In R, I would like to test the specification of a partial least square (PLS) model m1 against a non-nested alternative m2, applying the Davidson-MacKinnon J test. For a simple linear outcome Y it works quite well using the plsr estimator followed by the jtest command:
# Libraries and data
library(plsr)
library(plsRglm)
library(lmtest)
Z <- Cornell # illustration dataset coming with the plsrglm package
# Simple linear model
m1 <- plsr(Z$Y ~ Z$X1 + Z$X2 + Z$X3 + Z$X4 + Z$X5 ,2) # including X1
m2 <- plsr(Z$Y ~ Z$X6 + Z$X2 + Z$X3 + Z$X4 + Z$X5 ,2) # including X6 as alternative
jtest(m1,m2)
However, if Iuse the generalized linear model (plsRglm) estimator to account for a possible nonlinear distibution of an outcome, e.g.:
# Generalized Model
m1 <- plsRglm(Z$Y ~ Z$X1 + Z$X2 + Z$X3 + Z$X4 + Z$X5 ,2, modele = "pls-glm-family", family=Gamma(link = "log"), pvals.expli=TRUE)
m2 <- plsRglm(Z$Y ~ Z$X6 + Z$X2 + Z$X3 + Z$X4 + Z$X5 ,2, modele = "pls-glm-family", family=Gamma(link = "log"), pvals.expli=TRUE)
I am running into an error when using jtest:
> jtest(m1,m2)
Error in terms.default(formula1) : no terms component nor attribute
>
It seems that plsRglm does not save objects of class "formula", that jtest can handle. Has anybody a suggestion of how to edit my code to get this to work?
Thanks!
