Northeastern University R Regularization Glmnet Model and Lasso Project Build regularization models by using Lasso(least absolute shrinkage and selection o

Northeastern University R Regularization Glmnet Model and Lasso Project Build regularization models by using Lasso(least absolute shrinkage and selection operator) .Extend Lasso model fitting to big data that cannot be loaded into memory. You will fit solution paths for linear or logistic regression models penalized by Lasso over a grid of values for the regularization parameter lambda.I attached an exercise, you can follow a similar format. It is easy. I also posted some code I have done so far, and after the line 9 there has shows error, your welcome to correct me. Thank you so much to work for it. install.packages(‘ncvreg’)
library(ncvreg)
install.packages(‘bigmemory’)
library(bigmemory)
install.packages(‘biglasso’)
library(biglasso)
install.packages(‘lars’)
library(lars)
install.packages(‘glmnet’)
library(glmnet)
data(diabetes)
View(diabetes)
summary(x)
par(mfrow=c(2,5))
for(i in 1:10){
plot(x[,i], y)
abline(lm(y~x[,i]))
}
ols
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