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

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/rlme.r

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

1
2
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 [email protected] and Herb Susmann [email protected]

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

1
2
3
4
5
6
7
8
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.

herbps10/rlme documentation built on Jan. 9, 2018, 10:45 p.m.