This is the main function for specifying and fitting RCON/RCOR models in the package along with certain utility functions.
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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 
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
A model object of type 'RCOX'.
demo("gRcJSS") gives a more comprehensive demo.
S<f8>ren H<f8>jsgaard, sorenh@agrsci.dk
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  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)

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