pcalg (version 2.0-3)

gmG: Graphical Model 8-Dimensional Gaussian Example Data

Description

These two data sets contain a matrix containing information on eight gaussian variables and the corresonding DAG model.

Usage

data(gmG)

source

The data set is identical to the one generated by ## Used to generate "gmG" set.seed(40) p <- 8 n <- 5000 ## true DAG: vars <- c("Author", "Bar", "Ctrl", "Goal", paste0("V",5:8)) gGtrue <- randomDAG(p, prob = 0.3, V = vars) gmG <- list(x = rmvDAG(n, gGtrue, back.compatible=TRUE), g = gGtrue) gmG8 <- list(x = rmvDAG(n, gGtrue), g = gGtrue)

Details

The data was generated as indicated below. First, a random DAG model was generated, then 5000 samples were drawn from almost this model, for gmG: In the previous version, the data generation wgtMatrix had the non-zero weights in reversed order for each node. On the other hand, for gmG8, the correct weights were use in all cases

Examples

data(gmG)
str(gmG, max=3)
stopifnot(identical(gmG $ g, gmG8 $ g))
if(dev.interactive()) { ## to save time in tests
  round(as(gmG $ g, "Matrix"), 2) # weight ("adjacency") matrix
  plot(gmG $ g)
  pairs(gmG$x, gap = 0,
	panel=function(...) smoothScatter(..., add=TRUE))
}