Error: $ operator not defined for this S4 class while running hoslem.test

Question

I'm working on an optimization of a logistic regression model made with glm, the optimization is a lasso regression using glmnet. I want to compare both models using the output of a Hosmer Lemeshow test and I get this output. For the glm I get

> hl <- hoslem.test(trainingDatos$Exited, fitted(logit.Mod))
> hl

    Hosmer and Lemeshow goodness of fit (GOF) test

data:  trainingDatos$Exited, fitted(logit.Mod)
X-squared = 2.9161, df = 8, p-value = 0.9395

And when I try to run the test for the lasso regression I get

> hll <- hoslem.test(trainingDatos$Exited, fitted(lasso.model), g=10)
Error in cut.default(yhat, breaks = qq, include.lowest = TRUE) : 
  'x' must be numeric

I also tried to use the coefficients of the lasso regression to make it numeric and I get

> hll <- hoslem.test(trainingDatos$Exited, fitted(lasso.model$beta), g=10)
Error: $ operator not defined for this S4 class

But when I treat it as an S4

> hll <- hoslem.test(trainingDatos$Exited, fitted(lasso.model@beta), g=10)
Error in fitted(lasso.model@beta) : 
  trying to get slot "beta" from an object (class "lognet") that is not an S4 object

Any way to run the test for my lasso regression? Here is my full code for the lasso regression, can't share the database right now sorry

    #Creation of Training Data Set
input_ones <- Datos[which(Datos$Exited == 1), ] #All 1s
input_zeros <- Datos[which(Datos$Exited == 0), ] #All 0s
set.seed(100) 
#Training 1s
input_ones_training_rows <- sample(1:nrow(input_ones), 0.7*nrow(input_ones)) 
#Training 0s
input_zeros_training_rows <- sample(1:nrow(input_zeros), 0.7*nrow(input_ones)) 
training_ones <- input_ones[input_ones_training_rows, ]
training_zeros <- input_zeros[input_zeros_training_rows, ]
trainingDatos <- rbind(training_ones, training_zeros) 
library(glmnet)
#Conversion of training data into matrix form
x <- model.matrix(Exited ~ CreditScore + Geography + Gender
                  + Age + Tenure + Balance + IsActiveMember
                  + EstimatedSalary, trainingDatos)[,-1]
#Defining numeric response variable
y <- trainingDatos$Exited
sed.seed(100) 
#Grid search to find best lambda
cv.lasso<-cv.glmnet(x, y, alpha = 1, family = "binomial")
#Creation of the model
lasso.model <- glmnet(x, y, alpha = 1, family = "binomial", 
                      lambda = cv.lasso$lambda.1se)
coef(cv.lasso, cv.lasso$lambda.1se)
#Now trying to run the test
library(ResourceSelection)
set.seed(12657)
hll <- hoslem.test(trainingDatos$Exited, fitted(lasso.model), g=10)#numeric value error
hll <- hoslem.test(trainingDatos$Exited, fitted(lasso.model$beta), g=10)#$ not defined for S4
hll <- hoslem.test(trainingDatos$Exited, fitted(lasso.model@beta), g=10)#saying that beta is nos S4

Answer 1

glmnet uses a unique predict() method for obtaining fitted values. As rightly mentioned, the errors came from using fitted(). Meanwhile, running such tests could be easier with the gofcat package. Supported objects are passed directly to the functions. Your glm model, for instance, goes hosmerlem(logit.Mod).