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This is in reference to https://stats.stackexchange.com/questions/72251/an-example-lasso-regression-using-glmnet-for-binary-outcome I am trying to use the Cross Validation in GLMNET (i.e. cv.glmnet) for a binomial target variable. The glmnet works fine but the cv.glmnet throws an error here is the error log:
Error in storage.mode(y) = "double" : invalid to change the storage mode of a factor
In addition: Warning messages:
1: In Ops.factor(x, w) : ‘*’ not meaningful for factors
2: In Ops.factor(y, ybar) : ‘-’ not meaningful for factors
Data Types:
'data.frame': 490 obs. of 13 variables:
$ loan_id : Factor w/ 614 levels "LP001002","LP001003",..: 190 381 259 310 432 156 179 24 429 408 ...
$ gender : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 1 ...
$ married : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 2 2 2 2 1 ...
$ dependents : Factor w/ 4 levels "0","1","2","3+": 1 1 1 3 1 4 2 3 1 1 ...
$ education : Factor w/ 2 levels "Graduate","Not Graduate": 1 1 1 2 1 1 1 2 1 2 ...
$ self_employed : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ applicantincome : int 9328 3333 14683 7667 6500 39999 3750 3365 2920 2213 ...
$ coapplicantincome: num 0 2500 2100 0 0 ...
$ loanamount : int 188 128 304 185 105 600 116 112 87 66 ...
$ loan_amount_term : Factor w/ 10 levels "12","36","60",..: 6 9 9 9 9 6 9 9 9 9 ...
$ credit_history : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
$ property_area : Factor w/ 3 levels "Rural","Semiurban",..: 1 2 1 1 1 2 2 1 1 1 ...
$ loan_status : Factor w/ 2 levels "0","1": 2 2 1 2 1 2 2 1 2 2 ...
Codes Used:
xfactors<-model.matrix(loan_status ~ gender+married+dependents+education+self_employed+loan_amount_term+credit_history+property_area,data=data_train)[,-1]
x<-as.matrix(data.frame(applicantincome,coapplicantincome,loanamount,xfactors))
glmmod<-glmnet(x,y=as.factor(loan_status),alpha=1,family='binomial')
plot(glmmod,xvar="lambda")
grid()
cv.glmmod <- cv.glmnet(x,y=loan_status,alpha=1) #This Is Where It Throws The Error
861
cv. glmnet() performs cross-validation, by default 10-fold which can be adjusted using nfolds. A 10-fold CV will randomly divide your observations into 10 non-overlapping groups/folds of approx equal size. The first fold will be used for validation set and the model is fit on 9 folds.
glmnet .) alpha is for the elastic net mixing parameter α, with range α∈[0,1]. α=1 is lasso regression (default) and α=0 is ridge regression.
By default glmnet chooses the lambda. 1se . It is the largest λ at which the MSE is within one standard error of the minimal MSE. Along the lines of overfitting, this usually reduces overfitting by selecting a simpler model (less non zero terms) but whose error is still close to the model with the least error.
The credit for the answer goes to @user20650.
Suspect you need to add the familyto cv.glmnet as well. An example:
x <- model.matrix(am ~ 0 + . , data=mtcars)
cv.glmnet(x, y=factor(mtcars$am), alpha=1)
cv.glmnet(x, y=factor(mtcars$am), alpha=1, family="binomial")
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