CODEWITHSUNDEEP
rglmnetlasso-regressiondismo
erz
erz

Reputation: 1

Can dismo::evaluate() be used for a model fit with glmnet() or cv.glmnet()?

I'm using the glmnet package to create a species distribution model (SDM) based on a lasso regression. I've succesfully fit models using glmnet::cv.glmnet(), and I can use the predict() function to generate predicted probabilities for a given lambda value by setting s = lambda.min and type = "response".

I'm creating several different kinds of SDMs and had been using dismo::evaluate() to generate fit statistics (based on a testing dataset) and thresholds to convert probabilities to binary values. However, when I run dismo::evaluate() with a cv.glmnet (or glmnet) model, I get the following error:

Error in h(simpleError(msg, call)) : error in evaluating the argument 'x' in selecting a method for function 'as.matrix': not-yet-implemented method for <data.frame> %*%

This is confusing to me as I think the x argument in evaluate() isn't needed when I'm providing a matrix with predictor values at presence locations (p) and another matrix with values at absence locations (a). I'm wondering whether evaluate() doesn't work with these types of models? Thanks, and apologies if I've missed something obvious!

Upvotes: 0

Views: 55

Answers (1)

erz
erz

Reputation: 1

After spending more time on this, I don't think dismo::evaluate() works with glmnet objects when supplying "p" and "a" as matrices of predictor values. dismo::evaluate() converts them to data.frames before calling the predict() function. To solve my problem, I was able to create a new function based on dismo::evaluate() that supplies p or a as a matrix to the predict() function.

Upvotes: 0

Related Questions