rob.fitConGraph {rggm} | R Documentation |
You can obtain robustified estimates of a Gaussian concentration graph model.
rob.fitConGraph(amat, w.par, smpl.frm, it.limit = 200, tol = 1e-06)
amat |
An adjacent matrix |
w.par |
A non-negative real value, a robustness tuning parameter |
smpl.frm |
A data frame, a sample data |
it.limit |
a positive integer indicating the limit times of the iteration |
tol |
A small positive number idicating the tolerance used in the convergence tests |
it.limit
, the fitting procedure will be turminated.mhat |
Estimates of the mean vector |
Shat |
Estimates of the covariance matrix |
w.vec |
Estimates of the weight vector |
w.par |
The specified robustness tuning parameter |
it |
The number of the iteration |
tstat |
The test statistics for the overall goodness-of-fit |
df |
The degree of freedom for the model |
p.val |
The p-value of the model |
Masashi Miyamura
Masashi Miyamura and Yutaka Kano, 2006, Robust Gaussian graphical modeling, Journal of Multivariate Analysis, 97, 7, 1525–1550
require(mvtnorm) R <- matrix(c(1.0, 0.8, 0.0, 0.8, 1.0, 0.0, 0.0, 0.0, 1.0), 3, 3) # A sample data including five outliers df <- as.data.frame(rbind(rmvnorm(95, rep(0, 3), R), rmvnorm(5, rep(1, 3), R))) ug <- UG(~a*b*c) names(df) <- vertices(ug) rob.fitConGraph(ug, 0.3, df)