Elastic net model paths for some generalized linear models
glmnet-package.RdThis package fits lasso and elastic-net model paths for regression, logistic and multinomial regression using coordinate descent. The algorithm is extremely fast, and exploits sparsity in the input x matrix where it exists. A variety of predictions can be made from the fitted models.
Details
| Package: | glmnet |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2008-05-14 |
| License: | What license is it under? |
Very simple to use. Accepts x,y data for regression models, and
produces the regularization path over a grid of values for the tuning
parameter lambda. Only 5 functions: glmnetpredict.glmnetplot.glmnetprint.glmnetcoef.glmnet
References
Friedman, J., Hastie, T. and Tibshirani, R. (2008)
Regularization Paths for Generalized Linear Models via Coordinate
Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22,
doi:10.18637/jss.v033.i01
.
Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011)
Regularization Paths for Cox's Proportional
Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol.
39(5), 1-13,
doi:10.18637/jss.v039.i05
.
Tibshirani,Robert, Bien, J., Friedman, J., Hastie, T.,Simon, N.,Taylor, J. and
Tibshirani, Ryan. (2012) Strong Rules for Discarding Predictors in
Lasso-type Problems, JRSSB, Vol. 74(2), 245-266,
https://arxiv.org/abs/1011.2234.
Hastie, T., Tibshirani, Robert and Tibshirani, Ryan (2020) Best Subset,
Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons,
Statist. Sc. Vol. 35(4), 579-592,
https://arxiv.org/abs/1707.08692.
Glmnet webpage with four vignettes: https://glmnet.stanford.edu.
Author
Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer:
Trevor Hastie [email protected]
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))
#> s=0.010 s=0.005
#> [1,] 0.1514411 0.1260824
#> [2,] 0.5521237 0.5861328
#> [3,] -0.5837403 -0.6010315
#> [4,] 0.4842217 0.5120798
#> [5,] -0.1460407 -0.1599106
predict(fit1, type = "coef")
#> 21 x 66 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 66 column names ‘s0’, ‘s1’, ‘s2’ ... ]]
#>
#> (Intercept) 0.1212969 0.116204045 0.10978308 0.10393253 0.099649969
#> V1 . . . . .
#> V2 . -0.005248652 -0.02277941 -0.03875279 -0.053441576
#> V3 . . . . .
#> V4 . . . . .
#> V5 . . . . .
#> V6 . . . . .
#> V7 . -0.010456753 -0.02284906 -0.03414048 -0.044437694
#> V8 . . . . .
#> V9 . . . . .
#> V10 . . . . .
#> V11 . . . . .
#> V12 . . . . .
#> V13 . . . . .
#> V14 . . . . .
#> V15 . . . . -0.007745967
#> V16 . . . . .
#> V17 . . . . .
#> V18 . . . . .
#> V19 . . . . .
#> V20 . -0.018545816 -0.03572044 -0.05136932 -0.064320091
#>
#> (Intercept) 0.09636691 0.09337513 0.09064913 0.0882718525 0.08714231
#> V1 . . . . .
#> V2 -0.06690081 -0.07916587 -0.09034133 -0.1002291727 -0.10929421
#> V3 . . . . .
#> V4 . . . . .
#> V5 . . . . .
#> V6 . . . . .
#> V7 -0.05383289 -0.06239085 -0.07018855 -0.0772954626 -0.08641189
#> V8 . . . . .
#> V9 . . . -0.0033875504 -0.01084443
#> V10 . . . . .
#> V11 . . . . .
#> V12 . . . . .
#> V13 . . . . .
#> V14 . . . 0.0004977004 0.01212342
#> V15 -0.01938351 -0.02998270 -0.03964028 -0.0489339741 -0.05745956
#> V16 . . . . .
#> V17 . . . 0.0010126766 0.01644041
#> V18 . . . . .
#> V19 . . . . .
#> V20 -0.07534561 -0.08539293 -0.09454767 -0.1026967059 -0.11014976
#>
#> (Intercept) 0.085913807 0.084194335 0.083499083 8.316436e-02 0.082472251
#> V1 . 0.004457544 0.010288240 1.525860e-02 0.018942160
#> V2 -0.117493957 -0.124839130 -0.131622383 -1.379020e-01 -0.144704880
#> V3 . . . -5.371554e-05 -0.007120511
#> V4 . -0.002009599 -0.007189642 -1.249989e-02 -0.016915743
#> V5 . . . . .
#> V6 . . . . .
#> V7 -0.094815737 -0.103463302 -0.111724623 -1.193221e-01 -0.126191070
#> V8 . . 0.002616005 8.267272e-03 0.012659421
#> V9 -0.017622728 -0.023314070 -0.027723681 -3.125296e-02 -0.033824958
#> V10 . . . . .
#> V11 . . . . .
#> V12 -0.001719029 -0.014319800 -0.025258375 -3.482772e-02 -0.045727211
#> V13 . . . . .
#> V14 0.022860855 0.032825138 0.040969934 4.800172e-02 0.056325499
#> V15 -0.065544016 -0.075039947 -0.083882485 -9.220620e-02 -0.099388351
#> V16 . . . . .
#> V17 0.030504259 0.042799983 0.054523981 6.588069e-02 0.075403181
#> V18 . . . . .
#> V19 . . . . .
#> V20 -0.117041649 -0.122969120 -0.127761017 -1.321003e-01 -0.136677281
#>
#> (Intercept) 0.081977526 0.081638417 0.08129565 0.08098239 0.08069696
#> V1 0.022268430 0.024946622 0.02714335 0.02914376 0.03096646
#> V2 -0.150372387 -0.154729604 -0.15842239 -0.16178714 -0.16485297
#> V3 -0.013568610 -0.019810164 -0.02585192 -0.03135952 -0.03637784
#> V4 -0.020899914 -0.023783014 -0.02579406 -0.02762370 -0.02929080
#> V5 . . . . .
#> V6 . . . . .
#> V7 -0.132312827 -0.139378752 -0.14708171 -0.15409826 -0.16049147
#> V8 0.016691775 0.020172091 0.02313690 0.02583653 0.02829633
#> V9 -0.036550862 -0.039988231 -0.04363473 -0.04695733 -0.04998475
#> V10 . . . . .
#> V11 . . . . .
#> V12 -0.055361444 -0.066460721 -0.07868090 -0.08981745 -0.09996466
#> V13 -0.002198335 -0.006883180 -0.01175668 -0.01619670 -0.02024227
#> V14 0.063970209 0.071376286 0.07847282 0.08493988 0.09083242
#> V15 -0.105723167 -0.113745969 -0.12294821 -0.13133083 -0.13896877
#> V16 . -0.007066528 -0.01887052 -0.02962439 -0.03942291
#> V17 0.084260948 0.094282501 0.10476107 0.11430700 0.12300489
#> V18 . . . . .
#> V19 . . . . .
#> V20 -0.140889668 -0.144214160 -0.14682767 -0.14921027 -0.15138122
#>
#> (Intercept) 0.08043688 0.08019991 0.07999590 0.07979826 0.0796263669
#> V1 0.03262724 0.03414047 0.03552389 0.03677985 0.0379804903
#> V2 -0.16764645 -0.17019176 -0.17251611 -0.17462891 -0.1765698720
#> V3 -0.04095034 -0.04511664 -0.04888017 -0.05234162 -0.0555144785
#> V4 -0.03080980 -0.03219385 -0.03348651 -0.03463320 -0.0356212396
#> V5 . . . . .
#> V6 . . . . .
#> V7 -0.16631673 -0.17162449 -0.17642665 -0.18083591 -0.1848760289
#> V8 0.03053761 0.03257978 0.03445412 0.03614855 0.0376664656
#> V9 -0.05274323 -0.05525666 -0.05752562 -0.05961390 -0.0615230002
#> V10 . . . . -0.0005402559
#> V11 . . . . .
#> V12 -0.10921041 -0.11763480 -0.12523414 -0.13223407 -0.1387713503
#> V13 -0.02392846 -0.02728717 -0.03034052 -0.03312954 -0.0356677713
#> V14 0.09620148 0.10109358 0.10552530 0.10958879 0.1133178651
#> V15 -0.14592817 -0.15226932 -0.15800862 -0.16327611 -0.1682371362
#> V16 -0.04835096 -0.05648587 -0.06385167 -0.07060901 -0.0769796191
#> V17 0.13093009 0.13815123 0.14472088 0.15071681 0.1561910884
#> V18 . . . . .
#> V19 . . . . .
#> V20 -0.15335930 -0.15516166 -0.15680749 -0.15830355 -0.1597576836
#>
#> (Intercept) 0.07958930 0.079122065 0.078473604 0.0778504319 0.076959263
#> V1 0.03948731 0.040830468 0.042040411 0.0431423733 0.044302394
#> V2 -0.17848735 -0.180346762 -0.182081433 -0.1836664031 -0.184704108
#> V3 -0.05831508 -0.060849916 -0.063092933 -0.0651711136 -0.066948295
#> V4 -0.03635547 -0.036858307 -0.037264464 -0.0375994984 -0.037600837
#> V5 . . . . .
#> V6 . 0.001813735 0.004488296 0.0069436026 0.009411147
#> V7 -0.18845121 -0.191923206 -0.195091809 -0.1980411069 -0.201080650
#> V8 0.03897408 0.040292666 0.041559260 0.0427100197 0.044039305
#> V9 -0.06315562 -0.065058828 -0.066995679 -0.0687898080 -0.071110993
#> V10 -0.00437153 -0.007859419 -0.010958315 -0.0138292763 -0.016600473
#> V11 . . . -0.0001564292 -0.003049732
#> V12 -0.14527839 -0.151437398 -0.156981826 -0.1621681807 -0.167420606
#> V13 -0.03789816 -0.040242265 -0.042541442 -0.0446524973 -0.046883304
#> V14 0.11667439 0.119899374 0.122877282 0.1256296182 0.128358973
#> V15 -0.17353728 -0.178462445 -0.182868472 -0.1869782494 -0.191254724
#> V16 -0.08377648 -0.090038354 -0.095625044 -0.1008018951 -0.105783961
#> V17 0.16114853 0.165657586 0.169704332 0.1734210404 0.176978815
#> V18 . . . . .
#> V19 . . . . .
#> V20 -0.16166648 -0.163399694 -0.164980354 -0.1664263583 -0.167773181
#>
#> (Intercept) 0.076181611 0.075455436 0.07479331 0.07419000 0.07364028
#> V1 0.045347531 0.046302125 0.04717198 0.04796456 0.04868674
#> V2 -0.185686468 -0.186567800 -0.18737045 -0.18810178 -0.18876814
#> V3 -0.068536456 -0.070000127 -0.07133418 -0.07254973 -0.07365730
#> V4 -0.037675099 -0.037709065 -0.03773911 -0.03776645 -0.03779137
#> V5 . . . . .
#> V6 0.011605601 0.013633892 0.01548275 0.01716738 0.01870235
#> V7 -0.203769701 -0.206257578 -0.20852539 -0.21059176 -0.21247456
#> V8 0.045246417 0.046342947 0.04734197 0.04825224 0.04908165
#> V9 -0.073134058 -0.075016148 -0.07673208 -0.07829559 -0.07972021
#> V10 -0.019064825 -0.021341897 -0.02341752 -0.02530878 -0.02703202
#> V11 -0.005617614 -0.007989024 -0.01015060 -0.01212017 -0.01391478
#> V12 -0.172051906 -0.176353416 -0.18027494 -0.18384814 -0.18710391
#> V13 -0.048880960 -0.050716527 -0.05238942 -0.05391371 -0.05530258
#> V14 0.130797131 0.133042957 0.13508988 0.13695498 0.13865439
#> V15 -0.195046565 -0.198556899 -0.20175684 -0.20467255 -0.20732924
#> V16 -0.110212980 -0.114305675 -0.11803626 -0.12143548 -0.12453271
#> V17 0.180195718 0.183139083 0.18582126 0.18826517 0.19049197
#> V18 . . . . .
#> V19 . . . . .
#> V20 -0.169003678 -0.170124604 -0.17114595 -0.17207656 -0.17292450
#>
#> (Intercept) 0.0730251368 0.072264261 0.071549293 0.070896981 0.070302585
#> V1 0.0493465187 0.049956112 0.050512193 0.051018984 0.051480758
#> V2 -0.1894448010 -0.190225414 -0.190929407 -0.191570497 -0.192154620
#> V3 -0.0746654678 -0.075534269 -0.076339806 -0.077074236 -0.077743438
#> V4 -0.0377743728 -0.037746637 -0.037689216 -0.037635617 -0.037586730
#> V5 . . . . .
#> V6 0.0201601431 0.021576887 0.022897281 0.024101674 0.025199123
#> V7 -0.2143502493 -0.216320577 -0.218162742 -0.219843058 -0.221374168
#> V8 0.0498016542 0.050389662 0.050919759 0.051402536 0.051842417
#> V9 -0.0811178837 -0.082535428 -0.083869378 -0.085086583 -0.086195722
#> V10 -0.0285804093 -0.029869035 -0.031068553 -0.032162462 -0.033159228
#> V11 -0.0156084838 -0.017213884 -0.018708532 -0.020071690 -0.021313798
#> V12 -0.1902207291 -0.193188093 -0.195975233 -0.198517930 -0.200834863
#> V13 -0.0566235813 -0.057923735 -0.059126856 -0.060223870 -0.061223459
#> V14 0.1402668148 0.141820010 0.143261998 0.144576904 0.145775038
#> V15 -0.2098806757 -0.212361625 -0.214681564 -0.216797710 -0.218725952
#> V16 -0.1275082651 -0.130419126 -0.133134671 -0.135611393 -0.137868184
#> V17 0.1925960533 0.194644663 0.196529045 0.198246675 0.199811740
#> V18 . . . . .
#> V19 -0.0006851395 -0.002698469 -0.004560007 -0.006257227 -0.007803712
#> V20 -0.1736760167 -0.174309801 -0.174884654 -0.175408306 -0.175885432
#>
#> (Intercept) 0.069760993 0.06929407 0.06884415 0.06843228 0.06805685
#> V1 0.051901509 0.05228130 0.05263065 0.05294922 0.05323952
#> V2 -0.192686851 -0.19318123 -0.19362316 -0.19402500 -0.19439108
#> V3 -0.078353191 -0.07889504 -0.07940141 -0.07986379 -0.08028517
#> V4 -0.037542185 -0.03753965 -0.03750243 -0.03746562 -0.03743185
#> V5 . . . . .
#> V6 0.026199079 0.02707058 0.02790116 0.02866083 0.02935324
#> V7 -0.222769262 -0.22398173 -0.22514081 -0.22620095 -0.22716722
#> V8 0.052243219 0.05261433 0.05294709 0.05324979 0.05352557
#> V9 -0.087206332 -0.08807335 -0.08891286 -0.08968175 -0.09038265
#> V10 -0.034067445 -0.03486562 -0.03561987 -0.03630930 -0.03693766
#> V11 -0.022445563 -0.02343681 -0.02437681 -0.02523622 -0.02601951
#> V12 -0.202945970 -0.20477114 -0.20652481 -0.20812984 -0.20959283
#> V13 -0.062134249 -0.06293961 -0.06369607 -0.06438706 -0.06501679
#> V14 0.146866733 0.14782914 0.14873590 0.14956437 0.15031942
#> V15 -0.220482899 -0.22201096 -0.22347041 -0.22480543 -0.22602226
#> V16 -0.139924492 -0.14172084 -0.14342899 -0.14499083 -0.14641434
#> V17 0.201237770 0.20251478 0.20369911 0.20477968 0.20576436
#> V18 . . . . .
#> V19 -0.009212813 -0.01046281 -0.01163312 -0.01270183 -0.01367578
#> V20 -0.176320172 -0.17672102 -0.17708185 -0.17741033 -0.17770960
#>
#> (Intercept) 0.06771477 0.0674001263 0.0671180743 0.066858387 0.066646333
#> V1 0.05350403 0.0537450357 0.0539431093 0.054134249 0.054304009
#> V2 -0.19472463 -0.1950285485 -0.1952601807 -0.195482555 -0.195697347
#> V3 -0.08066912 -0.0810189667 -0.0811330175 -0.081282929 -0.081432751
#> V4 -0.03740106 -0.0373730118 -0.0374159295 -0.037440576 -0.037488965
#> V5 . -0.0001634331 -0.0006193258 -0.001014572 -0.001330018
#> V6 0.02998415 0.0305402045 0.0310085384 0.031440087 0.031803046
#> V7 -0.22804767 -0.2288674855 -0.2296935262 -0.230445189 -0.231056818
#> V8 0.05377685 0.0539926922 0.0541919201 0.054373151 0.054538323
#> V9 -0.09102130 -0.0915934168 -0.0921188519 -0.092598117 -0.092982147
#> V10 -0.03751021 -0.0380544983 -0.0386058625 -0.039106822 -0.039518426
#> V11 -0.02673323 -0.0273831210 -0.0279928107 -0.028547879 -0.029010842
#> V12 -0.21092590 -0.2121455628 -0.2132442674 -0.214261407 -0.215083839
#> V13 -0.06559059 -0.0661006561 -0.0665559237 -0.066973468 -0.067329233
#> V14 0.15100741 0.1516343820 0.1521600833 0.152652529 0.153073705
#> V15 -0.22713102 -0.2281526430 -0.2291556415 -0.230070457 -0.230814668
#> V16 -0.14771143 -0.1489119963 -0.1500746459 -0.151138217 -0.152012485
#> V17 0.20666158 0.2074906908 0.2083271138 0.209080871 0.209730251
#> V18 . . . . .
#> V19 -0.01456322 -0.0154021992 -0.0162477973 -0.017014651 -0.017666242
#> V20 -0.17798229 -0.1782294694 -0.1784338177 -0.178621174 -0.178799338
#>
#> (Intercept) 0.066432509 0.066234151 0.066052794 0.065887437 0.065770821
#> V1 0.054461721 0.054606375 0.054738361 0.054858656 0.054954878
#> V2 -0.195883476 -0.196050926 -0.196203114 -0.196341713 -0.196482524
#> V3 -0.081559946 -0.081673538 -0.081776592 -0.081870408 -0.081956736
#> V4 -0.037510881 -0.037526972 -0.037540969 -0.037553605 -0.037604611
#> V5 -0.001653525 -0.001954866 -0.002230631 -0.002482114 -0.002656706
#> V6 0.032158981 0.032487724 0.032788046 0.033061827 0.033269669
#> V7 -0.231673920 -0.232246966 -0.232771042 -0.233248910 -0.233591662
#> V8 0.054688270 0.054824880 0.054949368 0.055062800 0.055177952
#> V9 -0.093375310 -0.093741509 -0.094076598 -0.094382172 -0.094597221
#> V10 -0.039930515 -0.040312529 -0.040661769 -0.040980192 -0.041209401
#> V11 -0.029467996 -0.029890852 -0.030277268 -0.030629558 -0.030896247
#> V12 -0.215919689 -0.216696651 -0.217407317 -0.218055336 -0.218502022
#> V13 -0.067673690 -0.067991141 -0.068281032 -0.068545285 -0.068752504
#> V14 0.153480858 0.153855584 0.154197676 0.154509493 0.154754703
#> V15 -0.231565317 -0.232262880 -0.232900921 -0.233482720 -0.233901994
#> V16 -0.152886767 -0.153697365 -0.154438460 -0.155114166 -0.155610482
#> V17 0.210352293 0.210924466 0.211446787 0.211922883 0.212316394
#> V18 . . . . .
#> V19 -0.018298503 -0.018881510 -0.019413968 -0.019899347 -0.020282122
#> V20 -0.178956096 -0.179097644 -0.179226374 -0.179343624 -0.179458313
#>
#> (Intercept) 0.065605252 0.065504608 0.065396395 0.065292168 0.065195310
#> V1 0.055066596 0.055147334 0.055228147 0.055303892 0.055373609
#> V2 -0.196586377 -0.196701169 -0.196800138 -0.196887298 -0.196965644
#> V3 -0.082036404 -0.082106221 -0.082171709 -0.082230785 -0.082284358
#> V4 -0.037582513 -0.037620372 -0.037637341 -0.037645288 -0.037649945
#> V5 -0.002910308 -0.003061714 -0.003226612 -0.003385448 -0.003532986
#> V6 0.033531453 0.033708764 0.033890664 0.034063128 0.034222532
#> V7 -0.234064680 -0.234360471 -0.234676120 -0.234977896 -0.235257486
#> V8 0.055260857 0.055356317 0.055436567 0.055507673 0.055571825
#> V9 -0.094901703 -0.095088733 -0.095287624 -0.095479950 -0.095658991
#> V10 -0.041524255 -0.041721285 -0.041930377 -0.042130949 -0.042317146
#> V11 -0.031233031 -0.031461093 -0.031694475 -0.031916243 -0.032121410
#> V12 -0.219159031 -0.219546198 -0.219968213 -0.220376654 -0.220756901
#> V13 -0.068999955 -0.069175702 -0.069353144 -0.069519772 -0.069673205
#> V14 0.155046438 0.155254073 0.155463289 0.155659811 0.155840798
#> V15 -0.234474765 -0.234837136 -0.235219150 -0.235585730 -0.235925977
#> V16 -0.156269225 -0.156695894 -0.157142489 -0.157568522 -0.157963067
#> V17 0.212744120 0.213076309 0.213398925 0.213698780 0.213973906
#> V18 . . . . .
#> V19 -0.020733993 -0.021058955 -0.021384306 -0.021690085 -0.021971773
#> V20 -0.179549550 -0.179643476 -0.179725329 -0.179798757 -0.179865313
#>
#> (Intercept) 0.065106425 0.065025224 0.064951166 0.064883663 0.064822149
#> V1 0.055437363 0.055495529 0.055548554 0.055596876 0.055640908
#> V2 -0.197036668 -0.197101262 -0.197160078 -0.197213655 -0.197262468
#> V3 -0.082333087 -0.082377460 -0.082417882 -0.082454711 -0.082488267
#> V4 -0.037653316 -0.037656094 -0.037658527 -0.037660711 -0.037662690
#> V5 -0.003668351 -0.003792001 -0.003904771 -0.004007558 -0.004101225
#> V6 0.034368529 0.034501809 0.034623332 0.034734088 0.034835014
#> V7 -0.235513771 -0.235747801 -0.235961212 -0.236155721 -0.236332969
#> V8 0.055630067 0.055683064 0.055731329 0.055775299 0.055815360
#> V9 -0.095823403 -0.095973636 -0.096110666 -0.096235570 -0.096349394
#> V10 -0.042487967 -0.042644004 -0.042786310 -0.042916017 -0.043034217
#> V11 -0.032309392 -0.032481021 -0.032637521 -0.032780156 -0.032910133
#> V12 -0.221106087 -0.221425164 -0.221716201 -0.221981485 -0.222223236
#> V13 -0.069813539 -0.069941584 -0.070058314 -0.070164693 -0.070261628
#> V14 0.156006347 0.156157404 0.156295113 0.156420612 0.156534971
#> V15 -0.236238083 -0.236523162 -0.236783149 -0.237020117 -0.237236060
#> V16 -0.158324687 -0.158654893 -0.158956002 -0.159230441 -0.159480526
#> V17 0.214225222 0.214454422 0.214663332 0.214853706 0.215027175
#> V18 . . . . .
#> V19 -0.022229458 -0.022464594 -0.022678954 -0.022874310 -0.023052324
#> V20 -0.179925846 -0.179980965 -0.180031175 -0.180076921 -0.180118602
#>
#> (Intercept) 6.476601e-02
#> V1 5.568103e-02
#> V2 -1.973069e-01
#> V3 -8.251884e-02
#> V4 -3.766449e-02
#> V5 -4.186574e-03
#> V6 3.492698e-02
#> V7 -2.364945e-01
#> V8 5.585186e-02
#> V9 -9.645311e-02
#> V10 -4.314192e-02
#> V11 -3.302857e-02
#> V12 -2.224435e-01
#> V13 -7.034995e-02
#> V14 1.566392e-01
#> V15 -2.374328e-01
#> V16 -1.597084e-01
#> V17 2.151852e-01
#> V18 -1.189641e-05
#> V19 -2.321472e-02
#> V20 -1.801566e-01
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 s7
#> [1,] 0.49 0.4819105 0.4788050 0.4738934 0.4555989 0.4406458 0.4304908 0.4261773
#> [2,] 0.49 0.4872150 0.4775642 0.4715513 0.4766724 0.4837869 0.4911040 0.4994662
#> [3,] 0.49 0.4797147 0.4645708 0.4502638 0.4370692 0.4263187 0.4178806 0.4123160
#> [4,] 0.49 0.4808991 0.4587667 0.4381067 0.4121825 0.3839563 0.3559619 0.3277873
#> s8 s9 s10 s11 s12 s13 s14
#> [1,] 0.4221390 0.4172827 0.4099590 0.4031818 0.3981168 0.3974918 0.3994926
#> [2,] 0.5070446 0.5140558 0.5229282 0.5311383 0.5423292 0.5509907 0.5581493
#> [3,] 0.4071663 0.4026675 0.3983174 0.3942419 0.3894296 0.3874091 0.3849482
#> [4,] 0.3026907 0.2798634 0.2566786 0.2361538 0.2158872 0.1959762 0.1776731
#> s15 s16 s17 s18 s19 s20 s21
#> [1,] 0.4012593 0.4028152 0.4037715 0.3994227 0.3954805 0.3918029 0.3883535
#> [2,] 0.5646954 0.5706874 0.5785669 0.5916876 0.6062757 0.6200350 0.6325906
#> [3,] 0.3825997 0.3803669 0.3777405 0.3714814 0.3668084 0.3626456 0.3587648
#> [4,] 0.1617419 0.1478678 0.1360576 0.1259179 0.1167120 0.1085628 0.1013807
#> s22 s23 s24 s25 s26 s27
#> [1,] 0.38570035 0.38459934 0.38353021 0.38250220 0.38151889 0.3805827
#> [2,] 0.64355523 0.65238484 0.66044867 0.66780990 0.67452850 0.6806595
#> [3,] 0.35455473 0.34920808 0.34428110 0.33974408 0.33556902 0.3317296
#> [4,] 0.09505415 0.08948472 0.08454848 0.08016834 0.07627596 0.0728120
#> s28 s29 s30 s31 s32 s33
#> [1,] 0.37971489 0.37887574 0.37808525 0.37734303 0.37664800 0.37599875
#> [2,] 0.68625033 0.69135356 0.69600867 0.70025450 0.70412666 0.70765773
#> [3,] 0.32820504 0.32496446 0.32198986 0.31926099 0.31675885 0.31446574
#> [4,] 0.06973395 0.06697857 0.06451611 0.06231271 0.06033866 0.05856801
#> s34 s35 s36 s37 s38 s39
#> [1,] 0.37539361 0.37482755 0.37428816 0.37379614 0.37334018 0.37291795
#> [2,] 0.71087748 0.71381892 0.71672165 0.71936514 0.72177439 0.72397013
#> [3,] 0.31236516 0.31044729 0.30896053 0.30761381 0.30638031 0.30525026
#> [4,] 0.05697801 0.05554585 0.05410801 0.05279869 0.05161906 0.05055613
#> s40 s41 s42 s43 s44 s45
#> [1,] 0.37253651 0.3721751 0.37184198 0.37153512 0.37125266 0.37099288
#> [2,] 0.72596445 0.7277884 0.72945068 0.73096545 0.73234582 0.73360370
#> [3,] 0.30420805 0.3032601 0.30239385 0.30160158 0.30087710 0.30021478
#> [4,] 0.04961717 0.0487521 0.04796926 0.04726155 0.04662145 0.04604217
#> s46 s47 s48 s49 s50 s51
#> [1,] 0.37075417 0.37053497 0.37033384 0.37014940 0.36998036 0.36983061
#> [2,] 0.73474994 0.73579443 0.73674620 0.73761347 0.73840373 0.73911880
#> [3,] 0.29960946 0.29905638 0.29855114 0.29808971 0.29766837 0.29728129
#> [4,] 0.04551764 0.04504245 0.04461174 0.04422118 0.04386688 0.04355803
#> s52 s53 s54
#> [1,] 0.36968822 0.36955801 0.36950332
#> [2,] 0.73977512 0.74037338 0.74073979
#> [3,] 0.29692915 0.29660880 0.29634722
#> [4,] 0.04326675 0.04300082 0.04286161
predict(fit2, type = "nonzero")
#> $s0
#> NULL
#>
#> $s1
#> [1] 6 8
#>
#> $s2
#> [1] 6 8 20
#>
#> $s3
#> [1] 2 6 8 13 19 20
#>
#> $s4
#> [1] 2 4 6 8 13 19 20
#>
#> $s5
#> [1] 2 4 6 8 13 19 20
#>
#> $s6
#> [1] 2 4 5 6 8 13 17 19 20
#>
#> $s7
#> [1] 2 4 5 6 8 13 17 19 20
#>
#> $s8
#> [1] 2 4 5 6 8 12 13 17 19 20
#>
#> $s9
#> [1] 2 4 5 6 8 12 13 17 18 19 20
#>
#> $s10
#> [1] 2 4 5 6 8 12 13 17 18 19 20
#>
#> $s11
#> [1] 2 4 5 6 8 12 13 17 18 19 20
#>
#> $s12
#> [1] 2 4 5 6 7 8 9 12 13 16 17 18 19 20
#>
#> $s13
#> [1] 2 3 4 5 6 7 8 9 12 13 16 17 18 19 20
#>
#> $s14
#> [1] 2 3 4 5 6 7 8 9 12 13 16 17 18 19 20
#>
#> $s15
#> [1] 2 3 4 5 6 7 8 9 12 13 16 17 18 19 20
#>
#> $s16
#> [1] 2 3 4 5 6 7 8 9 12 13 16 17 18 19 20
#>
#> $s17
#> [1] 2 3 4 5 6 7 8 9 10 11 12 13 16 17 18 19 20
#>
#> $s18
#> [1] 2 3 4 5 6 7 8 9 10 11 12 13 16 17 18 19 20
#>
#> $s19
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 16 17 18 19 20
#>
#> $s20
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 16 17 18 19 20
#>
#> $s21
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 16 17 18 19 20
#>
#> $s22
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s23
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s24
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s25
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s26
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s27
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s28
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s29
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s30
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s31
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s32
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s33
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s34
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
#>
#> $s35
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s36
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s37
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s38
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s39
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s40
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s41
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s42
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#>
#> $s43
#> [1] 1 2 3 4 5 6 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
#>
#> $s54
#> [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)
#> , , s=0.01
#>
#> 1 2 3 4
#> [1,] 0.17373545 0.4506411 0.135443410 0.2401801
#> [2,] 0.08457102 0.4106629 0.262389951 0.2423761
#> [3,] 0.18019408 0.2051750 0.004246777 0.6103842
#>