R/integratepdf.grad.R

In DistanceDevelopment/mrds: Mark-Recapture Distance Sampling

#' Numerically integrates the non-normalised pdf or the detection function of 
#' observed distances over specified ranges. 
#' 
#' Gradient of the integral of the detection function, i.e., d beta/d theta in 
#' the documentation. This gradient of the integral is the same as the integral
#' of the gradient, thanks to Leibniz integral rule. 
#' 
#' For internal use only -- not to be called by \code{mrds} or \code{Distance}
#' users directly.
#' 
#' @param par.index the index of the parameter of interest
#' @param ddfobj the ddf object
#' @param width the truncation width
#' @param int.range vector with the lower and upper bound of the integration
#' @param left the left truncation. Defaults to zero.
#' @param pdf.based evaluate the non-normalised pdf or the detection function? 
#' Default is TRUE. 
#' @param point are the data from point transects (TRUE) or line transects 
#' (FALSE).
#' @param standardize TRUE if the non-standardised detection function should
#' be integrated. Only implemented for standardize = FALSE, so users should not
#' touch this argument and it can probably be removed. 
#' 
#' @author Felix Petersma

integratepdf.grad <- function(par.index, ddfobj, int.range, width, 
                              standardize = FALSE, point = FALSE, left = 0, 
                              pdf.based = TRUE) {
  
  ## If the non-standardised detection function is required, simply 
  ## integrate the gradient of the detection function directly.
  if (!standardize) {
    out <- integrate(distpdf.grad, lower = int.range[1], upper = int.range[2],
                     par.index = par.index, ddfobj = ddfobj, width = width,
                     standardize = FALSE, point = point, left = left,
                     pdf.based = pdf.based)$value
  } 
  
  return(out)
}