fitDag: Fitting of Gaussian DAG models
In ggm: Graphical Markov Models with Mixed Graphs

Description Usage Arguments Details Value Author(s) References See Also Examples

Fits linear recursive regressions with independent residuals specified by a DAG.

1	fitDag(amat, S, n)

`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

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.

`Shat`	the fitted covariance matrix.
`Ahat`	a square matrix of the fitted regression coefficients. The entry `Ahat[i,j]` is minus the regression coefficient of variable `i` in the regression equation `j`. Thus there is a non zero partial regression coefficient `Ahat[i,j]` corresponding to each non zero value `amat[j,i]` in the adjacency matrix.
`Dhat`	a vector containing the partial variances of each variable given the parents.
`dev`	the ‘deviance’ of the model.
`df`	the degrees of freedom.

Giovanni M. Marchetti

Cox, D. R. \& Wermuth, N. (1996). Multivariate dependencies. London: Chapman \& Hall.

DAG, swp.

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)