cv.lambda1: K-fold Crossvalidation for the ridge step
In dpcid: Differential Partial Correlation IDentification

Description Usage Arguments Details Value References Examples

K-fold crossvalidation for the choice of the lambda1.

1	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