R: Canonical Correlation Analysis

CCorA {vegan} | R Documentation

## Canonical Correlation Analysis

### Description

Canonical correlation analysis, following Brian McArdle's unpublished graduate course notes, plus improvements to allow the calculations in the case of very sparse and collinear matrices, and permutation test of Pillai's trace statistic.

### Usage

    CCorA(Y, X, stand.Y=FALSE, stand.X=FALSE, permutations = 0, ...)

    ## S3 method for class 'CCorA'
    biplot(x, plot.type="ov", xlabs, plot.axes = 1:2, int=0.5,
       col.Y="red", col.X="blue", cex=c(0.7,0.9), ...)

### Arguments

`Y` | Left matrix (object class: `matrix` or `data.frame`).
`X` | Right matrix (object class: `matrix` or `data.frame`).
`stand.Y` | Logical; should `Y` be standardized?
`stand.X` | Logical; should `X` be standardized?
`permutations` | a list of control values for the permutations as returned by the function `how`, or the number of permutations required, or a permutation matrix where each row gives the permuted indices.
`x` | `CCoaR` result object.
`plot.type` | A character string indicating which of the following plots should be produced: `"objects"`, `"variables"`, `"ov"` (separate graphs for objects and variables), or `"biplots"`. Any unambiguous subset containing the first letters of these names can be used instead of the full names.
`xlabs` | Row labels. The default is to use row names, `NULL` uses row numbers instead, and `NA` suppresses plotting row names completely.
`plot.axes` | A vector with 2 values containing the order numbers of the canonical axes to be plotted. Default: first two axes.
`int` | Radius of the inner circles plotted as visual references in the plots of the variables. Default: `int=0.5`. With `int=0`, no inner circle is plotted.
`col.Y` | Color used for objects and variables in the first data table (Y) plots. In biplots, the objects are in black.
`col.X` | Color used for objects and variables in the second data table (X) plots.
`cex` | A vector with 2 values containing the size reduction factors for the object and variable names, respectively, in the plots. Default values: `cex=c(0.7,0.9)`.
`...` | Other arguments passed to these functions. The function `biplot.CCorA` passes graphical arguments to `biplot` and `biplot.default`. `CCorA` currently ignores extra arguments.

### Details

Canonical correlation analysis (Hotelling 1936) seeks linear combinations of the variables of `Y` that are maximally correlated to linear combinations of the variables of `X`. The analysis estimates the relationships and displays them in graphs. Pillai's trace statistic is computed and tested parametrically (F-test); a permutation test is also available.

Algorithmic note – The blunt approach would be to read the two matrices, compute the covariance matrices, then the matrix `S12 %*% inv(S22) %*% t(S12) %*% inv(S11)`. Its trace is Pillai's trace statistic. This approach may fail, however, when there is heavy multicollinearity in very sparse data matrices. The safe approach is to replace all data matrices by their PCA object scores.

The function can produce different types of plots depending on the option chosen: `"objects"` produces two plots of the objects, one in the space of Y, the second in the space of X; `"variables"` produces two plots of the variables, one of the variables of Y in the space of Y, the second of the variables of X in the space of X; `"ov"` produces four plots, two of the objects and two of the variables; `"biplots"` produces two biplots, one for the first matrix (Y) and one for second matrix (X) solutions. For biplots, the function passes all arguments to `biplot.default`; consult its help page for configuring biplots.

### Value

Function `CCorA` returns a list containing the following elements:

`Pillai` | Pillai's trace statistic = sum of the canonical eigenvalues.
`Eigenvalues` | Canonical eigenvalues. They are the squares of the canonical correlations.
`CanCorr` | Canonical correlations.
`Mat.ranks` | Ranks of matrices `Y` and `X`.
`RDA.Rsquares` | Bimultivariate redundancy coefficients (R-squares) of RDAs of Y|X and X|Y.
`RDA.adj.Rsq` | `RDA.Rsquares` adjusted for `n` and the number of explanatory variables.
`nperm` | Number of permutations.
`p.Pillai` | Parametric probability value associated with Pillai's trace.
`p.perm` | Permutational probability associated with Pillai's trace.
`Cy` | Object scores in Y biplot.
`Cx` | Object scores in X biplot.
`corr.Y.Cy` | Scores of Y variables in Y biplot, computed as cor(Y,Cy).
`corr.X.Cx` | Scores of X variables in X biplot, computed as cor(X,Cx).
`corr.Y.Cx` | cor(Y,Cy) available for plotting variables Y in space of X manually.
`corr.X.Cy` | cor(X,Cx) available for plotting variables X in space of Y manually.
`control` | A list of control values for the permutations as returned by the function `how`.
`call` | Call to the CCorA function.

### Author(s)

Pierre Legendre, Departement de Sciences Biologiques, Universite de Montreal. Implemented in vegan with the help of Jari Oksanen.

### References

Hotelling, H. 1936. Relations between two sets of variates. Biometrika 28: 321-377.

Legendre, P. 2005. Species associations: the Kendall coefficient of concordance revisited. Journal of Agricultural, Biological, and Environmental Statistics 10: 226-245.

### Examples

    # Example using two mite groups. The mite data are available in vegan
    data(mite)
    # Two mite species associations (Legendre 2005, Fig. 4)
    group.1 <- c(1,2,4:8,10:15,17,19:22,24,26:30)
    group.2 <- c(3,9,16,18,23,25,31:35)
    # Separate Hellinger transformations of the two groups of species
    mite.hel.1 <- decostand(mite[,group.1], "hel")
    mite.hel.2 <- decostand(mite[,group.2], "hel")
    rownames(mite.hel.1) = paste("S",1:nrow(mite),sep="")
    rownames(mite.hel.2) = paste("S",1:nrow(mite),sep="")
    out <- CCorA(mite.hel.1, mite.hel.2)
    out
    biplot(out, "ob")                 # Two plots of objects
    biplot(out, "v", cex=c(0.7,0.6))  # Two plots of variables
    biplot(out, "ov", cex=c(0.7,0.6)) # Four plots (2 for objects, 2 for variables)
    biplot(out, "b", cex=c(0.7,0.6))  # Two biplots
    biplot(out, xlabs = NA, plot.axes = c(3,5))    # Plot axes 3, 5. No object names
    biplot(out, plot.type="biplots", xlabs = NULL) # Replace object names by numbers

    # Example using random numbers. No significant relationship is expected
    mat1 <- matrix(rnorm(60),20,3)
    mat2 <- matrix(rnorm(100),20,5)
    out2 = CCorA(mat1, mat2, permutations=99)
    out2
    biplot(out2, "b")

[Package vegan version 2.4-3 Index]