View source: R/allCorrelations.R
allCorrelations | R Documentation |
Compare all correlation measures in the package (or a subset)
allCorrelations( X1, X2, ncomp1, ncomp2, methods = c("SMI", "RV", "RV2", "RVadj", "PSI", "r1", "r2", "r3", "r4", "GCD"), digits = 3, plot = TRUE, xlab = "", ylab = "", ... )
X1 |
first |
X2 |
second |
ncomp1 |
maximum number of subspace components from the first |
ncomp2 |
maximum number of subspace components from the second |
methods |
|
digits |
number of digits for numerical output. |
plot |
logical indicating if plotting should be performed (default = TRUE). |
xlab |
optional x axis label. |
ylab |
optional y axis label. |
... |
additional arguments for |
For each of the coefficients a single scalar is computed to describe the similarity between the two input matrices. Note that some methods requires setting one or two numbers of components.
A single value measuring the similarity of two matrices.
Kristian Hovde Liland
SMI: Indahl, U.G.; Næs, T.; Liland, K.H.; 2018. A similarity index for comparing coupled matrices. Journal of Chemometrics; e3049.
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: Mayer, CD; Lorent, J; Horgan, GW. (2011). "Exploratory analysis of multiple omics datasets using the adjusted RV coefficient". Stat Appl Genet Mol Biol. 10(14).
PSI: Sibson, R; 1978. "Studies in the Robustness of Multidimensional Scaling: Procrustes Statistics". Journal of the Royal Statistical Society. Series B (Methodological), Vol. 40, No. 2, pp. 234-238.
Rozeboom: Rozeboom, WW; 1965. "Linear correlations between sets of variables". Psychometrika 30(1): 57-71.
Coxhead: Coxhead, P; 1974. "Measuring the releationship between two sets of variables". British Journal of Mathematical and Statistical Psychology 27: 205-212.
SMI
, RV
(RV2/RVadj), r1
(r2/r3/r4/GCD).
X1 <- scale( matrix( rnorm(100*300), 100,300), scale = FALSE) usv <- svd(X1) # Remove third principal component from X1 to produce X2 X2 <- usv$u[,-3] %*% diag(usv$d[-3]) %*% t(usv$v[,-3]) allCorrelations(X1,X2, ncomp1 = 5,ncomp2 = 5)
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