Update examples

Author: itsrainingdata

R/ccdrAlgorithm-main.R

@@ -58,25 +58,22 @@ NULL
 #'
 #' @examples
 #'
-#' \dontrun{
-#'
 #' ### Generate some random data
 #' dat <- matrix(rnorm(1000), nrow = 20)
-#' dat <- sparsebnData(dat, type = "continuous")
+#' dat <- sparsebnUtils::sparsebnData(dat, type = "continuous")
 #'
 #' # Run with default settings
-#' ccdr.run(data = dat)
+#' ccdr.run(data = dat, lambdas.length = 20)
 #'
 #' ### Optional: Adjust settings
-#' pp <- ncol(dat)
+#' pp <- ncol(dat$data)
 #'
 #' # Initialize algorithm with a random initial value
 #' init.betas <- matrix(0, nrow = pp, ncol = pp)
 #' init.betas[1,2] <- init.betas[1,3] <- init.betas[4,2] <- 1
 #'
 #' # Run with adjusted settings
-#' ccdr.run(data = dat, betas = init.betas, lambdas.length = 10, alpha = 10, verbose = TRUE)
-#' }
+#' ccdr.run(data = dat, betas = init.betas, lambdas.length = 20, alpha = 10, verbose = TRUE)
 #'
 #' @export
 ccdr.run <- function(data,
@@ -223,6 +220,7 @@ ccdr_call <- function(data,
         #   Still need to set start = 0, though.
         betas$start <- 0
     } # Type-checking for betas happens in ccdr_singleR
+
     # This parameter can be set by the user, but in order to prevent the algorithm from taking too long to run
     #  it is a good idea to keep the threshold used by default which is O(sqrt(pp))
     if(is.null(max.iters)){

R/ccdrAlgorithm-mvn.R

@@ -22,6 +22,22 @@
 #' @param ivn List of interventions (see \code{\link[sparsebnUtils]{sparsebnData}}). Must be a \code{list} with exactly \code{n} components.
 #' @param ivn.rand If \code{TRUE}, random N(0,1) values will be drawn for each intervention. Otherwise, these values need to supplied manually in \code{ivn}.
 #'
+#' @examples
+#'
+#' ### Generate observational data
+#' gr <- sparsebnUtils::random.graph(5, 5) # use sparsebnUtils package to generate a random graph
+#' gr.params <- runif(10) # there are 5 coefficients + 5 variances
+#' data.obs <- ccdrAlgorithm::generate_mvn_data(graph = gr,
+#'                                              n = 100,
+#'                                              params = gr.params)
+#'
+#' ### Generate experimental data
+#' ivn <- as.list(c(rep("V1", 50), rep("V2", 50))) # 50 interventions on V1, 50 interventions on V2
+#' data.ivn <- ccdrAlgorithm::generate_mvn_data(graph = gr,
+#'                                              n = 100,
+#'                                              params = gr.params,
+#'                                              ivn = ivn)
+#'
 #' @export
 generate_mvn_data <- function(graph, params, n = 1, ivn = NULL, ivn.rand = TRUE){