DHARMa (version 0.2.4)

recalculateResiduals: Recalculate residuals with grouping

Description

The purpose of this function is to recalculate scaled residuals per group, based on the simulations done by simulateResiduals

Usage

recalculateResiduals(simulationOutput, group = NULL, aggregateBy = sum)

Arguments

simulationOutput

an object with simualted residuals created by simulateResiduals

group

group of each data point

aggregateBy

function for the aggregation. Default is sum. This should only be changed if you know what you are doing. Note in particular that the expected residual distribution might not be flat any more if you choose general functions, such as sd etc.

Value

an object of class DHARMa, similar to what is returned by simulateResiduals, but with additional outputs for the new grouped calculations. Note that the relevant outputs are 2x in the object, the first is the grouped calculations (which is returned by $name access), and later another time, under identical name, the original output. Moreover, there is a function 'aggregateByGroup', which can be used to aggregate predictor variables in the same way as the variables calculated here

Examples

# NOT RUN {
library(lme4)

testData = createData(sampleSize = 200, overdispersion = 0.5, family = poisson())
fittedModel <- glmer(observedResponse ~ Environment1 + (1|group), 
                     family = "poisson", data = testData,
                     control=glmerControl(optCtrl=list(maxfun=20000) ))

simulationOutput <- simulateResiduals(fittedModel = fittedModel)

# plot residuals, quantreg = T is better but costs more time
plot(simulationOutput, quantreg = FALSE)

# the calculated residuals can be accessed via 
residuals(simulationOutput)
simulationOutput$scaledResiduals

# calculating summaries per group
simulationOutput = recalculateResiduals(simulationOutput, group = testData$group)
plot(simulationOutput, quantreg = FALSE)

# create simulations with refitting, n=5 is very low, set higher when using this
simulationOutput <- simulateResiduals(fittedModel = fittedModel, 
                                      n = 10, refit = TRUE)
plot(simulationOutput, quantreg = FALSE)

# grouping per random effect group works as above
simulationOutput = recalculateResiduals(simulationOutput, group = testData$group)
plot(simulationOutput, quantreg = FALSE)

# }