A class and set of methods to handle the results of Multiscale Codependence Analysis.
# S3 method for cdp
print(x, ...)# S3 method for cdp
plot(x, col, col.signif = 2, main = "", ...)
# S3 method for cdp
summary(object, ...)
# S3 method for cdp
fitted(object, selection, components = FALSE, ...)
# S3 method for cdp
residuals(object, selection, ...)
# S3 method for cdp
predict(object, selection, newdata, components = FALSE, ...)
cdp-class objects contain:
A list with two elements: the first being a copy of the
response (`Y`) and the second being a copy of the explanatory variables
(`X`). This is the variables that were given to MCA.
The eigenmap-class object that was given to
MCA.
A list with five elements: the first (`UpY`) is a matrix of the cross-products of structuring variable (`U`) and the response variable `Y`, the second (`UpX`) is a matrix of the cross-product of the structuring variable and the explanatory variables (`X`), the third (`C`) is a 3-dimensional array of the codependence coefficients, the fourth (`B`) is a 3-dimensional array of the coregression coefficients, and the fifth (`CM`) is a matrix of the multivariate codependence coefficients.
Results of statistical testing as performed by
test.cdp or permute.cdp. NULL if no
testing was performed, such as when only MCA had been
called. The results of statistical testing is a list containing the
following members:
The number of randomized permutations used by
permute.cdp for permutation testing. 0 or FALSE for
parametric testing obtained using test.cdp.
The indices of codependence coefficient describing statistically significant codependence between `Y` and `X`, in decreasing order of magnitude.
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.
Tests of every single response variable (a
3-dimensional array), had such tests been requested while calling the
testing function, NULL otherwise.
Details about permutation testing not shown in
`test$global` or `test$response`. NULL for parametric
testing.
A cdp-class object.
Further parameters to be passed to other functions or methods.
A vector of color values to be used for plotting the multivariate codependence coefficients.
Color of the frame used to mark the statistically significant codependence coefficients.
Text for the main title of the plot.
A cdp-class object.
A numeric vector of indices or character vector variable
names to test or force-use. Mandatory if object is untested.
A boolean specifying whether the components of fitted or predicted values associated with single eigenfunctions in the map should be returned.
A list with elements $X, $meanY, and $target that contain the information needed to make predictions (see details).
print(cdp): Print method for cdp-class objects.
plot(cdp): Plot method for cdp-class objects.
summary(cdp): Summary method for cdp-class objects.
fitted(cdp): Fitted method for cdp-class objects.
residuals(cdp): Residuals method for cdp-class objects.
predict(cdp): Predict method for cdp-class objects.
tools:::Rd_package_author("codep") Maintainer: tools:::Rd_package_maintainer("codep")
The `fitted`, `residuals`, and `predict` methods return a matrix of fitted,
residuals, or predicted values, respectively. The `fitted` and `predict`
methods return a list a list when argument `component` is TRUE. The
list contains the `fitted` or `predicted` values as a first element and an
array `components` as a second. That 3-dimensional array has one matrix for
each statistically significant codependence coefficient.
For making predictions, argument newdata may contain three elements:
`$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 arbitrary 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 argument 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.
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