#' Conditional mean for Beta Prime distribution
#'
#' betapr_flexpredict returns the conditional mean E(Y|X) of a model fitted via the function betapr_flexfit; where α has been specified to be a function of covariates the required value should be specified using the ‘features’ parameter. betapr_flexpredict also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.
#'@param model An object of class "mle2" produced using the function betapr_flexfit.
#'@param features A numeric vector specifying the value of covriates at which the conditional mean should be evaluated; the covariates in the vector should appear in the same order as they do in the model. Where a model does not depend on covariates the argument may be left blank.
#'@param draws The number of random draws from multivariate random normal representing correlated parameters. If parameter correlation is not required draws should be set to zero.
#'@details This function uses the two parameter parametrization of the Beta Prime distribution is used in Johnson and Kotz (1995). The tow parameter distribution ins a special case of the three parameter distribution, with σ = 1. The probability probability density function is used is:
#'@details f(y) = [y^α-1 (1+y)^-(α+β)]/Β(α,β)
#'@details The function returns:
#'@details E(Y|X) = α/(β-1)
#'@details α may be a function of covariates; in which case, the cannonical log link function is used.
#'@references Johnson, N.L., Kotz, S., Balakrishnan, N. (1995). Continuous Univariate Distributions, Volume 2 (2nd Edition), Wiley.
betapr_flexpredict <- function(model, features, draws = 5) {
mod <- as.data.frame(tidy(model))
#================================================================#
# linear predictor has intercept and is a function of covariates #
#================================================================#
if (isTRUE("Intercept" %in% mod[,1])) {
beta <- -1
while(isTRUE(beta<0)) {
params <- auto_cholesky(model = model, draws = draws)
beta <- params[1]
Intercept <- params[2]
betas <- params[3:length(params)]
}
alpha <- exp(Intercept + sum(features*betas))
return(as.numeric(alpha/(beta-1)))
#=====================================#
# mu is not a function of covariates #
#=====================================#
} else if (!isTRUE("beta1" %in% mod[,1])) {
beta <- -1
alpha <- -1
while(isTRUE((beta<0) | (alpha<0))) {
params <- auto_cholesky(model = model, draws = draws)
alpha <- params[1]
beta <- params[2]
}
return(as.numeric(alpha/(beta-1)))
#====================================================================#
# linear predictor has no intercept but is a function of covariates #
#====================================================================#
} else {
beta <- -1
while(isTRUE(beta<0)) {
params <- auto_cholesky(model = model, draws = draws)
beta <- params[1]
betas <- params[2:length(params)]
}
alpha <- exp(sum(features*betas))
return(as.numeric(alpha/(beta-1)))
}
}
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