R/predictCurve.R

In zhengfj1994/MeTEA: Targeted extraction from untargeted metabolomics dataset by using metabolomics database

#' @title Predict curve values
#'
#' @description Evaluate fitted curve values at \code{x} data points
#'
#' @param fittedCurve (peakPantheR_curveFit) A 'peakPantheR_curveFit': a list of
#' curve fitting parameters, curve shape model \code{curveModel} and nls.lm fit
#' status \code{fitStatus}.
#' @param x (numeric) values at which to evaluate the fitted curve
#'
#' @return  fitted curve values at x
#'
#' @details
#' ## Examples cannot be computed as the function is not exported:
#' ## Input a fitted curve
#' fittedCurve <- list(amplitude=275371.1, center=3382.577, sigma=0.07904697,
#'                     gamma=0.001147647, fitStatus=2,
#'                     curveModel='skewedGaussian')
#' class(fittedCurve)  <- 'peakPantheR_curveFit'
#' input_x <- c(3290, 3300, 3310, 3320, 3330, 3340, 3350, 3360, 3370, 3380,
#'             3390, 3400, 3410)
#'
#' ## Predict y at each input_x
#' pred_y  <- predictCurve(fittedCurve, input_x)
#' pred_y
#' # [1] 2.347729e-08 1.282668e-05 3.475590e-03 4.676579e-01 3.129420e+01
#' # [6] 1.043341e+03 1.736915e+04 1.447754e+05 6.061808e+05 1.280037e+06
#' # [11] 1.369651e+06 7.467333e+05 2.087477e+05
predictCurve <- function(fittedCurve, x) {

  # Check input
  if (!is.peakPantheR_curveFit(fittedCurve)) {
    stop("Error: \"fittedCurve\" must be a peakPantheR_curveFit!")
  }
  # known curveModel
  known_curveModel <- c("skewedGaussian", "emgGaussian")
  if (!(fittedCurve$curveModel %in% known_curveModel)) {
    stop(paste("Error: \"curveModel\" must be one of:",
               paste(known_curveModel, collapse=', ')))
  }

  # Select correct model
  if (fittedCurve$curveModel == "skewedGaussian")
  {
    yy <- skewedGaussian_minpack.lm(params = fittedCurve, xx = x)
  }
  else if (fittedCurve$curveModel == 'emgGaussian') {
    yy <- emgGaussian_minpack.lm(params = fittedCurve, xx = x)
  }
  # for future curve shapes
  # else if () {}

  return(yy)
}