fixes #413 note on color-option in vignette + fix help plotResiduals

DHARMa/R/plots.R

@@ -174,7 +174,7 @@ plotQQunif <- function(simulationOutput, testUniformity = TRUE, testOutliers = T
 #'
 #' @note If nObs > 10000, the scatter plot is replaced by graphics::smoothScatter
 #'
-#' #' @note The color for highlighting outliers and quantile lines/splines with significant tests can be changed by setting \code{options(DHARMaSignalColor = "red")} to a different color. See \code{getOption("DHARMaSignalColor")} for the current setting. This is convenient for a color-blind friendly display, since red and black are difficult for some people to separate.
+#' @note The color for highlighting outliers and quantile lines/splines with significant tests can be changed by setting \code{options(DHARMaSignalColor = "red")} to a different color. See \code{getOption("DHARMaSignalColor")} for the current setting. This is convenient for a color-blind friendly display, since red and black are difficult for some people to distinguish.
 #'
 #' @return If quantile tests are performed, the function returns them invisibly.
 #'

DHARMa/vignettes/DHARMa.Rmd

@@ -79,7 +79,7 @@ DHARMa aims at solving these problems by creating readily interpretable residual
 
 These steps are visualized in the following figure
 
-<img src="ECDFmotivation.png" width="400" class="center"/>
+<img src="ECDFmotivation.png" class="center" width="400"/>
 
 The key advantage of this definition is that the so-defined residuals always have the same, known distribution, independent of the model that is fit, if the model is correctly specified. To see this, note that, if the observed data was created from the same data-generating process that we simulate from, all values of the cumulative distribution should appear with equal probability. That means we expect the distribution of the residuals to be flat, regardless of the model structure (Poisson, binomial, random effects and so on).
 
@@ -189,7 +189,9 @@ If the predictor is a factor, or if there is just a small number of observations
 plotResiduals(simulationOutput, form = testData$group)
 ```
 
-See ?plotResiduas for details, but very shortly: under H0 (perfect model), we would expect those boxes to range homogeneously from 0.25-0.75. To see whether there are deviations from this expecation, the plot calculates a test for uniformity per box, and a test for homogeneity of variances between boxes. A positive test will be highlighted in red.
+See ?plotResiduas for details, but very shortly: under H0 (perfect model), we would expect those boxes to range homogeneously from 0.25-0.75. To see whether there are deviations from this expecation, the plot calculates a test for uniformity per box, and a test for homogeneity of variances between boxes. A positive test will be highlighted in red. \
+\
+**NOTE on plots**: The default color for highlighting outliers and significant tests is **red**. However, it can be changed by setting \code{options(DHARMaSignalColor = "red")} to a different color. This is convenient for a color-blind friendly display, since red and black are difficult for some people to disentangle.
 
 ## Goodness-of-fit tests on the scaled residuals
 
@@ -351,7 +353,7 @@ IMPORTANT INFO: we have made extensive simulations, which have shown that the va
 
 As support for these statements, here results of the simulation, which compares the uniform (KS) test with the standard simuation-based test (conditional and unconditional) and the Pearson-chi2 test (two-sided and greater) for an n=200 Poisson GLMM with 30 RE levels.
 
-<img src="dispersion.png" width="700" class="center"/>
+<img src="dispersion.png" class="center" width="700"/>
 
 Thus, my current recommendation is: for most users, use the default DHARMa test, but create simulations conditionally.