plot.mahalDist.SMNCens: Plot Mahalanobis distance for a fitted smn.clmm

In skewlmm: Scale Mixture of Skew-Normal Linear Mixed Models

Plot Mahalanobis distance for a fitted smn.clmm

Description

Plot method for objects of class "mahalDistCens". It also gives a quantile for outlier detection, based on the Mahalanobis distance theoretical distribution.

Usage

  ## S3 method for class 'mahalDistCens'
plot(x, fitobject, level = 0.99, nlabels = 3, ...)

Arguments

x	An object inheriting from class mahalDistCens, representing the Mahalanobis distance from a fitted scale mixture of normal censored linear mixed model.
fitobject	Optional. An object inheriting from class SMNclmm, representing the fitted scale mixture of normal linear mixed model that was used for calculating the Mahalanobis distance.
level	An optional numeric value in (0,1) indicating the level of the quantile. Default is 0.99.
nlabels	Number of observations that should be labeled. Default is 3.
...	Additional arguments.

Value

A ggplot object, plotting the index versus the Mahalanobis distance, if all subject have the same number of observations; or plotting the number of observations per subject versus the Mahalanobis, otherwise.

Author(s)

Fernanda L. Schumacher, Larissa A. Matos, Victor H. Lachos and Katherine L. Valeriano

See Also

ggplot, mahalDistCens, smn.clmm

Examples

nj1 = 5; m = 30
time = rep(1:nj1, times=m)
groups = as.factor(rep(1:m, each=nj1))
dat1 = rsmsn.clmm(time, groups, cbind(1,time), rep(1,m*nj1), sigma2=0.7,
                  D=0.5*diag(1), beta=c(1,2), depStruct="CS", phi=0.4)
# Estimation
fm1 = smn.clmm(dat1, formFixed=y~x, groupVar="ind", depStruct="CS", ci="ci",
               lcl="lcl", ucl="ucl", control=lmmControl(max.iter=30))
distance = mahalDistCens(fm1)
plot(distance, level=0.95, nlabels=2)

skewlmm documentation built on July 9, 2023, 7:29 p.m.