ordprobit.univar {mprobit} | R Documentation |
Maximum Likelihood for Ordinal Probit: Newton-Raphson minimization of negative log-likelihood.
ordprobit.univar(x,y,iprint=0,maxiter=20,toler=1.e-6)
x |
vector or matrix of explanatory variables. Each row corresponds to an observation and each column to a variable. The number of rows of x should equal the number of data values in y, and there should be fewer columns than rows. Missing values are not allowed. |
y |
numeric vector containing the ordinal response. The values must be in the range 1,2,..., number of categories. Missing values are not allowed. |
iprint |
logical indicator, default is FALSE, for whether the iterations for numerical maximum likelihood should be printed. |
maxiter |
maximum number of Newton-Raphson iterations, default = 20. |
toler |
tolerance for convergence in Newton-Raphson iterations, default = 1.e-6. |
If ordprobit for repeated measures ordinal probit fails to converge from the simple starting point in that function, this function ordprobit.univar should provide a better starting point. It is also equivalent to ordprobit with an identity latent correlation matrix.
The ordinal probit model is similar to the ordinal logit model (proportion odds logistic regression : polr in library MASS), The parameter estimate of ordinal logit are roughly 1.8 to 2 times those of ordinal probit (the signs of the parameters in polr may be different, as this function may be using a different orientation for the latent variable.
list of MLE of parameters and their associated standard errors, in the order cutpt1,...,cutpt(number of categ-1),b1,...b(number of covariates).
negloglik |
value of negative log-likelihood, evaluated at MLE |
cutpts |
MLE of ordered cutpoint parameters |
beta |
MLE of regression parameters |
cov |
estimated covariance matrix of the parameters |
Anderson, J.A. and Pemberton, J.D. (1985). The grouped continuous model for multivariate ordered categorical variables and covariate adjustment. Biometrics, 41, 875-885.
data(ordinalex) x=as.vector(ordinalex$x) y=ordinalex$y ord.univar = ordprobit.univar(x,y) print(ord.univar) startp=c(ord.univar$cutpts,ord.univar$beta,0.5) ord.exch <- ordprobit.exch(x,y,ordinalex$id,iprint=0,startpar=startp) print(ord.exch)