
Three different RV coefficients: RV, RV2 and adusted RV.
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)
first matrix
to be compared (data.frames
are also accepted).
second matrix
to be compared (data.frames
are also accepted).
logical
indicating if input matrices should be centered (default = TRUE).
logical
indicating if missing values are expected in X1
or X2
(only for RV and RV2).
Which version of RV adjusted to apply: "Maye" (default) or "Ghaziri"
RV adjusted is run using the RVadj
function.
A single value measuring the similarity of two matrices.
For each of the four coefficients a single scalar is computed to describe the similarity between the two input matrices.
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.
SMI
, r1
(r2/r3/r4/GCD), Rozeboom
, Coxhead
,
allCorrelations
(matrix correlation comparison), PCAcv (cross-validated PCA)
, PCAimpute (PCA based imputation)
.
# NOT RUN {
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)
# }
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