compar.gee: Comparative Analysis with GEEs

Description

This function performs the comparative analysis using generalized estimating equations as described by Paradis and Claude (2002).

Usage

compar.gee(formula, data = NULL, family = "gaussian", phy,
          scale.fix = FALSE, scale.value = 1)

Arguments

formula

a formula giving the model to be fitted.

data

the name of the data frame where the variables in formula are to be found; by default, the variables are looked for in the global environment.

family

a character string specifying the distribution assumed for the response; by default a Gaussian distribution (with link identity) is assumed (see ?family for details on specifying the distribution, and on changing the link funct

phy

an object of class "phylo".

scale.fix

logical, indicates whether the scale parameter should be fixed (TRUE) or estimated (FALSE, the default).

scale.value

if scale.fix = TRUE, gives the value for the scale (default: scale.value = 1).

Value

an object of class "compar.gee" with the following components:
callthe function call, including the formula.
nobsthe number of observations.
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).

Details

If a data frame is specified for the argument data and that data frame has rownames, then its values are matched to the tip labels of phy, otherwise its values are taken to be in the same order than the tip labels of phy. If data = NULL, then it is assumed that the variables are in the same order than the tip labels of phy.

References

Paradis, E. and Claude J. (2002) Analysis of comparative data using generalized estimating equations. Journal of theoretical Biology, 216, 000--000.

Examples

Run this code

### 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"

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