adonis.dispRity: adonis dispRity (from 'vegan::adonis2')

In dispRity: Measuring Disparity

adonis dispRity (from vegan::adonis2)

Description

Passing dispRity objects to the adonis2 function from the vegan package.

Usage

adonis.dispRity(
  data,
  formula = matrix ~ group,
  method = "euclidean",
  ...,
  warn = TRUE,
  matrix = 1
)

Arguments

data	A dispRity object with subsets
formula	The model formula (default is matrix ~ group, see details)
method	The distance method to be passed to adonis2 and eventually to vegdist (see details - default method ="euclidean")
...	Any optional arguments to be passed to adonis2
warn	logical, whether to print internal warnings (TRUE; default - advised) or not (FALSE).
matrix	numeric, which matrix to use (default is 1).

Details

The first element of the formula (the response) must be called matrix and the predictors must be existing in the subsets of the dispRity object.

If data$matrix[[1]] is not a distance matrix, distance is calculated using the dist function. The type of distance can be passed via the standard method argument that will be recycled by adonis2.

If the dispRity data has custom subsets with a single group, the formula is set to matrix ~ group.

If the dispRity data has custom subsets with multiple group categories (separated by a dot, e.g. c("group1.cat1", "group1.cat2", "group2.catA", "group2.catB") being two groups with two categories each), the default formula is matrix ~ first_group but can be set to any combination (e.g. matrix ~ first_group + second_group).

If the dispRity data has time subsets, the predictor is automatically set to time.

Author(s)

Thomas Guillerme

See Also

adonis2, test.dispRity, custom.subsets, chrono.subsets.

test.dispRity, custom.subsets, chrono.subsets

Examples

## Adonis with one groups 

## Generating a random character matrix
character_matrix <- sim.morpho(rtree(20), 50, rates = c(rnorm, 1, 0))
## Calculating the distance matrix
distance_matrix <- as.matrix(dist(character_matrix))
## Creating two groups
random_groups <- list("group1" = 1:10, "group2" = 11:20)

## Generating a dispRity object
random_disparity <- custom.subsets(distance_matrix, random_groups)
## Running a default NPMANOVA
adonis.dispRity(random_disparity)


## Adonis with multiple groups

## Creating a random matrix
random_matrix <- matrix(data = rnorm(90), nrow = 10, 
                     dimnames = list(letters[1:10]))
## Creating two groups with two states each
groups <- as.data.frame(matrix(data = c(rep(1,5), rep(2,5), rep(c(1,2), 5)),
         nrow = 10, ncol = 2, dimnames = list(letters[1:10], c("g1", "g2"))))

## Creating the dispRity object
multi_groups <- custom.subsets(random_matrix, groups)

## Running the NPMANOVA
adonis.dispRity(multi_groups, matrix ~ g1 + g2)

## Adonis with time

## Creating time series
data(BeckLee_mat50)
data(BeckLee_tree)
data(BeckLee_ages)
time_subsets <- chrono.subsets(BeckLee_mat50, BeckLee_tree, 
     method = "discrete", inc.nodes = FALSE, time = c(100, 85, 65, 0),
     FADLAD = BeckLee_ages)

## Running the NPMANOVA with time as a predictor
adonis.dispRity(time_subsets, matrix ~ time)

## Running the NPMANOVA with each time bin as a predictor
adonis.dispRity(time_subsets, matrix ~ chrono.subsets)

dispRity documentation built on Aug. 9, 2022, 5:11 p.m.