compar.gee  R Documentation 
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, newdata = NULL, type = c("link", "response"), ...)
formula 
a formula giving the model to be fitted. 
data 
the name of the data frame where the variables in

family 
a function specifying the distribution assumed for the
response; by default a Gaussian distribution (with link identity) is
assumed (see 
phy 
an object of class 
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 
object 
an object of class 
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

type 
a character string specifying the type of predicted values. By default, the linear (link) prediction is returned. 
... 
further arguments to be passed to 
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:
call 
the function call, including the formula. 
effect.assign 
a vector of integers assigning the coefficients
to the effects (used by 
nobs 
the number of observations. 
QIC 
the quasilikelihood information criterion as defined by Pan (2001). 
coefficients 
the estimated coefficients (or regression parameters). 
residuals 
the regression residuals. 
family 
a character string, the distribution assumed for the response. 
link 
a character string, the link function used for the mean function. 
scale 
the scale (or dispersion parameter). 
W 
the variancecovariance matrix of the estimated coefficients. 
dfP 
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.
The calculation of the phylogenetic degrees of freedom is likely to be approximative for nonBrownian correlation structures (this will be refined soon).
The calculation of the quasilikelihood information criterion (QIC) needs to be tested.
Emmanuel Paradis
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.
read.tree
, pic
,
compar.lynch
, drop1
### 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)
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