orQA (version 0.2.1)

est.lme: Estimate order restricted variance components for a two way mixed model with interaction.

Description

Estimates variance components of a two way cross classification mixed model with an order restricted fixed effect, a random effect and random interaction term.

Usage

est.lme(y,ia,ib)

Arguments

y

a numeric matrix, for the lines of which univariate variance decompositions should be estimated

ia

integer vector of length ncol(y) specifying the ordered levels of the order restricted fixed effect as integers between 1 and n where n is the number of levels

ib

integer vector or factor specifying the levels of the random term (e.g. individuals)

Value

sb

estimates of the random effect variance component

sg

estimates of the interaction term variance component

se

estimates of the residual error variance

tss

total sum of squares (useful to obtain normalized scales between estimates from different measurements)

Details

est.lme estimates the variance components for a two way cross classification mixed model with random interaction. The order restriction on the fixed effect used to improve the estimates by pooling levels of the fixed effect using isotonic regression according to the observed order in the measurements. Estimation is then done using functionality provided by the package .

References

Klinglmueller, F., Tuechler, T., Posch, M. (2010) "Cross Platform Comparison Of Microarray Data Using Order Restricted Inference" Under Review

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D.,R Development Core Team (2010) "nlme: Linear and Nonlinear Mixed Effects Models"

Barlow, R. E., Bartholomew, D. J., Bremner, J. M., and Brunk, H. D. (1972) "Statistical inference under order restrictions"; Wiley, London.

Robertson, T., Wright,F. T. and Dykstra, R. L. (1988) "Order Restricted Statistical Inference"; Wiley, New York.

Examples

g <- rep(1:4,each=10)
r <- rep(rep(1:2,each=5),4)

## No differences (global null)
nulldata <- matrix(rnorm(400),nc=40)

## estimation
res <- est.lme(nulldata,g,r)
round(apply(res,2,summary),2)