anacor {anacor}R Documentation

Simple and Canonical Correspondence Analysis

Description

This function performs simple and canonical CA for 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"), eps = 1e-06)

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

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", "centroid", "Benzecri", "Goodman".
eps Convergence criterion for reconstitution algorithm.
x Object of class "anacor" in print.anacor.
object Object of class "anacor" in summary.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. Furthermore, scores can be scaled around their centroid. Goodman scaling is based on Fisher-Maung decomposition.

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.
left.singvec Left singular vectors without trivial solution.
right.singvec Right singular vectors without trivial solution.
eigen.values Eigenvalues without trivial solution.
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., & Mair, P. (2007). Simple and canonical correspondence analysis using the R package anacor. Preprint available at http://gifi.stat.ucla.edu/anacor.pdf

See Also

plot.anacor

Examples


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


[Package anacor version 0.9-8 Index]