| Backward selection regression for GLMM {MXM} | R Documentation |
Backward selection regression for GLMM
Description
Backward selection regression for GLMM
Usage
glmm.bsreg(target, dataset, id, threshold = 0.05, wei = NULL, test = "testIndGLMMReg")
Arguments
target |
The class variable. This can be a numerical vector with continuous data, binary or discrete valued data. It can also be a factor variable with two levels only. |
dataset |
The dataset; provide a numerical a matrix (columns = variables, rows = samples). |
id |
This is a numerical vector of the same size as target denoting the groups or the subjects. |
threshold |
Threshold (suitable values in (0, 1)) for assessing p-values significance. Default value is 0.05. |
wei |
A vector of weights to be used for weighted regression. The default value is NULL. |
test |
This is for the type of regression to be used, "testIndGLMMReg", for Gaussian regression, "testIndGLMMLogistic for logistic regression or "testIndGLMMPois" for Poisson regression. |
Details
If the sample size is less than the number of variables a meesage will appear and no backward regression is performed.
Value
The output of the algorithm is S3 object including:
runtime |
The run time of the algorithm. A numeric vector. The first element is the user time, the second element is the system time and the third element is the elapsed time. |
info |
A matrix with the non selected variables and their latest test statistics and logged p-values. |
mat |
A matrix with the selected variables and their latest statistics and logged p-values. |
final |
The final regression model. |
Author(s)
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr
References
Eugene Demidenko (2013). Mixed Models: Theory and Applications with R, 2nd Edition. New Jersey: Wiley & Sons.
See Also
fbed.glmm.reg, ebic.glmm.bsreg, MMPC.glmm
Examples
## Not run:
require(lme4)
data(sleepstudy)
reaction <- sleepstudy$Reaction
days <- sleepstudy$Days
subject <- sleepstudy$Subject
x <- matrix(rnorm(180 * 200),ncol = 200) ## unrelated predictor variables
m1 <- glmm.bsreg(Reaction, x, subject)
m2 <- MMPC.glmm(target = reaction, group = subject, dataset = x)
## End(Not run)