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## File Name: mdmb_regression_R2.R
## File Version: 0.29
#**** explained variance
mdmb_regression_R2 <- function( linear.predictor, y, type, beta, index_sigma,
index_lambda, probit=FALSE )
{
R2 <- NULL
np <- length(beta)
var_y_pred <- stats::var(linear.predictor)
#***** logistic regression
# R2 form McKelvey and Zavoina
if (type=="logistic"){
var_resid <- 3.141593^2 / 3
}
if (type=="oprobit"){
var_resid <- 1
}
#***** yjt regression
if (type %in% c("yjt","bct") ){
sigma <- beta["sigma"]
lambda <- beta["lambda"]
if (type=="yjt"){
yt <- yj_trafo( y=y, lambda=lambda, probit=probit )
}
if (type=="bct"){
yt <- bc_trafo( y=y, lambda=lambda )
}
var_y <- stats::var(yt)
var_resid <- var_y - var_y_pred
}
#*** R2 formula
R2 <- var_y_pred / ( var_y_pred + var_resid )
#--- output
return(R2)
}
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