anacor: Simple and Canonical Correspondence Analysis

In anacor: Simple and Canonical Correspondence Analysis

Simple and Canonical Correspondence Analysis

Description

This function performs simple and canonical CA for possibly incomplete tables based on SVD. Different scaling methods for row and column scores are provided.

Usage

anacor(tab, ndim = 2, row.covariates, col.covariates, scaling = c("Benzecri","Benzecri"), 
ellipse = FALSE, eps = 1e-06)

## S3 method for class 'anacor'
print(x,...)

Arguments

tab	Data frame of dimension n times m with frequencies. Missings are coded as NA.
ndim	Number of dimensions.
row.covariates	Matrix with n rows containing covariates for the row scores.
col.covariates	Matrix with m rows containing covariates for the column scores.
scaling	A vector with two elements. The first one corresponds to the method for row scaling, the second one for column scaling. Available scaling methods are "standard", "Benzecri", "Goodman".
ellipse	If TRUE, confidence ellipses are computed.
eps	Convergence criterion for reconstitution algorithm.
x	Object of class "anacor" in print.anacor.
...	Additional arguments ignored.

Details

Missing values in tab are imputed using the reconstitution algorithm. Setting scaling to "standard" leads to standard coordinates. Principal coordinates can be computed by means of Benzecri decomposition. Goodman scaling is based on Fisher-Maung decomposition.

For large datasets it is suggested to set ellipse = FALSE. If ellipse = TRUE, make sure that there are no rows and columns that have full 0 entries.

Value

row.scores	Scaled row scores.
col.scores	Scaled column scores.
ndim	Number of dimensions extracted.
chisq	Total chi-square value.
chisq.decomp	Chi-square decomposition across dimensions with p-values.
singular.values	Singular values without trivial solution.
se.singular.values	Standard errors for the singular values.
stestmat	Test results for singular values (only for ellipse = TRUE).
left.singvec	Left singular vectors without trivial solution.
right.singvec	Right singular vectors without trivial solution.
eigen.values	Eigenvalues for the fitted dimensionality.
eigenvall	Full vector of eigenvalues (principal inertias).
datname	Name of the dataset.
tab	Table with imputed frequencies in case of missings.
row.covariates	Matrix with row covariates.
col.covariates	Matrix with column covariates.
scaling	Scaling Method.
bdmat	List of matrices with observed and fitted Benzecri distances for rows and columns.
rmse	Root mean squared error of Bezencri distances (rows and columns).
row.acov	Covariance matrix for row scores.
col.acov	Covariance matrix for column scores.
cancoef	List containing canonical coefficients (CCA only).
sitescores	List containing the site scores (CCA only).
isetcor	List containing the intraset correlations (CCA only).

Author(s)

Jan de Leeuw, Patrick Mair

References

De Leeuw, J. and Mair, P. (2009). Simple and Canonical Correspondence Analysis Using the R Package anacor. Journal of Statistical Software, 31(5), 1-18. https://www.jstatsoft.org/v31/i05/

See Also

plot.anacor

Examples

## simple CA on Tocher data, symmetric standard coordinates
data(tocher)
res <- anacor(tocher)
res

## simple CA on Tocher data, asymmetric coordinates
res <- anacor(tocher, scaling = c("standard", "Benzecri"))
res

## 2- and 5-dimensional solutions for bitterling data, Benzecri scaling
data(bitterling)
res1 <- anacor(bitterling, ndim = 2, scaling = c("Benzecri", "Benzecri"))
res2 <- anacor(bitterling, ndim = 5, scaling = c("Benzecri", "Benzecri"))
res1
res2

## Canonical CA on Maxwell data, Goodman scaling
data(maxwell)
res <- anacor(maxwell$table, row.covariates = maxwell$row.covariates, 
scaling = c("Goodman", "Goodman"))
res

anacor documentation built on June 10, 2022, 5:09 p.m.