bcl {weightedScores} | R Documentation |
Bivariate composite likelihood for multivariate normal copula with categorical and count regression.
bcl(r,b,gam,xdat,ydat,id,tvec,margmodel,corstr,link)
bcl.ord(r,b,gam,xdat,ydat,id,tvec,corstr,link)
r |
The vector of normal copula parameters. |
b |
The regression coefficients. |
gam |
The uinivariate parameters that are not regression coefficients. That is the parameter |
xdat |
|
ydat |
|
id |
An index for individuals or clusters. |
tvec |
A vector with the time indicator of individuals or clusters. |
margmodel |
Indicates the marginal model. Choices are “poisson” for Poisson, “bernoulli” for Bernoulli, and “nb1” , “nb2” for the NB1 and NB2 parametrization of negative binomial in Cameron and Trivedi (1998). |
corstr |
Indicates the latent correlation structure of normal copula. Choices are “exch”, “ar”, and “unstr” for exchangeable, ar(1) and unstructured correlation structure, respectively. |
link |
The link function. Choices are “log” for the log link function, “logit” for the logit link function, and “probit” for the probit link function. |
The CL1 composite likelihood in Zhao and Joe (2005). That is the sum of bivariate marginal log-likelihoods.
bcl.ord
is a variant of the code for ordinal (probit and logistic) regression.
The negative bivariate composite likelihood for multivariate normal copula with Poisson or binary or negative binomial or ordinal regression.
Aristidis K. Nikoloulopoulos A.Nikoloulopoulos@uea.ac.uk
Harry Joe harry.joe@ubc.ca
Zhao, Y. and Joe, H. (2005) Composite likelihood estimation in multivariate data analysis. The Canadian Journal of Statistics, 33, 335–356.
Cameron, A. C. and Trivedi, P. K. (1998) Regression Analysis of Count Data. Cambridge: Cambridge University Press.