fitDag {ggm} | R Documentation |
Fitting of Gaussian DAG models
Description
Fits linear recursive regressions with independent residuals specified by a DAG.
Usage
fitDag(amat, S, n)
Arguments
amat |
a square matrix with dimnames representing the adjacency matrix of the DAG |
S |
a symmetric positive definite matrix, the sample covariance matrix |
n |
an integer > 0, the sample size |
Details
fitDag
checks if the order of the nodes in adjacency matrix
is the same of S
and if not it reorders the adjacency matrix
to match the order of the variables in S
. The nodes
of the adjacency matrix may form a subset of the variables in S
.
Value
Shat |
the fitted covariance matrix. |
Ahat |
a square matrix of the fitted regression coefficients. The entry
|
Dhat |
a vector containing the partial variances of each variable given the parents. |
dev |
the ‘deviance’ of the model. |
df |
the degrees of freedom. |
Author(s)
Giovanni M. Marchetti
References
Cox, D. R. & Wermuth, N. (1996). Multivariate dependencies. London: Chapman & Hall.
See Also
Examples
dag <- DAG(y ~ x+u, x ~ z, z ~ u)
"S" <- structure(c(2.93, -1.7, 0.76, -0.06,
-1.7, 1.64, -0.78, 0.1,
0.76, -0.78, 1.66, -0.78,
-0.06, 0.1, -0.78, 0.81), .Dim = c(4,4),
.Dimnames = list(c("y", "x", "z", "u"), c("y", "x", "z", "u")))
fitDag(dag, S, 200)