inst/example

Author: Carol-seven

DESCRIPTION

@@ -42,5 +42,6 @@ RoxygenNote: 7.3.3
 RdMacros: Rdpack
 Suggests: 
     knitr,
+    MASS,
     rmarkdown
 VignetteBuilder: knitr

R/draw_sample.R

@@ -33,24 +33,8 @@
 #' @return
 #' A numeric vector of length \eqn{n} containing simulated data.
 #'
-#' @examples
-#' ## reproducibility for everything
-#' set.seed(1234)
-#'
-#' ## user-defined sampler
-#' my_gamma <- function(n) {
-#'   rgamma(n, shape = 10, scale = 0.5)
-#' }
-#' draw_sample(my_gamma, n = 10)
-#'
-#' my_unif <- function(n, min, max) {
-#'   runif(n, min = 0, max = 1)
-#' }
-#' draw_sample(my_unif, para = list(min = 1, max = 5), n = 10)
-#'
-#' ## base R distribution
-#' draw_sample("gamma", para = list(shape = 10, scale = 0.5), n = 10)
-#' draw_sample("unif", para = list(min = 1, max = 5), n = 10)
+#' @example
+#' inst/example/ex-draw_sample.R
 #'
 #' @noRd
 

R/expand_spec.R

@@ -21,10 +21,8 @@
 #' correspond to within-group blocks, and the remaining \eqn{K(K-1)/2}
 #' correspond to between-group blocks.
 #'
-#' @examples
-#' my_spec <- list("gamma", "unif")
-#' K <- 3
-#' expand_spec(my_spec, K)
+#' @example
+#' inst/example/ex-expand_spec.R
 #'
 #' @noRd
 

R/gen_prec_sbm.R

@@ -127,21 +127,8 @@
 #' \item{membership}{An integer vector specifying the group membership.}
 #' }
 #'
-#' @examples
-#' ## reproducibility for everything
-#' set.seed(1234)
-#'
-#' ## user-defined sampler
-#' my_gamma <- function(n) {
-#'   rgamma(n, shape = 10, scale = 0.5)
-#' }
-#'
-#' sim <- gen_prec_sbm(d = 20, K = 3,
-#'                     within.prob = 0.25, between.prob = 0.05,
-#'                     weight.dists = list(my_gamma, "unif"),
-#'                     weight.paras = list(NULL, c(min = 0, max = 5)),
-#'                     cond.target = 100)
-#' plot(sim)
+#' @example
+#' inst/example/ex-gen_prec_sbm.R
 #'
 #' @export
 

R/grasps.R

@@ -143,19 +143,8 @@
 #' @references
 #' \insertAllCited{}
 #'
-#' @examples
-#' ## reproducibility for everything
-#' set.seed(1234)
-#'
-#' ## n-by-d data matrix
-#' X <- matrix(rnorm(200), 10, 20)
-#'
-#' ## group membership
-#' membership <- c(rep(1,5), rep(2,5), rep(3,4), rep(4,6))
-#'
-#' ## lasso, BIC
-#' res <- grasps(X = X, membership = membership, penalty = "lasso", crit = "BIC")
-#' plot(res)
+#' @example
+#' inst/example/ex-grasps.R
 #'
 #' @export
 

R/plot.grasps.R

@@ -17,24 +17,8 @@
 #' A \code{ggplot2} heatmap showing the matrix entries. Dashed lines indicate
 #' group boundaries. The plot title also reports matrix dimension and sparsity.
 #'
-#' @examples
-#' ## reproducibility for everything
-#' set.seed(1234)
-#'
-#' ## user-defined sampler
-#' my_gamma <- function(n) {
-#'   rgamma(n, shape = 20, scale = 5)
-#' }
-#'
-#' ## block-structured precision matrix based on SBM
-#' sim <- gen_prec_sbm(d = 100, K = 5,
-#'                     within.prob = 0.5, between.prob = 0.05,
-#'                     weight.dists = list(my_gamma, "unif"),
-#'                     weight.paras = list(NULL, c(min = 0, max = 1)),
-#'                     cond.target = 100)
-#'
-#' ## visualization
-#' plot(sim)
+#' @example
+#' inst/example/ex-plot.grasps.R
 #'
 #' @export
 #' @noRd

R/sparsify_block_banded.R

@@ -22,26 +22,8 @@
 #' \item{membership}{An integer vector specifying the group membership.}
 #' }
 #'
-#' @examples
-#' ## reproducibility for everything
-#' set.seed(1234)
-#'
-#' ## precision matrix estimation
-#' X <- matrix(rnorm(200), 10, 20)
-#' membership <- c(rep(1,5), rep(2,5), rep(3,4), rep(4,6))
-#' est <- grasps(X, membership = membership, penalty = "lasso", crit = "BIC")
-#'
-#' ## default: keep blocks within ±1 of each group
-#' res1 <- sparsify_block_banded(est$hatOmega, membership, neighbor.range = 1)
-#' plot(res1)
-#'
-#' ## wider band: keep blocks within ±2 of each group
-#' res2 <- sparsify_block_banded(est$hatOmega, membership, neighbor.range = 2)
-#' plot(res2)
-#'
-#' ## special case: block-diagonal matrix
-#' res3 <- sparsify_block_banded(est$hatOmega, membership, neighbor.range = 0)
-#' plot(res3)
+#' @example
+#' inst/example/ex-sparsify_block_banded.R
 #'
 #' @export
 

README.Rmd

@@ -75,18 +75,20 @@ library(grasps)
 set.seed(1234)
 
 ## block-structured precision matrix based on SBM
-sim <- gen_prec_sbm(d = 100, K = 5,
+sim <- gen_prec_sbm(d = 60, K = 5,
                     within.prob = 0.5, between.prob = 0.05,
                     weight.dists = list("gamma", "unif"),
                     weight.paras = list(c(shape = 20, scale = 5), c(min = 0, max = 1)),
                     cond.target = 100)
 
 ## synthetic data
 library(MASS)
-X <- MASS::mvrnorm(n = 50, mu = rep(0, ncol(sim$Sigma)), Sigma = sim$Sigma)
+X <- MASS::mvrnorm(n = 30, mu = rep(0, 60), Sigma = sim$Sigma)
 
 ## solution
 res <- grasps(X = X, membership = sim$membership, penalty = "lasso", crit = "BIC")
+
+## visualization
 plot(res)
 ```
 

README.md

@@ -47,18 +47,20 @@ library(grasps)
 set.seed(1234)
 
 ## block-structured precision matrix based on SBM
-sim <- gen_prec_sbm(d = 100, K = 5,
+sim <- gen_prec_sbm(d = 60, K = 5,
                     within.prob = 0.5, between.prob = 0.05,
                     weight.dists = list("gamma", "unif"),
                     weight.paras = list(c(shape = 20, scale = 5), c(min = 0, max = 1)),
                     cond.target = 100)
 
 ## synthetic data
 library(MASS)
-X <- MASS::mvrnorm(n = 50, mu = rep(0, ncol(sim$Sigma)), Sigma = sim$Sigma)
+X <- MASS::mvrnorm(n = 30, mu = rep(0, 60), Sigma = sim$Sigma)
 
 ## solution
 res <- grasps(X = X, membership = sim$membership, penalty = "lasso", crit = "BIC")
+
+## visualization
 plot(res)
 ```
 

inst/example/ex-draw_sample.R

@@ -0,0 +1,22 @@
+library(grasps)
+
+## reproducibility for everything
+set.seed(1234)
+
+## user-defined sampler
+#### Case 1
+my_gamma <- function(n) {
+  rgamma(n, shape = 10, scale = 0.5)
+}
+draw_sample(my_gamma, n = 10)
+## Case 2
+my_unif <- function(n, min, max) {
+  runif(n, min = 0, max = 1)
+}
+draw_sample(my_unif, para = list(min = 1, max = 5), n = 10)
+
+## base R distribution
+#### Case 1
+draw_sample("gamma", para = list(shape = 10, scale = 0.5), n = 10)
+#### Case 2
+draw_sample("unif", para = list(min = 1, max = 5), n = 10)