# Akaike information criterion (AIC) and Bayesian information criterion (BIC) for the DPCID aic.dpcid returns the AIC values corresponding to the given lambda1 and lambda2 values for the DPCID. ```r crit.dpcid(A,B,l1,seq_l2,wd1,wd2,rho1_init,rho2_init,niter=1000,tol=1e-6,scaling=FALSE) ``` ## Arguments - `A`: An observed dataset from the first condition. - `B`: An observed dataset from the second condition. - `l1`: The selected lambda1 in cv.lambda1. - `seq_l2`: A sequence of tuning parameter lambda2 for the fusion penalty - `wd1`: The estimate of diagonal elements of the precision matrix of the first condition. - `wd2`: The estimate of diagonal elements of the precision matrix of the second condition. - `rho1_init`: An initial value for the partial correlation matrix of the first condition. - `rho2_init`: An initial value for the partial correlation matrix of the second condition. - `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. ## Returns - **aic**: A vector of aic values corresponding to a given sequence of tuning paramters. - **bic**: A vector of bic values corresponding to a given sequence of tuning paramters. ## Details crit.dpcid needs the estimates of the diagonal elements of two precision matrices. ## References 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. ## Examples ```r 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) A = scale(X1,center=TRUE,scale=FALSE) B = scale(X2,center=TRUE,scale=FALSE) shr_res = lshr.cov(A) PM1 = shr_res$shr_inv shr_res = lshr.cov(B) PM2 = shr_res$shr_inv wd1 = diag(PM1) wd2 = diag(PM2) rho1_init = -(1/sqrt(wd1))*PM1 rho1_init = t( 1/sqrt(wd1)*t(rho1_init)) diag(rho1_init) = 1 rho2_init = -(1/sqrt(wd2))*PM2 rho2_init = t( 1/sqrt(wd2)*t(rho2_init)) diag(rho2_init) = 1 l1 = 0.3 seq_l2 = seq(0.1,1,by=0.2) crit =crit.dpcid(A,B,l1,seq_l2,wd1,wd2,rho1_init,rho2_init) crit$aic crit$bic ```

crit.dpcid function