DHARMa (version 0.3.0)

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,
  seed = 123)

Arguments

simulationOutput

an object with simulated 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.

seed

the random seed to be used within DHARMa. The default setting, recommended for most users, is keep the random seed on a fixed value 123. This means that you will always get the same randomization and thus teh same result when running the same code. NULL = no new seed is set, but previous random state will be restored after simulation. FALSE = no seed is set, and random state will not be restored. The latter two options are only recommended for simulation experiments. See vignette for details.

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)

simulationOutput <- simulateResiduals(fittedModel = fittedModel)

# standard plot 
plot(simulationOutput)

# one of the possible test, for other options see ?testResiduals 
testOutliers(simulationOutput)

# for various other plots and tests, see the help / vignette

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

# transform residuals to other pdf, see ?residuals.DHARMa for details
residuals(simulationOutput, quantileFunction = qnorm, outlierValues = c(-7,7))

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

# create residuals with refitting, see ?simulateResiduals for details
# n=10 is very low, set higher when using this for real
simulationOutput <- simulateResiduals(fittedModel = fittedModel, 
                                      n = 10, refit = TRUE)
plot(simulationOutput, quantreg = FALSE)


# }