Caret train function for muliple data frames as function
Solution 1:
By writing predictor_iris <- "Species"
, you are basically saving a string object in predictor_iris
. Thus, when you run lda_ex
, I guess you incur in some error concerning the formula
object in train()
, since you are trying to predict a string using vectors of covariates.
Indeed, I tried the following toy example:
X = rnorm(1000)
Y = runif(1000)
predictor = "Y"
lm(predictor ~ X)
which gives an error about differences in the lengths of variables.
Let me modify your function:
lda_ex <- function(data, formula){
model <- train(formula, data,
method = "lda",
trControl = trainControl(method = "none"),
preProc = c("center","scale"))
return(model)
}
The key difference is that now we must pass in the whole formula
, instead of the predictor only. In that way, we avoid the string-related problem.
library(caret) # Recall to specify the packages needed to reproduce your examples!
data_iris <- iris
formula_iris = Species ~ . # Key difference!
iris_res <- lda_ex(data = data_iris, formula = formula_iris)