createData: Simulate test data

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

Simulate test data

Description

This function creates synthetic dataset with various problems such as overdispersion, zero-inflation, etc.

Usage

createData(sampleSize = 100, intercept = 0, fixedEffects = 1,
  quadraticFixedEffects = NULL, numGroups = 10, randomEffectVariance = 1,
  overdispersion = 0, family = poisson(), scale = 1, cor = 0,
  roundPoissonVariance = NULL, pZeroInflation = 0, binomialTrials = 1,
  temporalAutocorrelation = 0, spatialAutocorrelation = 0,
  factorResponse = FALSE, replicates = 1, hasNA = FALSE)

Arguments

sampleSize	sample size of the dataset.
intercept	intercept (linear scale).
fixedEffects	vector of fixed effects (linear scale).
quadraticFixedEffects	vector of quadratic fixed effects (linear scale).
numGroups	number of groups for the random effect.
randomEffectVariance	variance of the random effect (intercept).
overdispersion	if this is a numeric value, it will be used as the sd of a random normal variate that is added to the linear predictor. Alternatively, a random function can be provided that takes as input the linear predictor.
family	family.
scale	scale if the distribution has a scale (e.g. sd for the Gaussian)
cor	correlation between predictors.
roundPoissonVariance	if set, this creates a uniform noise on the possion response. The aim of this is to create heteroscedasticity.
pZeroInflation	probability to set any data point to zero.
binomialTrials	Number of trials for the binomial. Only active if family == binomial.
temporalAutocorrelation	strength of temporalAutocorrelation.
spatialAutocorrelation	strength of spatial Autocorrelation.
factorResponse	should the response be transformed to a factor (inteded to be used for 0/1 data).
replicates	number of datasets to create.
hasNA	should an NA be added to the environmental predictor (for test purposes).

Examples

testData = createData(sampleSize = 500, intercept = 2, fixedEffects = c(1), 
  overdispersion = 0, family = poisson(), quadraticFixedEffects = c(-3), 
  randomEffectVariance = 0)

par(mfrow = c(1,2))
plot(testData$Environment1, testData$observedResponse)
hist(testData$observedResponse)

# with zero-inflation

testData = createData(sampleSize = 500, intercept = 2, fixedEffects = c(1), 
  overdispersion = 0, family = poisson(), quadraticFixedEffects = c(-3), 
  randomEffectVariance = 0, pZeroInflation = 0.6)

par(mfrow = c(1,2))
plot(testData$Environment1, testData$observedResponse)
hist(testData$observedResponse)

# binomial with multiple trials

testData = createData(sampleSize = 40, intercept = 2, fixedEffects = c(1), 
                      overdispersion = 0, family = binomial(), quadraticFixedEffects = c(-3), 
                      randomEffectVariance = 0, binomialTrials = 20)

plot(observedResponse1 / observedResponse0 ~ Environment1, data = testData, ylab = "Proportion 1")


# spatial / temporal correlation

testData = createData(sampleSize = 100, family = poisson(), spatialAutocorrelation = 3, 
                      temporalAutocorrelation = 3)

plot(log(observedResponse) ~ time, data = testData)
plot(log(observedResponse) ~ x, data = testData)

DHARMa documentation built on Oct. 18, 2024, 5:09 p.m.