Comparative Analysis with GEEs
compar.gee performs the comparative analysis using generalized
estimating equations as described by Paradis and Claude (2002).
drop1 tests single effects of a fitted model output from
predict returns the predicted (fitted) values of the model.
compar.gee(formula, data = NULL, family = "gaussian", phy, corStruct, scale.fix = FALSE, scale.value = 1) ## S3 method for class 'compar.gee': drop1(object, scope, quiet = FALSE, ...) ## S3 method for class 'compar.gee': predict(object, type = c("link", "response"), ...)
If a data frame is specified for the argument
data, then its
rownames are matched to the tip labels of
phy. The user must be
careful here since the function requires that both series of names
perfectly match, so this operation may fail if there is a typing or
syntax error. If both series of names do not match, the values in the
data frame are taken to be in the same order than the tip labels of
phy, and a warning message is issued.
data = NULL, then it is assumed that the variables are in
the same order than the tip labels of
compar.gee returns an object of class
the following components:
drop1 returns an object of class
The calculation of the phylogenetic degrees of freedom is likely to be approximative for non-Brownian correlation structures (this will be refined soon).
The calculation of the quasilikelihood information criterion (QIC) needs to be tested.
Pan, W. (2001) Akaike's information criterion in generalized estimating equations. Biometrics, 57, 120--125.
Paradis, E. and Claude J. (2002) Analysis of comparative data using generalized estimating equations. Journal of theoretical Biology, 218, 175--185.
### The example in Phylip 3.5c (originally from Lynch 1991) ### (the same analysis than in help(pic)...) cat("((((Homo:0.21,Pongo:0.21):0.28,", "Macaca:0.49):0.13,Ateles:0.62):0.38,Galago:1.00);", file = "ex.tre", sep = "") tree.primates <- read.tree("ex.tre") X <- c(4.09434, 3.61092, 2.37024, 2.02815, -1.46968) Y <- c(4.74493, 3.33220, 3.36730, 2.89037, 2.30259) ### Both regressions... the results are quite close to those obtained ### with pic(). compar.gee(X ~ Y, phy = tree.primates) compar.gee(Y ~ X, phy = tree.primates) ### Now do the GEE regressions through the origin: the results are quite ### different! compar.gee(X ~ Y - 1, phy = tree.primates) compar.gee(Y ~ X - 1, phy = tree.primates) unlink("ex.tre") # delete the file "ex.tre"