To be specific, this list is about using R for teaching. Appropriate content might include things like sharing R tutorials for beginners, ideas for assignments or grading policies, educational simulations or demos using R, etc. How-To questions about using R should go to r-help. The bulk of posts here are not suitable for this list and I wonder how people end up here. When a person joins, do they get an initial email discribing what is appropriate content for this list, with suggestions of where to go for other sorts of questions? This might reduce the traffic in inappropriate questions, and notifications of same, and help those seeking help get to a place where they might actually get it;-) ----- Forwarded message from Albyn Jones <jones at reed.edu> ----- Date: Sat, 28 Jul 2018 22:12:31 -0700 From: Albyn Jones <jones at reed.edu> To: Baojun Sun <bsun1 at students.towson.edu> Cc: R-sig-teaching <r-sig-teaching at r-project.org> Subject: Re: [R-sig-teaching] (no subject) Is this a question about using R to teach statistical learning, or a question for r-help, or homework? On Sat, Jul 28, 2018 at 7:46 AM, Baojun Sun <bsun1 at students.towson.edu> wrote:
The book "Introduction to Statistical Learning" gives R scripts for its
labs. I found a script for ridge regression that works on the dataset the
book uses but is unusable on other datasets I own unless I clean the data.
I'm trying to understand the syntax for I need for data cleaning and am
stuck. I want to learn to do ridge regression. I tried using my own data
set on this script rather than the book example but get errors. If you use
your own data set rather than the Hitters dataset, then you'll get errors
unless you format your code. How do I change this script or clean any
dataset so that this script for ridge regression useable for all datasets?
library(ISLR)
fix(Hitters)
names(Hitters)
dim(Hitters)
sum(is.na(Hitters$Salary))
Hitters=na.omit(Hitters)
dim(Hitters)
sum(is.na(Hitters))
library(leaps)
x=model.matrix(Salary~.,Hitters)[,-1]
y=Hitters$Salary
# Ridge Regression
library(glmnet)
grid=10^seq(10,-2,length=100)
ridge.mod=glmnet(x,y,alpha=0,lambda=grid)
dim(coef(ridge.mod))
ridge.mod$lambda[50]
coef(ridge.mod)[,50]
sqrt(sum(coef(ridge.mod)[-1,50]^2))
ridge.mod$lambda[60]
coef(ridge.mod)[,60]
sqrt(sum(coef(ridge.mod)[-1,60]^2))
predict(ridge.mod,s=50,type="coefficients")[1:20,]
set.seed(1)
train=sample(1:nrow(x), nrow(x)/2)
test=(-train)
y.test=y[test]
ridge.mod=glmnet(x[train,],y[train],alpha=0,lambda=grid, thresh=1e-12)
ridge.pred=predict(ridge.mod,s=4,newx=x[test,])
mean((ridge.pred-y.test)^2)
mean((mean(y[train])-y.test)^2)
ridge.pred=predict(ridge.mod,s=1e10,newx=x[test,])
mean((ridge.pred-y.test)^2)
ridge.pred=predict(ridge.mod,s=0,newx=x[test,],exact=T)
mean((ridge.pred-y.test)^2)
lm(y~x, subset=train)
predict(ridge.mod,s=0,exact=T,type="coefficients")[1:20,]
set.seed(1)
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,])
mean((ridge.pred-y.test)^2)
out=glmnet(x,y,alpha=0)
predict(out,type="coefficients",s=bestlam)[1:20,]
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