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 compar.gee.

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.

If data = NULL, then it is assumed that the variables are in the same order than the tip labels of phy.


compar.gee returns an object of class "compar.gee" with the following components:callthe function call, including the formula.effect.assigna vector of integers assigning the coefficients to the effects (used by drop1).nobsthe number of observations.QICthe quasilikelihood information criterion as defined by Pan (2001).coefficientsthe estimated coefficients (or regression parameters).residualsthe regression residuals.familya character string, the distribution assumed for the response.linka character string, the link function used for the mean function.scalethe scale (or dispersion parameter).Wthe variance-covariance matrix of the estimated coefficients.dfPthe phylogenetic degrees of freedom (see Paradis and Claude for details on this).drop1 returns an object of class "anova".


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.

See Also

read.tree, pic, compar.lynch, drop1

### The example in Phylip 3.5c (originally from Lynch 1991)
### (the same analysis than in help(pic)...)
   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"
Documentation reproduced from package ape, version 2.7-3, License: GPL (>= 2)

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