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if(getRversion() >= '2.15.1') utils::globalVariables(c("variable", "residual"))
#' Function to plot squared residuals and the error model for an mkin object
#'
#' This function plots the squared residuals for the specified subset of the
#' observed variables from an mkinfit object. In addition, one or more dashed
#' line(s) show the fitted error model. A combined plot of the fitted model
#' and this error model plot can be obtained with \code{\link{plot.mkinfit}}
#' using the argument \code{show_errplot = TRUE}.
#'
#' @param object A fit represented in an \code{\link{mkinfit}} object.
#' @param obs_vars A character vector of names of the observed variables for
#' which residuals should be plotted. Defaults to all observed variables in
#' the model
#' @param xlim plot range in x direction.
#' @param xlab Label for the x axis.
#' @param ylab Label for the y axis.
#' @param maxy Maximum value of the residuals. This is used for the scaling of
#' the y axis and defaults to "auto".
#' @param legend Should a legend be plotted?
#' @param lpos Where should the legend be placed? Default is "topright". Will
#' be passed on to \code{\link{legend}}.
#' @param col_obs Colors for the observed variables.
#' @param pch_obs Symbols to be used for the observed variables.
#' @param frame Should a frame be drawn around the plots?
#' @param \dots further arguments passed to \code{\link{plot}}.
#' @return Nothing is returned by this function, as it is called for its side
#' effect, namely to produce a plot.
#' @author Johannes Ranke
#' @seealso \code{\link{mkinplot}}, for a way to plot the data and the fitted
#' lines of the mkinfit object.
#' @keywords hplot
#' @examples
#'
#' \dontrun{
#' model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#' fit <- mkinfit(model, FOCUS_2006_D, error_model = "tc", quiet = TRUE)
#' mkinerrplot(fit)
#' }
#'
#' @export
mkinerrplot <- function (object,
obs_vars = names(object$mkinmod$map),
xlim = c(0, 1.1 * max(object$data$predicted)),
xlab = "Predicted", ylab = "Squared residual",
maxy = "auto", legend= TRUE, lpos = "topright",
col_obs = "auto", pch_obs = "auto",
frame = TRUE,
...)
{
obs_vars_all <- as.character(unique(object$data$variable))
if (length(obs_vars) > 0){
obs_vars <- intersect(obs_vars_all, obs_vars)
} else obs_vars <- obs_vars_all
residuals <- subset(object$data, variable %in% obs_vars, residual)
if (maxy == "auto") maxy = max(residuals^2, na.rm = TRUE)
# Set colors and symbols
if (col_obs[1] == "auto") {
col_obs <- 1:length(obs_vars)
}
if (pch_obs[1] == "auto") {
pch_obs <- 1:length(obs_vars)
}
names(col_obs) <- names(pch_obs) <- obs_vars
plot(0, type = "n",
xlab = xlab, ylab = ylab,
xlim = xlim,
ylim = c(0, 1.2 * maxy), frame = frame, ...)
for(obs_var in obs_vars){
residuals_plot <- subset(object$data, variable == obs_var, c("predicted", "residual"))
points(residuals_plot[["predicted"]],
residuals_plot[["residual"]]^2,
pch = pch_obs[obs_var], col = col_obs[obs_var])
}
if (object$err_mod == "const") {
abline(h = object$errparms^2, lty = 2, col = 1)
}
if (object$err_mod == "obs") {
for (obs_var in obs_vars) {
sigma_name = paste0("sigma_", obs_var)
abline(h = object$errparms[sigma_name]^2, lty = 2,
col = col_obs[obs_var])
}
}
if (object$err_mod == "tc") {
sigma_plot <- function(predicted) {
sigma_twocomp(predicted,
sigma_low = object$errparms[1],
rsd_high = object$errparms[2])^2
}
plot(sigma_plot, from = 0, to = max(object$data$predicted),
add = TRUE, lty = 2, col = 1)
}
if (legend == TRUE) {
legend(lpos, inset = c(0.05, 0.05), legend = obs_vars,
col = col_obs[obs_vars], pch = pch_obs[obs_vars])
}
}
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