## 6.1. Aplicação aos dados do inquérito à força de trabalho
data("surveyData", package = "BDgraph")
head(surveyData, 5)
## income degree children pincome pdegree pchildren age
## [1,] NA 1 3 3 1 5 59
## [2,] 11 0 3 NA 0 7 59
## [3,] 8 1 1 NA 0 9 25
## [4,] 25 3 2 NA 0 5 55
## [5,] 100 3 2 4 3 2 56
sample.bdmcmc <- bdgraph( data = surveyData, method = "gcgm",
iter = 10000, burnin = 7000 )
## 10000 MCMC sampling ... in progress:
## 10%->20%->30%->40%->50%->60%->70%->80%->90%-> done
## $selected_g
## income degree children pincome pdegree pchildren age
## income 0 1 1 0 0 0 1
## degree 0 0 0 0 1 1 0
## children 0 0 0 0 1 1 1
## pincome 0 0 0 0 1 0 0
## pdegree 0 0 0 0 0 1 1
## pchildren 0 0 0 0 0 0 0
## age 0 0 0 0 0 0 0
##
## $p_links
## income degree children pincome pdegree pchildren age
## income 0 1 0.99 0.11 0.02 0.03 1.00
## degree 0 0 0.35 0.03 1.00 0.52 0.05
## children 0 0 0.00 0.08 0.71 0.99 1.00
## pincome 0 0 0.00 0.00 1.00 0.22 0.02
## pdegree 0 0 0.00 0.00 0.00 0.90 0.99
## pchildren 0 0 0.00 0.00 0.00 0.00 0.02
## age 0 0 0.00 0.00 0.00 0.00 0.00
##
## $K_hat
## income degree children pincome pdegree pchildren age
## income 2.82 -1.54 -0.29 -0.03 0.00 0.00 -0.44
## degree -1.54 3.96 0.09 0.00 -1.04 0.15 -0.01
## children -0.29 0.09 1.20 0.02 0.14 -0.20 -0.67
## pincome -0.03 0.00 0.02 5.42 -1.16 0.09 0.00
## pdegree 0.00 -1.04 0.14 -1.16 1.84 0.31 0.22
## pchildren 0.00 0.15 -0.20 0.09 0.31 1.82 0.00
## age -0.44 -0.01 -0.67 0.00 0.22 0.00 1.48