Dear All,
I am not going to discuss in detail the implementation of a model, but I
am rather puzzled because, even if I manage to train a model, then I do
not succeed in applying it to some test data.
I am sure I am making some trivial mistake, but so far I have been banging
my head against the floor.
When I run the following code
###############################################
library(glmnet)
test <- read.csv("test.csv", header=TRUE)
train <- read.csv("train.csv", header=TRUE)
for (i in seq(dim(train)[2])){
train[[i]] <- as.factor(train[[i]])
}
for (i in seq(dim(test)[2])){
test[[i]] <- as.factor(test[[i]])
}
X = sparse.model.matrix(as.formula(paste("ACTION ~",
paste(colnames(train[,-1]),
sep = "", collapse=" +"))), data = train)
model = cv.glmnet(X, train[,1], family = "binomial")
print("glmnet model completed")
predict(model,newx=test[,2:10], s="lambda.min")
######################################################
I get the error
Error in as.matrix(cbind2(1, newx) %*% nbeta) :
error in evaluating the argument 'x' in selecting a method for
function
'as.matrix': Error in cbind2(1, newx) %*% nbeta :
not-yet-implemented method for <data.frame> %*% <dgCMatrix>
However, even converting test to a matrix does not help.
Essentially, I am trying to train a linear model to deal with a set of
predictors that consist only of categorical variables.
The train and test datasets can be downloaded from
http://db.tt/23DzlIt3
http://db.tt/3GIcNpSE
Any suggestion is welcome.
Cheers
Lorenzo
