createDHARMa

optional scaled residuals from a simulation, e.g. Bayesian p-values. If those are not provided, simulated and true observations have to be provided.

scaledResiduals

matrix of observations simulated from the fitted model - row index for observations and colum index for simulations

simulatedResponse

observedResponse

fitted predicted response. Optional, but will be neccessary for some plots. If scaled residuals are Bayesian p-values, using the median posterior prediction as fittedPredictedResponse is recommended.

fittedPredictedResponse

if T, noise will be added at to the residuals to maintain a uniform expectations for integer responses (such as Poisson or Binomial). Unlike in <code><a rd-options="" href="/link/simulateResiduals?package=DHARMa&version=0.1.3" data-mini-rdoc="DHARMa::simulateResiduals">simulateResiduals</a></code>, the nature of the data is not automatically detected, so this MUST be set by the user appropriately

integerResponse


Convert simulated residuals to a DHARMa object


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.

Florian Hartig

DHARMa

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

createDHARMa function

if T, noise will be added at to the residuals to maintain a uniform expectations for integer responses (such as Poisson or Binomial). Unlike in <code><a rd-options='' href='simulateResiduals'>simulateResiduals</a></code>, the nature of the data is not automatically detected, so this MUST be set by the user appropriately

createDHARMa: Convert simulated residuals to a DHARMa object

Description

Usage

Arguments

Details