summary.oglmx {oglmx}

Summarizing Ordered Discrete Outcome Model Fits

Description

summary method for class "oglmx"

Usage

## S3 method for class 'oglmx'
summary(object, tol = 1e-20, ... )

## S3 method for class 'summary.oglmx'
print(x, ... )

Arguments

object

an object of class "oglmx"

tol

argument passed to qr.solve, defines the tolerance for detecting linear dependencies in the hessian matrix to be inverted.

...

additional arguments, currently ignored.

x

object of class summary.oglmx.

Value

regtype

character string describing the type of model estimated.

loglikelihood

log-likelihood for the estimated model.

estimate

matrix with four columns and number of rows equal to the number of estimated parameters. Columns of the matrix correspond to estimated coefficients, standard errors, t-statistics and (two-sided) p-values.

estimateDisplay

the same data as in estimate but separated into a list with elements for each type of parameter estimate. The first element is for parameters in the mean equation, second element for parameters in the variance equation and the final element is for threshold parameters.

no.iterations

number of iterations used in function that maximises the log-likelihood.

McFaddensR2

McFadden's R^2 aka Pseudo-R^2. Calculated as:

R^2=1-\log{L_{fit}}/\log{L_0}

where \log{L_{fit}} is the log-likelihood for the fitted model and \log{L_0} is the log-likelihood from an intercept only model that estimates the probability of each alternative to be the sample average.

AIC

Akaike Information Criterion, calculated as:

AIC=2k-2\log{L_{fit}}

where k is the number of estimated parameters.

coefficients

named vector of estimated parameters.

Author(s)

Carroll, Nathan [e-mail]

References

McFadden, D. (1973) Conditional Logit Analysis of Qualitative Choice Behavior in Frontiers in Econometrics. P.Zarembka (Ed.), New York, Academic Press.