rlme: Rank-based Estimates for Mixed-Effects Nested Models

In rlme: Rank-Based Estimation and Prediction in Random Effects Nested Models

Description

This function estimates fixed effects and predicts random effects in two- and three-level random effects nested models using three rank-based fittings (GR, GEER, JR) via the prediction method algorithm RPP.

Usage

rlme(f, data, method = "gr", print = FALSE, na.omit = TRUE,
  weight = "wil", rprpair = "hl-disp", verbose = FALSE)

Arguments

f	An object of class formula describing the mixed effects model. The syntax is same as in the lme4 package. Example: y ~ 1 + sex + age + (1 | region) + (1 | region:school) - sex and age are the fixed effects, region and school are the nested random effects, school is nested within region.
data	The dataframe to analyze. Data should be cleaned prior to analysis: cluster and subcluster columns are expected to be integers and in order (e.g. all clusters and subclusters )
method	string indicating the method to use (one of "gr", "jr", "reml", and "geer"). defaults to "gr".
print	Whether or not to print a summary of results. Defaults to false.
na.omit	Whether or not to omit rows containing NA values. Defaults to true.
weight	When weight="hbr", it uses hbr weights in GEE weights. By default, ="wil", it uses Wilcoxon weights. See the theory in the references.
rprpair	By default, it uses "hl-disp" in the random prediction procedure (RPP). Also, "med-mad" would be an alternative.
verbose	Boolean indicating whether to print out diagnostic messages.

Details

The iterative methods GR and GEER can be quite slow for large datasets; try JR for faster analysis. If you want to use the GR method, try using rprpair='med-mad'. This method avoids building a NxN covariance matrix which can quickly become unwieldly with large data.

Value

The function returns a list of class "rlme". Use summary.rlme to see a summary of the fit.

formula	The model formula.
method	The method used.
fixed.effects	Estimate of fixed effects.
random.effects	Estimate of random effects.
standard.residual	Residuals.
intra.class.correlations	Intra/inter-class correlationa estimates obtained from RPP.
t.value	t-values.
p.value	p-values.
location	Location.
scale	Scale.
y	The response variable y.
num.obs	Number of observations in provided dataset.
num.clusters	The number of clusters.
num.subclusters	The number of subclusters.
effect.err	Effect from error.
effect.cluster	Effect from cluster.
effect.subcluster	Effect from subcluster.
var.b	Variances of fixed effects estimate (Beta estimates).
xstar	Weighted design matrix with error covariance matrix.
ystar	Weighted response vector with its covariance matrix.
ehat	The raw residual.
ehats	The raw residual after weighted step. Scaled residual.

Author(s)

Yusuf Bilgic [e-mail] and Herb Susmann [e-mail]

References

Y. K. Bilgic. Rank-based estimation and prediction for mixed effects models in nested designs. 2012. URL http://scholarworks.wmich.edu/dissertations/40. Dissertation.

T. P. Hettmansperger and J. W. McKean. Robust Nonparametric Statistical Methods. Chapman Hall, 2012.

See Also

summary.rlme, plot.rlme, compare.fits

Examples

data(schools)

rlme.fit = rlme(y ~ 1 + sex + age + (1 | region) + (1 | region:school), schools, method="gr")
summary(rlme.fit)

# Try method="geer", "reml", "ml" and "jr" along with 
# rprpair="hl-disp" (not robust), and "med-mad" (robust),
# weight="hbr" is for the gee method.

rlme documentation built on May 2, 2019, 3:47 p.m.