Graphical Model 8-Dimensional Gaussian Example Data
These two data sets contain a matrix containing information on eight gaussian variables and the corresonding DAG model.
data(gmG)
gmG and gmG8 are each a list of two components
a numeric matrix 5000 * 8.
a graph, i.e., of formal class
"graphNEL" from package graph with 6 slots
.. ..@ nodes : chr [1:8] "1" "2" "3" "4" ...
.. ..@ edgeL :List of 8
........
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 used in all cases
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)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))
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