Iterative proportional fitting of graphical Gaussian model
Fit graphical Gaussian model by iterative proportional fitting.
ggmfit( S, n.obs, glist, start = NULL, eps = 1e-12, iter = 1000, details = 0, ... )
S |
Empirical covariance matrix |
n.obs |
Number of observations |
glist |
Generating class for model (a list) |
start |
Initial value for concentration matrix |
eps |
Convergence criterion |
iter |
Maximum number of iterations |
details |
Controlling the amount of output. |
... |
Optional arguments; currently not used |
ggmfit is based on a C implementation. ggmfitr is
implemented purely in R (and is provided mainly as a benchmark for the
C-version).
A list with
lrt |
Likelihood ratio statistic (-2logL) |
df |
Degrees of freedom |
logL |
log likelihood |
K |
Estimated concentration matrix (inverse covariance matrix) |
Søren Højsgaard, sorenh@math.aau.dk
## Fitting "butterfly model" to mathmark data
## Notice that the output from the two fitting functions is not
## entirely identical.
data(math)
ddd <- cov.wt(math, method="ML")
glist <- list(c("al", "st", "an"), c("me", "ve", "al"))
ggmfit (ddd$cov, ddd$n.obs, glist)
ggmfitr(ddd$cov, ddd$n.obs, glist)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.