RV {MatrixCorrelation}

RV coefficients

Description

Three different RV coefficients: RV, RV2 and adusted RV.

Usage

RV(X1, X2, center = TRUE, impute = FALSE)

RV2(X1, X2, center = TRUE, impute = FALSE)

RVadjMaye(X1, X2, center = TRUE)

RVadjGhaziri(X1, X2, center = TRUE)

RVadj(X1, X2, version = c("Maye", "Ghaziri"), center = TRUE)

Arguments

X1

first matrix to be compared (data.frames are also accepted).

X2

second matrix to be compared (data.frames are also accepted).

center

logical indicating if input matrices should be centered (default = TRUE).

impute

logical indicating if missing values are expected in X1 or X2 (only for RV and RV2).

version

Which version of RV adjusted to apply: "Maye" (default) or "Ghaziri" RV adjusted is run using the RVadj function.

Details

For each of the four coefficients a single scalar is computed to describe the similarity between the two input matrices.

Value

A single value measuring the similarity of two matrices.

Author(s)

Kristian Hovde Liland, Benjamin Leutner (RV2)

References

• RV: Robert, P.; Escoufier, Y. (1976). "A Unifying Tool for Linear Multivariate Statistical Methods: The RV-Coefficient". Applied Statistics 25 (3): 257-265.

• RV2: Smilde, AK; Kiers, HA; Bijlsma, S; Rubingh, CM; van Erk, MJ (2009). "Matrix correlations for high-dimensional data: the modified RV-coefficient". Bioinformatics 25(3): 401-5.

• Adjusted RV: Maye, CD; Lorent, J; Horgan, GW. (2011). "Exploratory analysis of multiple omics datasets using the adjusted RV coefficient". Stat Appl Genet Mol Biol. 10(14).

• Adjusted RV: El Ghaziri, A; Qannari, E.M. (2015) "Measures of association between two datasets; Application to sensory data", Food Quality and Preference 40 (A): 116-124.

See Also

SMI, r1 (r2/r3/r4/GCD), Rozeboom, Coxhead, allCorrelations (matrix correlation comparison), PCAcv (cross-validated PCA), PCAimpute (PCA based imputation).

Examples

X1  <- matrix(rnorm(100*300),100,300)
usv <- svd(X1)
X2  <- usv$u[,-3] %*% diag(usv$d[-3]) %*% t(usv$v[,-3])

RV(X1,X2)
RV2(X1,X2)
RVadj(X1,X2)

# Missing data
X1[c(1, 50, 400, 900)] <- NA
X2[c(10, 200, 450, 1200)] <- NA
RV(X1,X2, impute = TRUE)
RV2(X1,X2, impute = TRUE)