benchmarkP

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

controlValues

the test to be benchmarked - this should be a function that takes one of the controlValues as an input, and returns a p-value

getP

number of replicates per level of the controlValues

nRep

alpha

plot

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

parallel

This function runs 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 generalized linear mixed models from 'lme4'
(classes 'lmerMod', 'glmerMod'), generalized additive models ('gam' from 'mgcv'),
'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
classes. Alternatively, 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.

benchmarkP: General Type I/II benchmarks

Description

Usage

Arguments

See Also