# compar.gee

##### 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.

- Keywords
- regression

##### Usage

```
compar.gee(formula, data = NULL, family = "gaussian", phy, corStruct,
scale.fix = FALSE, scale.value = 1)
# S3 method for compar.gee
drop1(object, scope, quiet = FALSE, ...)
# S3 method for compar.gee
predict(object, newdata = NULL, type = c("link", "response"), ...)
```

##### 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 function 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 function).- phy
an object of class

`"phylo"`

(ignored if`corStruct`

is used).- corStruct
a (phylogenetic) correlation structure.

- 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`

).- object
an object of class

`"compar.gee"`

resulting from fitting`compar.gee`

.- scope
<unused>.

- quiet
a logical specifying whether to display a warning message about eventual ``marginality principle violation''.

- newdata
a data frame with column names matching the variables in the formula of the fitted object (see

`predict`

for details).- type
a character string specifying the type of predicted values. By default, the linear (link) prediction is returned.

- …
further arguments to be passed to

`drop1`

.

##### Details

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`

.

##### Value

`compar.gee`

returns an object of class `"compar.gee"`

with
the following components:

the function call, including the formula.

a vector of integers assigning the coefficients
to the effects (used by `drop1`

).

the number of observations.

the quasilikelihood information criterion as defined by Pan (2001).

the estimated coefficients (or regression parameters).

the regression residuals.

a character string, the distribution assumed for the response.

a character string, the link function used for the mean function.

the scale (or dispersion parameter).

the variance-covariance matrix of the estimated coefficients.

the phylogenetic degrees of freedom (see Paradis and Claude for details on this).

drop1 returns an object of class "anova".

predict returns a vector or a data frame if newdata is used.

##### Note

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.

##### References

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

##### Examples

```
# NOT RUN {
### The example in Phylip 3.5c (originally from Lynch 1991)
### (the same analysis than in help(pic)...)
tr <- "((((Homo:0.21,Pongo:0.21):0.28,Macaca:0.49):0.13,Ateles:0.62):0.38,Galago:1.00);"
tree.primates <- read.tree(text = tr)
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)
# }
```

*Documentation reproduced from package ape, version 4.1, License: GPL (>= 2)*