Skip to contents

Estimates the size of phenotypic spaces

Source: R/space_size.R
space_size.Rd

space_size

Usage

space_size(
  formula,
  data,
  cores = 1,
  method = "mcp",
  pb = TRUE,
  outliers = 0.95,
  ...
)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class).Must follow the form group ~ dim1 + dim2 where dim1 and dim2 are the dimensions of the phenotype space and group refers to the group labels.

data

Data frame containing columns for the dimensions of the phenotypic space (numeric) and a categorical or factor column with group labels.

cores

Numeric vector of length 1. Controls whether parallel computing is applied by specifying the number of cores to be used. Default is 1 (i.e. no parallel computing).

method

Character vector of length 1. Controls the method to be used for quantifying space size. Three metrics are available:

  • mcp: minimum convex polygon area using the function mcp. The minimum sample size (per group) must be 2 observations. Only works on 2-dimensional spaces.

  • density: kernel density area using the function kernelUD. The minimum sample size (per group) must be 6 observations. Only works on 2-dimensional spaces.

  • mst: minimum spanning tree using the function spantree. The minimum sample size (per group) must be 2 observations. This method is expected to be more robust to the influence of outliers. Note that mst is not a actually measuring area but distance between observations. However, it still help to quantify the size of the sub-region in trait space. Any number of dimensions can be used with this method.

  • ellipse: Calculate the size of an sub-region assuming an elliptical shape. The axes of the ellipse are estimated from the covariance matrix of the data points in the sub-region. Estimated with the function niche.size from the package 'nicheROVER'. The minimum sample size (per group) must be 1 observation. Any number of dimensions can be used with this method.

pb

Logical argument to control if progress bar is shown. Default is TRUE.

outliers

Numeric vector of length 1. A value between 0 and 1 controlling the proportion of outlier observations to be excluded. Outliers are determined as those farthest away from the sub-space centroid.

...

Additional arguments to be passed to kernelUD for kernel density estimation (when method = 'density'.

Value

A data frame containing the phenotypic space size for each group.

Details

The function quantifies the size of the phenotypic sub-spaces.

References

Araya-Salas, M, & K. Odom. 2022, PhenotypeSpace: an R package to quantify and compare phenotypic trait spaces R package version 0.1.0.

See also

rarefact_space_size, space_size_difference, rarefact_space_size_difference

Author

Marcelo Araya-Salas marcelo.araya@ucr.ac.cr)

Examples

{
# load data
data("example_space")

# plot data
xs <- tapply(example_space$dimension_1, example_space$group, mean)
ys <- tapply(example_space$dimension_2, example_space$group, mean)
plot(example_space[, c("dimension_1", "dimension_2")], 
col = example_space$color, pch = 20, cex = 1.8)
text(xs, ys, labels = names(xs), cex = 2.5)

# MCP spaces
space_size(
 formula = group ~ dimension_1 + dimension_2,
 data = example_space,
 method = "mcp")

# MST 
space_size(
 formula = group ~ dimension_1 + dimension_2,
 data = example_space,
 method = "mst")
}

#>   group   n      size
#> 1    G1 150  6.062202
#> 2    G2 200  9.179167
#> 3    G3 500 34.469612
#> 4    G4 300 16.668042
#> 5    G5 400 26.412904