Description Usage Arguments Details Value Author(s) References
Class and methods to handle Multiscale Codependence Analysis (mMCA)
1 2 3 4 5 6 7 8 9 10 11 12 | ## S3 method for class 'cdp'
print(x, ...)
## S3 method for class 'cdp'
plot(x, col, col.signif=2, main="", ...)
## S3 method for class 'cdp'
summary(object, ...)
## S3 method for class 'cdp'
fitted(object, selection, components=FALSE, ...)
## S3 method for class 'cdp'
residuals(object, selection, ...)
## S3 method for class 'cdp'
predict(object, selection, newdata, components=FALSE, ...)
|
x, object |
A |
col |
A vector of color values to be used for plotting the multivariate codependence coefficients. |
col.signif |
Color of the frame used to mark the statistically significant codependence coefficients . |
main |
Text for the main title of the plot. |
selection |
A numeric vector of indices or character vector variable
names to test or force-use. Mandatory if |
components |
A boolean specifying whether the components of fitted or predicted values associated with single eigenfunctions in the map should be returned. |
newdata |
A list with elements $X, $meanY, and $target that contain the information needed to make predictions (see details). |
... |
Further parameters to be passed to other functions or methods. |
The fitted
, residuals
, and predict
methods return
a matrix of fitted, residuals, or predicted values, respectively. The
fitted
and predict
methods return a list a list when the
parameter component
is TRUE
. The list contains the
fitted
or predicted
values as a first element and an
array components
as a second. That 3-dimensional array has one
matrix for each statistically significant codependence coefficient.
For making predictions, parameter newdata
may contain three
elements: $X
, a matrix of new values of the explanatory
variables, $meanY
, a vector of the predicted mean values of the
responses, and $target
, a matrix of target scores for arbitraty
locations within the study area. When no $X
is supplied, the
descriptor given to MCA
is recycled, while when no
$meanY
is supplied, the mean values of the response variables
given to MCA
are used. Finally, when element
$target
is omitted from newdata
, predictions are made at
the sites were observations were done. When none of the above is
provided, or if newdata
is omitted when calling the prediction
method, the behaviour of the predict
method is identical to
that of the fitted
method.
From version 0.7-1, cdp-class
replaces the former class
mca
used by codep-package
because the standard
package MASS
also had S3 methods for a class named mca
that were overwritten by those of codep-package
.
cdp-class
objects contain:
data |
A list with two elements: the first being a copy of the
response ( |
emobj |
The |
UpYXcb |
A list with five elements: the first ( |
test |
Results of statistical testing as performed by
|
$permute |
The number of randomized permutations used by
|
$significant |
The indices of codependence coefficient describing
statistically significant codependence between |
$global |
The testing table (a 5-column matrix) with phi statistics, degrees-of-freedom, and testwise and familywise probabilities of type I (alpha) error. It contains one line for each statistically significant global coefficient (if any) in addition to test results for the first, non-significant coefficient, on which the testing procedure stopped. |
$response |
Tests of every single response variable (a
3-dimensional array), had such tests been requested while calling
the testing function, |
$permutations |
Details about permutation testing not shown in
|
Guillaume Guénard, Département des sciences biologiques, Université de Montréal, Montréal, Québec, Canada.
Guénard, G., Legendre, P., Boisclair, D., and Bilodeau, M. 2010. Multiscale codependence analysis: an integrated approach to analyse relationships across scales. Ecology 91: 2952-2964
Guénard, G. Legendre, P. 2018. Bringing multivariate support to multiscale codependence analysis: Assessing the drivers of community structure across spatial scales. Meth. Ecol. Evol. 9: 292-304
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