Compositional Mean of Data Subsets

Splits the data into subsets and computes compositional mean for each.

Usage

condense(x, ...)

# S4 method for class 'CompositionMatrix'
condense(x, by, ignore_na = FALSE, ignore_zero = TRUE, verbose = FALSE, ...)

# S4 method for class 'GroupedComposition'
condense(
  x,
  by = NULL,
  ignore_na = FALSE,
  ignore_zero = TRUE,
  verbose = FALSE,
  ...
)

Arguments

x

A CompositionMatrix object.

...

Currently not used.

by

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

ignore_na

A logical scalar: should missing values be stripped before the computation proceeds?

ignore_zero

A logical scalar: should zeros be stripped before the computation proceeds?

verbose

A logical scalar: should R report extra information on progress?

Value

A CompositionMatrix object.

See also

mean(), aggregate()

Other statistics: aggregate(), 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, groups = 2)

## Compositional mean by group
condense(coda)
#> <GroupedComposition: 5 x 7>
#>      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