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codep (version 0.2-1)

mca-class: Class and methods for Multiscale Codependence Analysis

Description

Class and methods to handle Multiscale Codependence Analysis (MCA)

Usage

## S3 method for class 'mca':
print(x, ...)
## S3 method for class 'mca':
summary(object, ...)
## S3 method for class 'mca':
plot(x, ...)
## S3 method for class 'mca':
fitted(object, which=NA, components=FALSE, ...)
## S3 method for class 'mca':
residuals(object, which=NA, ...)
## S3 method for class 'mca':
predict(object, which=NA, newdata=NA, components=FALSE, ...)

Arguments

x
A mca-class object.
object
A mca-class object.
which
A numeric vector of indices or character vector variable names to test or force-use. Mandatory if mcaobj is untested.
components
A boolean specifying whether the components of fitted or predicted values associated with single eigenfunctions in the map should be returned.
newdata
A numeric vector containing new values of the explonatory variable.
...
Further parameters to be passed to other functions or methods (currently ignored).

Value

  • mca-class objects contain:
  • dataA copy of the response (y) and explanatory (x) variables that were given to MCA.
  • emobjThe eigenmap-class object that was given to MCA.
  • UpyxcbA 4 columns matrix containing the vectors of cross-products of structuring variable (U) with variable y and x, the codependence coefficients $\mathbf{c}$ and the coregression coefficients $\mathbf{b}$.
  • testResults of statistical testing as performed by test.mca or permute.mca. 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:
  • permuteThe number of randomized permutations used by permute.mca for permutation testing. 0 or FALSE for parametric testing obtained using test.mca.
  • significantThe indices of codependence coefficient describing statistically significant codependence between y and x, in decreasing order of magnitude.
  • test.tableThe testing table (a 4 columns matrix) with $\tau$ statistics, degrees-of-freedom, and testwise and familywise probabilities of type I ($\alpha$) error. It contains one line for each statistically significant coefficient (if any).
  • detailsDetails about permutation testing not shown in test.table. NULL for parametric testing.

encoding

utf8

Details

The fitted, residuals, and predict methods return a single-column 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 item and a matrix components as a second. This matrix has one column for each statistically significant codependence coefficient.

References

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