cabootcrsresults-class {cabootcrs}

A class containing the results from CA with bootstrapping

Description

This contains all of the usual output from simple or multiple CA, plus the results of the bootstrap analysis and the various settings used for this.

Details

The meanings and possible values for the settings are described in cabootcrs

Slots

br

The basic results from CA, class cabasicresults

datasetname

Name of the data set for printing, class "character"

DataMatrix

The sample data matrix, class "matrix"

rows

Number of rows, class "numeric"

columns

Number of columns, class "numeric"

rowlabels

Row category labels, class "character"

collabels

Column category labels, class "character"

varnames

Names of the variables, class "character"

Rowprinccoord

Principal coordinates for row points, class "matrix"

Colprinccoord

Principal coordinates for column points, class "matrix"

Rowstdcoord

Standard coordinates for row points, class "matrix"

Colstdcoord

Standard coordinates for column points, class "matrix"

RowCTR

Contributions for row points, class "matrix"

RowREP

Representations for row points, class "matrix"

ColCTR

Contributions for column points, class "matrix"

ColREP

Representations for column points, class "matrix"

RowVar

Variances for row points, class "matrix"

RowCov

Covariances for row points, class "array"

ColVar

Variances for column points, class "matrix"

ColCov

Covariances for column points, class "array"

inertiasum

Total inertia, class "numeric"

inertias

Axis inertias, class "matrix"

rowmasses

Masses of row points, class "numeric"

colmasses

Masses of column points, class "numeric"

nboots

Number of bootstrap replicates used to calculate the (co)variances, class "numeric".
If nboots=0 then standard CA or MCA is performed with no confidence regions produced.

resampledistn

Distribution used for resampling, class "character"

multinomialtype

Form of multinomial resampling used, class "character"

sameaxisorder

Number of resamples with no reordering in first six bootstrap axes, class "numeric"

poissonzeronewmean

Mean used for resampling zero cells, class "numeric"

newzeroreset

Option to reset resample zero cells, class "numeric"

printdims

Number of dimensions to print, though note that all are stored, class "numeric"

axisvariances

Number of axes for which variances were calculated and are stored, class "numeric"

bootcritR

Bootstrap critical values for row points, class "array"

bootcritC

Bootstrap critical values for column points, class "array"

usebootcrits

Whether to use bootstrap critical values for confidence ellipses, class "logical"

catype

Type of correspondence analysis performed, class "character"

mcatype

Type of multiple correspondence analysis performed, class "character"

mcaindividualboot

Whether the experimental method to bootstrap an indicator or doubled matrix was used, class "logical"

IndicatorMatrix

The indicator matrix derived from the data matrix, class "matrix"

Jk

The number of classes for each variable, class "numeric"

p

The number of variables, class "numeric"

mcalikertnoise

The noise value used in the experimental method to bootstrap an indicator or doubled matrix, class "numeric"

mcaadjustinertias

Whether MCA inertias were adjusted, class "logical"

mcauseadjustinertiasum

Whether the adjusted MCA inertia sum was used, class "logical"

mcaadjustcoords

Whether the MCA coordinates were adjusted, class "logical"

mcaadjustmassctr

Whether the MCA masses and contributions were adjusted, class "logical"

mcasupplementary

How supplementary points were calculated when bootstrapping a Burt matrix, class "character"

See Also

cabasicresults