Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 |
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
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.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # 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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