gmL: Latent Variable 4-Dim Graphical Model Data Example

In pcalg: Methods for Graphical Models and Causal Inference

Latent Variable 4-Dim Graphical Model Data Example

Description

This data set contains a matrix containing information on four gaussian variables and the corresonding DAG model containing four observed and one latent variable.

Usage

data(gmL)

Format

The format is a list of 2 components

x:

$ x: num [1:10000, 1:4] 0.924 -0.189 1.016 0.363 0.497 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:4] "2" "3" "4" "5"

g:

$ g:Formal class 'graphNEL' [package "graph"] with 6 slots .. ..@ nodes : chr [1:5] "1" "2" "3" "4" ... .. ..@ edgeL :List of 5 ........

Details

The data was generated as indicated below. First, a random DAG model was generated with five nodes; then 10000 samples were drawn from this model; finally, variable one was declared to be latent and the corresponding column was deleted from the simulated data set.

Source

    ## Used to generate "gmL"
    set.seed(47)
    p <- 5
    n <- 10000
    gGtrue <- randomDAG(p, prob = 0.3) ## true DAG
    myX <- rmvDAG(n, gGtrue)
    colnames(myX) <- as.character(1:5)
    gmL <- list(x = myX[,-1], g = gGtrue)
  

Examples

data(gmL)
str(gmL, max=3)

## the graph:
gmL$g
graph::nodes(gmL$g) ; str(graph::edges(gmL$g))
if(require("Rgraphviz"))
  plot(gmL$g, main = "gmL $ g -- latent variable example data")

pairs(gmL $x) # the data

pcalg documentation built on Sept. 26, 2023, 9:06 a.m.