Compute Summary Statistics of Data Subsets

Splits the data into subsets, computes summary statistics for each, and returns the result.

Usage

# S4 method for class 'CompositionMatrix'
aggregate(x, by, FUN, ..., simplify = TRUE, drop = TRUE)

# S4 method for class 'GroupedComposition'
aggregate(x, FUN, ..., simplify = TRUE)

Arguments

x

A CompositionMatrix object.

by

A vector or a list of grouping elements, each as long as the variables in x (see group()).

FUN

A function to compute the summary statistics.

...

Further arguments to be passed to FUN.

simplify

A logical scalar: should the results be simplified to a matrix if possible?

drop

A logical scalar indicating whether to drop unused combinations of grouping values.

Value

A matrix.

See also

Other statistics: condense(), covariance(), dist, mahalanobis(), margin(), mean(), pip(), quantile(), scale(), variance(), variance_total(), variation()

Author

N. Frerebeau

Examples

## Data from Aitchison 1986
data("slides")

## Coerce to a compositional matrix
coda <- as_composition(slides)

## Compositional mean by slide
aggregate(coda, by = slides$slide, FUN = mean)
#>      quartz microcline plagioclass    biotite   muscovite     opaques
#> A 0.2591344  0.3553997   0.3335045 0.02654833 0.013683701 0.007173544
#> B 0.2703079  0.3526640   0.3239803 0.03482515 0.008977167 0.006667495
#> C 0.2772923  0.3535327   0.3149545 0.03107089 0.011128347 0.007732587
#> D 0.2757090  0.3548635   0.3138702 0.03298438 0.010338266 0.009093408
#> E 0.2794701  0.3474061   0.3231178 0.02962410 0.010892785 0.006494703
#>    nonopaques
#> A 0.004555830
#> B 0.002578014
#> C 0.004288649
#> D 0.003141288
#> E 0.002994409

## Metric variance by slide
aggregate(coda, by = slides$slide, FUN = variance_total)
#>        [,1]
#> A 0.2775844
#> B 1.1398564
#> C 0.1356376
#> D 0.7032019
#> E 0.2810018