Splits the data into subsets, computes summary statistics for each, and returns the result.
Arguments
- x
A
CompositionMatrix
object.- by
A vector or a list of grouping elements, each as long as the variables in
x
. The elements are coerced to factors before use.- 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:
covariance()
,
dist
,
mahalanobis()
,
margin()
,
mean()
,
metric_var()
,
quantile()
,
scale()
,
variation()
Examples
## Create a data.frame
X <- data.frame(
samples = c("A", "A", "A", "B", "B", "B", "C", "C", "C"),
groups = c("X", "X", "X", NA, NA, NA, "Y", "Y", "Y"),
Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),
Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),
Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56)
)
## Coerce to a compositional matrix
Y <- as_composition(X)
## Compositional mean by sample
aggregate(Y, by = get_samples(Y), FUN = mean)
#> Ca Fe Na
#> A 0.4468157 0.4539051 0.09927924
#> B 0.4746506 0.4742013 0.05114805
#> C 0.3067545 0.6238351 0.06941036
## Metric variance by group
aggregate(Y, by = get_groups(Y), FUN = metric_var)
#> [,1]
#> X 0.1978911
#> Y 0.4275836