#' Asymptotic regression model
#'
#' Providing the mean function and the corresponding self starter function for the asymptotic regression model.
#'
#' The asymptotic regression model is a three-parameter model with mean function:
#' \deqn{ f(x) = c + (d-c)(1-\exp(-x/e))}
#' The parameter \eqn{c} is the lower limit (at \eqn{x=0}), the parameter \eqn{d} is the upper limit and the parameter \eqn{e>0} is determining the steepness of the increase as \eqn{x}.
#'
#' @param x numeric vector specifying the dose
#' @param c lower limit at at \eqn{x=0}
#' @param d upper limit
#' @param e \eqn{e>0} is determining the steepness of the increase as \eqn{x}
#'
#' @keywords models
#'
#' @rdname meAR
meAR.2 <- function(x, d, e){
.value <- d * (1 - exp(-exp(log(x) - log(e))))
.actualArgs <- c("d", "e")
t2 <- exp(log(x) - log(e))
t3 <- exp(-t2)
.grad <- cbind(1 - t3,
-d * xexpx(x/e, 1)/e)
dimnames(.grad) <- list(NULL, .actualArgs)
attr(.value, "gradient") <- .grad
return(.value)
}
#' @rdname meAR
meAR.3 <- function(x, c, d, e){
.value <- c + (d - c) * (1 - exp(-exp(log(x) - log(e))))
.actualArgs <- c("c", "d", "e")
t1 <- d - c
t2 <- exp(log(x) - log(e))
t3 <- exp(-t2)
.grad <- cbind(1 - (1 - t3),
1 - t3,
-t1 * xexpx(x/e, 1)/e)
dimnames(.grad) <- list(NULL, .actualArgs)
attr(.value, "gradient") <- .grad
return(.value)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.