Last updated: 2018-05-21
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library(glmnet)
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16
set.seed(1)
p = 100
n = 500
X = matrix(rnorm(n*p),ncol=p)
b = rnorm(p)
e = rnorm(n,0,sd=25)
Y = X %*% b + e
Now fit ols, ridge regression and lasso, and see some basic plots.
Y.ols = lm(Y~X)
Y.ridge = glmnet(X,Y,alpha=0)
plot(Y.ridge)
Y.lasso = glmnet(X,Y,alpha=1)
plot(Y.lasso)
The library also allows you to run cross-validation easily:
cv.ridge = cv.glmnet(X,Y,alpha=0)
plot(cv.ridge)
cv.lasso = cv.glmnet(X,Y,alpha=1)
plot(cv.lasso)
Extract coefficients from best cv fits.
b.ridge = predict(Y.ridge, type="coefficients", s = cv.ridge$lambda.min)
b.lasso = predict(Y.lasso, type="coefficients", s = cv.lasso$lambda.min)
b.ols = Y.ols$coefficients
Note that the fits include an intercept (unregularized, equal to the mean of Y).
length(b.lasso)
[1] 101
b.lasso[1]
[1] -1.38244
mean(Y)
[1] -1.233957
Compare the estimated coefficients with the truth:
btrue = c(0,b) # Here the 0 is the intercept (true value 0)
sum((btrue-0)^2) # This is error if we just estimate 0 for everything and ignore data. It is better than OLS!
[1] 85.30411
sum((btrue-Y.ols$coefficients)^2)
[1] 138.2715
sum((btrue-b.ridge)^2)
[1] 56.99797
sum((btrue-b.lasso)^2)
[1] 68.7212
sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X El Capitan 10.11.6
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] glmnet_2.0-16 foreach_1.4.4 Matrix_1.2-14
loaded via a namespace (and not attached):
[1] workflowr_1.0.1 Rcpp_0.12.16 codetools_0.2-15
[4] lattice_0.20-35 digest_0.6.15 rprojroot_1.3-2
[7] R.methodsS3_1.7.1 grid_3.3.2 backports_1.1.2
[10] git2r_0.21.0 magrittr_1.5 evaluate_0.10.1
[13] stringi_1.1.7 whisker_0.3-2 R.oo_1.22.0
[16] R.utils_2.6.0 rmarkdown_1.9 iterators_1.0.9
[19] tools_3.3.2 stringr_1.3.0 yaml_2.1.18
[22] htmltools_0.3.6 knitr_1.20
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