summary.CAvariants {CAvariants} | R Documentation |
Summary of numerical results from CA variants
Description
This function prints a numerical summary of the results from any of the six variants of correspondence analysis.
The input parameter is the name of the output of the main function CAvariants
.
Usage
## S3 method for class 'CAvariants'
summary(object, printdims, digits, ...)
Arguments
object |
The output of the main function |
printdims |
The number of dimensions that are used for summarising the numerical output of the analysis. By default, |
digits |
The number of decimal places used for displaying the numerical summaries of the analysis.
By default, |
... |
Further arguments passed to, or from, other functions. |
Value
The value of output returned depends on the type of correspondence analysis that is performed.
Inertias |
The inertia values, their percentage contribution to the total inertia and the cumulative percent inertias for the row and column variables. |
Generalised correlation matrix |
The matrix of generalised correlations when performing
an ordered correspondence analysis, |
Row principal coordinates |
The row principal coordinates when |
Column principal coordinates |
The column principal coordinates when |
Row standard coordinates |
The row standard coordinates when |
Column standard coordinates |
The column standard coordinates when |
Row principal polynomial coordinates |
The row principal polynomial coordinates
when |
Column principal polynomial coordinates |
The column principal coordinates when |
Row standard polynomial coordinates |
The row standard polynomial coordinates when |
Column standard polynomial coordinates |
The column standard polynomial coordinates when |
Total inertia |
The total inertia. For example, for non symmetrical correspondence analysis the numerator of the Goodman-Kruskal tau index, its C-statistic and p-value are returned. |
Polynomial components |
The polynomial components of the total inertia and their p-values.
The total inertia of the column space is partitioned to identify polynomial components.
when |
Inner product |
The inner product of the biplot coordinates for the two-dimensional plot. |
Author(s)
Rosaria Lombardo and Eric J. Beh
References
Beh EJ and Lombardo R 2014 Correspondence Analysis: Theory, Practice and New Strategies. Wiley.
Lombardo R Beh EJ 2016 Variants of Simple Correspondence Analysis. The R Journal, 8 (2), 167–184.
Lombardo R Beh EJ and Kroonenberg PM 2016 Modelling Trends in Ordered Correspondence Analysis Using Orthogonal
Polynomials. Psychometrika, 81(2), 325–349.
Examples
asbestos <- matrix(c(310, 36, 0, 0, 212, 158, 9, 0, 21, 35, 17, 4, 25, 102,
49, 18, 7, 35, 51, 28), 4, 5, dimnames = list(c("none","grade1", "grade2", "grade3"),
c("0-9", "10-19", "20-29", "30-39", "40")))
risasbestos <- CAvariants(asbestos, catype = "DOCA", firstaxis = 1, lastaxis = 2)
summary(risasbestos)