MidnightBSD Magusdevel/R-cran-glmnet
Lasso and elastic-net regularized generalized linear models
| Flavor | Version | Run | OSVersion | Arch | License | Restricted | Build | Fetch | Test | Scan | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.8.5_12 | 639 | 4.0 | amd64 | gpl2 | 0 | fail | untested | untested | untested |
License Permissions: dist-mirror dist-sell pkg-mirror pkg-sell auto-accept
Events
| Machine | Phase | Type | Time | Message |
|---|---|---|---|---|
| m4064b | info | 2026-05-27 14:58:01.948579 | Test Started | |
| m4064b | warn | 2026-05-27 15:01:59.601507 | MASTER_SITES contains non-HTTPS URLs: http://cran.utstat.utoronto.ca/src/contrib/, http://cran.utstat.utoronto.ca/src/contrib/Archive/glmnet/ | |
| m4064b | warn | 2026-05-27 15:01:59.61264 | fake-qa reported: /usr/local/lib/R/library/glmnet/libs/glmnet.so is linked to /usr/local/lib/gcc14/libgfortran.so.5 that does not belong to any package; /usr/local/lib/R/library/glmnet/libs/glmnet.so is linked to /usr/local/lib/gcc14/libquadmath.so.0 that does not belong to any package; /usr/local/lib/R/library/glmnet/libs/glmnet.so is linked to /usr/local/lib/R/lib/libR.so.4 that does not belong to any package | |
| m4064b | fail | 2026-05-27 15:01:59.616936 | make test returned non-zero: 1 | |
| m4064b | fail | 2026-05-27 15:01:59.683066 | Test complete. |
Build Log
[1m===> Testing for R-cran-glmnet-1.8.5_12[0m
* using log directory '/magus/work/usr/mports/devel/R-cran-glmnet/work/glmnet.Rcheck'
* using R version 4.4.0 (2024-04-24)
* using platform: amd64-portbld-midnightbsd4.0
* R was compiled by
MidnightBSD clang version 19.1.7 (https://github.com/llvm/llvm-project.git llvmorg-19.1.7-0-gcd708029e0b2)
GNU Fortran (MidnightBSD Ports Collection) 14.2.0
* running under: MidnightBSD m4064b 4.0.4 MidnightBSD 4.0.4 #9 stable/4.0-n13778-57927ace9b-dirty: Tue Mar 31 10:40:01 EDT 2026 root@m4064b:/usr/obj/usr/src/amd64.amd64/sys/GENERIC amd64
* using session charset: ASCII
* using options '--no-manual --no-build-vignettes'
* checking for file 'glmnet/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'glmnet' version '1.8-5'
* 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 executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package 'glmnet' can be installed ... OK
* used Fortran compiler: 'GNU Fortran (MidnightBSD Ports Collection) 14.2.0'
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... NOTE
Found a 'configure.in' file: 'configure.ac' has long been preferred.
* 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 whether startup messages can be suppressed ... 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 ... NOTE
auc: no visible global function definition for 'runif'
coef.cv.glmnet: no visible global function definition for 'coef'
coef.glmnet: no visible global function definition for 'predict'
cv.coxnet: no visible global function definition for 'predict'
cv.coxnet: no visible binding for global variable 'weighted.mean'
cv.elnet: no visible global function definition for 'predict'
cv.elnet: no visible binding for global variable 'weighted.mean'
cv.fishnet: no visible global function definition for 'predict'
cv.fishnet: no visible binding for global variable 'weighted.mean'
cv.glmnet: no visible global function definition for 'predict'
cv.glmnet: no visible binding for global variable 'median'
cv.lognet: no visible global function definition for 'predict'
cv.lognet: no visible binding for global variable 'weighted.mean'
cv.mrelnet: no visible global function definition for 'predict'
cv.mrelnet: no visible binding for global variable 'weighted.mean'
cv.multnet: no visible global function definition for 'predict'
cv.multnet: no visible binding for global variable 'weighted.mean'
cvcompute: no visible binding for global variable 'weighted.mean'
elnet: no visible global function definition for 'weighted.mean'
error.bars: no visible global function definition for 'segments'
getcoef: no visible global function definition for 'new'
getcoef.multinomial: no visible global function definition for 'new'
glmnet: no visible global function definition for 'as'
lambda.interp: no visible global function definition for 'approx'
mrelnet: no visible global function definition for 'weighted.mean'
plot.cv.glmnet: no visible global function definition for 'points'
plot.cv.glmnet: no visible global function definition for 'axis'
plot.cv.glmnet: no visible global function definition for 'abline'
plotCoef: no visible global function definition for 'matplot'
plotCoef: no visible global function definition for 'approx'
plotCoef: no visible global function definition for 'axis'
plotCoef: no visible global function definition for 'text'
predict.cv.glmnet: no visible global function definition for 'predict'
predict.glmnet: no visible global function definition for 'as'
rmult : : no visible global function definition for
'rmultinom'
zeromat: no visible global function definition for 'new'
Undefined global functions or variables:
abline approx as axis coef matplot median new points predict
rmultinom runif segments text weighted.mean
Consider adding
importFrom("graphics", "abline", "axis", "matplot", "points",
"segments", "text")
importFrom("methods", "as", "new")
importFrom("stats", "approx", "coef", "median", "predict", "rmultinom",
"runif", "weighted.mean")
to your NAMESPACE file (and ensure that your DESCRIPTION Imports field
contains 'methods').
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in shell scripts ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... NOTE
Package has both 'src/Makevars.in' and 'src/Makevars'.
Installation with --no-configure' is unlikely to work. If you intended
'src/Makevars' to be used on Windows, rename it to 'src/Makevars.win'
otherwise remove it. If 'configure' created 'src/Makevars', you need a
'cleanup' script.
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking compiled code ... OK
* checking usage of KIND in Fortran files ... OK
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ... ERROR
Running examples in 'glmnet-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: glmnet-package
> ### Title: Elastic net model paths for some generalized linear models
> ### Aliases: glmnet-package
> ### Keywords: models regression package
>
> ### ** Examples
>
> x=matrix(rnorm(100*20),100,20)
> y=rnorm(100)
> g2=sample(1:2,100,replace=TRUE)
> g4=sample(1:4,100,replace=TRUE)
> fit1=glmnet(x,y)
> predict(fit1,newx=x[1:5,],s=c(0.01,0.005))
1 2
[1,] 0.1651627 0.1895981
[2,] -0.5430803 -0.5561822
[3,] 0.3093809 0.3242407
[4,] 0.3453068 0.3581717
[5,] -0.4283461 -0.4420611
> predict(fit1,type="coef")
21 x 66 sparse Matrix of class "dgCMatrix"
[[ suppressing 66 column names 's0', 's1', 's2' ... ]]
(Intercept) -0.1417928 -0.1417887 -0.14178501 -0.1417478737 -0.138172119
V1 . . . . .
V2 . . . 0.0008919145 0.017974756
V3 . . . . 0.004490984
V4 . . . . .
V5 . . . . .
V6 . . . . .
V7 . . . . .
V8 . -0.0160778 -0.03072729 -0.0441276998 -0.056837237
V9 . . . . .
V10 . . . . .
V11 . . . . .
V12 . . . . .
V13 . . . . -0.012614957
V14 . . . . .
V15 . . . . .
V16 . . . . .
V17 . . . . .
V18 . . . . .
V19 . . . . .
V20 . . . . .
(Intercept) -0.13456053 -0.13126978 -0.12827136 -0.126024382 -0.12457388
V1 . . . 0.004378485 0.01375550
V2 0.03420188 0.04898754 0.06245968 0.074737991 0.08592887
V3 0.01584973 0.02619921 0.03562927 0.044128743 0.05175894
V4 . . . . .
V5 . . . . .
V6 . . . . .
V7 . . . . .
V8 -0.06749967 -0.07721492 -0.08606709 -0.094273527 -0.10192396
V9 . . . . .
V10 . . . . .
V11 . . . . .
V12 . . . . .
V13 -0.02635333 -0.03887123 -0.05027708 -0.060623473 -0.06999385
V14 . . . . .
V15 . . . . .
V16 . . . . .
V17 . . . . .
V18 . . . . .
V19 . . . . .
V20 . . . . .
(Intercept) -0.123092260 -0.12137748 -0.11981509 -0.119577235 -0.119702217
V1 0.021960097 0.02865998 0.03476503 0.040771967 0.046651303
V2 0.096321083 0.10623755 0.11527286 0.123933216 0.132184522
V3 0.059313151 0.06757379 0.07509966 0.081077849 0.086371361
V4 . . . . .
V5 . . . . .
V6 . . . . .
V7 . . . . .
V8 -0.109049143 -0.11589276 -0.12212845 -0.127596066 -0.132682865
V9 . . . . .
V10 0.003153469 0.01321132 0.02237508 0.031222991 0.039319686
V11 . . . . 0.002308117
V12 . . . . .
V13 -0.079134826 -0.08883813 -0.09767926 -0.105620022 -0.112892265
V14 . . . -0.002335272 -0.005610950
V15 . . . . .
V16 . . . . .
V17 . . . . .
V18 . . . . .
V19 . . . 0.005560349 0.011742684
V20 . . . . .
(Intercept) -0.119850090 -0.1199868655 -0.119715815 -0.119468192 -0.1192272147
V1 0.052801292 0.0586827820 0.064280981 0.069379972 0.0740259709
V2 0.140333921 0.1476821797 0.154347055 0.160422725 0.1659586694
V3 0.091215440 0.0957861307 0.100150855 0.104131143 0.1077578535
V4 0.000717184 0.0019027959 0.002684843 0.003390248 0.0040329530
V5 . . . . .
V6 . . . . .
V7 . . . . .
V8 -0.137696326 -0.1427242438 -0.147335354 -0.151537376 -0.1553661027
V9 . . . . .
V10 0.046449191 0.0522711059 0.057435932 0.062146503 0.0664386329
V11 0.009274153 0.0154379581 0.020655093 0.025409743 0.0297420108
V12 . 0.0003596194 0.004115996 0.007538125 0.0106562395
V13 -0.119669170 -0.1255045072 -0.130597548 -0.135239685 -0.1394694410
V14 -0.008903354 -0.0117027938 -0.014606102 -0.017252092 -0.0196630233
V15 . . . . .
V16 . 0.0027002708 0.006100211 0.009199773 0.0120239818
V17 . . . . .
V18 . . . . .
V19 0.017140319 0.0220285600 0.026530605 0.030633776 0.0343724390
V20 . . . . 0.0001943677
(Intercept) -0.118594121 -0.118101454 -0.117716309 -0.117365415 -0.117045694
V1 0.077392961 0.080468100 0.083247052 0.085778639 0.088085327
V2 0.171311368 0.176486131 0.181472369 0.186015234 0.190154524
V3 0.111995300 0.116318789 0.120697191 0.124686028 0.128320506
V4 0.004965750 0.005494256 0.005719535 0.005922562 0.006107554
V5 . . . . .
V6 . . . . .
V7 . . . . .
V8 -0.160029084 -0.164757650 -0.169510407 -0.173838068 -0.177781273
V9 . . . . .
V10 0.070685070 0.075055599 0.079480248 0.083513450 0.087188352
V11 0.033348320 0.036478248 0.039196967 0.041675838 0.043934494
V12 0.014006830 0.017192160 0.020215555 0.022970403 0.025480519
V13 -0.143052671 -0.146314298 -0.149283842 -0.151990048 -0.154455842
V14 -0.021813177 -0.023489092 -0.024767674 -0.025934431 -0.026997537
V15 . 0.002387192 0.006560286 0.010361270 0.013824585
V16 0.015008120 0.017832074 0.020497741 0.022926287 0.025139088
V17 . . . . .
V18 . . . . .
V19 0.038320080 0.041608083 0.044352667 0.046853984 0.049133090
V20 0.005471067 0.010028068 0.013998837 0.017616098 0.020912011
(Intercept) -0.116754375 -0.116488937 -0.116247079 -0.116030669 -0.115829571
V1 0.090187095 0.092102149 0.093847074 0.095447494 0.096895354
V2 0.193926090 0.197362601 0.200493822 0.203333221 0.205933830
V3 0.131632108 0.134649515 0.137398865 0.139878028 0.142162598
V4 0.006276112 0.006429695 0.006569635 0.006722119 0.006836386
V5 . . . . .
V6 . . . . .
V7 . . . . .
V8 -0.181374173 -0.184647891 -0.187630780 -0.190334643 -0.192812215
V9 . . . . .
V10 0.090536787 0.093587755 0.096367684 0.098880477 0.101189913
V11 0.045992496 0.047867671 0.049576261 0.051136806 0.052554973
V12 0.027767643 0.029851585 0.031750395 0.033481818 0.035058135
V13 -0.156702581 -0.158749726 -0.160615009 -0.162307971 -0.163857049
V14 -0.027966199 -0.028848808 -0.029653009 -0.030391994 -0.031059144
V15 0.016980229 0.019855534 0.022475404 0.024846242 0.027022584
V16 0.027155309 0.028992415 0.030666318 0.032183857 0.033574173
V17 . . . . .
V18 . . . . .
V19 0.051209727 0.053101881 0.054825942 0.056398003 0.057829233
V20 0.023915124 0.026651449 0.029144687 0.031411613 0.033481921
(Intercept) -0.1156442603 -0.1154728036 -0.115428980 -0.115377054 -0.1153141739
V1 0.0982144658 0.0992693064 0.100309383 0.101244996 0.1020971618
V2 0.2083036039 0.2103594724 0.212190893 0.213873721 0.2154076147
V3 0.1442445027 0.1461104449 0.147727717 0.149226282 0.1505924286
V4 0.0069402001 0.0069911034 0.007165004 0.007284834 0.0073927784
V5 0.0000434915 0.0008976257 0.001655467 0.002354820 0.0029923439
V6 . . . . .
V7 . -0.0002734381 -0.000931219 -0.001508970 -0.0020347529
V8 -0.1950744810 -0.1970933106 -0.198784613 -0.200350890 -0.2017786646
V9 . . . . .
V10 0.1032993418 0.1053102835 0.107013364 0.108596765 0.1100406165
V11 0.0538472279 0.0550756829 0.056275262 0.057356806 0.0583420986
V12 0.0364974848 0.0378876938 0.039201485 0.040390616 0.0414738838
V13 -0.1652717845 -0.1664264592 -0.167253596 -0.168036837 -0.1687514167
V14 -0.0316694566 -0.0323789602 -0.033130892 -0.033802786 -0.0344146885
V15 0.0290035616 0.0306960536 0.032145083 0.033489125 0.0347144201
V16 0.0348409838 0.0359343250 0.036827313 0.037655218 0.0384099518
V17 . . . . 0.0001728693
V18 . 0.0004831618 0.001271401 0.001971039 0.0025910116
V19 0.0591363268 0.0602807332 0.061259512 0.062154882 0.0629799525
V20 0.0353654271 0.0370884408 0.038709060 0.040184630 0.0415471548
(Intercept) -0.1152801350 -0.1151553146 -0.115037998 -0.114930997 -0.1148705036
V1 0.1027671732 0.1035332622 0.104227453 0.104859971 0.1054509215
V2 0.2167168630 0.2179902490 0.219156122 0.220218650 0.2211671502
V3 0.1517965809 0.1530497744 0.154194364 0.155237460 0.1561612895
V4 0.0073959149 0.0075202512 0.007624600 0.007719499 0.0078283316
V5 0.0035616422 0.0041515396 0.004690228 0.005181133 0.0056186561
V6 0.0000345520 0.0014280435 0.002693182 0.003845990 0.0048929626
V7 -0.0026358434 -0.0031599790 -0.003628768 -0.004055662 -0.0044615343
V8 -0.2029882272 -0.2040223037 -0.204972901 -0.205839217 -0.2065863970
V9 . . . . -0.0002936383
V10 0.1112468619 0.1124995495 0.113649227 0.114697084 0.1156851114
V11 0.0592434758 0.0601137461 0.060902296 0.061620783 0.0622524720
V12 0.0424062089 0.0433413660 0.044192493 0.044967974 0.0456208344
V13 -0.1693605100 -0.1700667101 -0.170716710 -0.171309211 -0.1718531231
V14 -0.0350451081 -0.0355980361 -0.036097289 -0.036552076 -0.0370307719
V15 0.0358444956 0.0369155982 0.037896193 0.038789813 0.0396233865
V16 0.0391585235 0.0398451139 0.040473215 0.041045559 0.0415352802
V17 0.0005447025 0.0008361926 0.001098305 0.001337002 0.0015729396
V18 0.0031200944 0.0035191185 0.003878926 0.004206693 0.0044768899
V19 0.0637460991 0.0644908331 0.065170148 0.065789135 0.0664377783
V20 0.0427993583 0.0441739953 0.045425288 0.046565405 0.0475985868
(Intercept) -0.114877357 -0.114879507 -0.114889415 -0.114892363 -0.114893876
V1 0.106032174 0.106550971 0.107041806 0.107473234 0.107864694
V2 0.222070148 0.222889708 0.223617550 0.224295702 0.224916063
V3 0.157026739 0.157813393 0.158502387 0.159153292 0.159749440
V4 0.008163893 0.008402939 0.008646522 0.008849467 0.009029738
V5 0.006219175 0.006735062 0.007175716 0.007601120 0.007992369
V6 0.006277064 0.007466245 0.008504581 0.009487596 0.010388890
V7 -0.004813221 -0.005132325 -0.005435072 -0.005702608 -0.005944147
V8 -0.207430355 -0.208170623 -0.208792751 -0.209404308 -0.209966411
V9 -0.001711621 -0.002932283 -0.003953084 -0.004958016 -0.005884796
V10 0.116987954 0.118153827 0.119139968 0.120099008 0.120983187
V11 0.062639398 0.063006737 0.063376865 0.063683834 0.063960166
V12 0.046104648 0.046538527 0.046936956 0.047299268 0.047627790
V13 -0.172487076 -0.173071253 -0.173563264 -0.174041671 -0.174484248
V14 -0.037607311 -0.038130321 -0.038605319 -0.039039413 -0.039435283
V15 0.040530686 0.041354442 0.042075675 0.042755983 0.043379913
V16 0.041917935 0.042272968 0.042583505 0.042877398 0.043146541
V17 0.001806325 0.002024416 0.002217229 0.002398310 0.002563809
V18 0.004470020 0.004477531 0.004527974 0.004539394 0.004543723
V19 0.067436110 0.068321334 0.069079451 0.069810074 0.070481568
V20 0.048678418 0.049635706 0.050486011 0.051280494 0.052005887
(Intercept) -0.114895045 -0.114896076 -0.114897009 -0.114897859 -0.114898633
V1 0.108221132 0.108545868 0.108841750 0.109111345 0.109356989
V2 0.225481768 0.225997294 0.226467034 0.226895046 0.227285034
V3 0.160293152 0.160788647 0.161240138 0.161651522 0.162026359
V4 0.009193232 0.009342080 0.009477686 0.009601241 0.009713821
V5 0.008349443 0.008674887 0.008971435 0.009241640 0.009487842
V6 0.011211000 0.011960215 0.012642894 0.013264929 0.013831705
V7 -0.006163810 -0.006363886 -0.006546177 -0.006712272 -0.006863611
V8 -0.210479380 -0.210946907 -0.211372921 -0.211761093 -0.212114780
V9 -0.006730985 -0.007502275 -0.008205090 -0.008845475 -0.009428972
V10 0.121790512 0.122526387 0.123196933 0.123807916 0.124364622
V11 0.064211433 0.064440297 0.064648817 0.064838811 0.065011925
V12 0.047926865 0.048199330 0.048447583 0.048673781 0.048879884
V13 -0.174888607 -0.175257220 -0.175593115 -0.175899175 -0.176178046
V14 -0.039796009 -0.040124689 -0.040424171 -0.040697047 -0.040945682
V15 0.043949087 0.044467805 0.044940459 0.045371126 0.045763534
V16 0.043391988 0.043615665 0.043819476 0.044005182 0.044174390
V17 0.002714641 0.002852075 0.002977301 0.003091401 0.003195366
V18 0.004546711 0.004549283 0.004551603 0.004553713 0.004555635
V19 0.071094311 0.071652762 0.072161624 0.072625284 0.073047754
V20 0.052667061 0.053269534 0.053818490 0.054318679 0.054774433
(Intercept) -0.114899338 -0.114899981 -0.114900566 -0.114901839 -0.114902389
V1 0.109580812 0.109784750 0.109970572 0.110156079 0.110295996
V2 0.227640378 0.227964153 0.228259165 0.228518079 0.228771308
V3 0.162367898 0.162679095 0.162962646 0.163193266 0.163453865
V4 0.009816398 0.009909863 0.009995025 0.010085563 0.010147576
V5 0.009712172 0.009916573 0.010102815 0.010249361 0.010423680
V6 0.014348130 0.014818678 0.015247423 0.015589794 0.015988400
V7 -0.007001505 -0.007127149 -0.007241632 -0.007338371 -0.007442148
V8 -0.212437048 -0.212730686 -0.212998238 -0.213198601 -0.213459741
V9 -0.009960632 -0.010445061 -0.010886455 -0.011211205 -0.011643983
V10 0.124871872 0.125334060 0.125755187 0.126089672 0.126479155
V11 0.065169661 0.065313384 0.065444338 0.065587624 0.065675830
V12 0.049067678 0.049238788 0.049394697 0.049528483 0.049667562
V13 -0.176432142 -0.176663666 -0.176874621 -0.177054930 -0.177236177
V14 -0.041172229 -0.041378650 -0.041566733 -0.041728459 -0.041893452
V15 0.046121082 0.046446866 0.046743708 0.046996072 0.047257044
V16 0.044328567 0.044469046 0.044597046 0.044702022 0.044818600
V17 0.003290094 0.003376407 0.003455052 0.003514304 0.003590866
V18 0.004557386 0.004558981 0.004560435 0.004588665 0.004568906
V19 0.073432693 0.073783435 0.074103018 0.074353079 0.074653706
V20 0.055189699 0.055568073 0.055912834 0.056197965 0.056511490
(Intercept) -0.114902919 -0.114903611 -0.114904101 -0.114904425 -0.114904657
V1 0.110448830 0.110579396 0.110695786 0.110801061 0.110896727
V2 0.228987253 0.229190534 0.229376140 0.229545503 0.229699946
V3 0.163647145 0.163841922 0.164021295 0.164185275 0.164334899
V4 0.010220039 0.010283246 0.010337557 0.010385564 0.010428761
V5 0.010547536 0.010672255 0.010789454 0.010897477 0.010996342
V6 0.016276335 0.016565666 0.016835993 0.017084437 0.017311525
V7 -0.007521763 -0.007600515 -0.007673043 -0.007739142 -0.007799312
V8 -0.213629065 -0.213810992 -0.213980625 -0.214136430 -0.214278849
V9 -0.011920841 -0.012212452 -0.012490566 -0.012748098 -0.012984157
V10 0.126763079 0.127044467 0.127309044 0.127553084 0.127776505
V11 0.065792337 0.065885808 0.065967497 0.066040882 0.066107394
V12 0.049777770 0.049887284 0.049986320 0.050076089 0.050157708
V13 -0.177389723 -0.177529376 -0.177660708 -0.177782102 -0.177893348
V14 -0.042028128 -0.042155479 -0.042273611 -0.042381897 -0.042480760
V15 0.047468980 0.047670900 0.047857383 0.048028327 0.048184467
V16 0.044906639 0.044993736 0.045074416 0.045148387 0.045215950
V17 0.003640750 0.003692283 0.003741755 0.003787606 0.003829614
V18 0.004588406 0.004596246 0.004597586 0.004596715 0.004595199
V19 0.074865102 0.075078614 0.075279796 0.075465309 0.075635087
V20 0.056749379 0.056985578 0.057203447 0.057402615 0.057584297
(Intercept) -0.114904842 -0.114905001 -0.114905143 -0.114905270 -0.114905386
V1 0.110983808 0.111063123 0.111135383 0.111201220 0.111261206
V2 0.229840723 0.229969015 0.230085916 0.230192436 0.230289493
V3 0.164471310 0.164595630 0.164708916 0.164812142 0.164906198
V4 0.010467927 0.010503548 0.010535981 0.010565524 0.010592440
V5 0.011086578 0.011168852 0.011243835 0.011312164 0.011374425
V6 0.017518684 0.017707524 0.017879617 0.018036431 0.018179318
V7 -0.007854103 -0.007904013 -0.007949484 -0.007990914 -0.008028662
V8 -0.214408778 -0.214527222 -0.214635164 -0.214733523 -0.214823147
V9 -0.013199729 -0.013396318 -0.013575499 -0.013738783 -0.013887568
V10 0.127980452 0.128166412 0.128335897 0.128490342 0.128631072
V11 0.066167875 0.066222942 0.066273102 0.066318801 0.066360438
V12 0.050232016 0.050299701 0.050361366 0.050417550 0.050468742
V13 -0.177994939 -0.178087585 -0.178172029 -0.178248980 -0.178319099
V14 -0.042570904 -0.042653060 -0.042727924 -0.042796140 -0.042858297
V15 0.048326873 0.048456675 0.048574963 0.048682748 0.048780960
V16 0.045277567 0.045333730 0.045384910 0.045431546 0.045474040
V17 0.003867963 0.003902928 0.003934794 0.003963833 0.003990292
V18 0.004593567 0.004591994 0.004590531 0.004589187 0.004587959
V19 0.075790040 0.075931315 0.076060071 0.076177398 0.076284306
V20 0.057749906 0.057900827 0.058038349 0.058163656 0.058277832
(Intercept) -0.114905491
V1 0.111315864
V2 0.230377928
V3 0.164991899
V4 0.010616965
V5 0.011431155
V6 0.018309513
V7 -0.008063057
V8 -0.214904810
V9 -0.014023138
V10 0.128759301
V11 0.066398376
V12 0.050515386
V13 -0.178382990
V14 -0.042914932
V15 0.048870448
V16 0.045512759
V17 0.004014401
V18 0.004586839
V19 0.076381718
V20 0.058381866
> plot(fit1,xvar="lambda")
> fit2=glmnet(x,g2,family="binomial")
> predict(fit2,type="response",newx=x[2:5,])
s0 s1 s2 s3 s4 s5 s6
[1,] 0.2006707 0.2039833 0.2082066 0.2142023 0.21982403 0.22509049 0.21284155
[2,] 0.2006707 0.1772041 0.1514758 0.1201875 0.09163274 0.06552467 0.07100726
[3,] 0.2006707 0.2139580 0.2182372 0.2081231 0.19900077 0.19074808 0.18024357
[4,] 0.2006707 0.1982384 0.1895203 0.1699092 0.15207185 0.13581199 0.12634638
s7 s8 s9 s10 s11 s12
[1,] 0.20095744 0.20348825 0.22058887 0.22530507 0.22132822 0.21779882
[2,] 0.09252652 0.09604202 0.08058595 0.06693564 0.05476611 0.04350937
[3,] 0.14073716 0.08390237 0.01064485 -0.05394641 -0.11126787 -0.16388075
[4,] 0.11251215 0.10277040 0.09740835 0.09485215 0.09431164 0.09385802
s13 s14 s15 s16 s17 s18
[1,] 0.21466137 0.21186770 0.20893533 0.19135488 0.17204394 0.140657542
[2,] 0.03310110 0.02347970 0.01483302 0.01513083 0.01262498 0.001994054
[3,] -0.21218708 -0.25654849 -0.29707248 -0.32707090 -0.35667735 -0.391688555
[4,] 0.09347621 0.09315444 0.09230610 0.07189066 0.05246517 0.032390216
s19 s20 s21 s22 s23 s24
[1,] 0.10249436 0.067378346 0.035130114 0.006126494 -0.01543003 -0.03153510
[2,] 0.01144203 0.021927685 0.031543390 0.031610389 0.02465821 0.01933113
[3,] -0.42854714 -0.462399607 -0.493419075 -0.519931610 -0.54203392 -0.56405826
[4,] 0.01749789 0.003957317 -0.008523194 -0.031654670 -0.06400958 -0.09473659
s25 s26 s27 s28 s29 s30
[1,] -0.04688687 -0.06201435 -0.07640507 -0.09239776 -0.107108217 -0.120619291
[2,] 0.01652410 0.01673773 0.01579850 0.01173413 0.004785888 -0.001561899
[3,] -0.58621312 -0.60618771 -0.62114878 -0.63176161 -0.639042530 -0.645700328
[4,] -0.12237140 -0.14788053 -0.17287661 -0.19816779 -0.222355068 -0.244522783
s31 s32 s33 s34 s35 s36
[1,] -0.133023679 -0.14440707 -0.15484892 -0.16435592 -0.17313448 -0.18117773
[2,] -0.007358724 -0.01265246 -0.01748665 -0.02183968 -0.02587403 -0.02955827
[3,] -0.651789537 -0.65735753 -0.66244786 -0.66705379 -0.67130803 -0.67519560
[4,] -0.264833845 -0.28343831 -0.30047444 -0.31600758 -0.33028327 -0.34334474
s37 s38 s39 s40 s41 s42
[1,] -0.1885436 -0.19528671 -0.20145744 -0.20701487 -0.21067597 -0.21392622
[2,] -0.0329218 -0.03599222 -0.03879478 -0.04124538 -0.04190075 -0.04239211
[3,] -0.6787471 -0.68199103 -0.68495351 -0.68770684 -0.69094337 -0.69383043
[4,] -0.3552913 -0.36621509 -0.37620116 -0.38544512 -0.39580319 -0.40520766
s43 s44 s45 s46 s47 s48
[1,] -0.21698783 -0.22018846 -0.22318536 -0.22577530 -0.22827899 -0.23057916
[2,] -0.04293518 -0.04296617 -0.04299912 -0.04291514 -0.04294457 -0.04298186
[3,] -0.69652971 -0.69913838 -0.70153455 -0.70359224 -0.70559206 -0.70742188
[4,] -0.41384946 -0.42279454 -0.43103993 -0.43843617 -0.44530830 -0.45158712
s49 s50 s51 s52 s53
[1,] -0.23268099 -0.23460023 -0.23635232 -0.23795156 -0.23941105
[2,] -0.04301714 -0.04304969 -0.04307963 -0.04310713 -0.04313237
[3,] -0.70909139 -0.71061419 -0.71200302 -0.71326958 -0.71442453
[4,] -0.45731665 -0.46254382 -0.46731213 -0.47166144 -0.47562822
> predict(fit2,type="nonzero")
$s0
NULL
$s1
[1] 17
$s2
[1] 15 17
$s3
[1] 15 17
$s4
[1] 15 17
$s5
[1] 15 17
$s6
[1] 12 15 17
$s7
[1] 1 12 15 17
$s8
[1] 1 12 13 15 17
$s9
[1] 1 12 13 15 17
$s10
[1] 1 5 12 13 15 17
$s11
[1] 1 5 12 13 15 17
$s12
[1] 1 5 12 13 15 17
$s13
[1] 1 5 12 13 15 17
$s14
[1] 1 5 12 13 15 17
$s15
[1] 1 5 12 13 14 15 17
$s16
[1] 1 5 12 13 14 15 17
$s17
[1] 1 5 7 12 13 14 15 17
$s18
[1] 1 5 7 9 12 13 14 15 16 17
$s19
[1] 1 5 7 9 12 13 14 15 16 17 20
$s20
[1] 1 5 7 9 12 13 14 15 16 17 20
$s21
[1] 1 5 7 9 12 13 14 15 16 17 20
$s22
[1] 1 4 5 7 9 12 13 14 15 16 17 20
$s23
[1] 1 4 5 7 8 9 12 13 14 15 16 17 20
$s24
[1] 1 2 4 5 7 8 9 12 13 14 15 16 17 20
$s25
[1] 1 2 4 5 7 8 9 11 12 13 14 15 16 17 20
$s26
[1] 1 2 4 5 7 8 9 11 12 13 14 15 16 17 19 20
$s27
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 19 20
$s28
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s29
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s30
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s31
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s32
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s33
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s34
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s35
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s36
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s37
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s38
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s39
[1] 1 2 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s40
[1] 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s41
[1] 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s42
[1] 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s43
[1] 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s44
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s45
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s46
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s47
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s48
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s49
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s50
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s51
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s52
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$s53
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
> fit3=glmnet(x,g4,family="multinomial")
> predict(fit3,newx=x[1:3,],type="response",s=0.01)
Error in dimnames(x) <- dn :
length of 'dimnames' [1] not equal to array extent
Calls: predict -> predict.glmnet -> rownames<-
Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... NOTE
Package vignette without corresponding tangle output:
'Coxnet.rnw'
* checking running R code from vignettes ...
'Coxnet.rnw'... OK
OK
* checking re-building of vignette outputs ... SKIPPED
* DONE
Status: 1 ERROR, 4 NOTEs
See
'/magus/work/usr/mports/devel/R-cran-glmnet/work/glmnet.Rcheck/00check.log'
for details.
*** Error code 1
Stop.
make: stopped in /usr/mports/devel/R-cran-glmnet
Links
Depends On
- devel/binutils (build)
- lang/gcc14 (build)
- math/R (build)
- lang/gcc14 (run)
- math/R (run)
Depend Of
NothingCategories
CVEs
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