| rlme {rlme} | R Documentation |
Rank-based Estimates for Mixed-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 yekabe@hotmail.com and Herb Susmann hps1@geneseo.edu
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.