Reputation: 8044
Why there is no possibility to pass only 1 explanatory variable to model in glmnet
function from glmnet
package when it is possible in glm
function from base?
Code and error are below:
> modelX<-glm( ifelse(train$cliks <1,0,1)~(sparseYY[,40]), family="binomial")
> summary(modelX)
Call:
glm(formula = ifelse(train$cliks < 1, 0, 1) ~ (sparseYY[, 40]),
family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.2076 -0.2076 -0.2076 -0.2076 2.8641
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.82627 0.00823 -464.896 <2e-16 ***
sparseYY[, 40] -0.25844 0.15962 -1.619 0.105
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 146326 on 709677 degrees of freedom
Residual deviance: 146323 on 709676 degrees of freedom
AIC: 146327
Number of Fisher Scoring iterations: 6
> modelY<-glmnet( y =ifelse(train$cliks <1,0,1), x =(sparseYY[,40]), family="binomial" )
Błąd wif (is.null(np) | (np[2] <= 1)) stop("x should be a matrix with 2 or more columns")
Upvotes: 7
Views: 6655
Reputation: 2522
Here is an answer I got to this question from the maintainer of the package (Trevor Hastie):
glmnet is designed to select variables from a (large) collection. Allowing for 1 variable would have created a lot of edge case programming, and I was not interested in doing that. Sorry!
Upvotes: 12
Reputation: 6659
I don't know why, but it's some kind of internal limitation. It does not have to do with the family as Roman claimed above.
glmnet(x = as.matrix(iris[2:4]), y = as.matrix(iris[1]))
## long output
glmnet(x = as.matrix(iris[1]), y = as.matrix(iris[1]))
Error in glmnet(x = as.matrix(iris[2]), y = as.matrix(iris[1])) :
x should be a matrix with 2 or more columns
It's a simple check in the code https://github.com/cran/glmnet/blob/master/R/glmnet.R#L20
Upvotes: 1
Reputation: 70633
Because the documentation says so.
For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class).
You have two options. Either construct a matrix where two columns represent counts, or, convert x
into a factor with two levels.
Upvotes: -2