Logistic Ridge Regression predict ROC/ AUC and accuracy testing - r

Question

I am trying to fit Logistic Ridge Regression and developed the model as follows; I need help with the coding for testing it for accuracy and ROC/AUC curve with threshold value.
My coding is as follows:
Fitting the model
library(glmnet)
library(caret)
data1<-read.csv("D:\\Research\\Final2.csv",header=T,sep=",")
str(data1)
'data.frame': 154 obs. of 12 variables:
$ Earningspershare : num 12 2.69 8.18 -0.91 3.04 ...
$ NetAssetsPerShare: num 167.1 17.2 41.1 14.2 33 ...
$ Dividendpershare : num 3 1.5 1.5 0 1.25 0 0 0 0 0.5 ...
$ PE : num 7.35 8.85 6.66 -5.27 18.49 ...
$ PB : num 0.53 1.38 1.33 0.34 1.7 0.23 0.5 3.1 0.5 0.3 ...
$ ROE : num 0.08 0.16 0.27 -0.06 0.09 -0.06 -0.06 0.15 0.09 0.
$ ROA : num 0.02 0.09 0.14 -0.03 0.05 -0.04 -0.05 0.09 0.03 0
$ Log_MV : num 8.65 10.38 9.81 8.3 10.36 ..
$ Return_yearly : int 0 1 0 0 0 0 0 0 0 0 ...
$ L3 : int 0 0 0 0 0 0 0 0 0 0 ...
$ L6 : int 0 0 0 0 0 0 0 0 0 0 ...
$ Sector : int 2 2 2 2 2 1 2 2 4 1 ...
smp_size <- floor(0.8 * nrow(data1))
set.seed(123)
train_ind <- sample(seq_len(nrow(data1)), size = smp_size)
train <- data1[train_ind, ]
test <- data1[-train_ind, ]
train$Return_yearly <-as.factor(train$Return_yearly)
train$L3 <-as.factor(train$L3)
train$L6 <-as.factor(train$L6)
train$Sector <-as.factor(train$Sector)
train$L3 <-model.matrix( ~ L3 - 1, data=train)
train$L6 <-model.matrix( ~ L6 - 1, data=train)
train$Sector<-model.matrix( ~ Sector - 1, data=train)
x <- model.matrix(Return_yearly ~., train)
y <- train$Return_yearly
ridge.mod <- glmnet(x, y=as.factor(train$Return_yearly), family='binomial', alpha=0, nlambda=100, lambda.min.ratio=0.0001)
set.seed(1)
cv.out <- cv.glmnet(x, y=as.factor(train$Return_yearly), family='binomial', alpha=0, nfolds = 5, type.measure = "auc", nlambda=100, lambda.min.ratio=0.0001)
plot(cv.out)
best.lambda <- cv.out$lambda.min
best.lambda
[1] 5.109392
Testing the model
test$L3 <-as.factor(test$L3)
test$L6 <-as.factor(test$L6)
test$Sector <-as.factor(test$Sector)
test$Return_yearly <-as.factor(test$Return_yearly)
test$L3 <-model.matrix( ~ L3 - 1, data=test)
test$L6 <-model.matrix( ~ L6 - 1, data=test)
test$Sector<-model.matrix( ~ Sector - 1, data=test)
newx <- model.matrix(Return_yearly ~., test)
y.pred <- as.matrix(ridge.mod,newx=newx, type="class",data=test)
comparing for accuracy testing; error pops up, unable to continue
compare <- cbind (actual=test$Return_yearly, y.pred)
Warning message:
In cbind(actual = test$Return_yearly, y.pred) :
number of rows of result is not a multiple of vector length (arg 1)

Answer 1

Without a reproducible dataset here's a guess:
The train and test matrices have different columns as the result of converting L3 and L6 to factors. By default, as.factor() creates as many levels in a factor as there are unique values, so if by chance the train/test split has different unique values of L3 or L6, the number of dummy variables created by model.matrix() will be different as well.
Possible solution: do as.factor() before train/test split, or supply as.factor with the complete levels, like
train$L3 <- as.factor(train$L3, levels = unique(data1$L3))

Answer 2

Use the following code to plot the accuracy and sensitivity.
ROC_Pre <- prediction(ROC_Pre, data$LSD)
ROC <- performance(ROC_Pre, "tpr", "fpr")
plot(ROC)