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Convert pairwise distance matrices to rectangular matrices

Source: R/distance_to_rectangular.R
distance_to_rectangular.Rd

distance_to_rectangular converts binary triangular matrices to rectangular matrices using Multidimensional Scaling.

Usage

distance_to_rectangular(
  distance.matrix,
  labels = names(distance.matrix),
  n.dimensions = 2,
  metric = TRUE,
  ...
)

Arguments

distance.matrix

Distance matrix (i.e. object of class 'dist'). Can be created using the function dist or converted to using as.dist.

labels

Character vector or factor containing labels to be used for rows/columns in the output data frame. Default is names(distance.matrix). Must be the same length as the number of observations in 'distance.matrix'.

n.dimensions

Integer vector of length 1 indicating the number of of dimensions to represent distances in a new space. Default is 2.

metric

Logical argument to control if Metric (a.k.a. Classical, TRUE, default) or Non-Metric MUltidimensional Scaling (FALSE) is used to project in a new n-dimension space. Non-Metric MDS is conducted using the function isoMDS while Classical MDS uses the function cmdscale. So yes, it is a silly wrapper over those 2 functions.

...

Additional arguments to be passed to train (only used for machine learning models).

Value

A data frame with the new dimensions representing the position of observations in a new n-dimension space. If metric = FALSE the output data frame is embedded in a list that also includes the stress value.

Details

It is a silly wrapper over 2 multidimensional scaling functions (isoMDS and cmdscale) that simplifies the calculation of Multidimensional Scaling and formatting of its output to be used with other functions in the package.

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

distance_to_rectangular, rectangular_to_triangular

Author

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

Examples

{
data("example_space")

dist_example <- dist(example_space[example_space$group %in% c("G1", "G2"), 
c("dimension_1", "dimension_2")])

# convert into a 2-dimension space
rect_example <- distance_to_rectangular(distance.matrix = dist_example,
metric = TRUE)

head(rect_example)

# \donttest{
# convert into a 2-dimension space with non-metric MDS
rect_example <- distance_to_rectangular(distance.matrix = dist_example, 
metric = FALSE, maxit = 3)
 # }
}
#> Calculating non-metric multidimensional scaling: initial  value 0.000000 
#> final  value 0.000000 
#> converged