Matineh Rahmatbakhsh
| Snapshot Date: 2026-05-06 13:45 -0400 (Wed, 06 May 2026) |
| git_url: https://git.bioconductor.org/packages/HPiP |
| git_branch: devel |
| git_last_commit: a85ff66 |
| git_last_commit_date: 2026-04-28 08:56:55 -0400 (Tue, 28 Apr 2026) |
| Back to Build/check report for BioC 3.24: simplified long |
|
This page was generated on 2026-05-07 11:33 -0400 (Thu, 07 May 2026).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 24.04.4 LTS) | x86_64 | 4.6.0 RC (2026-04-17 r89917) -- "Because it was There" | 4879 |
| 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 1007/2366 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| HPiP 1.19.0 (landing page) Matineh Rahmatbakhsh
| nebbiolo2 | Linux (Ubuntu 24.04.4 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: 1.19.0 |
| Command: /home/biocbuild/bbs-3.24-bioc/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/bbs-3.24-bioc/R/site-library --timings HPiP_1.19.0.tar.gz |
| StartedAt: 2026-05-07 00:53:25 -0400 (Thu, 07 May 2026) |
| EndedAt: 2026-05-07 01:08:22 -0400 (Thu, 07 May 2026) |
| EllapsedTime: 896.9 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: HPiP.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### /home/biocbuild/bbs-3.24-bioc/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/bbs-3.24-bioc/R/site-library --timings HPiP_1.19.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/home/biocbuild/bbs-3.24-bioc/meat/HPiP.Rcheck’
* using R version 4.6.0 RC (2026-04-17 r89917)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
* running under: Ubuntu 24.04.4 LTS
* using session charset: UTF-8
* current time: 2026-05-07 04:53:25 UTC
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.19.0’
* 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 34.389 0.409 34.828
var_imp 33.592 0.479 34.114
FSmethod 33.439 0.402 33.842
pred_ensembel 13.185 0.296 12.125
enrichfindP 0.558 0.038 10.785
* 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-3.24-bioc/meat/HPiP.Rcheck/00check.log’
for details.
HPiP.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.24-bioc/R/bin/R CMD INSTALL HPiP ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.24-bioc/R/site-library’ * installing *source* package ‘HPiP’ ... ** this is package ‘HPiP’ version ‘1.19.0’ ** 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 4.6.0 RC (2026-04-17 r89917) -- "Because it was There"
Copyright (C) 2026 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
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 94.582532
iter 10 value 93.619666
iter 20 value 93.618418
iter 30 value 93.610709
final value 93.610679
converged
Fitting Repeat 2
# weights: 103
initial value 108.676471
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 105.174846
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 107.952071
iter 10 value 93.582419
final value 93.582418
converged
Fitting Repeat 5
# weights: 103
initial value 94.361173
final value 94.052910
converged
Fitting Repeat 1
# weights: 305
initial value 110.217777
final value 93.187879
converged
Fitting Repeat 2
# weights: 305
initial value 108.428958
final value 93.582418
converged
Fitting Repeat 3
# weights: 305
initial value 99.022144
final value 94.052910
converged
Fitting Repeat 4
# weights: 305
initial value 100.452470
iter 10 value 94.052910
iter 10 value 94.052910
iter 10 value 94.052910
final value 94.052910
converged
Fitting Repeat 5
# weights: 305
initial value 110.529089
iter 10 value 93.582418
iter 10 value 93.582418
iter 10 value 93.582418
final value 93.582418
converged
Fitting Repeat 1
# weights: 507
initial value 98.491957
iter 10 value 93.540753
final value 93.540689
converged
Fitting Repeat 2
# weights: 507
initial value 101.000609
iter 10 value 90.512881
iter 20 value 85.478059
iter 30 value 85.456319
iter 40 value 85.434179
final value 85.428445
converged
Fitting Repeat 3
# weights: 507
initial value 95.969401
iter 10 value 93.582433
final value 93.582418
converged
Fitting Repeat 4
# weights: 507
initial value 104.826454
iter 10 value 89.647133
iter 20 value 85.973383
final value 85.971879
converged
Fitting Repeat 5
# weights: 507
initial value 131.860433
iter 10 value 93.582418
iter 10 value 93.582418
iter 10 value 93.582418
final value 93.582418
converged
Fitting Repeat 1
# weights: 103
initial value 103.190308
iter 10 value 92.041121
iter 20 value 83.208100
iter 30 value 81.596483
iter 40 value 80.146425
iter 50 value 79.503781
iter 60 value 79.478636
iter 70 value 79.242882
iter 80 value 79.185822
final value 79.185816
converged
Fitting Repeat 2
# weights: 103
initial value 100.792758
iter 10 value 94.058163
iter 20 value 93.654672
iter 30 value 89.682135
iter 40 value 86.954187
iter 50 value 86.786916
iter 60 value 84.252254
iter 70 value 83.496419
final value 83.492712
converged
Fitting Repeat 3
# weights: 103
initial value 99.548499
iter 10 value 94.056862
iter 20 value 93.375617
iter 30 value 89.488459
iter 40 value 81.814476
iter 50 value 80.554086
iter 60 value 80.197446
iter 70 value 79.211774
final value 79.191319
converged
Fitting Repeat 4
# weights: 103
initial value 100.891336
iter 10 value 94.039502
iter 20 value 92.492542
iter 30 value 88.219870
iter 40 value 84.246623
iter 50 value 83.776900
iter 60 value 83.504521
iter 70 value 83.492724
final value 83.492713
converged
Fitting Repeat 5
# weights: 103
initial value 95.977512
iter 10 value 94.056347
iter 20 value 92.330290
iter 30 value 86.029378
iter 40 value 84.388870
iter 50 value 83.609825
final value 83.502999
converged
Fitting Repeat 1
# weights: 305
initial value 99.668831
iter 10 value 89.240691
iter 20 value 81.779604
iter 30 value 81.223001
iter 40 value 79.502079
iter 50 value 78.931218
iter 60 value 78.516707
iter 70 value 78.365836
iter 80 value 78.270025
iter 90 value 78.115857
iter 100 value 78.052325
final value 78.052325
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 102.316041
iter 10 value 94.047693
iter 20 value 93.592138
iter 30 value 93.464058
iter 40 value 85.139862
iter 50 value 84.285841
iter 60 value 83.984320
iter 70 value 82.568049
iter 80 value 81.782903
iter 90 value 80.992722
iter 100 value 80.481694
final value 80.481694
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 101.944801
iter 10 value 93.913694
iter 20 value 92.890424
iter 30 value 87.944486
iter 40 value 84.451905
iter 50 value 82.880681
iter 60 value 80.329833
iter 70 value 80.113696
iter 80 value 79.785163
iter 90 value 79.434632
iter 100 value 79.400621
final value 79.400621
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 100.501027
iter 10 value 94.012392
iter 20 value 88.651821
iter 30 value 82.743316
iter 40 value 80.875605
iter 50 value 79.298161
iter 60 value 79.116766
iter 70 value 78.568261
iter 80 value 78.284101
iter 90 value 78.216705
iter 100 value 77.769483
final value 77.769483
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 112.869815
iter 10 value 93.687229
iter 20 value 87.947435
iter 30 value 84.962111
iter 40 value 82.994957
iter 50 value 82.769561
iter 60 value 81.469214
iter 70 value 79.512127
iter 80 value 78.848661
iter 90 value 78.143617
iter 100 value 77.919219
final value 77.919219
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 111.556925
iter 10 value 93.643313
iter 20 value 87.650629
iter 30 value 82.733536
iter 40 value 81.382827
iter 50 value 80.057324
iter 60 value 79.338837
iter 70 value 78.845973
iter 80 value 78.615051
iter 90 value 78.558118
iter 100 value 78.292003
final value 78.292003
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 114.048527
iter 10 value 93.598865
iter 20 value 86.901097
iter 30 value 83.856894
iter 40 value 83.512319
iter 50 value 83.263941
iter 60 value 83.199096
iter 70 value 82.826540
iter 80 value 80.970118
iter 90 value 79.702333
iter 100 value 78.657952
final value 78.657952
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 107.176877
iter 10 value 95.075649
iter 20 value 86.555391
iter 30 value 85.204097
iter 40 value 84.112761
iter 50 value 82.303922
iter 60 value 81.916762
iter 70 value 81.432799
iter 80 value 81.097129
iter 90 value 80.200522
iter 100 value 79.637485
final value 79.637485
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 108.037426
iter 10 value 96.550609
iter 20 value 89.637302
iter 30 value 83.414315
iter 40 value 80.812092
iter 50 value 80.416122
iter 60 value 80.030636
iter 70 value 79.338943
iter 80 value 78.878072
iter 90 value 78.330169
iter 100 value 78.175145
final value 78.175145
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 123.486960
iter 10 value 94.365326
iter 20 value 94.097875
iter 30 value 87.509168
iter 40 value 83.162396
iter 50 value 80.610422
iter 60 value 79.835932
iter 70 value 79.633558
iter 80 value 78.858413
iter 90 value 78.692494
iter 100 value 78.552769
final value 78.552769
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 102.821818
iter 10 value 93.914338
iter 20 value 92.608797
final value 87.937133
converged
Fitting Repeat 2
# weights: 103
initial value 104.711138
final value 94.054726
converged
Fitting Repeat 3
# weights: 103
initial value 101.693918
final value 94.054549
converged
Fitting Repeat 4
# weights: 103
initial value 103.170278
final value 94.054290
converged
Fitting Repeat 5
# weights: 103
initial value 97.198738
final value 92.982095
converged
Fitting Repeat 1
# weights: 305
initial value 101.670534
iter 10 value 94.057236
iter 20 value 93.741054
final value 93.582606
converged
Fitting Repeat 2
# weights: 305
initial value 96.353013
iter 10 value 94.056627
iter 20 value 94.005456
final value 93.582604
converged
Fitting Repeat 3
# weights: 305
initial value 107.112550
iter 10 value 93.587917
iter 20 value 93.584602
iter 30 value 93.124922
iter 40 value 85.943462
iter 50 value 79.840023
iter 60 value 79.163386
iter 70 value 79.149485
iter 80 value 79.149031
final value 79.148980
converged
Fitting Repeat 4
# weights: 305
initial value 103.754238
iter 10 value 94.057493
iter 20 value 94.052951
final value 94.052920
converged
Fitting Repeat 5
# weights: 305
initial value 99.547282
iter 10 value 93.587373
iter 20 value 93.404781
iter 30 value 87.300077
iter 40 value 84.619335
iter 40 value 84.619334
iter 40 value 84.619334
final value 84.619334
converged
Fitting Repeat 1
# weights: 507
initial value 99.751551
iter 10 value 93.296976
iter 20 value 92.954416
iter 30 value 83.132928
iter 40 value 82.994990
iter 50 value 82.072402
iter 60 value 81.979134
iter 70 value 81.977883
iter 80 value 81.943861
iter 90 value 81.915386
iter 100 value 81.914909
final value 81.914909
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 99.504608
iter 10 value 93.590341
iter 20 value 93.532021
final value 93.528746
converged
Fitting Repeat 3
# weights: 507
initial value 107.241954
iter 10 value 94.061011
iter 20 value 94.042077
iter 30 value 86.930489
iter 40 value 84.647649
final value 84.560896
converged
Fitting Repeat 4
# weights: 507
initial value 102.035395
iter 10 value 93.589910
iter 20 value 93.228455
iter 30 value 93.048515
iter 40 value 93.008815
final value 93.008401
converged
Fitting Repeat 5
# weights: 507
initial value 95.238818
iter 10 value 90.122816
iter 20 value 84.010901
iter 30 value 84.003372
iter 40 value 83.741981
iter 50 value 83.612437
iter 60 value 83.608064
iter 70 value 83.606716
iter 80 value 83.048727
iter 90 value 82.701179
iter 100 value 82.692997
final value 82.692997
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 114.666501
iter 10 value 91.722524
iter 20 value 91.614731
final value 91.614716
converged
Fitting Repeat 2
# weights: 103
initial value 114.536193
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 101.000282
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 97.154077
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 103.144221
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 111.446694
final value 94.254545
converged
Fitting Repeat 2
# weights: 305
initial value 100.223435
final value 94.026542
converged
Fitting Repeat 3
# weights: 305
initial value 108.511493
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 106.535747
iter 10 value 94.485133
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 100.404860
iter 10 value 87.939562
final value 87.936688
converged
Fitting Repeat 1
# weights: 507
initial value 102.107366
iter 10 value 92.297719
iter 20 value 91.476641
final value 91.462788
converged
Fitting Repeat 2
# weights: 507
initial value 105.149680
final value 94.026542
converged
Fitting Repeat 3
# weights: 507
initial value 108.304880
final value 94.482478
converged
Fitting Repeat 4
# weights: 507
initial value 97.358564
iter 10 value 94.021035
final value 94.020991
converged
Fitting Repeat 5
# weights: 507
initial value 105.817278
final value 94.484210
converged
Fitting Repeat 1
# weights: 103
initial value 99.085056
iter 10 value 92.943570
iter 20 value 92.782248
iter 30 value 87.785658
iter 40 value 86.731760
iter 50 value 84.265040
iter 60 value 83.169906
iter 70 value 82.637469
iter 80 value 82.478979
iter 90 value 81.820753
iter 100 value 80.979433
final value 80.979433
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 113.373988
iter 10 value 94.469616
iter 20 value 94.127792
iter 30 value 93.237338
iter 40 value 92.766221
iter 50 value 88.528930
iter 60 value 85.469417
iter 70 value 84.489065
iter 80 value 82.494878
iter 90 value 81.390186
iter 100 value 81.111044
final value 81.111044
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 97.781155
iter 10 value 94.388061
iter 20 value 87.719671
iter 30 value 85.333377
iter 40 value 84.569295
iter 50 value 82.279246
iter 60 value 81.593779
iter 70 value 80.791897
iter 80 value 80.766535
iter 90 value 80.741309
final value 80.741304
converged
Fitting Repeat 4
# weights: 103
initial value 98.023529
iter 10 value 94.325090
iter 20 value 92.973076
iter 30 value 92.930873
iter 40 value 92.619936
iter 50 value 90.283620
iter 60 value 84.461158
iter 70 value 82.926681
iter 80 value 82.300291
iter 90 value 81.888277
iter 100 value 81.282277
final value 81.282277
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 102.767082
iter 10 value 94.315386
iter 20 value 91.631673
iter 30 value 91.131746
iter 40 value 90.311100
iter 50 value 89.905371
iter 60 value 84.388569
iter 70 value 82.471021
iter 80 value 81.300526
iter 90 value 80.855854
iter 100 value 80.795899
final value 80.795899
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 106.971696
iter 10 value 94.448526
iter 20 value 92.397943
iter 30 value 90.339720
iter 40 value 88.053725
iter 50 value 86.159640
iter 60 value 83.891651
iter 70 value 80.720582
iter 80 value 80.151266
iter 90 value 79.928796
iter 100 value 79.790247
final value 79.790247
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 106.978290
iter 10 value 91.731261
iter 20 value 86.509681
iter 30 value 86.290790
iter 40 value 85.044383
iter 50 value 82.019254
iter 60 value 81.404420
iter 70 value 80.455715
iter 80 value 80.238402
iter 90 value 80.003209
iter 100 value 79.732154
final value 79.732154
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 101.547175
iter 10 value 92.319289
iter 20 value 86.672568
iter 30 value 85.896243
iter 40 value 85.103513
iter 50 value 83.831107
iter 60 value 80.895803
iter 70 value 80.513374
iter 80 value 79.917344
iter 90 value 79.571368
iter 100 value 79.542555
final value 79.542555
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 108.405855
iter 10 value 94.367304
iter 20 value 91.069713
iter 30 value 86.541700
iter 40 value 85.664885
iter 50 value 83.922503
iter 60 value 81.860934
iter 70 value 80.666347
iter 80 value 80.143198
iter 90 value 79.744417
iter 100 value 79.657225
final value 79.657225
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 127.735376
iter 10 value 94.309005
iter 20 value 84.889547
iter 30 value 81.780932
iter 40 value 80.320245
iter 50 value 79.594854
iter 60 value 79.445648
iter 70 value 79.284821
iter 80 value 79.088937
iter 90 value 79.062866
iter 100 value 79.028378
final value 79.028378
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 112.238113
iter 10 value 93.445260
iter 20 value 87.458325
iter 30 value 84.612332
iter 40 value 83.013713
iter 50 value 81.030633
iter 60 value 79.751183
iter 70 value 79.331107
iter 80 value 79.167544
iter 90 value 79.090566
iter 100 value 78.978342
final value 78.978342
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 106.015481
iter 10 value 94.501025
iter 20 value 94.455035
iter 30 value 92.960763
iter 40 value 86.015663
iter 50 value 84.575284
iter 60 value 83.884923
iter 70 value 83.233336
iter 80 value 82.160809
iter 90 value 80.915319
iter 100 value 80.463877
final value 80.463877
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 106.531782
iter 10 value 95.012565
iter 20 value 91.830030
iter 30 value 90.334384
iter 40 value 85.068654
iter 50 value 84.053085
iter 60 value 82.891302
iter 70 value 82.767453
iter 80 value 81.863436
iter 90 value 81.165180
iter 100 value 80.009068
final value 80.009068
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 110.491500
iter 10 value 94.288719
iter 20 value 92.983571
iter 30 value 92.770233
iter 40 value 85.693702
iter 50 value 85.353753
iter 60 value 83.194535
iter 70 value 81.266990
iter 80 value 81.048177
iter 90 value 80.181128
iter 100 value 79.706153
final value 79.706153
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 115.311424
iter 10 value 94.738371
iter 20 value 90.868294
iter 30 value 83.802953
iter 40 value 82.707416
iter 50 value 82.231623
iter 60 value 81.627786
iter 70 value 80.094772
iter 80 value 79.833721
iter 90 value 79.713562
iter 100 value 79.599639
final value 79.599639
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 99.075359
final value 94.485839
converged
Fitting Repeat 2
# weights: 103
initial value 102.501349
final value 94.485582
converged
Fitting Repeat 3
# weights: 103
initial value 101.976871
final value 94.485943
converged
Fitting Repeat 4
# weights: 103
initial value 95.873070
final value 94.485847
converged
Fitting Repeat 5
# weights: 103
initial value 101.778928
iter 10 value 94.485888
iter 20 value 94.484217
final value 94.484214
converged
Fitting Repeat 1
# weights: 305
initial value 105.680420
iter 10 value 93.374162
iter 20 value 92.626041
iter 30 value 92.624186
iter 40 value 92.621101
iter 50 value 92.619771
iter 60 value 89.146937
iter 70 value 81.951710
iter 80 value 78.728960
iter 90 value 78.427370
final value 78.360745
converged
Fitting Repeat 2
# weights: 305
initial value 96.295783
iter 10 value 93.706508
iter 20 value 93.439746
iter 30 value 92.185776
iter 40 value 91.929641
iter 50 value 91.921161
iter 60 value 91.921018
iter 70 value 91.920747
iter 80 value 91.920347
iter 80 value 91.920347
iter 80 value 91.920347
final value 91.920347
converged
Fitting Repeat 3
# weights: 305
initial value 104.205224
iter 10 value 94.031407
iter 20 value 94.030051
iter 30 value 94.029096
iter 40 value 94.027068
iter 50 value 94.020886
iter 60 value 86.695317
final value 85.710063
converged
Fitting Repeat 4
# weights: 305
initial value 97.321557
iter 10 value 89.426535
iter 20 value 87.693895
iter 30 value 87.436605
iter 40 value 87.407830
iter 50 value 87.406351
iter 60 value 84.228557
iter 70 value 84.176436
iter 80 value 84.175659
iter 90 value 83.577624
iter 100 value 82.114216
final value 82.114216
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 106.654966
iter 10 value 94.488944
iter 20 value 94.157403
iter 30 value 85.911667
iter 40 value 83.452132
final value 83.451909
converged
Fitting Repeat 1
# weights: 507
initial value 106.510937
iter 10 value 94.035421
iter 20 value 94.028244
iter 30 value 85.589355
iter 40 value 80.717180
iter 50 value 79.352748
iter 60 value 78.773276
iter 70 value 78.671832
iter 80 value 78.671268
iter 90 value 78.669623
final value 78.668430
converged
Fitting Repeat 2
# weights: 507
initial value 104.286077
iter 10 value 94.083364
iter 20 value 94.019240
iter 30 value 88.886707
iter 40 value 88.703070
iter 50 value 88.700687
iter 60 value 88.281052
iter 70 value 88.275622
iter 80 value 88.271986
iter 90 value 87.147015
iter 100 value 86.452764
final value 86.452764
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 107.475443
iter 10 value 94.034730
iter 20 value 92.884425
iter 30 value 90.419967
iter 40 value 90.268870
iter 50 value 85.105141
final value 83.852029
converged
Fitting Repeat 4
# weights: 507
initial value 102.506174
iter 10 value 93.256088
iter 20 value 92.886404
iter 30 value 92.724630
iter 40 value 92.654154
iter 50 value 90.249209
iter 60 value 82.443816
iter 70 value 82.026016
iter 80 value 81.566567
iter 90 value 81.439309
iter 100 value 81.386692
final value 81.386692
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 104.972074
iter 10 value 92.524980
iter 20 value 92.233186
iter 30 value 92.231525
iter 40 value 92.226526
iter 50 value 89.812993
iter 60 value 83.873656
final value 83.799394
converged
Fitting Repeat 1
# weights: 103
initial value 96.067739
final value 94.052910
converged
Fitting Repeat 2
# weights: 103
initial value 96.845890
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 96.331486
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 99.587771
final value 94.032967
converged
Fitting Repeat 5
# weights: 103
initial value 99.861688
final value 94.052910
converged
Fitting Repeat 1
# weights: 305
initial value 117.156979
final value 93.900821
converged
Fitting Repeat 2
# weights: 305
initial value 98.150474
final value 94.052910
converged
Fitting Repeat 3
# weights: 305
initial value 96.453983
final value 93.551913
converged
Fitting Repeat 4
# weights: 305
initial value 102.444495
final value 93.551913
converged
Fitting Repeat 5
# weights: 305
initial value 100.160395
final value 94.032968
converged
Fitting Repeat 1
# weights: 507
initial value 103.680674
iter 10 value 94.029318
final value 94.029316
converged
Fitting Repeat 2
# weights: 507
initial value 96.224636
final value 94.032967
converged
Fitting Repeat 3
# weights: 507
initial value 100.159348
iter 10 value 93.713468
iter 20 value 93.552561
final value 93.551913
converged
Fitting Repeat 4
# weights: 507
initial value 113.650425
iter 10 value 94.047455
iter 20 value 94.029317
iter 20 value 94.029316
iter 20 value 94.029316
final value 94.029316
converged
Fitting Repeat 5
# weights: 507
initial value 97.562531
final value 94.052910
converged
Fitting Repeat 1
# weights: 103
initial value 100.763363
iter 10 value 93.561037
iter 20 value 86.041195
iter 30 value 84.069267
iter 40 value 83.511742
iter 50 value 83.392904
iter 60 value 83.392563
final value 83.392558
converged
Fitting Repeat 2
# weights: 103
initial value 96.872715
iter 10 value 94.045494
iter 20 value 90.803317
iter 30 value 84.562760
iter 40 value 84.225279
iter 50 value 83.562875
iter 60 value 83.428845
iter 70 value 83.396001
final value 83.392558
converged
Fitting Repeat 3
# weights: 103
initial value 98.160906
iter 10 value 93.994857
iter 20 value 87.687111
iter 30 value 87.398702
iter 40 value 87.137807
iter 50 value 85.584320
iter 60 value 84.170178
iter 70 value 83.797460
iter 80 value 83.564574
iter 90 value 83.404823
iter 100 value 83.321556
final value 83.321556
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 102.757901
iter 10 value 94.054446
iter 20 value 85.951438
iter 30 value 84.445664
iter 40 value 84.223632
iter 50 value 83.621436
iter 60 value 83.570619
iter 70 value 83.403420
iter 80 value 83.393131
iter 90 value 83.392591
final value 83.392558
converged
Fitting Repeat 5
# weights: 103
initial value 103.992254
iter 10 value 89.706046
iter 20 value 85.668387
iter 30 value 85.021077
iter 40 value 83.866525
iter 50 value 83.597987
iter 60 value 83.367123
iter 70 value 83.124402
iter 80 value 82.759461
iter 90 value 82.729679
final value 82.729677
converged
Fitting Repeat 1
# weights: 305
initial value 98.741905
iter 10 value 87.194683
iter 20 value 84.378066
iter 30 value 83.314794
iter 40 value 82.843709
iter 50 value 82.621465
iter 60 value 81.022607
iter 70 value 80.295203
iter 80 value 80.070705
iter 90 value 79.876883
iter 100 value 79.450912
final value 79.450912
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 116.757430
iter 10 value 94.074849
iter 20 value 85.239490
iter 30 value 83.800884
iter 40 value 83.610364
iter 50 value 83.257118
iter 60 value 81.258759
iter 70 value 80.554624
iter 80 value 80.447838
iter 90 value 80.373893
iter 100 value 80.232533
final value 80.232533
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 109.878803
iter 10 value 94.132499
iter 20 value 94.055334
iter 30 value 93.905775
iter 40 value 90.947164
iter 50 value 86.462286
iter 60 value 83.332810
iter 70 value 82.828457
iter 80 value 82.740535
iter 90 value 81.436174
iter 100 value 80.871893
final value 80.871893
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 101.396438
iter 10 value 94.016885
iter 20 value 88.255908
iter 30 value 85.194709
iter 40 value 82.163533
iter 50 value 81.056286
iter 60 value 80.090390
iter 70 value 79.941120
iter 80 value 79.899296
iter 90 value 79.868136
iter 100 value 79.833569
final value 79.833569
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 99.987012
iter 10 value 94.084294
iter 20 value 93.905945
iter 30 value 84.540164
iter 40 value 84.203962
iter 50 value 83.597474
iter 60 value 81.885369
iter 70 value 81.076424
iter 80 value 80.827276
iter 90 value 80.765779
iter 100 value 80.485640
final value 80.485640
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 112.357636
iter 10 value 92.187537
iter 20 value 90.873370
iter 30 value 89.394424
iter 40 value 86.168672
iter 50 value 83.011402
iter 60 value 81.267176
iter 70 value 80.208980
iter 80 value 79.900976
iter 90 value 79.720697
iter 100 value 79.599217
final value 79.599217
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 126.425861
iter 10 value 94.077755
iter 20 value 89.499190
iter 30 value 88.156004
iter 40 value 83.066098
iter 50 value 81.763713
iter 60 value 80.947008
iter 70 value 80.256325
iter 80 value 79.969852
iter 90 value 79.685040
iter 100 value 79.456116
final value 79.456116
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 108.698955
iter 10 value 91.425993
iter 20 value 84.014127
iter 30 value 82.520618
iter 40 value 81.446460
iter 50 value 80.878894
iter 60 value 79.863102
iter 70 value 79.580506
iter 80 value 79.527897
iter 90 value 79.404794
iter 100 value 79.279451
final value 79.279451
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 112.812890
iter 10 value 94.012160
iter 20 value 88.543539
iter 30 value 87.037586
iter 40 value 84.484671
iter 50 value 84.031688
iter 60 value 83.811342
iter 70 value 83.580389
iter 80 value 81.612236
iter 90 value 81.140302
iter 100 value 80.695017
final value 80.695017
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 107.366715
iter 10 value 88.726026
iter 20 value 86.781549
iter 30 value 83.237847
iter 40 value 81.566752
iter 50 value 80.967178
iter 60 value 79.933389
iter 70 value 79.552851
iter 80 value 79.136375
iter 90 value 79.053277
iter 100 value 78.954676
final value 78.954676
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 100.673432
final value 94.054401
converged
Fitting Repeat 2
# weights: 103
initial value 104.762528
final value 94.051579
converged
Fitting Repeat 3
# weights: 103
initial value 100.699071
final value 94.054509
converged
Fitting Repeat 4
# weights: 103
initial value 94.987283
final value 94.054570
converged
Fitting Repeat 5
# weights: 103
initial value 101.288962
final value 94.054583
converged
Fitting Repeat 1
# weights: 305
initial value 98.230723
iter 10 value 94.057612
iter 20 value 94.049586
iter 30 value 93.559367
iter 40 value 93.554899
iter 50 value 93.553357
iter 60 value 93.547987
iter 70 value 85.601891
iter 80 value 83.699940
iter 90 value 83.643192
iter 100 value 83.564663
final value 83.564663
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 94.884807
iter 10 value 94.037887
iter 20 value 93.900231
iter 30 value 86.409212
iter 40 value 85.880626
iter 50 value 85.880517
iter 60 value 85.803730
iter 70 value 85.801739
final value 85.801700
converged
Fitting Repeat 3
# weights: 305
initial value 104.690064
iter 10 value 94.040047
iter 20 value 93.814994
iter 30 value 86.231284
iter 40 value 84.007414
iter 50 value 83.998060
iter 60 value 83.997116
iter 70 value 83.834702
iter 80 value 83.833318
final value 83.833198
converged
Fitting Repeat 4
# weights: 305
initial value 101.807461
iter 10 value 94.057911
iter 20 value 94.053079
iter 30 value 86.210774
final value 85.255525
converged
Fitting Repeat 5
# weights: 305
initial value 99.308520
iter 10 value 94.057214
iter 20 value 93.999972
iter 30 value 85.112962
iter 40 value 83.236193
iter 50 value 82.832341
iter 60 value 82.832060
iter 70 value 82.830895
iter 80 value 82.829922
iter 90 value 81.502967
iter 100 value 81.268783
final value 81.268783
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 97.977900
iter 10 value 94.041174
iter 20 value 93.770468
iter 30 value 86.276714
iter 40 value 82.725008
iter 50 value 82.611483
iter 60 value 82.610780
final value 82.610778
converged
Fitting Repeat 2
# weights: 507
initial value 94.189371
final value 94.061347
converged
Fitting Repeat 3
# weights: 507
initial value 95.653316
iter 10 value 94.025875
iter 20 value 94.025099
iter 30 value 93.618278
iter 40 value 92.246813
iter 50 value 86.722673
iter 60 value 85.616199
iter 70 value 85.555206
iter 80 value 83.370159
iter 90 value 82.206619
iter 100 value 82.156024
final value 82.156024
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 99.919717
iter 10 value 87.645518
iter 20 value 85.208643
iter 30 value 84.824236
iter 40 value 83.948762
iter 50 value 83.813684
iter 60 value 83.009022
iter 70 value 82.902694
iter 80 value 82.900896
iter 90 value 82.694806
iter 100 value 82.074441
final value 82.074441
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 123.262843
iter 10 value 94.041990
iter 20 value 94.035967
iter 30 value 94.033410
final value 94.033368
converged
Fitting Repeat 1
# weights: 103
initial value 110.604486
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 95.304899
final value 94.467391
converged
Fitting Repeat 3
# weights: 103
initial value 95.414416
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 114.594109
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 95.951307
iter 10 value 90.334110
iter 20 value 88.986567
iter 30 value 88.073499
iter 40 value 88.072302
iter 50 value 87.983783
final value 87.983604
converged
Fitting Repeat 1
# weights: 305
initial value 104.705696
iter 10 value 94.312071
final value 94.159617
converged
Fitting Repeat 2
# weights: 305
initial value 103.963630
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 105.120097
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 103.058698
iter 10 value 93.768456
iter 20 value 93.408642
iter 30 value 93.407345
final value 93.407293
converged
Fitting Repeat 5
# weights: 305
initial value 98.919400
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 111.287979
iter 10 value 94.467392
iter 10 value 94.467391
iter 10 value 94.467391
final value 94.467391
converged
Fitting Repeat 2
# weights: 507
initial value 130.174149
iter 10 value 94.467416
final value 94.467391
converged
Fitting Repeat 3
# weights: 507
initial value 133.688350
iter 10 value 94.483810
iter 10 value 94.483810
iter 10 value 94.483810
final value 94.483810
converged
Fitting Repeat 4
# weights: 507
initial value 98.401140
final value 94.467392
converged
Fitting Repeat 5
# weights: 507
initial value 98.730591
final value 94.484211
converged
Fitting Repeat 1
# weights: 103
initial value 100.739549
iter 10 value 93.537090
iter 20 value 90.861152
iter 30 value 90.354592
iter 40 value 90.278413
iter 50 value 85.261144
iter 60 value 83.317885
iter 70 value 83.156147
iter 80 value 82.555855
iter 90 value 81.843898
iter 100 value 81.763421
final value 81.763421
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 100.094186
iter 10 value 94.482014
iter 20 value 94.450105
iter 30 value 93.931017
iter 40 value 93.459015
iter 50 value 93.202692
iter 60 value 93.177441
iter 70 value 88.522903
iter 80 value 86.971687
iter 90 value 83.944919
iter 100 value 82.236136
final value 82.236136
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 104.399694
iter 10 value 94.429848
iter 20 value 87.687572
iter 30 value 85.894165
iter 40 value 85.478346
iter 50 value 85.334954
iter 60 value 85.248942
iter 70 value 83.196270
iter 80 value 82.758239
iter 90 value 82.126368
iter 100 value 81.764379
final value 81.764379
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 102.659782
iter 10 value 94.067150
iter 20 value 92.910701
iter 30 value 92.765379
iter 40 value 92.743313
iter 50 value 92.707836
iter 60 value 86.451207
iter 70 value 85.437390
iter 80 value 83.703303
iter 90 value 83.325926
iter 100 value 82.755990
final value 82.755990
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 102.119994
iter 10 value 93.601165
iter 20 value 88.207662
iter 30 value 87.289900
iter 40 value 86.468139
iter 50 value 86.270022
iter 60 value 86.130932
iter 70 value 86.104671
iter 80 value 84.972984
iter 90 value 84.207993
iter 100 value 83.917837
final value 83.917837
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 107.789334
iter 10 value 94.387322
iter 20 value 90.731394
iter 30 value 86.735862
iter 40 value 83.561750
iter 50 value 82.702867
iter 60 value 81.743488
iter 70 value 81.159083
iter 80 value 80.822405
iter 90 value 80.751358
iter 100 value 80.660728
final value 80.660728
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 110.370159
iter 10 value 94.069266
iter 20 value 89.563497
iter 30 value 87.112141
iter 40 value 84.966714
iter 50 value 84.639021
iter 60 value 84.613424
iter 70 value 82.858044
iter 80 value 80.866139
iter 90 value 80.430410
iter 100 value 80.312801
final value 80.312801
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 110.921906
iter 10 value 93.584526
iter 20 value 90.262376
iter 30 value 88.279809
iter 40 value 87.980659
iter 50 value 83.831370
iter 60 value 82.405155
iter 70 value 81.982885
iter 80 value 81.716945
iter 90 value 80.831250
iter 100 value 80.644741
final value 80.644741
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 104.304081
iter 10 value 94.381934
iter 20 value 91.164424
iter 30 value 88.460410
iter 40 value 86.988830
iter 50 value 85.903389
iter 60 value 85.654853
iter 70 value 85.614260
iter 80 value 85.263806
iter 90 value 82.758774
iter 100 value 81.525358
final value 81.525358
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 101.996605
iter 10 value 94.499350
iter 20 value 88.143931
iter 30 value 87.888768
iter 40 value 87.758718
iter 50 value 85.420115
iter 60 value 83.416026
iter 70 value 81.777334
iter 80 value 81.255280
iter 90 value 81.113889
iter 100 value 80.949487
final value 80.949487
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 111.319056
iter 10 value 94.574407
iter 20 value 87.581530
iter 30 value 86.031874
iter 40 value 84.798120
iter 50 value 82.409929
iter 60 value 81.830169
iter 70 value 81.371011
iter 80 value 81.189912
iter 90 value 80.812038
iter 100 value 80.587897
final value 80.587897
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 123.030177
iter 10 value 94.584496
iter 20 value 91.876706
iter 30 value 87.406659
iter 40 value 83.593057
iter 50 value 82.796684
iter 60 value 81.543795
iter 70 value 80.954502
iter 80 value 80.630816
iter 90 value 80.324346
iter 100 value 80.067437
final value 80.067437
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 113.876650
iter 10 value 95.072636
iter 20 value 94.503435
iter 30 value 93.054133
iter 40 value 89.350585
iter 50 value 85.175829
iter 60 value 83.828951
iter 70 value 83.351405
iter 80 value 83.257471
iter 90 value 83.038557
iter 100 value 82.958966
final value 82.958966
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 110.635478
iter 10 value 94.255073
iter 20 value 87.191341
iter 30 value 85.407037
iter 40 value 84.225784
iter 50 value 83.895630
iter 60 value 83.509925
iter 70 value 83.498962
iter 80 value 83.183154
iter 90 value 81.952316
iter 100 value 80.909274
final value 80.909274
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 135.071701
iter 10 value 94.364128
iter 20 value 88.213940
iter 30 value 85.630793
iter 40 value 84.371155
iter 50 value 83.396637
iter 60 value 83.057632
iter 70 value 82.883008
iter 80 value 81.937371
iter 90 value 81.568168
iter 100 value 81.283158
final value 81.283158
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 104.374325
final value 94.486075
converged
Fitting Repeat 2
# weights: 103
initial value 97.426196
final value 94.485470
converged
Fitting Repeat 3
# weights: 103
initial value 97.430781
final value 94.485946
converged
Fitting Repeat 4
# weights: 103
initial value 98.955995
iter 10 value 93.111776
iter 20 value 93.111688
final value 93.111570
converged
Fitting Repeat 5
# weights: 103
initial value 97.727781
final value 94.485706
converged
Fitting Repeat 1
# weights: 305
initial value 94.804000
iter 10 value 94.472239
iter 20 value 94.347226
iter 30 value 88.023558
iter 40 value 85.960810
iter 50 value 83.358000
iter 60 value 83.263200
iter 70 value 83.261433
iter 80 value 83.260413
iter 90 value 83.197688
iter 100 value 82.559220
final value 82.559220
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 101.629169
iter 10 value 94.488875
iter 20 value 94.479314
iter 30 value 87.568344
iter 40 value 86.363478
final value 86.352133
converged
Fitting Repeat 3
# weights: 305
initial value 107.754945
iter 10 value 94.481500
iter 20 value 92.669258
iter 30 value 86.828058
iter 40 value 86.575270
iter 50 value 85.806327
iter 60 value 85.800994
final value 85.800875
converged
Fitting Repeat 4
# weights: 305
initial value 103.347498
iter 10 value 94.488385
iter 20 value 94.451785
iter 30 value 91.826652
iter 40 value 89.663384
iter 50 value 87.358614
iter 60 value 87.341366
iter 70 value 87.340900
iter 80 value 87.334907
iter 90 value 87.114433
final value 87.114278
converged
Fitting Repeat 5
# weights: 305
initial value 94.791134
iter 10 value 94.489077
iter 20 value 94.484243
final value 94.484224
converged
Fitting Repeat 1
# weights: 507
initial value 95.517478
iter 10 value 94.272618
iter 20 value 94.269763
iter 30 value 93.876870
iter 40 value 93.790221
iter 50 value 93.788546
iter 60 value 93.787516
iter 70 value 93.759370
iter 80 value 92.360055
iter 90 value 91.850500
iter 100 value 86.770926
final value 86.770926
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 98.667458
iter 10 value 94.492003
iter 20 value 94.374578
iter 30 value 89.843861
iter 40 value 88.340530
iter 50 value 87.581676
iter 60 value 87.343862
iter 70 value 87.329788
iter 80 value 87.311774
iter 90 value 87.304741
iter 100 value 85.922551
final value 85.922551
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 96.529035
iter 10 value 94.332067
iter 20 value 92.517024
iter 30 value 91.120085
iter 40 value 88.777193
iter 50 value 86.887945
iter 60 value 86.640823
iter 70 value 86.250389
iter 80 value 86.248800
iter 90 value 86.248405
iter 100 value 86.207100
final value 86.207100
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 102.836053
iter 10 value 87.247138
iter 20 value 85.364658
iter 30 value 85.363913
iter 40 value 85.294741
iter 50 value 84.454270
iter 60 value 81.956883
iter 70 value 81.941787
iter 80 value 81.939162
iter 90 value 81.939084
iter 100 value 81.939045
final value 81.939045
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 109.220066
iter 10 value 87.388124
iter 20 value 85.421328
iter 30 value 85.374930
iter 40 value 84.937585
iter 50 value 84.223391
iter 60 value 82.356420
iter 70 value 80.097339
iter 80 value 79.627370
iter 90 value 79.467048
iter 100 value 79.362029
final value 79.362029
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 101.582592
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 103.311887
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 94.743383
iter 10 value 92.363236
final value 92.227947
converged
Fitting Repeat 4
# weights: 103
initial value 96.230016
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 109.319403
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 110.090211
final value 94.484211
converged
Fitting Repeat 2
# weights: 305
initial value 98.072559
iter 10 value 94.443238
iter 10 value 94.443238
final value 94.443238
converged
Fitting Repeat 3
# weights: 305
initial value 116.121974
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 96.195459
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 101.142266
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 96.176014
iter 10 value 88.111131
iter 20 value 84.169957
iter 30 value 82.672867
iter 40 value 82.462635
iter 50 value 82.452036
iter 60 value 82.451953
final value 82.451951
converged
Fitting Repeat 2
# weights: 507
initial value 100.155388
iter 10 value 93.722235
final value 93.721266
converged
Fitting Repeat 3
# weights: 507
initial value 103.810964
iter 10 value 94.228699
final value 94.228678
converged
Fitting Repeat 4
# weights: 507
initial value 131.381899
final value 94.443243
converged
Fitting Repeat 5
# weights: 507
initial value 96.986294
final value 94.484211
converged
Fitting Repeat 1
# weights: 103
initial value 101.313033
iter 10 value 94.469456
iter 20 value 94.251694
iter 30 value 94.223106
iter 40 value 88.519049
iter 50 value 88.111483
iter 60 value 87.407976
iter 70 value 85.219494
iter 80 value 84.151943
iter 90 value 83.513612
iter 100 value 83.111841
final value 83.111841
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 110.878160
iter 10 value 94.488156
iter 20 value 94.486442
iter 30 value 94.179097
iter 40 value 93.792801
iter 50 value 90.907122
iter 60 value 86.695928
iter 70 value 84.884805
iter 80 value 84.817095
iter 90 value 84.808590
final value 84.808587
converged
Fitting Repeat 3
# weights: 103
initial value 98.155200
iter 10 value 94.473193
iter 20 value 92.168264
iter 30 value 87.824905
iter 40 value 86.678606
iter 50 value 85.198339
iter 60 value 84.823610
iter 70 value 84.808590
final value 84.808587
converged
Fitting Repeat 4
# weights: 103
initial value 102.030208
iter 10 value 94.355743
iter 20 value 89.595305
iter 30 value 86.136793
iter 40 value 84.932145
iter 50 value 84.864493
iter 60 value 84.320901
iter 70 value 84.245064
iter 80 value 84.217784
iter 90 value 84.210545
final value 84.210542
converged
Fitting Repeat 5
# weights: 103
initial value 101.253354
iter 10 value 94.523364
iter 20 value 92.392478
iter 30 value 86.369598
iter 40 value 86.187590
iter 50 value 85.608515
iter 60 value 84.498784
iter 70 value 84.212865
final value 84.210542
converged
Fitting Repeat 1
# weights: 305
initial value 110.415591
iter 10 value 94.946475
iter 20 value 89.243583
iter 30 value 86.234085
iter 40 value 84.591467
iter 50 value 84.521912
iter 60 value 83.961897
iter 70 value 83.895351
iter 80 value 83.744042
iter 90 value 83.542492
iter 100 value 83.489422
final value 83.489422
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 101.435323
iter 10 value 93.097899
iter 20 value 84.671481
iter 30 value 84.072344
iter 40 value 83.266866
iter 50 value 83.056728
iter 60 value 82.895996
iter 70 value 82.840650
iter 80 value 82.564721
iter 90 value 82.284715
iter 100 value 82.033135
final value 82.033135
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 105.302566
iter 10 value 92.707524
iter 20 value 88.719787
iter 30 value 87.819080
iter 40 value 86.501147
iter 50 value 82.964649
iter 60 value 82.055056
iter 70 value 81.676024
iter 80 value 81.314339
iter 90 value 81.242618
iter 100 value 81.206542
final value 81.206542
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 102.591675
iter 10 value 94.531594
iter 20 value 94.487112
iter 30 value 94.448640
iter 40 value 93.757084
iter 50 value 92.157453
iter 60 value 91.993375
iter 70 value 91.602118
iter 80 value 84.623941
iter 90 value 83.143050
iter 100 value 82.524648
final value 82.524648
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 103.170551
iter 10 value 94.581991
iter 20 value 93.253074
iter 30 value 91.509210
iter 40 value 85.579862
iter 50 value 83.855115
iter 60 value 82.856655
iter 70 value 82.274196
iter 80 value 82.122753
iter 90 value 81.637997
iter 100 value 81.405942
final value 81.405942
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 125.418266
iter 10 value 95.527538
iter 20 value 93.994830
iter 30 value 87.305859
iter 40 value 86.574834
iter 50 value 85.960426
iter 60 value 84.484312
iter 70 value 84.405260
iter 80 value 84.255879
iter 90 value 83.113574
iter 100 value 82.005194
final value 82.005194
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 117.507566
iter 10 value 90.096983
iter 20 value 89.332499
iter 30 value 88.093443
iter 40 value 84.835754
iter 50 value 82.914253
iter 60 value 82.521378
iter 70 value 82.363209
iter 80 value 81.965587
iter 90 value 81.558270
iter 100 value 81.347602
final value 81.347602
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 103.379796
iter 10 value 95.708644
iter 20 value 94.592551
iter 30 value 92.850006
iter 40 value 89.224574
iter 50 value 84.864889
iter 60 value 84.043480
iter 70 value 83.926496
iter 80 value 83.815970
iter 90 value 82.329743
iter 100 value 81.699248
final value 81.699248
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 106.116370
iter 10 value 94.912032
iter 20 value 91.316945
iter 30 value 86.924995
iter 40 value 83.775782
iter 50 value 82.434902
iter 60 value 82.111351
iter 70 value 81.877578
iter 80 value 81.797836
iter 90 value 81.565721
iter 100 value 81.454736
final value 81.454736
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 111.646225
iter 10 value 94.591085
iter 20 value 94.413434
iter 30 value 94.212231
iter 40 value 93.483680
iter 50 value 85.558362
iter 60 value 84.800558
iter 70 value 83.467313
iter 80 value 82.993748
iter 90 value 82.789024
iter 100 value 82.461748
final value 82.461748
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 103.086336
final value 94.485987
converged
Fitting Repeat 2
# weights: 103
initial value 104.527008
final value 94.485938
converged
Fitting Repeat 3
# weights: 103
initial value 108.375420
iter 10 value 89.909568
iter 20 value 88.374241
iter 30 value 87.286887
iter 40 value 87.263497
final value 87.263389
converged
Fitting Repeat 4
# weights: 103
initial value 100.804364
final value 94.485954
converged
Fitting Repeat 5
# weights: 103
initial value 102.496434
iter 10 value 94.485784
iter 20 value 94.294501
iter 30 value 85.710574
iter 40 value 85.707141
iter 50 value 85.706264
iter 60 value 85.674044
final value 85.672957
converged
Fitting Repeat 1
# weights: 305
initial value 99.884219
iter 10 value 94.489163
iter 20 value 94.255667
iter 30 value 85.516538
iter 40 value 85.392402
final value 85.388767
converged
Fitting Repeat 2
# weights: 305
initial value 99.702413
iter 10 value 94.489880
iter 20 value 94.477043
iter 30 value 93.149885
iter 40 value 85.002385
iter 50 value 84.406611
iter 60 value 84.383091
iter 70 value 84.380131
iter 70 value 84.380131
final value 84.380131
converged
Fitting Repeat 3
# weights: 305
initial value 106.383530
iter 10 value 94.488793
iter 20 value 94.410251
iter 30 value 86.276218
iter 40 value 84.043542
iter 50 value 83.765519
iter 60 value 83.712574
iter 70 value 83.709223
iter 80 value 83.703248
iter 90 value 83.702758
iter 100 value 82.071188
final value 82.071188
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 104.994524
iter 10 value 94.448535
iter 20 value 94.389506
final value 94.383829
converged
Fitting Repeat 5
# weights: 305
initial value 125.231345
iter 10 value 94.448260
iter 20 value 94.347545
iter 30 value 94.253239
final value 94.253177
converged
Fitting Repeat 1
# weights: 507
initial value 100.530619
iter 10 value 94.262162
iter 20 value 94.257279
iter 30 value 94.256959
iter 40 value 94.206959
iter 50 value 87.792951
iter 60 value 83.429180
iter 70 value 83.413585
iter 80 value 83.385866
final value 83.381891
converged
Fitting Repeat 2
# weights: 507
initial value 100.317399
iter 10 value 94.147258
iter 20 value 94.049934
iter 30 value 92.377811
iter 40 value 92.377001
iter 50 value 92.367685
iter 60 value 89.232989
iter 70 value 84.943562
iter 80 value 84.390343
iter 90 value 83.922891
iter 100 value 83.696296
final value 83.696296
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 96.017520
iter 10 value 92.902294
iter 20 value 92.429374
iter 30 value 91.818906
iter 40 value 91.730075
iter 50 value 91.401352
iter 60 value 91.399962
final value 91.397838
converged
Fitting Repeat 4
# weights: 507
initial value 99.445932
iter 10 value 91.760902
iter 20 value 91.311421
iter 30 value 91.131145
iter 40 value 89.363002
iter 50 value 86.966052
iter 60 value 86.433569
iter 70 value 86.392266
iter 80 value 86.376389
iter 90 value 86.375819
iter 100 value 85.827424
final value 85.827424
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 97.232315
iter 10 value 94.492377
iter 20 value 94.437401
iter 30 value 86.769080
iter 40 value 86.738523
iter 50 value 84.871134
iter 60 value 84.823100
iter 70 value 84.815263
iter 80 value 84.806091
iter 90 value 84.805836
iter 100 value 84.322996
final value 84.322996
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 127.003944
iter 10 value 117.894681
iter 20 value 117.636195
iter 30 value 108.629860
iter 40 value 108.528377
iter 50 value 107.940045
iter 60 value 107.182403
iter 60 value 107.182402
iter 60 value 107.182402
final value 107.182402
converged
Fitting Repeat 2
# weights: 305
initial value 132.472783
iter 10 value 117.763503
iter 20 value 117.695867
iter 30 value 117.523235
final value 117.511532
converged
Fitting Repeat 3
# weights: 305
initial value 123.752330
iter 10 value 117.892714
iter 20 value 117.760001
iter 30 value 117.222972
iter 40 value 103.169268
iter 50 value 102.342492
iter 60 value 102.338080
iter 60 value 102.338080
final value 102.338080
converged
Fitting Repeat 4
# weights: 305
initial value 127.504238
iter 10 value 117.895074
iter 20 value 117.765198
iter 30 value 109.078299
iter 40 value 107.257807
iter 50 value 107.229790
iter 60 value 106.642332
iter 70 value 102.778715
iter 80 value 102.138994
iter 90 value 101.084552
iter 100 value 100.970595
final value 100.970595
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 119.129861
iter 10 value 117.763781
iter 20 value 117.696547
iter 30 value 117.508609
iter 40 value 106.197848
iter 50 value 103.838493
final value 103.836189
converged
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 -- Thu May 7 00:58:37 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
40.931 1.371 89.784
HPiP.Rcheck/HPiP-Ex.timings
| name | user | system | elapsed | |
| FSmethod | 33.439 | 0.402 | 33.842 | |
| FreqInteractors | 0.424 | 0.024 | 0.448 | |
| calculateAAC | 0.031 | 0.002 | 0.033 | |
| calculateAutocor | 0.255 | 0.018 | 0.273 | |
| calculateCTDC | 0.071 | 0.001 | 0.072 | |
| calculateCTDD | 0.465 | 0.001 | 0.465 | |
| calculateCTDT | 0.132 | 0.000 | 0.132 | |
| calculateCTriad | 0.384 | 0.002 | 0.387 | |
| calculateDC | 0.086 | 0.001 | 0.087 | |
| calculateF | 0.294 | 0.000 | 0.295 | |
| calculateKSAAP | 0.094 | 0.000 | 0.094 | |
| calculateQD_Sm | 1.708 | 0.006 | 1.714 | |
| calculateTC | 1.443 | 0.031 | 1.475 | |
| calculateTC_Sm | 0.273 | 0.001 | 0.274 | |
| corr_plot | 34.389 | 0.409 | 34.828 | |
| enrichfindP | 0.558 | 0.038 | 10.785 | |
| enrichfind_hp | 0.081 | 0.001 | 0.997 | |
| enrichplot | 0.514 | 0.002 | 0.517 | |
| filter_missing_values | 0.001 | 0.000 | 0.001 | |
| getFASTA | 0.517 | 0.021 | 4.071 | |
| getHPI | 0.001 | 0.000 | 0.001 | |
| get_negativePPI | 0.002 | 0.001 | 0.003 | |
| get_positivePPI | 0 | 0 | 0 | |
| impute_missing_data | 0.002 | 0.000 | 0.003 | |
| plotPPI | 0.096 | 0.001 | 0.096 | |
| pred_ensembel | 13.185 | 0.296 | 12.125 | |
| var_imp | 33.592 | 0.479 | 34.114 | |