runBenchmarks

the statistics to be benchmarked. Should return one value, or a vector of values. If controlValues are given, must accept a paramteter control

calculateStatistics

a vector with a control parameter (e.g. to vary the strength of a problem the test should be specific to)

controlValues

number of replicates per level of the controlValues

nRep

alpha

whether to use parallel computations. Possible values are F, T (sets the cores automatically to number of available cores -1), or an integer number for the number of cores that should be used for the cluster

parallel

additional parameters to calculateStatistics

This function runs statistical benchmarks, including Power / Type I error simulations for an arbitrary test with a control parameter

The 'DHARMa' package uses a simulation-based approach to create
readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
models. Currently supported are linear and generalized linear (mixed) models from 'lme4'
(classes 'lmerMod', 'glmerMod'), 'glmmTMB' and 'spaMM', generalized additive models ('gam' from
'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
classes. Moreover, externally created simulations, e.g. posterior predictive simulations
from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well.
The resulting residuals are standardized to values between 0 and 1 and can be interpreted
as intuitively as residuals from a linear regression. The package also provides a number of
plot and test functions for typical model misspecification problems, such as
over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.

runBenchmarks: Benchmark calculations

Description

Usage

Arguments