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Object Summaries

Source: R/AllGenerics.R, R/coerce.R, R/summary.R
summary.Rd

Provides a summary of the results of a multivariate data analysis.

Usage

# S4 method for class 'MultivariateSummary'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

# S4 method for class 'CA'
summary(
  object,
  ...,
  axes = c(1, 2),
  margin = 1,
  active = TRUE,
  sup = TRUE,
  rank = NULL
)

# S4 method for class 'PCA'
summary(
  object,
  ...,
  axes = c(1, 2),
  margin = 1,
  active = TRUE,
  sup = TRUE,
  rank = NULL
)

Arguments

x

A MultivariateSummary object.

row.names

A character vector giving the row names for the data frame, or NULL.

optional

A logical scalar: should the names of the variables in the data frame be checked? If FALSE then the names of the variables in the data frame are checked to ensure that they are syntactically valid variable names and are not duplicated.

...

Currently not used.

object

A CA, MCA or PCA object.

axes

A length-two numeric vector giving the dimensions to be summarized.

margin

A length-one numeric vector giving the subscript which the data will be summarized: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

active

A logical scalar: should the active observations be summarized?

sup

A logical scalar: should the supplementary observations be summarized?

rank

An integer value specifying the maximal number of components to be kept in the results. Deprecated, use axes instead.

See also

Other summary: describe(), tidy()

Author

N. Frerebeau

Examples

## Data from Lebart et al. 2006, p. 170-172
data("colours")

## Compute correspondence analysis
X <- ca(colours)

## Rows summary
summary(X, margin = 1)
#> # Correspondence Analysis (CA)
#> 
#> ## Eigenvalues
#> 
#> |    | eigenvalues | variance | cumulative |
#> | :- | ----------: | -------: | ---------: |
#> | F1 |       0.209 |   89.373 |     89.373 |
#> | F2 |       0.022 |    9.515 |     98.888 |
#> | F3 |       0.003 |    1.112 |        100 |
#> 
#> ## Active rows
#> 
#> |          | inertia | F1_coord | F1_contrib | F1_cos2 | F2_coord | F2_contrib | F2_cos2 |
#> | :------- | ------: | -------: | ---------: | ------: | -------: | ---------: | ------: |
#> | marron   | 250.487 |   -0.492 |     43.116 |   0.967 |   -0.088 |     13.042 |   0.031 |
#> | noisette |  83.321 |   -0.213 |      3.401 |   0.542 |    0.167 |     19.804 |   0.336 |
#> | vert     | 148.785 |    0.162 |      1.355 |   0.176 |    0.339 |      55.91 |   0.773 |
#> | bleu     | 306.566 |    0.547 |     52.128 |   0.977 |   -0.083 |     11.244 |   0.022 |

## Columns summary
summary(X, margin = 2)
#> # Correspondence Analysis (CA)
#> 
#> ## Eigenvalues
#> 
#> |    | eigenvalues | variance | cumulative |
#> | :- | ----------: | -------: | ---------: |
#> | F1 |       0.209 |   89.373 |     89.373 |
#> | F2 |       0.022 |    9.515 |     98.888 |
#> | F3 |       0.003 |    1.112 |        100 |
#> 
#> ## Active columns
#> 
#> |         | inertia | F1_coord | F1_contrib | F1_cos2 | F2_coord | F2_contrib | F2_cos2 |
#> | :------ | ------: | -------: | ---------: | ------: | -------: | ---------: | ------: |
#> | brun    | 303.812 |   -0.505 |     22.246 |   0.838 |   -0.215 |     37.877 |   0.152 |
#> | chatain |  25.428 |   -0.148 |      5.086 |   0.864 |    0.033 |      2.319 |   0.042 |
#> | roux    | 125.862 |    -0.13 |      0.964 |   0.133 |     0.32 |     55.131 |   0.812 |
#> | blond   |  702.91 |    0.835 |     71.704 |   0.993 |    -0.07 |      4.673 |   0.007 |