Class and methods for Phylogenetic Eigenvector Maps (PEM)
Class and methods to handle Phylogenetic Eigenvector Maps (PEM).
## 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, ...) x |
A |
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. |
... |
Further parameters to be passed to other functions or methods (currently ignored). |
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.
A PEM-class object contains:
x |
the |
sp |
a |
B |
the influence matrix for those vertices that are tips, |
ne |
the number of edges, |
nsp |
the number of tips, |
Bc |
the column-centred influence matrix, |
means |
the column means of |
dist |
edge lengths, |
a |
the steepness parameter (see |
psi |
the relative evolution rate along the edges (see
|
w |
edge weights, |
BcW |
the weighted and column-centred influence matrix, |
d |
the singular values of |
u |
the eigenvectors (left singular vectors) of |
vt |
the right singular vectors of |
In addition to these standard component, function,
PEM.fitSimple and PEM.forcedSimple add the
following members, which are necessary to make predictions:
S2 |
the variance(s) of the response(s), |
y |
a copy of the response(s), and |
opt |
the list returned by |
as well as a copy of the estimated weighting parameters as edge properties.
Guillaume Guénard, Département des sciences biologiques, Université de Montréal, Montréal, Québec, Canada.
Guénard, G., Legendre, P., and Peres-Neto, P. 2013. Phylogenetic eigenvector maps (PEM): a framework to model and predict species traits. Meth. Ecol. Evol. In press.
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