rcox {gRc}

Main function for specifying RCON/RCOR models.

Description

This is the main function for specifying and fitting RCON/RCOR models in the package along with certain utility functions.

Usage

rcox(
  gm = NULL,
  vcc = NULL,
  ecc = NULL,
  type = c("rcon", "rcor"),
  method = "ipm",
  fit = TRUE,
  data = NULL,
  S = NULL,
  n = NULL,
  Kstart = NULL,
  control = list(),
  details = 1,
  trace = 0
)

Arguments

gm

Generating class for a grapical Gaussian model, see 'Examples' for an illustration

vcc

List of vertex colour classes for the model

ecc

List of edge colour classes for the model

type

Type of model. Default is RCON

method

Estimation method; see 'Details' below.

fit

Should the model be fitted

data

A dataframe

S

An empirical covariance matrix (as alternative to giving data as a dataframe)

n

The number of observations (which is needed if data is specified as an empirical covariance matrix)

Kstart

An initial value for K. Can be omitted.

control

Controlling the fitting algorithms

details

Controls the amount of output

trace

Debugging info

Details

Estimation methods:

* 'ipm' (default) is iterative partial maximization which when finished calculates the information matrix so that approximate variances of the parameters can be obtained using vcov().

* 'ipms' is iterative partial maximization without calculating the information matrix. This is the fastest method.

* 'scoring' is stabilised Fisher scoring.

* 'matching' is score matching followed by one step with Fisher scoring.

* 'hybrid1' is for internal use and should not be called directly

Value

A model object of type 'RCOX'.

Author(s)

Søren Højsgaard, [e-mail]

Examples

data(math)
gm  = ~al:an:st
vcc = list(~me+st, ~ve+an, ~al)
ecc = list(~me:ve+me:al, ~ve:al+al:st)

m1 <- rcox(gm=gm, vcc=vcc, ecc=ecc, data=math, method='matching')
m2 <- rcox(gm=gm, vcc=vcc, ecc=ecc, data=math, method='scoring')
m3 <- rcox(gm=gm, vcc=vcc, ecc=ecc, data=math, method='ipm')

m1
m2
m3

summary(m1)
summary(m2)
summary(m3)

coef(m1)
coef(m2)
coef(m3)

vcov(m1)
vcov(m2)
vcov(m3)