summary.aldvmm {aldvmm}

Summarizing Adjusted Limited Dependent Variable Mixture Model Fits

Description

The method summary.aldvmm for the generic function summary creates an object of class "summary.aldvmm" including key results from an object of class "aldvmm".

Usage

## S3 method for class 'aldvmm'
summary(object, digits = max(3L, getOption("digits") - 3L), level = 0.95, ...)

Arguments

object

an object inheriting from class 'aldvmm'.

digits

an integer value of the number of digits in the output table.

level

a numeric value of the confidence interval between 0 and 1.

...

further arguments passed to or from other methods.

Value

summary.aldvmm returns an object of class "summary.aldvmm" including the following elements.

call

a character value including the model call captured by match.call.

summary

a data frame generated by aldvmm.sum including a formatted summary of model results for printing.

terms

a list of objects of class terms.

contrasts

a nested list of character values showing contrasts of factors used in models of component means ("beta") and probabilities of component membership ("delta").

coef

a numeric vector of parameter estimates.

cov

a numeric matrix object with covariances of parameters.

n

a scalar representing the number of complete observations with no missing values that were used in the estimation.

df.residual

an integer value of the residual degrees of freedom.

df.null

an integer value of the residual degrees of freedom of a null model including intercepts and standard errors.

iter

an integer value of the number of iterations used in optimization.

ll

a numeric value of the negative log-likelihood -ll.

aic

a numeric value of the Akaike information criterion AIC = 2n_{par} - 2ll.

bic

a numeric value of the Bayesian information criterion BIC = n_{par}*log(n_{obs}) - 2ll.

k

a numeric value of the number of components.

lcoef

a character vector of labels for objects including results on distributions (default "beta") and the probabilities of component membership (default "delta").

lcpar

a character vector of labels for objects including constant distribution parameters (default "sigma" for dist = "normal").

lcmp

a character value of the label for objects including results on different components (default "Comp")

lvar

a list including 2 character vectors of covariate names for model parameters of distributions ("beta") and the multinomial logit ("delta").