Class and methods to handle Phylogenetic Eigenvector Maps (PEM).
# S3 method for PEM
print(x, ...)# S3 method for PEM
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
# S3 method for PEM
predict(
object,
targets,
lmobject,
newdata,
interval = c("none", "confidence", "prediction"),
level = 0.95,
...
)
A PEM-class object contains:
The graph-class object that was used to
build the PEM (see PEM.build).
A logical vector specifying which vertex is
a tip.
The influence matrix for those vertices that are tips.
The number of edges.
The number of species (tips).
The column-centred influence matrix.
The column means of B.
Edge lengths.
The steepness parameter (see PEM.build for
details).
The relative evolution rate along the edges (see
PEM.build for details).
Edge weights.
The weighted and column-centred influence matrix.
The singular values of BcW.
The eigenvectors (left singular vectors) of BcW.
The right singular vectors of BcW.
In addition to these standard component, function,
PEM.fitSimple and PEM.forcedSimple add the
following members, which are necessary to make predictions:
The variances of responses (one value for each response).
A copy of the responses.
The list returned by optim.
The estimated weighting parameters are also given as an edge property.
A PEM-class object containing a Phylogenetic
Eigenvector Map.
Additional parameters to be passed to the method. Currently ignored.
Included for method consistency reason; ignored.
Included for method consistency reason; ignored.
A PEM-class object.
Output of getGraphLocations.
An object of class ‘lm’ (see lm for
details).
Auxiliary trait values.
The kind of limits (confidence or prediction) to return with
the predictions; interval="none": do not return a confidence interval.
Probability of the confidence of prediction interval.
print.PEM: Print method for PEM-class objects
as.data.frame.PEM: Method as.data.frame for PEM-class objects
predict.PEM: Predict method for PEM-class objects
tools:::Rd_package_author("MPSEM") Maintainer: tools:::Rd_package_maintainer("MPSEM")
The print method provides the number of eigenvectors,
the number of observations these vectors are spanning, and their associated
eigenvalues.
The as.data.frame method extracts the eigenvectors from the
object and allows one to use PEM-class objects as data
parameter in function such as lm and glm.
The predict object is a barebone interface meant to make
predictions. It must be given species locations with respect to the
phylogenetic graph (target), which are provided by function
getGraphLocations and a linear model in the form of an object
from lm. The user must provide auxiliary trait values if
lmobject involves such trait.
Guénard, G., Legendre, P., and Peres-Neto, P. 2013. Phylogenetic eigenvector maps (PEM): a framework to model and predict species traits. Meth. Ecol. Evol. 4: 1120--1131
PEM.build, PEM-class