healy.plot {skewlmm}

Healy-type plot from a smn.lmm or smsn.lmm object

Description

It creates a Healy-type plot from a smn.lmm or smsn.lmm object, for goodness-of-fit assessment.

Usage

healy.plot(object, dataPlus = NULL, dotsize = 0.4, calcCI = FALSE,
           levelCI, MCiter, seed, ...)

Arguments

object

An object inheriting from class SMN or SMSN, representing a fitted scale mixture of (skew) normal linear mixed model.

dataPlus

Optional. Expanded dataset that should be used instead the one used for fitting. This is necessary for unbalanced datasets, since Haley's plot requires all subject to have the same number of observations.

dotsize

Optional. Dotsize used in ggplot.

calcCI

TRUE or FALSE (default). A logical value indicating if Monte Carlo confidence intervals should be computed for the conditionally independent model, which can be used for testing if the autocorrelations are zero.

levelCI

An optional numeric value in (0,1) indicating the confidence level that should be used in the Monte Carlo confidence intervals. Default is 0.95.

MCiter

An optional discrete value indicating the number of Monte Carlo samples that should be used to compute the confidence intervals. Default is 300.

seed

An optional value used to specify seeds inside the function. Default is to use a random seed.

...

Additional arguments.

Details

It constructs a Healy-type plot (Healy, 1968) by plotting the nominal probability values 1/n,2/n,...,n/n against the theoretical cumulative probabilities of the ordered observed Mahalanobis distances. If the fitted model is appropriate, the plot should resemble a straight line through the origin with unit slope. If calcCI=TRUE, the plot presents two dashed blue lines containing approximated confidence intervals by considering that the fitted model is correct.

Value

A ggplot object.

Author(s)

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

References

Healy, M. J. R. (1968). Multivariate normal plotting. Journal of the Royal Statistical Society: Series C (Applied Statistics), 17(2), 157-161.

Schumacher, F. L., Lachos, V. H., and Matos, L. A. (2021). Scale mixture of skew-normal linear mixed models with within-subject serial dependence. Statistics in Medicine 40(7), 1790-1810.

See Also

ggplot, smn.lmm, smsn.lmm, mahalDist, acfresid

Examples

fm1 = smn.lmm(distance ~ age+Sex, data=nlme::Orthodont, groupVar="Subject")
healy.plot(fm1)

## computing simulated bands
healy.plot(fm1, calcCI=TRUE)