Matineh Rahmatbakhsh
| Snapshot Date: 2026-03-23 13:45 -0400 (Mon, 23 Mar 2026) |
| git_url: https://git.bioconductor.org/packages/HPiP |
| git_branch: RELEASE_3_22 |
| git_last_commit: 6cf0d22 |
| git_last_commit_date: 2025-12-28 18:31:13 -0400 (Sun, 28 Dec 2025) |
| Back to Build/check report for BioC 3.22: simplified long |
|
This page was generated on 2026-03-24 11:57 -0400 (Tue, 24 Mar 2026).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" | 4893 |
| 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 1006/2361 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| HPiP 1.16.1 (landing page) Matineh Rahmatbakhsh
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) / x86_64 | OK | OK | OK | | ||||||||
| See other builds for HPiP in R Universe. | ||||||||||||||
|
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: 1.16.1 |
| Command: /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings HPiP_1.16.1.tar.gz |
| StartedAt: 2026-03-24 00:24:17 -0400 (Tue, 24 Mar 2026) |
| EndedAt: 2026-03-24 00:39:03 -0400 (Tue, 24 Mar 2026) |
| EllapsedTime: 886.1 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: HPiP.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings HPiP_1.16.1.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck’
* using R version 4.5.2 (2025-10-31)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.3 LTS
* using session charset: UTF-8
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.16.1’
* 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 loading without being on the library search path ... 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 ... OK
* 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
corr_plot 34.721 0.508 35.272
FSmethod 34.106 0.434 34.594
var_imp 33.879 0.602 34.502
pred_ensembel 12.807 0.087 11.616
enrichfindP 0.582 0.044 10.113
* 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE
Status: 2 NOTEs
See
‘/home/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck/00check.log’
for details.
HPiP.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL HPiP ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’ * installing *source* package ‘HPiP’ ... ** this is package ‘HPiP’ version ‘1.16.1’ ** 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 version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu
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 95.281627
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 98.583453
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 94.854880
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 94.750223
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 99.732456
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 97.052619
iter 10 value 94.484230
final value 94.484211
converged
Fitting Repeat 2
# weights: 305
initial value 111.808382
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 111.719240
iter 10 value 84.857559
iter 20 value 82.684216
iter 30 value 82.563642
final value 82.563559
converged
Fitting Repeat 4
# weights: 305
initial value 103.974497
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 96.744225
iter 10 value 93.092131
final value 93.088889
converged
Fitting Repeat 1
# weights: 507
initial value 140.493098
final value 94.484211
converged
Fitting Repeat 2
# weights: 507
initial value 97.459886
final value 94.484211
converged
Fitting Repeat 3
# weights: 507
initial value 96.333530
final value 94.484211
converged
Fitting Repeat 4
# weights: 507
initial value 111.147621
final value 94.443243
converged
Fitting Repeat 5
# weights: 507
initial value 107.276313
final value 94.443243
converged
Fitting Repeat 1
# weights: 103
initial value 102.526723
iter 10 value 94.400477
iter 20 value 93.610863
iter 30 value 93.513572
iter 40 value 93.042798
iter 50 value 90.585845
iter 60 value 84.936639
iter 70 value 84.522229
iter 80 value 84.216888
iter 90 value 83.945996
iter 100 value 83.331207
final value 83.331207
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 97.378045
iter 10 value 94.489428
iter 20 value 92.033734
iter 30 value 86.595484
iter 40 value 86.229407
iter 50 value 85.820660
iter 60 value 83.308355
iter 70 value 83.159851
iter 80 value 82.960114
iter 90 value 82.957355
final value 82.957017
converged
Fitting Repeat 3
# weights: 103
initial value 101.481247
iter 10 value 94.449461
iter 20 value 93.894081
iter 30 value 93.863342
iter 40 value 85.221207
iter 50 value 84.388995
iter 60 value 83.522736
iter 70 value 83.388174
iter 80 value 80.927587
iter 90 value 80.657092
iter 100 value 80.538809
final value 80.538809
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 98.766815
iter 10 value 93.835521
iter 20 value 84.786481
iter 30 value 82.221956
iter 40 value 82.059174
iter 50 value 81.979773
iter 60 value 81.592198
iter 70 value 80.289887
iter 80 value 80.019251
iter 90 value 79.876308
iter 100 value 79.652040
final value 79.652040
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 96.094736
iter 10 value 94.486436
iter 20 value 94.069520
iter 30 value 93.542716
iter 40 value 86.935307
iter 50 value 84.058682
iter 60 value 83.684922
iter 70 value 83.342908
final value 83.342042
converged
Fitting Repeat 1
# weights: 305
initial value 116.615277
iter 10 value 94.780040
iter 20 value 94.486590
iter 30 value 85.899537
iter 40 value 83.609939
iter 50 value 83.461339
iter 60 value 80.868341
iter 70 value 79.998764
iter 80 value 79.844019
iter 90 value 79.472594
iter 100 value 79.190974
final value 79.190974
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 111.023808
iter 10 value 88.333417
iter 20 value 85.583348
iter 30 value 84.098268
iter 40 value 83.640296
iter 50 value 82.878929
iter 60 value 82.399737
iter 70 value 82.002329
iter 80 value 81.843604
iter 90 value 81.665411
iter 100 value 81.620327
final value 81.620327
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 99.909565
iter 10 value 94.524300
iter 20 value 94.309399
iter 30 value 93.839774
iter 40 value 86.126019
iter 50 value 84.153928
iter 60 value 83.462935
iter 70 value 83.376712
iter 80 value 83.174004
iter 90 value 82.855175
iter 100 value 82.192395
final value 82.192395
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 103.721167
iter 10 value 94.484386
iter 20 value 85.137246
iter 30 value 81.803233
iter 40 value 81.381708
iter 50 value 80.108698
iter 60 value 79.966637
iter 70 value 79.803583
iter 80 value 79.665371
iter 90 value 78.797288
iter 100 value 78.531991
final value 78.531991
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 109.272229
iter 10 value 94.451694
iter 20 value 86.552029
iter 30 value 83.226976
iter 40 value 82.890510
iter 50 value 82.697679
iter 60 value 82.496457
iter 70 value 82.410283
iter 80 value 82.366912
iter 90 value 82.330377
iter 100 value 81.657894
final value 81.657894
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 105.637353
iter 10 value 94.855937
iter 20 value 89.375972
iter 30 value 85.370991
iter 40 value 84.597465
iter 50 value 82.492814
iter 60 value 82.174980
iter 70 value 82.058349
iter 80 value 81.246336
iter 90 value 80.635823
iter 100 value 80.218146
final value 80.218146
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 117.550061
iter 10 value 94.171617
iter 20 value 85.154810
iter 30 value 84.464089
iter 40 value 83.122778
iter 50 value 82.392426
iter 60 value 81.991684
iter 70 value 80.824461
iter 80 value 79.294706
iter 90 value 78.325943
iter 100 value 78.183129
final value 78.183129
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 108.055739
iter 10 value 91.979508
iter 20 value 85.923944
iter 30 value 83.871358
iter 40 value 82.794538
iter 50 value 81.122121
iter 60 value 80.309190
iter 70 value 79.398530
iter 80 value 79.175389
iter 90 value 78.880356
iter 100 value 78.662878
final value 78.662878
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 118.368430
iter 10 value 93.477047
iter 20 value 91.723731
iter 30 value 91.057567
iter 40 value 89.858314
iter 50 value 88.088428
iter 60 value 85.108826
iter 70 value 83.488408
iter 80 value 79.189712
iter 90 value 78.583060
iter 100 value 78.497415
final value 78.497415
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 104.008001
iter 10 value 94.462395
iter 20 value 93.693326
iter 30 value 93.215022
iter 40 value 87.557493
iter 50 value 86.408610
iter 60 value 86.072018
iter 70 value 82.323465
iter 80 value 81.598805
iter 90 value 80.953177
iter 100 value 80.462464
final value 80.462464
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 108.374111
final value 94.485871
converged
Fitting Repeat 2
# weights: 103
initial value 101.686154
final value 94.485778
converged
Fitting Repeat 3
# weights: 103
initial value 98.705258
final value 94.486121
converged
Fitting Repeat 4
# weights: 103
initial value 99.367738
final value 94.485919
converged
Fitting Repeat 5
# weights: 103
initial value 95.789155
final value 94.485719
converged
Fitting Repeat 1
# weights: 305
initial value 107.879575
iter 10 value 93.778154
iter 20 value 93.775600
iter 30 value 85.197180
iter 40 value 84.891659
iter 50 value 84.891311
iter 60 value 82.834909
iter 70 value 81.997611
iter 80 value 81.994366
iter 90 value 81.935086
iter 100 value 81.934547
final value 81.934547
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 97.011041
iter 10 value 94.375075
iter 20 value 94.317288
iter 30 value 94.315947
iter 40 value 93.819757
iter 50 value 93.815665
iter 60 value 93.815284
iter 70 value 88.302326
iter 80 value 81.681601
iter 90 value 81.430442
iter 100 value 81.357940
final value 81.357940
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 103.957699
iter 10 value 93.185255
iter 20 value 93.183357
iter 30 value 91.656387
iter 40 value 85.166760
iter 50 value 83.086525
iter 60 value 83.067518
iter 70 value 83.067424
final value 83.067214
converged
Fitting Repeat 4
# weights: 305
initial value 97.272458
iter 10 value 94.448538
iter 20 value 94.439327
iter 30 value 93.814214
iter 30 value 93.814214
iter 30 value 93.814214
final value 93.814214
converged
Fitting Repeat 5
# weights: 305
initial value 100.046067
iter 10 value 94.448448
iter 20 value 93.954232
iter 30 value 92.540270
final value 92.540261
converged
Fitting Repeat 1
# weights: 507
initial value 115.561055
iter 10 value 94.490493
iter 20 value 94.222173
iter 30 value 90.609343
iter 40 value 89.594646
iter 50 value 89.583771
iter 60 value 89.576499
iter 70 value 89.570650
iter 80 value 89.568549
iter 90 value 89.555373
iter 100 value 89.368371
final value 89.368371
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 100.216772
iter 10 value 94.099479
iter 20 value 94.063581
iter 30 value 93.477003
iter 40 value 91.978822
iter 50 value 81.826533
iter 60 value 81.165896
iter 70 value 81.155893
iter 80 value 81.155557
final value 81.155353
converged
Fitting Repeat 3
# weights: 507
initial value 121.653151
iter 10 value 92.382991
iter 20 value 85.272160
iter 30 value 82.761613
iter 40 value 82.381484
iter 50 value 82.379929
iter 60 value 82.372272
iter 70 value 82.285941
iter 80 value 82.280009
iter 90 value 82.269357
iter 100 value 82.257440
final value 82.257440
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 120.384340
iter 10 value 94.492164
iter 20 value 94.485018
iter 30 value 94.073827
iter 40 value 93.438912
final value 93.438868
converged
Fitting Repeat 5
# weights: 507
initial value 113.616325
iter 10 value 94.492488
iter 20 value 94.462463
iter 30 value 91.436792
iter 40 value 91.035654
final value 91.035652
converged
Fitting Repeat 1
# weights: 103
initial value 98.089166
iter 10 value 90.792175
iter 20 value 89.700868
iter 30 value 89.687981
iter 40 value 89.687304
final value 89.687241
converged
Fitting Repeat 2
# weights: 103
initial value 99.157829
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 95.981333
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 100.696329
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 96.979032
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 101.588522
iter 10 value 94.242009
iter 20 value 94.054118
final value 94.053605
converged
Fitting Repeat 2
# weights: 305
initial value 115.284919
final value 94.322897
converged
Fitting Repeat 3
# weights: 305
initial value 98.417377
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 101.995814
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 106.659339
iter 10 value 93.772974
iter 10 value 93.772973
iter 10 value 93.772973
final value 93.772973
converged
Fitting Repeat 1
# weights: 507
initial value 129.073722
iter 10 value 94.466823
iter 10 value 94.466823
iter 10 value 94.466823
final value 94.466823
converged
Fitting Repeat 2
# weights: 507
initial value 96.933360
final value 94.484211
converged
Fitting Repeat 3
# weights: 507
initial value 95.906527
iter 10 value 93.514920
iter 20 value 90.427059
iter 30 value 90.331335
iter 40 value 90.322683
iter 50 value 90.252750
iter 60 value 90.223953
iter 70 value 90.223297
iter 80 value 87.631668
iter 90 value 86.551835
iter 100 value 85.926612
final value 85.926612
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 102.912907
final value 94.112570
converged
Fitting Repeat 5
# weights: 507
initial value 98.547764
final value 94.484211
converged
Fitting Repeat 1
# weights: 103
initial value 107.586479
iter 10 value 94.406904
iter 20 value 91.239571
iter 30 value 90.423157
iter 40 value 85.333268
iter 50 value 84.708444
iter 60 value 84.154877
iter 70 value 83.975239
iter 80 value 83.919685
iter 90 value 82.969537
iter 100 value 81.988778
final value 81.988778
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 110.785110
iter 10 value 94.364158
iter 20 value 87.851920
iter 30 value 86.500639
iter 40 value 86.024516
iter 50 value 84.989443
iter 60 value 84.666598
iter 70 value 84.597728
final value 84.593415
converged
Fitting Repeat 3
# weights: 103
initial value 97.456814
iter 10 value 94.492687
iter 20 value 88.359135
iter 30 value 86.788257
iter 40 value 86.697299
iter 50 value 86.518562
iter 60 value 83.494435
iter 70 value 83.404697
iter 80 value 83.401405
final value 83.401394
converged
Fitting Repeat 4
# weights: 103
initial value 98.910650
iter 10 value 94.334483
iter 20 value 93.801333
iter 30 value 87.757631
iter 40 value 87.435045
iter 50 value 86.955550
iter 60 value 82.717389
iter 70 value 81.721269
iter 80 value 81.652558
iter 90 value 81.650595
final value 81.650462
converged
Fitting Repeat 5
# weights: 103
initial value 116.096149
iter 10 value 94.486612
iter 20 value 87.214838
iter 30 value 86.457147
iter 40 value 86.174776
iter 50 value 85.436126
iter 60 value 84.739568
iter 70 value 84.072108
iter 80 value 83.913117
final value 83.907955
converged
Fitting Repeat 1
# weights: 305
initial value 107.020462
iter 10 value 94.838290
iter 20 value 94.471972
iter 30 value 94.175366
iter 40 value 94.139949
iter 50 value 89.931844
iter 60 value 86.433711
iter 70 value 83.229988
iter 80 value 82.207426
iter 90 value 81.475284
iter 100 value 81.012588
final value 81.012588
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 101.650943
iter 10 value 94.424438
iter 20 value 92.797950
iter 30 value 92.681083
iter 40 value 91.187356
iter 50 value 87.305989
iter 60 value 85.262593
iter 70 value 84.195566
iter 80 value 83.244581
iter 90 value 82.110528
iter 100 value 80.836609
final value 80.836609
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 101.309575
iter 10 value 95.510541
iter 20 value 91.478891
iter 30 value 85.203189
iter 40 value 83.205546
iter 50 value 80.982249
iter 60 value 80.591479
iter 70 value 80.420526
iter 80 value 80.325101
iter 90 value 80.242769
iter 100 value 80.228474
final value 80.228474
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 106.824780
iter 10 value 94.521648
iter 20 value 94.070627
iter 30 value 87.922324
iter 40 value 85.395579
iter 50 value 84.871161
iter 60 value 83.465870
iter 70 value 82.969664
iter 80 value 81.591372
iter 90 value 81.052913
iter 100 value 80.526745
final value 80.526745
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 99.561724
iter 10 value 88.227961
iter 20 value 83.863156
iter 30 value 83.460481
iter 40 value 82.629746
iter 50 value 81.134202
iter 60 value 80.393886
iter 70 value 79.919376
iter 80 value 79.861221
iter 90 value 79.848806
iter 100 value 79.843498
final value 79.843498
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 115.009808
iter 10 value 94.423549
iter 20 value 89.100311
iter 30 value 84.138341
iter 40 value 83.065356
iter 50 value 82.632214
iter 60 value 81.912630
iter 70 value 81.565414
iter 80 value 81.139183
iter 90 value 80.327013
iter 100 value 80.035981
final value 80.035981
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 106.958028
iter 10 value 96.382758
iter 20 value 93.360241
iter 30 value 86.074540
iter 40 value 82.736111
iter 50 value 81.688601
iter 60 value 81.050558
iter 70 value 80.359757
iter 80 value 80.242209
iter 90 value 79.972346
iter 100 value 79.953798
final value 79.953798
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 126.949917
iter 10 value 94.422302
iter 20 value 90.342986
iter 30 value 85.571815
iter 40 value 81.897489
iter 50 value 80.886533
iter 60 value 80.806153
iter 70 value 80.642931
iter 80 value 80.504571
iter 90 value 80.467854
iter 100 value 80.452040
final value 80.452040
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 129.731667
iter 10 value 94.461009
iter 20 value 94.010759
iter 30 value 93.685043
iter 40 value 86.522720
iter 50 value 84.498818
iter 60 value 82.024880
iter 70 value 81.503808
iter 80 value 81.052646
iter 90 value 80.767021
iter 100 value 80.516160
final value 80.516160
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 105.769460
iter 10 value 94.183321
iter 20 value 91.875699
iter 30 value 91.038087
iter 40 value 90.661131
iter 50 value 88.051084
iter 60 value 84.084436
iter 70 value 83.189491
iter 80 value 81.866100
iter 90 value 80.835778
iter 100 value 80.557185
final value 80.557185
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 96.592344
final value 94.485885
converged
Fitting Repeat 2
# weights: 103
initial value 104.522220
final value 94.485702
converged
Fitting Repeat 3
# weights: 103
initial value 122.501557
final value 94.485596
converged
Fitting Repeat 4
# weights: 103
initial value 104.300474
final value 94.485954
converged
Fitting Repeat 5
# weights: 103
initial value 124.488181
final value 94.485782
converged
Fitting Repeat 1
# weights: 305
initial value 106.323096
iter 10 value 94.489684
iter 20 value 94.482502
iter 30 value 91.951682
final value 91.833908
converged
Fitting Repeat 2
# weights: 305
initial value 101.600890
iter 10 value 94.489225
iter 20 value 94.484752
iter 30 value 94.113161
iter 40 value 94.000127
iter 50 value 93.995693
final value 93.995667
converged
Fitting Repeat 3
# weights: 305
initial value 97.928592
iter 10 value 94.433362
iter 20 value 94.432539
iter 30 value 94.428312
iter 40 value 94.427826
final value 94.427818
converged
Fitting Repeat 4
# weights: 305
initial value 98.647242
iter 10 value 94.486364
iter 20 value 87.702186
iter 30 value 85.709668
iter 40 value 85.681329
iter 50 value 85.346367
final value 85.343708
converged
Fitting Repeat 5
# weights: 305
initial value 102.154751
iter 10 value 94.489513
iter 20 value 94.440183
iter 30 value 93.465248
iter 40 value 93.343153
iter 50 value 93.342984
final value 93.342968
converged
Fitting Repeat 1
# weights: 507
initial value 103.798180
iter 10 value 94.492449
iter 20 value 94.451516
iter 30 value 86.760905
iter 40 value 84.676277
iter 50 value 84.440699
iter 60 value 83.677700
iter 70 value 83.231789
iter 80 value 83.009226
iter 90 value 82.432162
iter 100 value 82.430018
final value 82.430018
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 95.545334
iter 10 value 94.458110
iter 20 value 86.749914
iter 30 value 85.813608
iter 40 value 85.803889
iter 50 value 85.391422
iter 60 value 84.609331
iter 70 value 81.129089
iter 80 value 79.539043
iter 90 value 79.137851
iter 100 value 79.134985
final value 79.134985
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 109.905080
iter 10 value 94.491858
iter 20 value 94.431346
iter 30 value 86.204447
iter 40 value 86.160459
iter 50 value 85.764845
final value 85.709406
converged
Fitting Repeat 4
# weights: 507
initial value 97.165924
iter 10 value 94.491907
iter 20 value 94.420658
iter 30 value 86.466124
iter 40 value 85.501891
iter 50 value 85.489525
iter 60 value 85.489276
iter 70 value 84.366024
iter 80 value 83.412816
iter 90 value 80.919031
iter 100 value 80.918382
final value 80.918382
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 102.953559
iter 10 value 94.492022
iter 20 value 92.583372
iter 30 value 84.938617
iter 40 value 84.219086
iter 50 value 82.820946
iter 60 value 79.697235
iter 70 value 79.414901
iter 80 value 79.343743
iter 90 value 79.343084
iter 100 value 79.341576
final value 79.341576
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 112.612680
final value 94.052910
converged
Fitting Repeat 2
# weights: 103
initial value 99.565972
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 98.219813
final value 94.011429
converged
Fitting Repeat 4
# weights: 103
initial value 98.003226
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 97.058827
final value 94.052910
converged
Fitting Repeat 1
# weights: 305
initial value 98.080751
final value 94.052910
converged
Fitting Repeat 2
# weights: 305
initial value 94.503459
final value 94.052910
converged
Fitting Repeat 3
# weights: 305
initial value 94.597090
final value 93.102857
converged
Fitting Repeat 4
# weights: 305
initial value 122.160879
final value 94.052910
converged
Fitting Repeat 5
# weights: 305
initial value 112.552886
final value 94.008696
converged
Fitting Repeat 1
# weights: 507
initial value 120.188713
final value 94.008696
converged
Fitting Repeat 2
# weights: 507
initial value 95.873518
iter 10 value 84.819629
iter 20 value 84.705726
iter 30 value 84.184199
iter 40 value 84.138067
iter 50 value 84.137188
final value 84.137146
converged
Fitting Repeat 3
# weights: 507
initial value 98.426416
iter 10 value 91.803961
iter 20 value 90.699930
iter 30 value 90.697292
final value 90.697280
converged
Fitting Repeat 4
# weights: 507
initial value 93.914621
iter 10 value 91.849970
iter 20 value 90.768196
final value 90.767904
converged
Fitting Repeat 5
# weights: 507
initial value 104.080637
final value 94.052910
converged
Fitting Repeat 1
# weights: 103
initial value 103.456221
iter 10 value 94.055018
iter 20 value 92.886625
iter 30 value 91.865740
iter 40 value 91.427532
iter 50 value 85.309473
iter 60 value 82.863354
iter 70 value 82.249148
iter 80 value 82.060110
iter 90 value 81.799507
iter 100 value 81.233750
final value 81.233750
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 98.709281
iter 10 value 94.057755
iter 20 value 90.076563
iter 30 value 86.260724
iter 40 value 85.130066
iter 50 value 84.636528
iter 60 value 82.531635
iter 70 value 82.328310
iter 80 value 82.273562
iter 90 value 82.265368
iter 100 value 82.248920
final value 82.248920
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 98.991530
iter 10 value 88.452328
iter 20 value 85.409320
iter 30 value 83.247982
iter 40 value 82.982558
iter 50 value 82.070524
iter 60 value 81.986045
iter 70 value 81.924490
iter 80 value 81.869338
iter 90 value 81.857682
final value 81.857666
converged
Fitting Repeat 4
# weights: 103
initial value 98.148045
iter 10 value 94.056888
iter 20 value 91.492205
iter 30 value 83.533428
iter 40 value 82.709690
iter 50 value 82.330222
iter 60 value 82.279838
iter 70 value 82.256766
iter 80 value 82.253324
iter 90 value 82.248772
iter 90 value 82.248772
iter 90 value 82.248772
final value 82.248772
converged
Fitting Repeat 5
# weights: 103
initial value 96.919958
iter 10 value 94.042582
iter 20 value 84.786487
iter 30 value 84.379621
iter 40 value 82.969677
iter 50 value 82.472677
iter 60 value 82.289631
iter 70 value 82.267454
iter 80 value 82.258770
iter 90 value 82.248899
final value 82.248773
converged
Fitting Repeat 1
# weights: 305
initial value 100.325786
iter 10 value 93.975257
iter 20 value 86.178448
iter 30 value 85.453327
iter 40 value 84.151524
iter 50 value 83.049305
iter 60 value 82.124818
iter 70 value 80.797083
iter 80 value 80.130639
iter 90 value 79.786294
iter 100 value 79.754263
final value 79.754263
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 125.525785
iter 10 value 94.090369
iter 20 value 92.847777
iter 30 value 90.437614
iter 40 value 86.110503
iter 50 value 85.268137
iter 60 value 82.330771
iter 70 value 81.491757
iter 80 value 80.978712
iter 90 value 80.790415
iter 100 value 80.642127
final value 80.642127
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 101.987962
iter 10 value 94.051311
iter 20 value 91.279587
iter 30 value 91.160663
iter 40 value 89.259853
iter 50 value 82.577198
iter 60 value 82.113207
iter 70 value 81.918668
iter 80 value 81.715562
iter 90 value 81.616558
iter 100 value 81.410036
final value 81.410036
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 118.240079
iter 10 value 94.067070
iter 20 value 93.495627
iter 30 value 87.207657
iter 40 value 84.407491
iter 50 value 83.611093
iter 60 value 82.432555
iter 70 value 81.349187
iter 80 value 81.277766
iter 90 value 80.913017
iter 100 value 80.705499
final value 80.705499
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 101.563820
iter 10 value 95.570849
iter 20 value 84.892716
iter 30 value 82.597049
iter 40 value 82.293489
iter 50 value 82.217038
iter 60 value 82.129256
iter 70 value 82.070866
iter 80 value 82.054351
iter 90 value 82.037195
iter 100 value 82.035284
final value 82.035284
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 106.221534
iter 10 value 94.443002
iter 20 value 94.055915
iter 30 value 92.735174
iter 40 value 88.813516
iter 50 value 84.714342
iter 60 value 82.571487
iter 70 value 80.493510
iter 80 value 80.107547
iter 90 value 79.842612
iter 100 value 79.556099
final value 79.556099
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 109.619253
iter 10 value 96.099986
iter 20 value 92.496610
iter 30 value 84.910891
iter 40 value 82.511662
iter 50 value 82.207837
iter 60 value 81.684223
iter 70 value 80.716197
iter 80 value 80.248241
iter 90 value 79.837166
iter 100 value 79.386362
final value 79.386362
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 111.963917
iter 10 value 94.239348
iter 20 value 92.557371
iter 30 value 88.037258
iter 40 value 83.534139
iter 50 value 80.792169
iter 60 value 79.740685
iter 70 value 79.238262
iter 80 value 79.085542
iter 90 value 79.015364
iter 100 value 78.988254
final value 78.988254
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 106.689913
iter 10 value 94.104785
iter 20 value 93.072573
iter 30 value 91.010409
iter 40 value 88.459144
iter 50 value 82.642749
iter 60 value 81.857376
iter 70 value 81.513044
iter 80 value 80.610247
iter 90 value 79.960677
iter 100 value 79.447461
final value 79.447461
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 129.743465
iter 10 value 94.009669
iter 20 value 85.202162
iter 30 value 84.541865
iter 40 value 82.311816
iter 50 value 82.042314
iter 60 value 81.967630
iter 70 value 81.915065
iter 80 value 81.362100
iter 90 value 80.499076
iter 100 value 80.249611
final value 80.249611
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 94.140685
final value 94.054703
converged
Fitting Repeat 2
# weights: 103
initial value 96.923421
iter 10 value 89.700648
iter 20 value 84.166743
iter 30 value 84.164467
iter 40 value 84.161840
iter 40 value 84.161839
iter 40 value 84.161839
final value 84.161839
converged
Fitting Repeat 3
# weights: 103
initial value 100.231260
final value 94.054617
converged
Fitting Repeat 4
# weights: 103
initial value 95.626502
final value 94.054561
converged
Fitting Repeat 5
# weights: 103
initial value 101.726871
final value 94.054609
converged
Fitting Repeat 1
# weights: 305
initial value 94.167523
iter 10 value 94.013483
iter 20 value 93.964300
iter 30 value 92.972824
iter 40 value 92.267471
iter 50 value 92.248408
iter 60 value 92.092387
final value 92.088699
converged
Fitting Repeat 2
# weights: 305
initial value 100.939527
iter 10 value 94.057552
iter 20 value 94.036198
iter 30 value 85.163000
final value 85.161680
converged
Fitting Repeat 3
# weights: 305
initial value 111.553504
iter 10 value 94.058036
iter 20 value 93.527460
iter 30 value 82.972302
iter 40 value 82.856973
iter 50 value 82.043827
iter 60 value 80.139094
iter 70 value 78.995988
iter 80 value 78.962830
iter 90 value 78.961282
iter 90 value 78.961282
final value 78.961282
converged
Fitting Repeat 4
# weights: 305
initial value 94.508921
iter 10 value 94.057080
iter 20 value 89.415996
iter 30 value 82.588641
iter 40 value 82.272679
iter 50 value 82.261752
final value 82.261108
converged
Fitting Repeat 5
# weights: 305
initial value 95.090836
iter 10 value 94.013416
iter 20 value 94.008881
iter 30 value 93.881332
iter 40 value 92.572776
iter 50 value 85.432117
iter 60 value 84.620912
iter 70 value 84.617549
iter 80 value 84.554209
iter 90 value 84.551494
iter 100 value 84.551095
final value 84.551095
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 108.938297
iter 10 value 93.919478
iter 20 value 93.045308
iter 30 value 84.320357
final value 84.161025
converged
Fitting Repeat 2
# weights: 507
initial value 97.770168
iter 10 value 94.016979
iter 20 value 94.013310
iter 30 value 94.009182
final value 94.008761
converged
Fitting Repeat 3
# weights: 507
initial value 98.309648
iter 10 value 94.017157
iter 20 value 94.015078
iter 30 value 94.014568
iter 40 value 93.903703
iter 50 value 86.073168
iter 60 value 84.410419
iter 70 value 81.322460
iter 80 value 78.826061
iter 90 value 78.821204
iter 100 value 78.705517
final value 78.705517
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 102.494261
iter 10 value 94.064897
iter 20 value 89.705651
iter 30 value 87.837112
iter 40 value 82.420566
iter 50 value 82.417471
iter 60 value 82.288099
final value 82.287891
converged
Fitting Repeat 5
# weights: 507
initial value 96.254880
iter 10 value 94.016893
iter 20 value 93.936900
iter 30 value 91.688955
iter 40 value 91.597657
iter 50 value 86.128573
iter 60 value 81.092530
iter 70 value 79.950364
iter 80 value 79.812133
iter 90 value 79.803716
iter 100 value 79.803295
final value 79.803295
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 93.846464
iter 10 value 85.059789
iter 20 value 85.054740
final value 85.054738
converged
Fitting Repeat 2
# weights: 103
initial value 100.038414
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 111.326372
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 97.620733
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 97.943160
final value 94.052910
converged
Fitting Repeat 1
# weights: 305
initial value 110.329519
final value 94.052910
converged
Fitting Repeat 2
# weights: 305
initial value 98.323867
iter 10 value 91.771434
final value 91.771429
converged
Fitting Repeat 3
# weights: 305
initial value 107.089567
final value 94.052910
converged
Fitting Repeat 4
# weights: 305
initial value 94.181670
final value 93.582418
converged
Fitting Repeat 5
# weights: 305
initial value 101.397449
final value 94.052910
converged
Fitting Repeat 1
# weights: 507
initial value 100.841155
final value 94.052910
converged
Fitting Repeat 2
# weights: 507
initial value 109.702763
iter 10 value 90.434225
iter 20 value 87.301076
iter 30 value 86.881139
iter 40 value 85.701489
iter 50 value 85.689430
final value 85.689286
converged
Fitting Repeat 3
# weights: 507
initial value 100.044394
final value 93.890110
converged
Fitting Repeat 4
# weights: 507
initial value 103.884062
final value 93.582418
converged
Fitting Repeat 5
# weights: 507
initial value 102.852644
final value 94.052910
converged
Fitting Repeat 1
# weights: 103
initial value 97.130387
iter 10 value 94.249918
iter 20 value 94.010841
iter 30 value 93.032336
iter 40 value 89.126339
iter 50 value 86.153606
iter 60 value 86.021575
iter 70 value 85.795324
iter 80 value 85.548651
iter 90 value 85.488015
final value 85.487882
converged
Fitting Repeat 2
# weights: 103
initial value 102.964111
iter 10 value 93.924951
iter 20 value 93.682487
iter 30 value 91.159293
iter 40 value 88.480751
iter 50 value 88.206871
iter 60 value 85.225371
iter 70 value 84.759262
iter 80 value 84.663954
iter 90 value 82.270008
iter 100 value 82.183128
final value 82.183128
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 98.231370
iter 10 value 93.920706
iter 20 value 90.355294
iter 30 value 87.864109
iter 40 value 85.703420
iter 50 value 85.276487
iter 60 value 85.202539
iter 70 value 83.258891
iter 80 value 82.397525
iter 90 value 81.883888
iter 100 value 81.822237
final value 81.822237
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 101.816103
iter 10 value 94.056661
iter 20 value 90.385430
iter 30 value 89.484090
iter 40 value 88.559426
iter 50 value 84.255058
iter 60 value 83.562463
iter 70 value 83.546570
iter 80 value 83.343573
iter 90 value 83.269971
final value 83.266062
converged
Fitting Repeat 5
# weights: 103
initial value 106.123239
iter 10 value 94.365198
iter 20 value 94.056794
iter 30 value 93.871599
iter 40 value 93.744346
iter 50 value 93.609568
iter 60 value 93.540343
iter 70 value 93.528338
final value 93.528332
converged
Fitting Repeat 1
# weights: 305
initial value 102.787802
iter 10 value 93.639856
iter 20 value 89.437540
iter 30 value 87.010902
iter 40 value 86.548389
iter 50 value 85.098522
iter 60 value 83.605666
iter 70 value 82.914326
iter 80 value 82.359610
iter 90 value 82.015320
iter 100 value 81.787338
final value 81.787338
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 100.595791
iter 10 value 94.065497
iter 20 value 88.453032
iter 30 value 87.832586
iter 40 value 86.993834
iter 50 value 86.656128
iter 60 value 85.096765
iter 70 value 83.297122
iter 80 value 82.398903
iter 90 value 82.103124
iter 100 value 82.036477
final value 82.036477
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 107.937911
iter 10 value 94.065037
iter 20 value 93.715539
iter 30 value 90.835644
iter 40 value 89.220558
iter 50 value 88.943445
iter 60 value 85.407154
iter 70 value 84.526708
iter 80 value 83.996820
iter 90 value 82.498374
iter 100 value 81.762662
final value 81.762662
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 103.570553
iter 10 value 94.846189
iter 20 value 93.758421
iter 30 value 92.712906
iter 40 value 86.824639
iter 50 value 86.653051
iter 60 value 86.539726
iter 70 value 85.543907
iter 80 value 84.639743
iter 90 value 83.935320
iter 100 value 82.707889
final value 82.707889
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 127.090036
iter 10 value 93.773871
iter 20 value 92.232081
iter 30 value 89.769231
iter 40 value 86.507347
iter 50 value 85.226046
iter 60 value 83.602721
iter 70 value 83.085041
iter 80 value 82.173632
iter 90 value 81.624417
iter 100 value 81.512358
final value 81.512358
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 107.325027
iter 10 value 94.053257
iter 20 value 88.878282
iter 30 value 86.449527
iter 40 value 85.599308
iter 50 value 83.866539
iter 60 value 83.043017
iter 70 value 82.804387
iter 80 value 82.747782
iter 90 value 82.032473
iter 100 value 81.345690
final value 81.345690
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 108.273958
iter 10 value 94.134441
iter 20 value 93.423173
iter 30 value 90.572402
iter 40 value 85.747970
iter 50 value 84.057407
iter 60 value 81.726985
iter 70 value 81.091168
iter 80 value 80.967809
iter 90 value 80.890531
iter 100 value 80.861499
final value 80.861499
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 108.909466
iter 10 value 94.353477
iter 20 value 93.914677
iter 30 value 90.462061
iter 40 value 89.646449
iter 50 value 88.057287
iter 60 value 87.420879
iter 70 value 85.380050
iter 80 value 83.869826
iter 90 value 82.472950
iter 100 value 82.213250
final value 82.213250
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 109.273181
iter 10 value 89.909562
iter 20 value 88.305583
iter 30 value 85.696228
iter 40 value 82.556648
iter 50 value 81.675132
iter 60 value 81.250382
iter 70 value 81.099863
iter 80 value 80.870864
iter 90 value 80.829971
iter 100 value 80.769147
final value 80.769147
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 139.938107
iter 10 value 94.081799
iter 20 value 90.242342
iter 30 value 86.189943
iter 40 value 85.731286
iter 50 value 84.730045
iter 60 value 84.559879
iter 70 value 84.435112
iter 80 value 82.939225
iter 90 value 82.255895
iter 100 value 82.049098
final value 82.049098
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 106.675443
final value 94.054291
converged
Fitting Repeat 2
# weights: 103
initial value 95.954504
iter 10 value 94.054566
final value 94.052949
converged
Fitting Repeat 3
# weights: 103
initial value 100.378991
iter 10 value 93.318604
iter 10 value 93.318603
iter 10 value 93.318603
final value 93.318603
converged
Fitting Repeat 4
# weights: 103
initial value 97.576779
final value 94.054734
converged
Fitting Repeat 5
# weights: 103
initial value 96.488437
final value 94.054541
converged
Fitting Repeat 1
# weights: 305
initial value 101.725950
iter 10 value 93.588568
iter 20 value 93.586425
iter 30 value 93.583646
final value 93.582679
converged
Fitting Repeat 2
# weights: 305
initial value 99.720459
iter 10 value 93.532263
iter 20 value 93.421871
iter 30 value 93.417415
iter 40 value 93.018036
iter 50 value 87.951410
iter 60 value 82.623686
iter 70 value 81.699903
iter 80 value 80.393305
iter 90 value 80.384262
iter 100 value 80.382456
final value 80.382456
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 103.188048
iter 10 value 94.057611
iter 20 value 94.052952
iter 30 value 93.532827
iter 40 value 89.134912
iter 50 value 88.892926
iter 60 value 86.005109
iter 70 value 83.874795
iter 80 value 83.610540
iter 90 value 83.396379
iter 100 value 81.598719
final value 81.598719
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 123.449645
iter 10 value 94.057389
iter 20 value 93.994123
iter 30 value 93.539811
final value 93.539511
converged
Fitting Repeat 5
# weights: 305
initial value 110.742434
iter 10 value 94.057685
iter 20 value 94.052309
iter 30 value 88.171870
iter 40 value 85.733062
iter 50 value 85.196860
iter 60 value 84.570897
final value 84.565637
converged
Fitting Repeat 1
# weights: 507
initial value 96.178990
iter 10 value 93.772614
iter 20 value 93.724050
final value 93.583360
converged
Fitting Repeat 2
# weights: 507
initial value 94.380660
iter 10 value 94.060384
iter 20 value 93.384693
iter 30 value 86.545549
iter 40 value 86.423008
iter 50 value 86.410470
iter 60 value 86.179599
iter 70 value 86.133686
final value 86.133432
converged
Fitting Repeat 3
# weights: 507
initial value 96.439448
iter 10 value 94.055025
final value 93.890486
converged
Fitting Repeat 4
# weights: 507
initial value 95.237528
iter 10 value 93.590636
iter 20 value 93.583913
iter 30 value 89.279456
iter 40 value 87.618399
iter 50 value 87.615682
iter 60 value 87.481889
iter 70 value 85.069518
iter 80 value 81.569160
iter 90 value 81.456034
iter 100 value 81.452030
final value 81.452030
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 95.155194
iter 10 value 94.061273
iter 20 value 93.913557
iter 30 value 89.092712
iter 40 value 87.088593
iter 50 value 86.492972
iter 60 value 86.425793
final value 86.420247
converged
Fitting Repeat 1
# weights: 103
initial value 107.871869
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 97.833985
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 97.356462
final value 94.354396
converged
Fitting Repeat 4
# weights: 103
initial value 94.905077
iter 10 value 94.144543
final value 94.144481
converged
Fitting Repeat 5
# weights: 103
initial value 113.924315
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 99.142855
final value 94.484211
converged
Fitting Repeat 2
# weights: 305
initial value 106.779212
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 111.607453
iter 10 value 94.418091
iter 20 value 92.609659
iter 30 value 92.023559
final value 92.023529
converged
Fitting Repeat 4
# weights: 305
initial value 99.086960
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 113.674446
final value 94.354396
converged
Fitting Repeat 1
# weights: 507
initial value 101.392847
final value 94.354396
converged
Fitting Repeat 2
# weights: 507
initial value 96.195874
iter 10 value 94.141603
iter 20 value 94.114235
final value 94.114232
converged
Fitting Repeat 3
# weights: 507
initial value 98.009419
final value 94.484211
converged
Fitting Repeat 4
# weights: 507
initial value 92.985234
iter 10 value 87.646757
iter 20 value 87.345881
iter 30 value 86.888376
iter 40 value 86.887526
final value 86.887524
converged
Fitting Repeat 5
# weights: 507
initial value 112.238405
iter 10 value 94.484128
iter 10 value 94.484127
iter 10 value 94.484127
final value 94.484127
converged
Fitting Repeat 1
# weights: 103
initial value 97.015205
iter 10 value 94.416341
iter 20 value 93.207569
iter 30 value 88.610412
iter 40 value 85.392473
iter 50 value 84.798984
iter 60 value 84.368576
iter 70 value 84.025308
iter 80 value 83.954952
final value 83.954891
converged
Fitting Repeat 2
# weights: 103
initial value 99.209353
iter 10 value 94.486510
iter 20 value 93.291753
iter 30 value 88.674670
iter 40 value 86.313229
iter 50 value 86.046904
iter 60 value 85.863282
iter 70 value 85.849672
final value 85.849670
converged
Fitting Repeat 3
# weights: 103
initial value 97.471834
iter 10 value 94.481370
iter 20 value 94.399706
iter 30 value 94.382456
iter 40 value 94.335012
iter 50 value 92.328274
iter 60 value 88.765956
iter 70 value 88.600816
final value 88.513287
converged
Fitting Repeat 4
# weights: 103
initial value 99.548632
iter 10 value 94.454261
iter 20 value 91.736202
iter 30 value 90.464421
iter 40 value 89.172432
iter 50 value 88.475591
iter 60 value 87.173649
iter 70 value 84.940325
iter 80 value 84.929698
final value 84.929105
converged
Fitting Repeat 5
# weights: 103
initial value 97.972069
iter 10 value 94.484125
iter 20 value 88.249657
iter 30 value 85.602856
iter 40 value 84.995770
iter 50 value 84.622831
iter 60 value 84.481123
iter 70 value 84.480745
final value 84.480735
converged
Fitting Repeat 1
# weights: 305
initial value 115.902839
iter 10 value 94.474378
iter 20 value 90.653802
iter 30 value 87.196890
iter 40 value 86.768651
iter 50 value 86.503078
iter 60 value 86.076773
iter 70 value 85.522830
iter 80 value 85.159930
iter 90 value 84.836773
iter 100 value 84.641805
final value 84.641805
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 107.541125
iter 10 value 95.137923
iter 20 value 94.899975
iter 30 value 92.822292
iter 40 value 92.408669
iter 50 value 92.291079
iter 60 value 92.046752
iter 70 value 91.504767
iter 80 value 86.932732
iter 90 value 86.048385
iter 100 value 84.904079
final value 84.904079
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 113.133013
iter 10 value 94.162265
iter 20 value 91.331262
iter 30 value 86.517830
iter 40 value 85.835930
iter 50 value 84.946908
iter 60 value 83.376770
iter 70 value 83.084599
iter 80 value 82.505089
iter 90 value 82.183109
iter 100 value 82.078948
final value 82.078948
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 101.352464
iter 10 value 94.210439
iter 20 value 88.255494
iter 30 value 85.573024
iter 40 value 83.510393
iter 50 value 82.669589
iter 60 value 81.754545
iter 70 value 81.404120
iter 80 value 81.360012
iter 90 value 81.236148
iter 100 value 81.193131
final value 81.193131
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 99.456903
iter 10 value 92.277171
iter 20 value 89.068885
iter 30 value 86.560991
iter 40 value 85.243283
iter 50 value 84.643762
iter 60 value 84.534604
iter 70 value 83.817375
iter 80 value 82.486949
iter 90 value 81.882289
iter 100 value 81.832841
final value 81.832841
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 132.054085
iter 10 value 95.146623
iter 20 value 87.611619
iter 30 value 86.811435
iter 40 value 85.892502
iter 50 value 85.701158
iter 60 value 85.274460
iter 70 value 83.801656
iter 80 value 81.963641
iter 90 value 81.811549
iter 100 value 81.692393
final value 81.692393
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 121.966220
iter 10 value 95.565032
iter 20 value 94.253391
iter 30 value 87.747071
iter 40 value 86.789451
iter 50 value 84.210450
iter 60 value 82.455181
iter 70 value 82.117488
iter 80 value 82.102545
iter 90 value 82.089553
iter 100 value 82.054519
final value 82.054519
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 105.213821
iter 10 value 91.746867
iter 20 value 87.587561
iter 30 value 86.865463
iter 40 value 83.832009
iter 50 value 82.092469
iter 60 value 81.931177
iter 70 value 81.726122
iter 80 value 81.711609
iter 90 value 81.664788
iter 100 value 81.633701
final value 81.633701
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 103.861024
iter 10 value 91.478933
iter 20 value 87.821421
iter 30 value 85.257453
iter 40 value 84.906663
iter 50 value 84.053850
iter 60 value 82.998368
iter 70 value 82.412003
iter 80 value 81.696050
iter 90 value 81.384627
iter 100 value 81.161568
final value 81.161568
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 111.208373
iter 10 value 95.361518
iter 20 value 94.628099
iter 30 value 87.640787
iter 40 value 84.141392
iter 50 value 82.675456
iter 60 value 82.103414
iter 70 value 81.734488
iter 80 value 81.685918
iter 90 value 81.520378
iter 100 value 81.379710
final value 81.379710
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 102.315811
iter 10 value 94.485778
iter 20 value 88.385528
iter 30 value 86.945711
iter 40 value 86.944078
iter 50 value 86.746680
final value 86.623753
converged
Fitting Repeat 2
# weights: 103
initial value 97.241837
final value 94.485954
converged
Fitting Repeat 3
# weights: 103
initial value 101.258407
final value 94.485794
converged
Fitting Repeat 4
# weights: 103
initial value 98.117552
iter 10 value 94.533520
iter 20 value 94.526149
iter 30 value 94.498317
iter 40 value 94.484291
iter 50 value 94.452050
iter 60 value 92.924024
iter 70 value 92.923440
iter 80 value 92.922939
final value 92.922907
converged
Fitting Repeat 5
# weights: 103
initial value 96.946826
final value 94.485888
converged
Fitting Repeat 1
# weights: 305
initial value 98.260022
iter 10 value 94.359435
iter 20 value 94.351822
iter 30 value 86.703115
iter 40 value 86.556681
iter 50 value 86.443513
iter 50 value 86.443512
iter 50 value 86.443512
final value 86.443512
converged
Fitting Repeat 2
# weights: 305
initial value 96.638087
iter 10 value 94.488983
iter 20 value 94.321146
iter 30 value 92.889709
iter 40 value 92.839024
iter 50 value 92.834881
final value 92.834853
converged
Fitting Repeat 3
# weights: 305
initial value 95.523214
iter 10 value 88.833835
iter 20 value 87.879813
iter 30 value 87.739438
iter 40 value 87.714111
iter 50 value 87.658395
iter 60 value 87.650780
iter 70 value 87.650165
iter 80 value 87.155810
iter 90 value 85.878907
iter 100 value 85.865089
final value 85.865089
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 97.935177
iter 10 value 94.485937
iter 20 value 94.167591
iter 30 value 90.737690
iter 40 value 86.613026
iter 50 value 86.545096
iter 60 value 86.491660
iter 70 value 86.466454
final value 86.465961
converged
Fitting Repeat 5
# weights: 305
initial value 113.828094
iter 10 value 94.327405
iter 20 value 93.106069
iter 30 value 87.811971
iter 40 value 86.231823
iter 50 value 86.205714
iter 60 value 86.204671
final value 86.204664
converged
Fitting Repeat 1
# weights: 507
initial value 100.650467
iter 10 value 94.484486
iter 20 value 94.477181
iter 30 value 94.361457
iter 40 value 93.701228
iter 50 value 93.084437
iter 60 value 93.014476
iter 70 value 92.372275
iter 80 value 85.488578
iter 90 value 83.761418
iter 100 value 83.713500
final value 83.713500
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 96.104029
iter 10 value 94.490722
iter 20 value 94.473015
iter 30 value 92.886132
iter 40 value 92.876573
iter 50 value 92.504135
iter 60 value 91.779891
iter 70 value 91.698341
iter 80 value 91.661106
iter 90 value 91.550583
final value 91.550410
converged
Fitting Repeat 3
# weights: 507
initial value 95.876779
iter 10 value 94.126678
iter 20 value 94.083870
iter 30 value 90.806361
iter 40 value 90.634087
iter 50 value 90.615071
iter 60 value 86.246842
iter 70 value 82.337460
iter 80 value 80.992561
iter 90 value 80.845251
iter 100 value 80.834304
final value 80.834304
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 102.615947
iter 10 value 94.284152
iter 20 value 94.273912
iter 30 value 94.244226
iter 40 value 92.971769
iter 50 value 90.612264
iter 60 value 87.149211
iter 70 value 86.658346
iter 80 value 86.553952
iter 90 value 86.551056
iter 100 value 86.550096
final value 86.550096
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 97.979969
iter 10 value 94.210844
iter 20 value 94.206565
iter 30 value 92.680853
iter 40 value 86.941695
iter 50 value 86.442400
iter 60 value 82.944552
iter 70 value 81.937716
iter 80 value 81.425832
iter 90 value 81.419333
iter 100 value 81.418094
final value 81.418094
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 124.341548
iter 10 value 117.894730
iter 20 value 114.390402
iter 30 value 108.730778
iter 40 value 108.542989
iter 50 value 108.529273
iter 60 value 107.328642
iter 70 value 104.546741
iter 80 value 102.543600
iter 90 value 102.280804
iter 100 value 102.092362
final value 102.092362
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 127.509177
iter 10 value 117.895034
iter 20 value 117.890339
iter 30 value 117.065025
iter 40 value 107.485866
iter 50 value 106.854072
iter 60 value 106.657579
iter 70 value 105.656456
iter 80 value 103.424079
iter 90 value 101.467937
iter 100 value 101.414311
final value 101.414311
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 130.986652
iter 10 value 117.209012
iter 20 value 117.102679
iter 30 value 111.169359
iter 40 value 108.511140
iter 50 value 108.508114
final value 108.507990
converged
Fitting Repeat 4
# weights: 305
initial value 152.140652
iter 10 value 117.895526
iter 20 value 117.891336
iter 30 value 117.696746
iter 40 value 114.523287
iter 50 value 114.287552
iter 60 value 109.174605
iter 70 value 104.466481
iter 80 value 103.188729
iter 90 value 102.240783
iter 100 value 101.158351
final value 101.158351
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 135.744760
iter 10 value 117.895236
iter 20 value 117.702061
iter 30 value 110.818461
iter 40 value 107.698760
iter 50 value 107.536295
iter 60 value 103.964526
iter 70 value 100.239480
iter 80 value 100.104913
iter 90 value 100.057869
iter 100 value 100.056401
final value 100.056401
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 -- Tue Mar 24 00:29:24 2026
***********************************************
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
41.591 1.059 82.321
HPiP.Rcheck/HPiP-Ex.timings
| name | user | system | elapsed | |
| FSmethod | 34.106 | 0.434 | 34.594 | |
| FreqInteractors | 0.503 | 0.025 | 0.529 | |
| calculateAAC | 0.038 | 0.000 | 0.039 | |
| calculateAutocor | 0.347 | 0.010 | 0.358 | |
| calculateCTDC | 0.086 | 0.000 | 0.087 | |
| calculateCTDD | 0.562 | 0.001 | 0.563 | |
| calculateCTDT | 0.201 | 0.004 | 0.206 | |
| calculateCTriad | 0.347 | 0.010 | 0.358 | |
| calculateDC | 0.087 | 0.001 | 0.088 | |
| calculateF | 0.331 | 0.000 | 0.330 | |
| calculateKSAAP | 0.101 | 0.003 | 0.103 | |
| calculateQD_Sm | 1.666 | 0.125 | 1.790 | |
| calculateTC | 1.531 | 0.083 | 1.614 | |
| calculateTC_Sm | 0.240 | 0.003 | 0.244 | |
| corr_plot | 34.721 | 0.508 | 35.272 | |
| enrichfindP | 0.582 | 0.044 | 10.113 | |
| enrichfind_hp | 0.077 | 0.002 | 0.940 | |
| enrichplot | 0.523 | 0.001 | 0.524 | |
| filter_missing_values | 0.001 | 0.000 | 0.001 | |
| getFASTA | 0.512 | 0.039 | 3.494 | |
| getHPI | 0.001 | 0.000 | 0.001 | |
| get_negativePPI | 0.003 | 0.000 | 0.003 | |
| get_positivePPI | 0.001 | 0.000 | 0.000 | |
| impute_missing_data | 0.003 | 0.000 | 0.002 | |
| plotPPI | 0.101 | 0.000 | 0.101 | |
| pred_ensembel | 12.807 | 0.087 | 11.616 | |
| var_imp | 33.879 | 0.602 | 34.502 | |