 10 value 93.876675
iter 20 value 81.908312
iter 30 value 81.529756
iter 40 value 80.545313
iter 50 value 79.604121
iter 60 value 79.124218
final value 79.092135
converged
Fitting Repeat 4
# weights: 103
initial value 100.235492
iter 10 value 94.055233
iter 20 value 92.979064
iter 30 value 92.867352
iter 40 value 92.792019
iter 50 value 92.790091
iter 60 value 92.787951
iter 70 value 92.786532
iter 80 value 92.644561
iter 90 value 86.917207
iter 100 value 86.316934
final value 86.316934
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 103.515509
iter 10 value 94.051555
iter 20 value 84.210113
iter 30 value 82.928908
iter 40 value 82.202839
iter 50 value 82.091350
final value 82.088631
converged
Fitting Repeat 1
# weights: 305
initial value 103.980226
iter 10 value 94.005094
iter 20 value 85.146271
iter 30 value 82.765035
iter 40 value 82.190279
iter 50 value 81.995372
iter 60 value 81.829425
iter 70 value 81.790494
iter 80 value 81.569707
iter 90 value 81.092086
iter 100 value 80.499789
final value 80.499789
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 111.117980
iter 10 value 94.039372
iter 20 value 92.679837
iter 30 value 92.598639
iter 40 value 86.147008
iter 50 value 82.906343
iter 60 value 81.880117
iter 70 value 81.666134
iter 80 value 80.177395
iter 90 value 79.844857
iter 100 value 79.308295
final value 79.308295
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 99.521759
iter 10 value 93.902106
iter 20 value 91.368687
iter 30 value 86.566617
iter 40 value 84.529545
iter 50 value 83.244402
iter 60 value 82.863782
iter 70 value 82.512959
iter 80 value 81.460270
iter 90 value 79.970424
iter 100 value 78.811701
final value 78.811701
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 108.270565
iter 10 value 94.037770
iter 20 value 93.536228
iter 30 value 92.667131
iter 40 value 91.058700
iter 50 value 84.711693
iter 60 value 84.114800
iter 70 value 82.443838
iter 80 value 80.751488
iter 90 value 79.324982
iter 100 value 78.536360
final value 78.536360
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 113.244932
iter 10 value 94.172267
iter 20 value 93.000839
iter 30 value 92.728901
iter 40 value 89.544035
iter 50 value 83.207898
iter 60 value 82.779168
iter 70 value 80.281795
iter 80 value 78.555899
iter 90 value 78.285857
iter 100 value 77.651287
final value 77.651287
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 105.718234
iter 10 value 92.727149
iter 20 value 85.161545
iter 30 value 83.940532
iter 40 value 82.407552
iter 50 value 80.341420
iter 60 value 78.662361
iter 70 value 77.927525
iter 80 value 77.421441
iter 90 value 77.345281
iter 100 value 77.223237
final value 77.223237
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 116.749170
iter 10 value 94.823234
iter 20 value 93.884694
iter 30 value 87.570913
iter 40 value 83.049842
iter 50 value 80.186216
iter 60 value 78.781626
iter 70 value 77.839694
iter 80 value 77.667845
iter 90 value 77.449718
iter 100 value 77.035803
final value 77.035803
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 114.159671
iter 10 value 87.563074
iter 20 value 83.130398
iter 30 value 82.696434
iter 40 value 80.728593
iter 50 value 79.258555
iter 60 value 78.791950
iter 70 value 78.032433
iter 80 value 77.504827
iter 90 value 77.137991
iter 100 value 77.086772
final value 77.086772
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 104.059082
iter 10 value 93.795619
iter 20 value 92.736229
iter 30 value 91.700752
iter 40 value 83.793591
iter 50 value 80.649839
iter 60 value 79.158366
iter 70 value 78.572436
iter 80 value 78.321227
iter 90 value 78.251284
iter 100 value 78.092687
final value 78.092687
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 123.075695
iter 10 value 94.010156
iter 20 value 89.063052
iter 30 value 85.681850
iter 40 value 82.563944
iter 50 value 80.391316
iter 60 value 80.097297
iter 70 value 79.924564
iter 80 value 79.484979
iter 90 value 79.139765
iter 100 value 78.665954
final value 78.665954
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 100.514928
final value 94.054600
converged
Fitting Repeat 2
# weights: 103
initial value 96.039620
final value 94.054667
converged
Fitting Repeat 3
# weights: 103
initial value 97.206860
final value 94.054523
converged
Fitting Repeat 4
# weights: 103
initial value 100.486259
final value 94.054509
converged
Fitting Repeat 5
# weights: 103
initial value 101.021418
iter 10 value 94.074872
iter 20 value 94.053483
iter 30 value 84.001745
iter 40 value 81.326052
final value 81.319928
converged
Fitting Repeat 1
# weights: 305
initial value 100.026546
iter 10 value 93.333305
iter 20 value 93.329191
iter 30 value 91.869138
iter 40 value 83.296123
iter 50 value 80.261882
iter 60 value 80.211985
iter 70 value 80.112657
iter 80 value 80.068242
final value 80.067987
converged
Fitting Repeat 2
# weights: 305
initial value 95.134084
iter 10 value 92.768140
iter 20 value 92.640068
iter 30 value 92.486655
final value 92.486218
converged
Fitting Repeat 3
# weights: 305
initial value 125.843904
iter 10 value 94.058008
iter 20 value 94.053009
iter 30 value 91.368248
iter 40 value 90.345127
iter 50 value 87.580811
final value 87.579611
converged
Fitting Repeat 4
# weights: 305
initial value 106.196201
iter 10 value 94.057894
iter 20 value 94.052961
final value 94.052915
converged
Fitting Repeat 5
# weights: 305
initial value 96.131829
iter 10 value 94.057600
iter 20 value 93.690225
iter 30 value 88.981583
iter 40 value 88.628259
iter 50 value 85.257058
iter 60 value 85.253118
iter 70 value 85.239230
iter 80 value 85.218128
iter 90 value 82.157233
iter 100 value 80.831297
final value 80.831297
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 101.422167
iter 10 value 90.285295
iter 20 value 81.327022
iter 30 value 80.807558
iter 40 value 79.586214
iter 50 value 78.995886
iter 60 value 78.989415
iter 60 value 78.989415
iter 60 value 78.989415
final value 78.989415
converged
Fitting Repeat 2
# weights: 507
initial value 112.577296
iter 10 value 93.337095
iter 20 value 93.332030
iter 30 value 92.553237
final value 92.516462
converged
Fitting Repeat 3
# weights: 507
initial value 97.690335
iter 10 value 92.732938
iter 20 value 92.705371
iter 30 value 92.698034
iter 40 value 92.597962
iter 50 value 90.550179
iter 60 value 86.485120
iter 70 value 84.931815
iter 80 value 84.917127
iter 90 value 84.915047
iter 100 value 84.430602
final value 84.430602
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 101.982221
iter 10 value 93.122528
iter 20 value 93.095508
iter 30 value 93.090359
iter 40 value 92.249264
iter 50 value 87.940940
iter 60 value 78.416779
iter 70 value 78.218312
iter 80 value 78.213404
iter 90 value 77.873438
iter 100 value 76.571707
final value 76.571707
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 108.459900
iter 10 value 92.428274
iter 20 value 92.424465
iter 30 value 92.420739
iter 40 value 92.419753
iter 50 value 91.420674
iter 60 value 89.940519
iter 70 value 80.922555
iter 80 value 79.447695
iter 90 value 78.058351
iter 100 value 77.147024
final value 77.147024
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 95.034879
final value 94.479532
converged
Fitting Repeat 2
# weights: 103
initial value 97.604501
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 96.411029
final value 94.467391
converged
Fitting Repeat 4
# weights: 103
initial value 98.802113
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 97.877606
final value 94.479532
converged
Fitting Repeat 1
# weights: 305
initial value 111.253995
final value 94.467391
converged
Fitting Repeat 2
# weights: 305
initial value 111.661864
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 125.854224
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 122.389478
iter 10 value 94.484211
iter 10 value 94.484211
iter 10 value 94.484211
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 123.369912
iter 10 value 94.275363
iter 10 value 94.275363
iter 10 value 94.275363
final value 94.275363
converged
Fitting Repeat 1
# weights: 507
initial value 98.896641
final value 94.467391
converged
Fitting Repeat 2
# weights: 507
initial value 107.221635
final value 94.467391
converged
Fitting Repeat 3
# weights: 507
initial value 99.268285
final value 94.484211
converged
Fitting Repeat 4
# weights: 507
initial value 95.457793
iter 10 value 94.467391
iter 10 value 94.467391
iter 10 value 94.467391
final value 94.467391
converged
Fitting Repeat 5
# weights: 507
initial value 96.407178
iter 10 value 91.643874
final value 91.604177
converged
Fitting Repeat 1
# weights: 103
initial value 110.575457
iter 10 value 93.730896
iter 20 value 85.243480
iter 30 value 84.898572
iter 40 value 84.554589
iter 50 value 84.428808
iter 60 value 83.079620
iter 70 value 82.685133
iter 80 value 82.667210
final value 82.665410
converged
Fitting Repeat 2
# weights: 103
initial value 99.589344
iter 10 value 94.655520
iter 20 value 94.474564
iter 30 value 94.472042
iter 40 value 88.295631
iter 50 value 85.101426
iter 60 value 84.654726
iter 70 value 83.976154
iter 80 value 82.645588
iter 90 value 82.463704
iter 100 value 82.353653
final value 82.353653
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 107.328139
iter 10 value 94.388888
iter 20 value 92.539977
iter 30 value 90.331736
iter 40 value 84.975787
iter 50 value 83.330303
iter 60 value 82.936205
iter 70 value 82.705506
iter 80 value 82.661665
iter 90 value 82.639505
iter 100 value 82.619628
final value 82.619628
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 101.172613
iter 10 value 89.273296
iter 20 value 84.242923
iter 30 value 83.591477
iter 40 value 83.223058
iter 50 value 83.073658
iter 60 value 82.883520
iter 70 value 82.482998
iter 80 value 82.325080
iter 90 value 82.244281
iter 100 value 82.153506
final value 82.153506
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 98.549527
iter 10 value 94.484352
iter 20 value 88.693904
iter 30 value 84.857556
iter 40 value 84.645262
iter 50 value 83.128142
iter 60 value 82.659785
iter 70 value 82.619680
final value 82.619622
converged
Fitting Repeat 1
# weights: 305
initial value 126.981412
iter 10 value 94.508956
iter 20 value 89.405061
iter 30 value 86.699733
iter 40 value 84.023661
iter 50 value 83.020425
iter 60 value 82.788226
iter 70 value 82.691981
iter 80 value 82.653534
iter 90 value 82.624466
iter 100 value 82.558138
final value 82.558138
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 108.711120
iter 10 value 94.490687
iter 20 value 94.360251
iter 30 value 92.339726
iter 40 value 84.879076
iter 50 value 83.538652
iter 60 value 82.603468
iter 70 value 81.698965
iter 80 value 81.458955
iter 90 value 81.371401
iter 100 value 81.362857
final value 81.362857
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 102.174711
iter 10 value 94.712450
iter 20 value 94.507537
iter 30 value 93.566130
iter 40 value 92.059715
iter 50 value 90.861202
iter 60 value 87.525045
iter 70 value 84.850103
iter 80 value 82.293158
iter 90 value 82.023988
iter 100 value 81.771625
final value 81.771625
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 99.989160
iter 10 value 93.696775
iter 20 value 87.230424
iter 30 value 86.964858
iter 40 value 86.520318
iter 50 value 84.259809
iter 60 value 83.999468
iter 70 value 83.692277
iter 80 value 82.743499
iter 90 value 81.850338
iter 100 value 81.166426
final value 81.166426
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 119.509297
iter 10 value 94.394254
iter 20 value 86.120138
iter 30 value 85.152761
iter 40 value 84.138727
iter 50 value 82.970186
iter 60 value 81.477144
iter 70 value 81.000466
iter 80 value 80.887546
iter 90 value 80.806436
iter 100 value 80.799428
final value 80.799428
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 105.775035
iter 10 value 94.624777
iter 20 value 93.497536
iter 30 value 88.817760
iter 40 value 84.152838
iter 50 value 83.470316
iter 60 value 83.083101
iter 70 value 81.680112
iter 80 value 80.961175
iter 90 value 80.744283
iter 100 value 80.715732
final value 80.715732
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 107.173142
iter 10 value 94.826930
iter 20 value 89.323137
iter 30 value 88.141659
iter 40 value 85.891974
iter 50 value 84.697544
iter 60 value 83.655954
iter 70 value 83.287959
iter 80 value 83.041103
iter 90 value 82.159099
iter 100 value 81.988302
final value 81.988302
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 104.586897
iter 10 value 93.819508
iter 20 value 84.277596
iter 30 value 83.376212
iter 40 value 82.349136
iter 50 value 81.458792
iter 60 value 81.226290
iter 70 value 81.049953
iter 80 value 80.991389
iter 90 value 80.907304
iter 100 value 80.754186
final value 80.754186
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 126.330570
iter 10 value 94.436750
iter 20 value 87.410946
iter 30 value 84.374375
iter 40 value 83.150499
iter 50 value 81.272857
iter 60 value 80.892761
iter 70 value 80.783071
iter 80 value 80.639874
iter 90 value 80.524145
iter 100 value 80.351588
final value 80.351588
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 106.635144
iter 10 value 93.912161
iter 20 value 87.381467
iter 30 value 86.269545
iter 40 value 84.218132
iter 50 value 83.031615
iter 60 value 82.748364
iter 70 value 82.324883
iter 80 value 81.733372
iter 90 value 81.062132
iter 100 value 80.958645
final value 80.958645
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 95.248887
final value 94.485688
converged
Fitting Repeat 2
# weights: 103
initial value 108.885002
final value 94.486012
converged
Fitting Repeat 3
# weights: 103
initial value 105.565403
final value 94.486011
converged
Fitting Repeat 4
# weights: 103
initial value 99.565724
final value 94.486088
converged
Fitting Repeat 5
# weights: 103
initial value 101.455388
final value 94.485850
converged
Fitting Repeat 1
# weights: 305
initial value 96.983339
iter 10 value 93.592316
iter 20 value 93.286059
iter 30 value 93.276839
iter 40 value 90.484774
iter 50 value 88.153039
iter 60 value 85.675401
final value 85.649749
converged
Fitting Repeat 2
# weights: 305
initial value 97.158319
iter 10 value 87.805918
iter 20 value 87.652816
iter 30 value 85.814155
iter 40 value 85.548730
iter 40 value 85.548730
final value 85.548730
converged
Fitting Repeat 3
# weights: 305
initial value 95.998264
iter 10 value 94.487969
iter 20 value 94.483221
final value 94.467415
converged
Fitting Repeat 4
# weights: 305
initial value 103.414577
iter 10 value 94.472067
iter 20 value 94.467600
final value 94.467571
converged
Fitting Repeat 5
# weights: 305
initial value 109.635034
iter 10 value 94.280312
iter 20 value 92.580085
iter 30 value 88.193917
iter 40 value 88.180746
iter 50 value 88.180076
iter 60 value 88.179669
iter 70 value 87.630534
iter 80 value 83.630318
iter 90 value 83.618004
iter 100 value 83.617259
final value 83.617259
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 95.298633
iter 10 value 94.490679
iter 20 value 93.133900
iter 30 value 90.551464
iter 40 value 90.281428
iter 50 value 90.281204
iter 60 value 90.275744
iter 70 value 83.529383
iter 80 value 83.374234
iter 90 value 83.361124
iter 100 value 83.358723
final value 83.358723
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 106.126961
iter 10 value 94.270391
iter 20 value 94.266644
iter 30 value 94.263434
final value 94.263082
converged
Fitting Repeat 3
# weights: 507
initial value 97.524014
iter 10 value 94.493147
iter 20 value 94.473097
final value 94.467487
converged
Fitting Repeat 4
# weights: 507
initial value 115.814180
iter 10 value 94.475491
iter 20 value 94.467858
final value 94.467408
converged
Fitting Repeat 5
# weights: 507
initial value 123.276956
iter 10 value 94.475899
iter 20 value 94.459279
iter 30 value 90.721158
iter 40 value 89.199673
iter 50 value 87.845168
iter 60 value 87.757410
iter 70 value 87.755610
final value 87.751189
converged
Fitting Repeat 1
# weights: 103
initial value 98.368772
final value 94.052910
converged
Fitting Repeat 2
# weights: 103
initial value 101.424143
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 99.292715
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 96.070769
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 97.554433
final value 94.052910
converged
Fitting Repeat 1
# weights: 305
initial value 100.305441
final value 94.052910
converged
Fitting Repeat 2
# weights: 305
initial value 96.494866
iter 10 value 93.924078
iter 20 value 93.871624
iter 30 value 93.868978
iter 30 value 93.868978
final value 93.868973
converged
Fitting Repeat 3
# weights: 305
initial value 101.417879
final value 94.052910
converged
Fitting Repeat 4
# weights: 305
initial value 98.061577
final value 94.052910
converged
Fitting Repeat 5
# weights: 305
initial value 95.393514
final value 93.915746
converged
Fitting Repeat 1
# weights: 507
initial value 96.493022
final value 93.915746
converged
Fitting Repeat 2
# weights: 507
initial value 102.926425
iter 10 value 86.339237
final value 85.486387
converged
Fitting Repeat 3
# weights: 507
initial value 112.625252
final value 93.969040
converged
Fitting Repeat 4
# weights: 507
initial value 96.415478
final value 93.915746
converged
Fitting Repeat 5
# weights: 507
initial value 97.766335
final value 94.052910
converged
Fitting Repeat 1
# weights: 103
initial value 106.888212
iter 10 value 93.922323
iter 20 value 88.114810
iter 30 value 84.818778
iter 40 value 84.377519
iter 50 value 83.620866
iter 60 value 83.341537
final value 83.323453
converged
Fitting Repeat 2
# weights: 103
initial value 100.534663
iter 10 value 94.068082
iter 20 value 93.084435
iter 30 value 87.326470
iter 40 value 84.982385
iter 50 value 83.728398
iter 60 value 83.024438
iter 70 value 82.586565
iter 80 value 82.429922
iter 90 value 82.339002
iter 100 value 82.133944
final value 82.133944
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 101.259675
iter 10 value 94.254848
iter 20 value 93.315768
iter 30 value 90.050271
iter 40 value 89.420302
iter 50 value 87.125276
iter 60 value 84.766419
iter 70 value 83.626653
iter 80 value 83.489861
iter 90 value 83.345703
iter 100 value 83.318452
final value 83.318452
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 106.254476
iter 10 value 94.056684
iter 20 value 85.311609
iter 30 value 85.037290
iter 40 value 84.814128
iter 50 value 83.867172
iter 60 value 83.738013
iter 70 value 83.733143
iter 80 value 83.731503
iter 90 value 83.725096
final value 83.723186
converged
Fitting Repeat 5
# weights: 103
initial value 98.163990
iter 10 value 94.054360
iter 20 value 88.756129
iter 30 value 85.188137
iter 40 value 84.929848
iter 50 value 84.632652
iter 60 value 83.824177
iter 70 value 83.757511
iter 80 value 83.725285
iter 90 value 83.723447
final value 83.723186
converged
Fitting Repeat 1
# weights: 305
initial value 106.822184
iter 10 value 94.631823
iter 20 value 92.438553
iter 30 value 85.244844
iter 40 value 83.632335
iter 50 value 82.840392
iter 60 value 81.628880
iter 70 value 81.155258
iter 80 value 80.853623
iter 90 value 80.803777
iter 100 value 80.779193
final value 80.779193
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 99.077670
iter 10 value 94.044274
iter 20 value 92.630196
iter 30 value 92.229637
iter 40 value 92.154326
iter 50 value 85.695615
iter 60 value 85.186763
iter 70 value 85.059301
iter 80 value 83.300612
iter 90 value 80.950023
iter 100 value 80.683004
final value 80.683004
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 105.066454
iter 10 value 96.208678
iter 20 value 87.649084
iter 30 value 87.067272
iter 40 value 85.546887
iter 50 value 84.695618
iter 60 value 82.407845
iter 70 value 82.061465
iter 80 value 82.007005
iter 90 value 81.983759
iter 100 value 81.966283
final value 81.966283
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 99.690441
iter 10 value 93.961226
iter 20 value 92.112151
iter 30 value 90.229958
iter 40 value 88.420475
iter 50 value 85.513780
iter 60 value 85.288043
iter 70 value 83.862043
iter 80 value 82.730245
iter 90 value 82.215961
iter 100 value 82.191454
final value 82.191454
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 102.533257
iter 10 value 94.585267
iter 20 value 89.894565
iter 30 value 86.152519
iter 40 value 85.487625
iter 50 value 84.916457
iter 60 value 84.781890
iter 70 value 83.109793
iter 80 value 82.290152
iter 90 value 82.150609
iter 100 value 81.974120
final value 81.974120
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 111.602408
iter 10 value 93.190150
iter 20 value 88.081569
iter 30 value 85.232842
iter 40 value 84.108704
iter 50 value 83.831071
iter 60 value 82.157315
iter 70 value 81.489949
iter 80 value 80.753876
iter 90 value 80.496220
iter 100 value 80.074505
final value 80.074505
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 129.135175
iter 10 value 96.412844
iter 20 value 94.837061
iter 30 value 86.299041
iter 40 value 85.836852
iter 50 value 85.564364
iter 60 value 84.637553
iter 70 value 83.023324
iter 80 value 82.155900
iter 90 value 81.808529
iter 100 value 81.279159
final value 81.279159
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 127.137134
iter 10 value 93.996126
iter 20 value 89.364499
iter 30 value 87.001175
iter 40 value 84.924014
iter 50 value 84.117165
iter 60 value 83.794319
iter 70 value 83.193294
iter 80 value 82.115242
iter 90 value 81.838609
iter 100 value 81.366351
final value 81.366351
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 101.781669
iter 10 value 93.760380
iter 20 value 86.750851
iter 30 value 83.481467
iter 40 value 82.938825
iter 50 value 82.194621
iter 60 value 81.024016
iter 70 value 80.796203
iter 80 value 80.604729
iter 90 value 80.362997
iter 100 value 80.220468
final value 80.220468
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 112.351366
iter 10 value 94.062430
iter 20 value 91.977766
iter 30 value 88.184052
iter 40 value 87.153552
iter 50 value 85.405135
iter 60 value 82.976277
iter 70 value 82.014709
iter 80 value 80.947508
iter 90 value 80.428631
iter 100 value 80.193046
final value 80.193046
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 95.509583
final value 94.054515
converged
Fitting Repeat 2
# weights: 103
initial value 97.098154
iter 10 value 94.054338
iter 20 value 94.044334
iter 30 value 89.019694
iter 40 value 89.019006
iter 50 value 89.016486
iter 60 value 89.016186
iter 70 value 87.369002
iter 80 value 84.191525
final value 83.668831
converged
Fitting Repeat 3
# weights: 103
initial value 112.340953
final value 94.054553
converged
Fitting Repeat 4
# weights: 103
initial value 96.933266
final value 94.054567
converged
Fitting Repeat 5
# weights: 103
initial value 96.233982
final value 94.054800
converged
Fitting Repeat 1
# weights: 305
initial value 102.923714
iter 10 value 94.057926
iter 20 value 88.636758
iter 30 value 83.020235
iter 40 value 83.018861
final value 83.018691
converged
Fitting Repeat 2
# weights: 305
initial value 106.426832
iter 10 value 94.057887
iter 20 value 94.052925
iter 20 value 94.052924
iter 20 value 94.052924
final value 94.052924
converged
Fitting Repeat 3
# weights: 305
initial value 97.163867
iter 10 value 90.458333
iter 20 value 85.767189
iter 30 value 85.531443
iter 40 value 85.510217
iter 50 value 84.202693
final value 84.201893
converged
Fitting Repeat 4
# weights: 305
initial value 97.307804
iter 10 value 94.057488
iter 20 value 93.534164
iter 30 value 83.772902
iter 40 value 83.642075
iter 50 value 83.641019
iter 60 value 83.640450
iter 70 value 83.637155
final value 83.635716
converged
Fitting Repeat 5
# weights: 305
initial value 95.886327
iter 10 value 93.920589
iter 20 value 90.766825
iter 30 value 85.751384
final value 85.748894
converged
Fitting Repeat 1
# weights: 507
initial value 111.006740
iter 10 value 93.924177
iter 20 value 93.916153
iter 30 value 92.014373
iter 40 value 85.109995
iter 50 value 83.887572
iter 60 value 83.887336
final value 83.884635
converged
Fitting Repeat 2
# weights: 507
initial value 98.798305
iter 10 value 94.060964
iter 20 value 94.012906
iter 30 value 92.721253
iter 40 value 87.239762
iter 50 value 84.952354
iter 60 value 83.718141
iter 70 value 82.430111
iter 80 value 81.990670
iter 90 value 81.804971
iter 100 value 81.705247
final value 81.705247
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 103.829916
iter 10 value 94.061149
iter 20 value 93.817409
iter 30 value 92.585320
iter 40 value 92.571369
final value 92.571147
converged
Fitting Repeat 4
# weights: 507
initial value 100.870676
iter 10 value 93.584569
iter 20 value 93.443585
iter 30 value 93.441896
iter 40 value 93.439706
iter 50 value 93.398544
iter 60 value 93.375875
iter 70 value 91.665463
iter 80 value 91.616106
iter 90 value 86.512235
iter 100 value 85.649333
final value 85.649333
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 120.222866
iter 10 value 93.977528
iter 20 value 93.947517
iter 30 value 93.862981
iter 40 value 93.037294
iter 50 value 91.558736
iter 60 value 91.329200
iter 70 value 91.245278
iter 80 value 90.678872
iter 90 value 90.674776
iter 100 value 90.674205
final value 90.674205
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 150.499154
iter 10 value 131.467453
iter 20 value 124.607604
iter 30 value 106.585546
iter 40 value 105.564550
iter 50 value 103.697117
iter 60 value 102.422081
iter 70 value 102.224473
iter 80 value 102.007683
iter 90 value 101.806780
iter 100 value 101.411540
final value 101.411540
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 137.067335
iter 10 value 117.896641
iter 20 value 114.859954
iter 30 value 114.513937
iter 40 value 114.340872
iter 50 value 113.391233
iter 60 value 112.184665
iter 70 value 111.419068
iter 80 value 105.911004
iter 90 value 103.206034
iter 100 value 102.316171
final value 102.316171
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 131.039832
iter 10 value 117.873927
iter 20 value 108.617699
iter 30 value 106.197306
iter 40 value 105.882834
iter 50 value 104.195141
iter 60 value 103.340262
iter 70 value 102.766652
iter 80 value 102.363148
iter 90 value 102.233194
iter 100 value 102.081790
final value 102.081790
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 124.339908
iter 10 value 117.709814
iter 20 value 114.453918
iter 30 value 111.220145
iter 40 value 110.660430
iter 50 value 110.088645
iter 60 value 110.027437
iter 70 value 106.656915
iter 80 value 104.900470
iter 90 value 104.562561
iter 100 value 103.892618
final value 103.892618
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 141.924638
iter 10 value 117.960769
iter 20 value 117.902735
iter 30 value 117.890174
iter 40 value 116.635591
iter 50 value 109.869520
iter 60 value 108.850058
iter 70 value 108.517425
iter 80 value 108.362670
iter 90 value 105.221251
iter 100 value 104.821652
final value 104.821652
stopped after 100 iterations
svmRadial
ranger
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
RUNIT TEST PROTOCOL -- Fri Dec 19 20:15:22 2025
***********************************************
Number of test functions: 7
Number of errors: 0
Number of failures: 0
1 Test Suite :
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7
Number of errors: 0
Number of failures: 0
Warning messages:
1: `repeats` has no meaning for this resampling method.
2: executing %dopar% sequentially: no parallel backend registered
>
>
>
>
> proc.time()
user system elapsed
20.382 0.488 73.876
HPiP.Rcheck/HPiP-Ex.timings
| name | user | system | elapsed | |
| FSmethod | 19.119 | 0.986 | 21.123 | |
| FreqInteractors | 0.170 | 0.011 | 0.194 | |
| calculateAAC | 0.013 | 0.002 | 0.015 | |
| calculateAutocor | 0.124 | 0.020 | 0.148 | |
| calculateCTDC | 0.031 | 0.004 | 0.035 | |
| calculateCTDD | 0.156 | 0.010 | 0.171 | |
| calculateCTDT | 0.064 | 0.006 | 0.082 | |
| calculateCTriad | 0.148 | 0.016 | 0.174 | |
| calculateDC | 0.035 | 0.004 | 0.039 | |
| calculateF | 0.107 | 0.003 | 0.115 | |
| calculateKSAAP | 0.036 | 0.003 | 0.039 | |
| calculateQD_Sm | 0.833 | 0.067 | 0.917 | |
| calculateTC | 0.560 | 0.055 | 0.617 | |
| calculateTC_Sm | 0.124 | 0.010 | 0.136 | |
| corr_plot | 18.980 | 0.987 | 20.952 | |
| enrichfindP | 0.195 | 0.037 | 12.885 | |
| enrichfind_hp | 0.015 | 0.002 | 0.861 | |
| enrichplot | 0.169 | 0.011 | 0.183 | |
| filter_missing_values | 0 | 0 | 0 | |
| getFASTA | 0.033 | 0.006 | 3.535 | |
| getHPI | 0.001 | 0.000 | 0.001 | |
| get_negativePPI | 0.000 | 0.000 | 0.001 | |
| get_positivePPI | 0 | 0 | 0 | |
| impute_missing_data | 0 | 0 | 0 | |
| plotPPI | 0.040 | 0.002 | 0.042 | |
| pred_ensembel | 6.575 | 0.132 | 6.294 | |
| var_imp | 18.564 | 0.945 | 20.708 | |