Description Usage Arguments Details Value References Examples
DPCID is a procedure for the differential partial correlation identification with the ridge and the fusion penalties. This function conducts the two stage procedure (diagonal and partial correlation steps).
1 |
A |
An observed dataset from the first condition. |
B |
An observed dataset from the second condition. |
lambda1 |
A tuning parameter for the ridge penalty. |
lambda2 |
A a tuning parameter for the fusion penalty between two precision matrices. |
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. |
In the first step (lshr.cov), each precision matrix is estimated from the optimal linear shrinkage covariance matrix. In the second step (dpcid_core), two partial correlation matrices are jointly estimated with a given tuning parameters lambda1 and lambda2 and fixed diagonal elements of two precision matrices.
rho1 |
An estimated partial correlatioin matrix of the first condition. |
rho2 |
An estimated partial correlatioin matrix of the second condition. |
wd1 |
A vector of estimated diagonal elements of the first precision matrices. |
wd2 |
A vector of estimated diagonal elements of the second precision matrices. |
diff_edge |
An index matrix of different edges between two conditions. |
n_diff |
The number of different edges between two conditions. |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | 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)
lambda1 = 0.2
lambda2 = 0.2
res = dpcid(X1,X2,lambda1,lambda2,niter=1000,tol=1e-6)
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