print.MCAvariants {MCAvariants}

Main printing function

Description

This function prints results of classical or ordered multiple correspondence analysis. The input parameter is the name of the output of the main function MCAvariants.

Usage

## S3 method for class 'MCAvariants'
print(x, printdims = 2,...)

Arguments

x

The output of the main function CAvariants.

printdims

The number of dimensions, printdims, that are used to generate the correspondence plot and for summarising the numerical output of the analysis. By default, printdims = 2.

...

Further arguments passed to or from other methods.

Details

This function uses another function (called printwithaxes) for specifying the number of matrix dimensions to print.

Value

The value of output returned depends on the kind of multiple correspondence analysis performed.

DataTable

The Burt data table.

Row coordinates

Rows in principal coordinates: the first 10.

Column coordinates

Column in principal coordinates.

Polynomials

Polynomial functions of each variable. When catype is omca.

Linear Percentage of Clusters

The percentage of individuals belonging to each cluster. When catype is omca.

Polynomial Components of Total Inertia

The decomposition of total inertia via orthogonal polynomials. When catype is omca.

Degree of Freedom

Degree of Freedom of Polynomial Component. When catype is omca.

Inertia values

Inertia values of super-indicator and Burt table.

Benzecri's Inertia values

Adjusted Inertia values, percentages and cumulative.

Total Degree of Freedom

The degree of freedom of total inertia.

Total inertia of X

Total inertia of Super-Indicator table

Total inertia of B

Total inertia of BURT table.

Chi-square values

Chi-square values of BURT Inertia.

Total Chi-square values

Chi-square values of total Inertia of Burt table.

Author(s)

Rosaria Lombardo

References

Lombardo R and Meulman JJ (2010) Journal of Classification, 27, 191-210.
Beh EJ Lombardo R (2014) Correspondence Analysis, Theory, Practice and New Strategies. Wiley

Examples

res=MCAvariants(satisfaction, catype = "omca", np = 5, vordered=c(TRUE,TRUE,TRUE,TRUE,TRUE))
print(res)