| mmiGEE | R Documentation |
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.
mmiGEE(object, data, trace = FALSE)
object |
A model of class |
data |
A data frame or set of vectors of equal length. |
trace |
A logical indicating whether or not to print results to console. |
Calculates the relative importance of each variable
using multi-model inference methods in a Generalized Estimating Equations
framework implemented in GEE.
mmiGEE returns a list containing the following elements
resultA matrix containing slopes, degrees of freedom, quasilikelihood, QIC, delta, and weight values for the set of candidate models. The models are ranked by QIC.
rviA vector containing the relative importance of each variable in the regression.
Gudrun Carl, Sam Levin
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
GEE, qic.calc, MuMIn
# 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)
## End(Not run)
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