RV | R Documentation |
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)
X1 |
first |
X2 |
second |
center |
|
impute |
|
version |
Which version of RV adjusted to apply: "Maye" (default) or "Ghaziri"
RV adjusted is run using the |
For each of the four coefficients a single scalar is computed to describe the similarity between the two input matrices.
A single value measuring the similarity of two matrices.
Kristian Hovde Liland, Benjamin Leutner (RV2)
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)
.
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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