Error message in R:
Error in cv.glmnet(x[train, ], y[train], alpha = 0) :
could not find function "cv.glmnet"
Calls: ... handle -> withCallingHandlers -> withVisible -> eval -> eval
In addition: Warning messages:
1: package 'knitr' was built under R version 3.6.3
2: package 'ISLR' was built under R version 3.6.3
3: In block_exec(params) :
Failed to tidy R code in chunk 'unnamed-chunk-2'. Reason:
Error in loadNamespace(name) : there is no package called 'formatR'
4: In block_exec(params) :
Failed to tidy R code in chunk 'unnamed-chunk-3'. Reason:
Error in loadNamespace(name) : there is no package called 'formatR'
5: In block_exec(params) :
Failed to tidy R code in chunk 'unnamed-chunk-4'. Reason:
Error in loadNamespace(name) : there is no package called 'formatR'
Execution halted
title: "HW3"
author:
- Me
header-includes:
- \usepackage{bbm}
- \usepackage{amssymb}
- \usepackage{amsmath}
- \usepackage{graphicx}
- \usepackage{natbib}
- \usepackage{float}
- \floatplacement{figure}{H}
output:
pdf_document: default
fontsize: 11pt
library(knitr); library(ISLR)
opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE)
1. Exercise 9(a), 9(b), 9(c), 9(d) and 9(g) in Section 6.8 of the Text. Please run set.seed(1) before you run any other codes, so that your results are reproducible for grading. Please answer 9(g) with respect to the results obtained in 9(a), 9(b), 9(c) and 9(d). The College data set is in the library ISLR
set.seed(1)
data(College)
n = 1:nrow(College)
train = sample(n, 777*.7)
index = n[-train]
test = sample(index, 777*.3)
train_set = College[train, ]
test_set = College[test, ]
summary(College)
lm.fit = lm(Apps~., data = train_set)
p = predict(lm.fit, test_set)
MSE = mean((p - test_set$Apps)^2)
MSE
x=model.matrix(Apps~.,College)[,-1]
y=College$Apps
set.seed(1)
train=sample(1:nrow(x), nrow(x)/2)
test=(-train)
y.test=y[test]
cv.out=cv.glmnet(x[train,],y[train],alpha=0)
plot(cv.out)
bestlam=cv.out$lambda.min
bestlam
ridge.pred=predict(ridge.mod,s=bestlam,newx=x[test,])
MSE = mean((ridge.pred-y.test)^2)
out=glmnet(x,y,alpha=0)
predict(out,type="coefficients",s=bestlam)[1:18,]
MSE
lasso.mod=glmnet(x[train,],y[train],alpha=1,lambda=grid)
plot(lasso.mod)
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=1)
plot(cv.out)
bestlam=cv.out$lambda.min
lasso.pred=predict(lasso.mod,s=bestlam,newx=x[test,])
MSE = mean((lasso.pred-y.test)^2)
MSE
out=glmnet(x,y,alpha=1,lambda=grid)
lasso.coef=predict(out,type="coefficients",s=bestlam)[1:18,]
lasso.coef
lasso.coef[lasso.coef!=0]
for this to work. No need to call