This is simply a wrapper for predict.sgdnet() with type = "coefficients".

# S3 method for sgdnet
coef(object, ...)

Arguments

object

a model of class 'sgdnet', typically from a call to sgdnet().

...

passed on to predict.sgdnet()

Value

A sparse matrix with intercept in the first row and betas in the rest.

See also

Examples

fit <- sgdnet(matrix(rnorm(100), 50, 2), rnorm(50)) coef(fit)
#> 3 x 100 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 100 column names ‘s0’, ‘s1’, ‘s2’ ... ]]
#> #> (Intercept) 0.02453368 0.024068506 0.023733143 0.02328706 0.02291342 #> V1 . -0.004709041 -0.009027845 -0.01305898 -0.01666998 #> V2 . . . . . #> #> (Intercept) 0.02266761 0.02241577 0.02210770 0.02182443 0.02164676 #> V1 -0.01992895 -0.02295983 -0.02566424 -0.02811193 -0.03039725 #> V2 . . . . . #> #> (Intercept) 0.02157120 0.02126566 0.02132656 0.02092764 0.02084428 #> V1 -0.03231923 -0.03433885 -0.03594343 -0.03765516 -0.03901878 #> V2 . . . . . #> #> (Intercept) 0.02059249 0.02058808 0.0202328446 0.019697967 0.019163617 #> V1 -0.04030840 -0.04147442 -0.0429259739 -0.044566402 -0.046019331 #> V2 . . 0.0009190186 0.002674266 0.004223642 #> #> (Intercept) 0.01865670 0.01831044 0.017916418 0.01748736 0.01724966 #> V1 -0.04737266 -0.04857374 -0.049810352 -0.05075367 -0.05170107 #> V2 0.00566842 0.00692855 0.008168374 0.00926951 0.01019633 #> #> (Intercept) 0.01682454 0.01670599 0.01644116 0.01614995 0.01599362 #> V1 -0.05258144 -0.05324176 -0.05403744 -0.05461156 -0.05525803 #> V2 0.01121322 0.01188970 0.01270662 0.01342121 0.01398338 #> #> (Intercept) 0.01577439 0.01560199 0.01543887 0.01535457 0.01524653 #> V1 -0.05573996 -0.05621593 -0.05670536 -0.05707308 -0.05740215 #> V2 0.01451912 0.01501050 0.01555352 0.01596216 0.01630936 #> #> (Intercept) 0.01505398 0.01501152 0.01487099 0.01476569 0.01467383 #> V1 -0.05778235 -0.05808414 -0.05839597 -0.05866189 -0.05889967 #> V2 0.01669230 0.01693754 0.01734385 0.01760146 0.01782491 #> #> (Intercept) 0.01466247 0.01457021 0.01447934 0.01447829 0.01441418 #> V1 -0.05909194 -0.05924139 -0.05940993 -0.05956547 -0.05973555 #> V2 0.01803731 0.01824516 0.01845970 0.01856413 0.01872576 #> #> (Intercept) 0.01440215 0.01431663 0.01430803 0.01424788 0.01421991 #> V1 -0.05984823 -0.05996429 -0.06010485 -0.06018299 -0.06031075 #> V2 0.01887296 0.01904183 0.01911864 0.01924778 0.01936695 #> #> (Intercept) 0.01419993 0.01417551 0.01415129 0.01414071 0.01411355 #> V1 -0.06037211 -0.06047024 -0.06052691 -0.06061691 -0.06063582 #> V2 0.01941639 0.01950727 0.01958560 0.01964936 0.01971197 #> #> (Intercept) 0.01407289 0.01405817 0.01405033 0.01404878 0.01402143 #> V1 -0.06070491 -0.06073792 -0.06076882 -0.06080526 -0.06085236 #> V2 0.01978252 0.01984688 0.01985636 0.01990956 0.01994907 #> #> (Intercept) 0.01400416 0.01400869 0.01400647 0.01400041 0.01397698 #> V1 -0.06090717 -0.06092069 -0.06096075 -0.06095496 -0.06098622 #> V2 0.01996975 0.02000005 0.02004674 0.02006997 0.02008541 #> #> (Intercept) 0.01396539 0.01395222 0.01395750 0.01396623 0.01395277 #> V1 -0.06101674 -0.06103360 -0.06105267 -0.06107276 -0.06109401 #> V2 0.02010981 0.02014128 0.02015784 0.02017163 0.02018892 #> #> (Intercept) 0.01393396 0.01393342 0.01393813 0.01393659 0.01392901 #> V1 -0.06110215 -0.06110832 -0.06111833 -0.06112423 -0.06113926 #> V2 0.02020452 0.02020727 0.02022349 0.02023585 0.02024350 #> #> (Intercept) 0.01392795 0.01392528 0.01391826 0.01391922 0.01391753 #> V1 -0.06114500 -0.06115160 -0.06115885 -0.06116579 -0.06117439 #> V2 0.02025848 0.02026371 0.02027386 0.02027758 0.02028243 #> #> (Intercept) 0.01391575 0.01391590 0.01391353 0.01390838 0.01390862 #> V1 -0.06118108 -0.06118349 -0.06118622 -0.06119154 -0.06119514 #> V2 0.02029084 0.02029257 0.02029781 0.02030572 0.02030709 #> #> (Intercept) 0.01390959 0.01390724 0.01390620 0.01390650 0.01390497 #> V1 -0.06119879 -0.06120131 -0.06120524 -0.06120843 -0.06120862 #> V2 0.02031118 0.02031479 0.02031809 0.02032110 0.02032114 #> #> (Intercept) 0.01390238 0.01390141 0.01390225 0.01390215 0.01390130 #> V1 -0.06121124 -0.06121360 -0.06121602 -0.06121739 -0.06121751 #> V2 0.02032398 0.02032771 0.02032803 0.02032901 0.02033140 #> #> (Intercept) 0.01390137 0.01390046 0.01389967 0.01389984 0.01389981 #> V1 -0.06121929 -0.06122095 -0.06122194 -0.06122309 -0.06122342 #> V2 0.02033221 0.02033343 0.02033501 0.02033552 0.02033660