multilevelTools (version 0.2.0)

modelDiagnostics.lme: modelDiagnostics method for lme objects

Description

This function creates a number of diagnostics for lme models.

Usage

# S3 method for lme
modelDiagnostics(
  object,
  ev.perc = 0.001,
  robust = FALSE,
  distr = "normal",
  standardized = TRUE,
  ...
)

Value

A logical (is.modelDiagnostics) or a modelDiagnostics object (list) for

as.modelDiagnostics and

modelDiagnostics.

Arguments

object

A fitted model object of class lme.

ev.perc

A real number between 0 and 1 indicating the proportion of the theoretical distribution beyond which values are considered extreme values (possible outliers). Defaults to .001.

robust

Whether to use robust mean and standard deviation estimates for normal distribution

distr

A character string given the assumed distribution. Passed on to testDistribution. Defaults to “normal”.

standardized

A logical whether to use standardized pearson residuals. Defaults to TRUE generally where possible but may depend on method.

...

Additional arguments, passed to residualDiagnostics.

Examples

library(JWileymisc)
sleep[1,1] <- NA
m <- nlme::lme(extra ~ group, data = sleep,
 random = ~ 1 | ID, na.action = "na.omit")

 md <- modelDiagnostics(m, ev.perc = .1)
 md$extremeValues

 plot(md)

data(aces_daily, package = "JWileymisc")
m <- nlme::lme(PosAff ~ STRESS, data = aces_daily,
  random = ~ 1 + STRESS | UserID, na.action = "na.omit")
md <- modelDiagnostics(m, ev.perc = .001)
md$extremeValues
plot(md)


m <- nlme::lme(extra ~ 1, data = sleep, random = ~ 1 | ID/group,
  na.action = "na.omit")

md <- modelDiagnostics(m, ev.perc = .1)
md$extremeValues
plot(md)
rm(m, md, sleep)