hist.DHARMa: Histogram of DHARMa residuals

In DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models

Histogram of DHARMa residuals

Description

The function produces a histogram from a DHARMa output

Usage

## S3 method for class 'DHARMa'
hist(x, breaks = seq(-0.02, 1.02, len = 53),
  col = c("red", rep("lightgrey", 50), "red"),
  main = "Hist of DHARMa residuals",
  xlab = "Residuals (outliers are marked red)", cex.main = 1, ...)

Arguments

x	a DHARMa simulation output (class DHARMa)
breaks	breaks for hist() function
col	col for hist bars
main	plot main
xlab	plot xlab
cex.main	plot cex.main
...	other arguments to be passed on to hist

See Also

plotSimulatedResiduals, plotResiduals

Examples

testData = createData(sampleSize = 200, family = poisson(), 
                      randomEffectVariance = 1, numGroups = 10)
fittedModel <- glm(observedResponse ~ Environment1, 
                   family = "poisson", data = testData)
simulationOutput <- simulateResiduals(fittedModel = fittedModel)

######### main plotting function #############

# for all functions, quantreg = T will be more
# informative, but slower

plot(simulationOutput, quantreg = FALSE)

#############  Distribution  ######################

plotQQunif(simulationOutput = simulationOutput, 
           testDispersion = FALSE,
           testUniformity = FALSE,
           testOutliers = FALSE)

hist(simulationOutput )

#############  residual plots  ###############

# rank transformation, using a simulationOutput
plotResiduals(simulationOutput, rank = TRUE, quantreg = FALSE)

# smooth scatter plot - usually used for large datasets, default for n > 10000
plotResiduals(simulationOutput, rank = TRUE, quantreg = FALSE, smoothScatter = TRUE)

# residual vs predictors, using explicit values for pred, residual 
plotResiduals(simulationOutput, form = testData$Environment1, 
              quantreg = FALSE)

# if pred is a factor, or if asFactor = T, will produce a boxplot
plotResiduals(simulationOutput, form = testData$group)

# All these options can also be provided to the main plotting function

# If you want to plot summaries per group, use
simulationOutput = recalculateResiduals(simulationOutput, group = testData$group)
plot(simulationOutput, quantreg = FALSE) 
# we see one residual point per RE

DHARMa documentation built on Sept. 9, 2022, 1:06 a.m.