cv.lambda1 {dpcid} | R Documentation |
K-fold crossvalidation for the choice of the lambda1.
cv.lambda1(A,B, nfold,seq_lambda1,niter=1000,tol=1e-6,scaling=FALSE)
A |
An observed dataset from the first condition. |
B |
An observed dataset from the second condition. |
nfold |
the number of folds in the crossvalidation(i.e., K in K-fold cross validation) |
seq_lambda1 |
A sequence of tuning parameters for the ridge penalty |
niter |
A total number of iterations in the block-wise coordinate descent. |
tol |
A tolerance for the convergence. |
scaling |
a logical flag for scaling variable to have unit variance. Default is FALSE. |
cv.lambda1 returns a vector of the K-fold crossvalidated errors and matrices of the initial estimates of the precision matrices.
cv |
A vector of crossvalidated errors corresponding to a given sequence of tuning paramters. |
pm1 |
A matrix of the inverse of the linear shrinkage covariance estimates for the first condition. |
pm2 |
A matrix of the inverse of the linear shrinkage covariance estimates for the second condition. |
Yu, D., Lee, S. H., Lim, J., Xiao, G., Craddock, R. C., and Biswal, B. B. (2018). Fused Lasso Regression for Identifying Differential Correlations in Brain Connectome Graphs. Statistical Analysis and Data Mining, 11, 203–226.
library(MASS) ## True precision matrix omega1 <- matrix(0,5,5) omega1[1,2] <- omega1[1,3] <- omega1[1,4] <- 1 omega1[2,3] <- omega1[3,4] <- 1.5 omega1 <- t(omega1) + omega1 diag(omega1) <- 3 omega2 <- matrix(0,5,5) omega2[1,3] <- omega2[1,5] <- 1.5 omega2[2,3] <- omega2[2,4] <- 1.5 omega2 <- t(omega2) + omega2 diag(omega2) <- 3 Sig1 = solve(omega1) Sig2 = solve(omega2) X1 = mvrnorm(50,rep(0,5),Sig1) X2 = mvrnorm(50,rep(0,5),Sig2) nfold = 5 seq_lam1 = seq(0.5,3,by=0.5) cv_vec = cv.lambda1(X1,X2,nfold,seq_lam1,niter=1000,tol=1e-6)$cv