R/get.sub.model.dest.degrad.mle.out.R

In Auburngrads/SMRD: Statistical Methods For Reliability Data

#' Title
#'
#' @param x 
#' @param stresses 
#' @param ... 
#'
#' @return NULL
#' @export
#'
#' @examples
#' \dontrun{
#' 
#' AdhesiveStrength.ddd <- frame.to.ddd(adhesivestrength, 
#'                                      response.column = "pounds",
#'                                      time.column = "days",
#'                                      x.columns = "celsius",
#'                                      data.title = "AdhesiveStrength Strength Data",
#'                                      time.units = "Days")
#' 
#' AdhesiveStrength.gmle <- dest.degrad.mle(AdhesiveStrength.ddd,
#'                                          distribution = "normal",
#'                                          transformation.response = "log", 
#'                                          transformation.x = "arrhenius", 
#'                                          transformation.time = "linear")
#' 
#' get.sub.model.dest.degrad.mle.out(AdhesiveStrength.gmle,c(50,60,70))
#' 
#' 
#' # Or more simply
#' get.sub.model(AdhesiveStrength.gmle,c(50,60,70))
#' 
#' }
get.sub.model.dest.degrad.mle.out <-
function (x, stresses,...)
{
    transformation.x <- fix.inverse.relationship(x$model$transformation.x)
    trans.stresses <- as.matrix(stresses)
    for (i in 1:ncol(trans.stresses)) {
        trans.stresses[, i] <- f.relationship(trans.stresses[,
            i], subscript.relationship(transformation.x, i))
    }
    origparam <- x$origparam
    beta2.names <- paste("beta", seq(2, ncol(trans.stresses) +
        1), sep = "")
    beta2.vec <- origparam[beta2.names]
    x.beta <- trans.stresses %*% beta2.vec
    return(list(x.value = stresses, intercept = rep(origparam["beta0"],
        nrow(trans.stresses)), slope = origparam["beta1"] * exp(x.beta)))
}