fitDag {ggm} | R Documentation |
Fits linear recursive regressions with independent residuals specified by a DAG.
fitDag(gmat, Syy, n)
gmat |
a square Boolean matrix representing the edge matrix of the DAG |
Syy |
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 edge matrix
is the same of Syy
and if not it reorders the edge matrix
to match the order of the variables in Syy
. The nodes
of the edge matrix may form a subset of the variables in Syy
.
A |
the matrix of the fitted regression coefficients. Each coefficient has the sign reversed. |
B |
the inverse of matrix B. |
Delta |
a vector containing the partial covariances of each variable given the parents. |
Shat |
the fitted covariance matrix. |
Khat |
the fitted concentration matrix. |
dev |
the `deviance' (-2 log L) of the model. |
df |
the degrees of freedom. |
Giovanni M. Marchetti
Cox, D. R. & Wermuth, N. (1996). Multivariate dependencies. London: Chapman & Hall.
dag <- DAG(y ~ x+u, x ~ z, z ~ u) "V" <- 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, V, 200)