oprobit_dist.Rd
Fits and evaluates the ordinal probit model.
#---- ordinal probit model doprobit(x, thresh, max_val=99) fit_oprobit(x, par_init=NULL, weights=NULL) # S3 method for fit_oprobit coef(object, ...) # S3 method for fit_oprobit logLik(object, ...) # S3 method for fit_oprobit summary(object, digits=4, file=NULL, ...) # S3 method for fit_oprobit vcov(object, ...)
x | Numeric vector |
---|---|
thresh | Vector of thresholds |
max_val | Maximum value for computing thresholds |
par_init | Optional vector of initial parameters |
weights | Optional vector of sampling weights |
object | Object of class |
digits | Number of digits used for rounding in |
file | File name for the |
... | Further arguments to be passed |
Vector or an object of fitted distribution depending on the called function
See oprobit_regression
for fitting a regression model in which
the response variable follows an ordinal probit model.
############################################################################# # EXAMPLE 1: Fit an ordinal probit distribution ############################################################################# #-- simulate data set.seed(987) N <- 1500 # define thresholds thresh <- c(0,.3, .7, 1.6) # latent continuous data yast <- stats::rnorm(N) # discretized ordinal data x <- as.numeric( cut( yast, c(-Inf,thresh,Inf) ) ) - 1 #-- fit ordinal probit distribution mod <- mdmb::fit_oprobit(x=x) summary(mod) logLik(mod) vcov(mod)