Add functionality to plot the error model

by plotting squared residuals against predicted values, and
showing the variance function used in the fitted error model.

Rebuild docs

1 files changed, 7 insertions, 7 deletions

diff --git a/R/mkinresplot.R b/R/mkinresplot.R
index 3650ef4b..739a80e9 100644
--- a/R/mkinresplot.R
+++ b/R/mkinresplot.R
@@ -23,7 +23,7 @@ mkinresplot <- function (object,
   xlab = "Time", ylab = "Residual",

   maxabs = "auto", legend= TRUE, lpos = "topright", ...)

 {

-	obs_vars_all <- as.character(unique(object$data$variable))

+  obs_vars_all <- as.character(unique(object$data$variable))

 

   if (length(obs_vars) > 0){

       obs_vars <- intersect(obs_vars_all, obs_vars)

@@ -33,18 +33,18 @@ mkinresplot <- function (object,
 

   if (maxabs == "auto") maxabs = max(abs(residuals), na.rm = TRUE)

 

-	col_obs <- pch_obs <- 1:length(obs_vars)

- 	names(col_obs) <- names(pch_obs) <- obs_vars

+  col_obs <- 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(-1.2 * maxabs, 1.2 * maxabs), ...)

 

-	for(obs_var in obs_vars){

-		residuals_plot <- subset(object$data, variable == obs_var, c("time", "residual"))

-		points(residuals_plot, pch = pch_obs[obs_var], col = col_obs[obs_var])

-	}

+  for(obs_var in obs_vars){

+    residuals_plot <- subset(object$data, variable == obs_var, c("time", "residual"))

+    points(residuals_plot, pch = pch_obs[obs_var], col = col_obs[obs_var])

+  }

 

   abline(h = 0, lty = 2)