The goal of dineR is to enable users of all backgrounds to easily and computationally efficiently perform differential network estimation.
You can install the released version of dineR from CRAN with:
This is a basic example which shows you how to solve a common problem:
library(dineR) # Data Generation n_X <- 100 n_Y <- n_X p_X <- 100 p_Y <- p_X #case <- "sparse" case <- "asymsparse" data <- data_generator(n = n_X, p = p_X, seed = 123) X <- data$X Y <- data$Y diff_Omega <- data$diff_Omega paste("The number of non-zero entries in the differential network is: ", sum(diff_Omega!=0)) # Estimation Preliminaries (All of the parameters are now optional as the function has pre-specified defaults) loss <- "lasso" nlambda <- 50 tuning <- "AIC" stop_tol <- 1e-4 perturb <- F correlation <- F max_iter <- 500 lambda_min_ratio <- 0.5 #gamma <- 1 #Only if we use EBIC # Estimation result <- estimation(X, Y, loss = loss, nlambda = nlambda, tuning = tuning, stop_tol = stop_tol, perturb = perturb, correlation = correlation, max_iter = max_iter, lambda_min_ratio = lambda_min_ratio) # Results print(result$path[][1:5,1:5]) result$elapse
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.