cor_diss {resemble}

Correlation and moving correlation dissimilarity measurements (cor_diss)

Description

Computes correlation and moving correlation dissimilarity matrices.

Usage

cor_diss(Xr, Xu = NULL, ws = NULL,
         center = TRUE, scale = FALSE)

Arguments

Xr

a matrix.

Xu

an optional matrix containing data of a second set of observations.

ws

for moving correlation dissimilarity, an odd integer value which specifies the window size. If ws = NULL, then the window size will be equal to the number of variables (columns), i.e. instead moving correlation, the normal correlation will be used. See details.

center

a logical indicating if the spectral data Xr (and Xu if specified) must be centered. If Xu is provided, the data is scaled on the basis of \(Xr \cup Xu\).

scale

a logical indicating if Xr (and Xu if specified) must be scaled. If Xu is provided the data is scaled on the basis of \(Xr \cup Xu\).

Details

The correlation dissimilarity \(d\) between two observations \(x_i\) and \(x_j\) is based on the Perason's correlation coefficient (\(\rho\)) and it can be computed as follows:

\[d(x_i, x_j) = \frac{1}{2}((1 - \rho(x_i, x_j)))\]

The above formula is used when ws = NULL. On the other hand (when ws != NULL) the moving correlation dissimilarity between two observations \(x_i\) and \(x_j\) is computed as follows:

\[d(x_i, x_j; ws) = \frac{1}{2 ws}\sum_{k=1}^{p-ws}1 - \rho(x_{i,(k:k+ws)}, x_{j,(k:k+ws)})\]

where \(ws\) represents a given window size which rolls sequentially from 1 up to \(p - ws\) and \(p\) is the number of variables of the observations.

The function does not accept input data containing missing values.

Value

a matrix of the computed dissimilarities.

Author(s)

Antoine Stevens and Leonardo Ramirez-Lopez

Examples

library(prospectr)
data(NIRsoil)

Xu <- NIRsoil$spc[!as.logical(NIRsoil$train), ]
Xr <- NIRsoil$spc[as.logical(NIRsoil$train), ]

cor_diss(Xr = Xr)

cor_diss(Xr = Xr, Xu = Xu)

cor_diss(Xr = Xr, ws = 41)

cor_diss(Xr = Xr, Xu = Xu, ws = 41)