Merge pull request #439 from florianhartig/testOutliersBugfix

Update tests.R

DHARMa/R/tests.R

@@ -153,6 +153,7 @@ testQuantiles <- function(simulationOutput, predictor = NULL, quantiles = c(0.25
 #' @param type either default, bootstrap or binomial. See details
 #' @param nBoot number of boostrap replicates. Only used ot type = "bootstrap"
 #' @param plot if TRUE, the function will create an additional plot
+#' @param plotBoostrap if plot should be produced of outlier frequencies calculated under the bootstrap
 #' @details DHARMa residuals are created by simulating from the fitted model, and comparing the simulated values to the observed data. It can occur that all simulated values are higher or smaller than the observed data, in which case they get the residual value of 0 and 1, respectively. I refer to these values as simulation outliers, or simply outliers.
 #'
 #' Because no data was simulated in the range of the observed value, we don't know "how strongly" these values deviate from the model expectation, so the term "outlier" should be used with a grain of salt. It is not a judgment about the magnitude of the residual deviation, but simply a dichotomous sign that we are outside the simulated range. Moreover, the number of outliers will decrease as we increase the number of simulations.
@@ -169,7 +170,7 @@ testQuantiles <- function(simulationOutput, predictor = NULL, quantiles = c(0.25
 #' @author Florian Hartig
 #' @seealso [testResiduals], [testUniformity], [testOutliers], [testDispersion], [testZeroInflation], [testGeneric], [testTemporalAutocorrelation], [testSpatialAutocorrelation], [testQuantiles], [testCategorical]
 #' @export
-testOutliers <- function(simulationOutput, alternative = c("two.sided", "greater", "less"), margin = c("both", "upper", "lower"), type = c("default","bootstrap", "binomial"), nBoot = 100, plot = TRUE){
+testOutliers <- function(simulationOutput, alternative = c("two.sided", "greater", "less"), margin = c("both", "upper", "lower"), type = c("default","bootstrap", "binomial"), nBoot = 100, plot = TRUE, plotBoostrap = FALSE){
 
   # check inputs
   alternative = match.arg(alternative)
@@ -284,12 +285,17 @@ testOutliers <- function(simulationOutput, alternative = c("two.sided", "greater
     names(out$parameter) = "observations"
     out$estimate = outliers
     names(out$estimate) = paste("outlier frequency (expected:", mean(frequBoot),")")
+
+    if(plotBoostrap == T){
+      hist(frequBoot, xlim = range(frequBoot, outliers), col = "lightgrey", main = "Bootstrapped outlier frequency")
+      abline(v = mean(frequBoot), col = 1, lwd = 2)
+      abline(v = outliers, col = "red", lwd = 2)
+
+      # legend("center", c(paste("p=", round(out$p.value, digits = 5)), paste("Deviation ", ifelse(out$p.value < 0.05, "significant", "n.s."))), text.col = ifelse(out$p.value < 0.05, "red", "black" ))
+    }
 
     if(plot == T) {
-
-      opar <- par(mfrow = c(1,2))
-      on.exit(par(opar))
-
+
       hist(simulationOutput, main = "")
 
       main = ifelse(out$p.value <= 0.05,
@@ -299,12 +305,6 @@ testOutliers <- function(simulationOutput, alternative = c("two.sided", "greater
       title(main = main, cex.main = 1,
             col.main = ifelse(out$p.value <= 0.05, "red", "black"))
 
-      hist(frequBoot, xlim = range(frequBoot, outliers), col = "lightgrey")
-      abline(v = mean(frequBoot), col = 1, lwd = 2)
-      abline(v = outliers, col = "red", lwd = 2)
-
-      # legend("center", c(paste("p=", round(out$p.value, digits = 5)), paste("Deviation ", ifelse(out$p.value < 0.05, "significant", "n.s."))), text.col = ifelse(out$p.value < 0.05, "red", "black" ))
-
     }
   }
   return(out)