caper (version 0.5.2)

pgls-methods: Generic model methods for 'pgls' models.

Description

These are simple summary methods, accessor functions and summary and print methods for 'pgls' models.

Usage

# S3 method for pgls
coef(object, ...)
# S3 method for pgls
residuals(object, phylo = FALSE, ...)
# S3 method for pgls
fitted(object, ...)
# S3 method for pgls
predict(object, newdata, ...)
# S3 method for pgls
summary(object, ...)
# S3 method for pgls
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for summary.pgls
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for pgls
nobs(object, ...)

Arguments

object

An object of class 'pgls'.

x

An object of class 'pgls'.

phylo

Return phylogenetically corrected residuals or ordinary residuals (see details).

newdata

Alternative data for predicting from 'pgls' models.

digits

Number of digits to show in summary methods.

...

Further arguments to methods.

Value

The 'summary' method returns an object of class 'summary.pgls' containing:

call

The original function call creating the model.

df

A vector of the degrees of freedom used to estimate parameters and the residual degrees of freedom.

sigma

The square root of the estimated variance of the random error.

residuals

The phylogenetically corrected residuals.

coefficients

A table of model coefficient, standard errors and t values.

param

A vector of branch length parameters used in the model.

mlVals

A vector showing which branch length parameters have been optimised.

param.CI

A list of length three containing confidence intervals and p values on parameter bounds for each parameter.

fstatistic

A vector of the F value, numerator and denominator degrees of freedom for the model.

r.squared

The r^2 for the model.

adj.r.squared

The adjusted r^2 for the model.

Details

Phylogenetically corrected residuals from 'pgls' models [TODO].

See Also

pgls

Examples

# NOT RUN {
data(shorebird)
shorebird <- comparative.data(shorebird.tree, shorebird.data, Species, vcv=TRUE, vcv.dim=3)
mod1 <- pgls(log(Egg.Mass) ~ log(M.Mass) * log(F.Mass), shorebird) 
print(mod1)

mod1.sum <- summary(mod1)
print(mod1.sum)
# }