mrfit: Multiple rankings (MR) estimates for cluster correlated...

In kloke/mrfit: Multiple rankings estimates

Description

Returns the MR fit (and results for inference) for a linear model with cluster correlated data. The objective function is a sum of dispersion functions, one for each cluster/center/block.

Usage

mrfit(x, y, center, beta0 = jaeckel(x, y)$par)

Arguments

x	n by p design matrix
y	n by 1 response vector
center	n by 1 vector denoting cluster membership
beta0	initial fit (default is JR estimate)

Details

The multiple rankings (MR) dispersion function is defined as D(beta) = sum Dj(beta) where Dj(beta) is Jaeckel's (1972) dispersion function for the jth cluster/center/block. A separate set of rankings is computed for each center. D(beta) is the objective function to minimize for the MR estimate of beta.

Value

Returns an object of class mrfit

betahat	Estimated regression coefficients
tauhat	Estimate of the scale parameter tau
disp	Value of the MR dispersion function evaluated at betahat
sebetahat	estimated standard errors of the regression coefficients
x	Centered design matrix
y	response vector
center	vector denoting cluster membership

Author(s)

John Kloke [e-mail]

References

Hettmansperger, T.P. and McKean J.W. (2011), Robust Nonparametric Statistical Methods, 2nd ed., New York: Chapman-Hall.

Rashid, M.M., McKean, J.W., Kloke, J.D. (2011). R Estimates and Associated Inferences for Mixed Models with Covariates in a Multi-Center Clinical Trial. Statistics in Biopharmaceutical Research.

Examples

# a simple simulated example w/ 4 equal sized blocks #
x<-matrix(rnorm(40),ncol=2); y<-rnorm(20) ; g<-rep(1:4,each=5)
fit<-mrfit(x,y,g)

kloke/mrfit documentation built on May 20, 2019, 12:34 p.m.