PEM-class {MPSEM} | R Documentation |
Class and Methods for Phylogenetic Eigenvector Maps (PEM)
Description
Class and methods to handle Phylogenetic Eigenvector Maps (PEM).
Usage
## S3 method for class 'PEM'
print(x, ...)
## S3 method for class 'PEM'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
## S3 method for class 'PEM'
predict(
object,
targets,
lmobject,
newdata,
interval = c("none", "confidence", "prediction"),
level = 0.95,
...
)
Arguments
x |
A |
... |
Additional parameters to be passed to the method. Currently ignored. |
row.names |
Included for method consistency reason; ignored. |
optional |
Included for method consistency reason; ignored. |
object |
A |
targets |
Output of |
lmobject |
An object of class ‘lm’ (see |
newdata |
Auxiliary trait values. |
interval |
The kind of limits (confidence or prediction) to return with
the predictions; |
level |
Probability of the confidence of prediction interval. |
Format
A PEM-class
object contains:
- x
The
graph-class
object that was used to build the PEM (seePEM.build
).- sp
A
logical
vector specifying which vertex is a tip.- B
The influence matrix for those vertices that are tips.
- ne
The number of edges.
- nsp
The number of species (tips).
- Bc
The column-centred influence matrix.
- means
The column means of
B
.- dist
Edge lengths.
- a
The steepness parameter (see
PEM.build
for details).- psi
The relative evolution rate along the edges (see
PEM.build
for details).- w
Edge weights.
- BcW
The weighted and column-centred influence matrix.
- d
The singular values of
BcW
.- u
The eigenvectors (left singular vectors) of
BcW
.- vt
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:
- S2
The variances of responses (one value for each response).
- y
A copy of the responses.
- opt
The list returned by
optim
.
The estimated weighting parameters are also given as an edge property.
Details
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.
Functions
-
print.PEM
: Print method for PEM-class objects -
as.data.frame.PEM
: Methodas.data.frame
for PEM-class objects -
predict.PEM
: Predict method for PEM-class objects
Author(s)
Guillaume Guenard, with contribution from Pierre Legendre Maintainer: Guillaume Guenard <guillaume.guenard@gmail.com>
References
GuĂ©nard, 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