cabootcrsresults-class {cabootcrs} | R Documentation |

## 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

*cabootcrs*version 2.1.0 Index]