cabootcrsresults-class {cabootcrs} | R Documentation |

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.

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

`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"`

[Package *cabootcrs* version 2.1.0 Index]