dispRity (version 1.5.0)

dispRity: Calculates disparity from a matrix.

Description

Calculates disparity from a matrix, a list of matrices or subsets of a matrix, where the disparity metric can be user specified.

Usage

dispRity(
  data,
  metric,
  dimensions,
  ...,
  between.groups = FALSE,
  verbose = FALSE
)

Arguments

data

A matrix or a dispRity object (see details).

metric

A vector containing one to three functions. At least of must be a dimension-level 1 or 2 function (see details).

dimensions

Optional, a numeric value or proportion of the dimensions to keep.

...

Optional arguments to be passed to the metric.

between.groups

A logical value indicating whether to run the calculations between groups (TRUE) or not (FALSE - default) or a numeric list of pairs of groups to run (see details).

verbose

A logical value indicating whether to be verbose or not.

Value

This function outputs a dispRity object containing:

matrix

the multidimensional space (a matrix).

call

A list containing the called arguments.

subsets

A list containing matrices pointing to the elements present in each subsets.

disparity

A list containing the disparity in each subsets.

Use summary.dispRity to summarise the dispRity object.

Details

The dispRity object given to the data argument can be: a list of matrices (typically output from the functions chrono.subsets or custom.subsets), a bootstrapped matrix output from boot.matrix, a list of disparity measurements calculated from the dispRity function or a matrix object with rows as elements and columns as dimensions. In any of these cases, the data is considered as the multidimensional space and is not transformed (e.g. if ordinated with negative eigen values, no correction is applied to the matrix).

metric should be input as a vector of functions. The functions are sorted and used by dimension-level from 3 to 1 (see dispRity.metric and make.metric). Typically dimension-level 3 functions take a matrix and output a matrix; dimension-level 2 functions take a matrix and output a vector and dimension-level 1 functions take a matrix or a vector and output a single value. When more than one function is input, they are treated first by dimension-level (i.e. 3, 2 and finally 1). Note that the functions can only take one metric of each dimension-level and thus can only take a maximum of three arguments!

Some metric functions are built into the dispRity package: see dispRity.metric For user specified metrics, please use make.metric to ensure that the metric will work.

HINT: if using more than three functions you can always create your own function that uses more than one function (e.g. my_function <- function(matrix) cor(var(matrix)) is perfectly valid and allows one to use two dimension-level 3 functions - the correlation of the variance-covariance matrix in this case).

The between.groups argument runs the disparity between groups rather within groups. If between.groups = TRUE, the disparity will be calculated using the following inputs:

• if the input is an output from custom.subsets, the series are run in a pairwise manner using metric(matrix, matrix2). For example for a custom.subset contains 3 subsets m1, m2 and m3, the code loops through: metric(m1, m2), metric(m2, m3) and metric(m1, m3) (looping through list(c(1,2), c(2,3), c(3,1))).

• if the input is an output from chrono.subsets, the series are run in a paired series manner using metric(matrix, matrix2). For example for a chrono.subsets contains 3 subsets m1, m2, m3 and m4, the code loops through: metric(m1, m2) and metric(m2, m3) (looping through list(c(1,2), c(2,3), c(3,4))).

In both cases it is also possible to specify the input directly by providing the list to loop through. For example using between.groups = list(c(1,2), c(2,1), c(4,8)) will apply the metric to the 1st and 2nd subsets, the 2nd and first and the 4th and 8th (in that specific order).

See Also

custom.subsets, chrono.subsets, boot.matrix, dispRity.metric, summary.dispRity, plot.dispRity.

Examples

# NOT RUN {
## Load the Beck & Lee 2014 data
data(BeckLee_mat50)

## Calculating the disparity as the sum of variances from a single matrix
sum_of_variances <- dispRity(BeckLee_mat50, metric = c(sum, variances))
summary(sum_of_variances)
## Bootstrapping this value
bootstrapped_data <- boot.matrix(BeckLee_mat50, bootstraps = 100)
dispRity(bootstrapped_data, metric = c(sum, variances))

## Calculating the disparity from a customised subset
## Generating the subsets
customised_subsets <- custom.subsets(BeckLee_mat50,
     list(group1 = 1:(nrow(BeckLee_mat50)/2),
          group2 = (nrow(BeckLee_mat50)/2):nrow(BeckLee_mat50)))
## Bootstrapping the data
bootstrapped_data <- boot.matrix(customised_subsets, bootstraps = 100)
## Calculating the sum of variances
sum_of_variances <- dispRity(bootstrapped_data, metric = c(sum, variances))
summary(sum_of_variances)

## Calculating disparity with different metrics of different dimension-levels
## Disparity is calculated as the distribution of the variances in each
## dimension (output are distributions)
disparity_level2 <- dispRity(BeckLee_mat50, metric = variances)
## Disparity is calculated as the mean of the variances in each dimension 
## (output are single values)
disparity_level1 <- dispRity(disparity_level2, metric = mean)
## Both disparities have the same means but dimension-level 1 has no quantiles
summary(disparity_level2)
summary(disparity_level1)


# }