View source: R/residuals.galamm.R
| residuals.galamm | R Documentation |
Computes residuals for models fit with galamm() using the definitions in
Chapter 8 of \insertCitedunnGeneralizedLinearModels2018;textualgalamm.
Define y as the response and \hat{\mu} as the model fit. Importantly,
\hat{\mu} includes all random effects. Also define V(\cdot) as the
variance function of the model family, and w as the weight. The Pearson
residual is then
r_{P} = (y - \hat{\mu})/\sqrt{V(\hat{\mu}) / w}.
Furthermore, let sgn(\cdot) be the function which returns the sign of its
argument and let d(y, \hat{\mu}) be the model deviance. The deviance
residual is then
r_{D} = sgn(y - \hat{\mu}) \sqrt{w d(y, \hat{\mu})}.
## S3 method for class 'galamm'
residuals(object, type = c("pearson", "deviance"), scaled = FALSE, ...)
object |
An object of class |
type |
Character of length one describing the type of residuals to be
returned. One of |
scaled |
Logical value specifying whether to scale the residuals by
their standard deviation. Defaults to |
... |
Optional arguments passed on to other methods. Currently not used. |
Numeric vector of residual values.
fitted.galamm() for model fitted values, predict.galamm() for
model predictions, and plot.galamm() for diagnostic plots. The generic
function is residuals().
Other details of model fit:
VarCorr(),
appraise.galamm(),
coef.galamm(),
confint.galamm(),
derivatives.galamm(),
deviance.galamm(),
factor_loadings.galamm(),
family.galamm(),
fitted.galamm(),
fixef(),
formula.galamm(),
llikAIC(),
logLik.galamm(),
model.frame.galamm(),
nobs.galamm(),
predict.galamm(),
print.VarCorr.galamm(),
ranef.galamm(),
response(),
sigma.galamm(),
vcov.galamm()
# Poisson GLMM
count_mod <- galamm(
formula = y ~ lbas * treat + lage + v4 + (1 | subj),
data = epilep, family = poisson
)
# Extract residuals
residuals(count_mod)
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