Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/similarity.variables.R
Get similarity matrix for variables of mixed types
1 2 | similarity.variables(data, method = c("associationMeasures", "distcor"),
associationFun = association, check.psd = TRUE, make.psd = TRUE)
|
data |
data frame with variables of interest |
method |
method to calculate distances: combination of association measures ( |
associationFun |
only applies if |
check.psd |
only applies if |
make.psd |
only applies if |
A similarity matrix for variables can be derived by combining different measures of association or by a distance correlation approach. For the association measure approach, for each pair of variables, similarity coefficients s_ij are calculated, see association
for details. If the similarity matrix is (made) positive semi-definite, distances d_ij = sqrt(1 - s_ij) have metric properties (Gower, 1971), which means for instance that the triangular inequality holds.
The distance correlation approach uses generalized distance correlation based on Gower's similarity coefficient between sample elements.
Matrix of similarity values for each pair of variables
Manuela Hummel, Dominic Edelmann
Hummel M, Edelmann D, Kopp-Schneider A (2017). Clustering of samples and variables with mixed-type data. PLOS ONE, 12(11):e0188274.
Gower J (1971). A general coefficient of similarity and some of its properties. Biometrics, 27:857-871.
Szekely GJ, Rizzo ML, Bakirov NK (2007). Measuring and testing dependence by correlation of distances. The Annals of Statistics, 35.6:2769-2794.
Lyons R (2013). Distance covariance in metric spaces. The Annals of Probability, 41.5:3284-3305.
association
, dist.variables
, dendro.variables
, dist.subjects
, mix.heatmap
1 2 3 4 | data(mixdata)
S1 <- similarity.variables(mixdata)
S2 <- similarity.variables(mixdata, method="distcor")
|
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