# Expected Network Replicability Compute expected network replicability in any number of replication attempts. This works for any kind of correlation, assuming it is possible to obtain the standard error (analytically or with a bootstrap). ```r enr(net, n, alpha = 0.05, replications = 2, type = "pearson") ``` ## Arguments - `net`: True network of dimensions **p** by **p**. - `n`: Integer. The samples size, assumed equal in the replication attempts. - `alpha`: The desired significance level (defaults to `0.05`). Note that 1 - alpha corresponds to specificity. - `replications`: Integer. The desired number of replications. - `type`: Character string. Which type of correlation coefficients to be computed. Options include `"pearson"` (default) and `"spearman"`. ## Returns An list of class `enr`, including a variety of information used by other functions (e.g., to plot the results). ## Note This method was introduced in if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="williams2020learning;textual",package="GGMnonreg",cached_env=.Rdpack.currefs) . ## Examples ```r # correlations cors <- cor(GGMnonreg::ptsd) # inverse inv <- solve(cors) # partials pcors <- -cov2cor(inv) # set values to zero pcors <- ifelse(abs(pcors) < 0.05, 0, pcors) fit_enr <- enr(net = pcors, n = 500, replications = 2) # intuition for the method # location of edges index <- which(pcors[upper.tri(diag(20))] != 0) # convert network into correlation matrix diag(pcors) <- 1 cors_new <- corpcor::pcor2cor(pcors) # replicated edges R <- NA # increase 100 to, say, 5,000 for(i in 1:100){ # two replications Y1 <- MASS::mvrnorm(500, rep(0, 20), cors_new) Y2 <- MASS::mvrnorm(500, rep(0, 20), cors_new) # estimate network 1 fit1 <- ggm_inference(Y1, boot = FALSE) # estimate network 2 fit2 <- ggm_inference(Y2, boot = FALSE) # number of replicated edges (detected in both networks) R[i] <- sum( rowSums( cbind(fit1$adj[upper.tri(diag(20))][index], fit2$adj[upper.tri(diag(20))][index]) ) == 2) } # combine simulation and analytic cbind.data.frame( data.frame(simulation = sapply(seq(0, 0.9, 0.1), function(x) { mean(R > round(length(index) * x) ) })), data.frame(analytic = round(fit_enr$cdf, 3)) ) # average replicability (simulation) mean(R / length(index)) # average replicability (analytic) fit_enr$ave_pwr ``` ## References if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_all_ref(.Rdpack.currefs)

enr function