hist.DHARMa {DHARMa}

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