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#' Predict method for bivRegr
#'
#' Obtains predictions for a bivRegr object, that is the bivRegr means,
#' variances, and correlation for given covariate values.
#'
#' @param object A bivRegr fit.
#' @param newdata A data frame defining the covariate values for prediction. Default
#' is NULL and the prediction will be done in the original data.
#' @param ... Additional predict options.
#' @return This function returns prediction of bivRegr mean, variance and correlation models.
#' @export
predict.bivRegr <- function(object, newdata = NULL, ...) {
vars <- unique(c(
names(object$mu1$model)[-1], names(object$mu2$model)[-1],
names(object$var1$model)[-1], names(object$var2$model)[-1],
names(object$rho$model)[-1]
))
vars <- vars[-which(vars == "(weights)")]
hat_mu1 <- if (is.null(newdata)) {
predict(object$mu1)
} else {
predict(object$mu1, newdata = newdata)
}
hat_mu2 <- if (is.null(newdata)) {
predict(object$mu2)
} else {
predict(object$mu2, newdata = newdata)
}
hat_sd1 <- if (is.null(newdata)) {
sqrt(exp(predict(object$var1)))
} else {
sqrt(exp(predict(object$var1, newdata = newdata)))
}
hat_sd2 <- if (is.null(newdata)) {
sqrt(exp(predict(object$var2)))
} else {
sqrt(exp(predict(object$var2, newdata = newdata)))
}
if (is.null(newdata)) {
R <- predict(object$rho)
R[R > 1.8] <- 1.8
R[R < (-1.8)] <- -1.8
R <- tanh(R)
} else {
R <- predict(object$rho, newdata = newdata)
R[R > 1.8] <- 1.8
R[R < (-1.8)] <- -1.8
R <- tanh(R)
}
hat_rho <- R
if (is.null(newdata)) {
`Predicted Parameters` <- as.data.frame(cbind(object$data[, vars], hat_mu1, hat_sd1, hat_mu2, hat_sd2, hat_rho))
} else {
`Predicted Parameters` <- as.data.frame(cbind(newdata, hat_mu1, hat_sd1, hat_mu2, hat_sd2, hat_rho))
}
if (is.null(newdata)) {
l <- dim(object$data[, vars])[2]
} else {
l <- dim(newdata)[2]
}
names(`Predicted Parameters`)[l + c(1:5)] <- c(
paste0("Mean ", names(object$mu1$model)[1]),
paste0("Std.dev. ", names(object$mu1$model)[1]), paste0("Mean ", names(object$mu2$model)[1]),
paste0("Std.dev. ", names(object$mu2$model)[1]),
paste0("Corr. (", names(object$mu1$model)[1], "-", names(object$mu2$model)[1], ")")
)
return(`Predicted Parameters`)
}
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