spind (version 2.2.1)

mmiGEE: Multi-model inference for GEE models

Description

mmiGEE is a multimodel inference approach evaluating the relative importance of predictors used in GEE.

@details It performs automatically generated model selection and creates a model selection table according to the approach of multi-model inference (Burnham & Anderson, 2002). QIC is used to obtain model selection weights and to rank the models. Moreover, mmiGEE calculates relative variable importance of a given model. Finally, this function requires that all predictor variables be continuous.

Usage

mmiGEE(object, data, trace = FALSE)

Arguments

object

A model of class GEE.

data

A data frame or set of vectors of equal length.

trace

A logical indicating whether or not to print results to console.

Value

mmiGEE returns a list containing the following elements

result

A matrix containing slopes, degrees of freedom, quasilikelihood, QIC, delta, and weight values for the set of candidate models. The models are ranked by QIC.

rvi

A vector containing the relative importance of each variable in the regression.

Details

Calculates the relative importance of each variable using multi-model inference methods in a Generalized Estimating Equations framework implemented in GEE.

References

Burnham, K.P. & Anderson, D.R. (2002) Model selection and multimodel inference. Springer, New York.

Carl G & Kuehn I, 2007. Analyzing Spatial Autocorrelation in Species Distributions using Gaussian and Logit Models, Ecol. Model. 207, 159 - 170

See Also

GEE, qic.calc, MuMIn

Examples

# NOT RUN {
# data (for demonstration only)
library(MASS)
data(birthwt)

# impose an artificial (not fully appropriate) grid structure

x <- rep(1:14, 14)
y <- as.integer(gl(14, 14))
coords <- cbind(x[-(190:196)], y[-(190:196)])

# }
# NOT RUN {
formula <- formula(low ~ race + smoke +  bwt)

mgee <- GEE(formula,
            family = "gaussian",
            data = birthwt,
            coord = coords,
            corstr = "fixed",
            scale.fix = TRUE)

mmi <- mmiGEE(mgee, birthwt)

# }