lexpit-class: Class "lexpit"

In blm: Binomial Linear Regression

Class "lexpit"

Description

Class for linear-expit regression (lexpit).

Objects from the Class

Objects can be created by calls of the form new("lexpit", ...).

Slots

coef.linear:

vector of fitted linear coefficients

coef.expit:

vector of fitted expit coefficients

vcov.linear:

matrix of variance-covariate estimates for linear coef

vcov.expit:

matrix of variance-covariate estimates for expit coef

formula.linear:

model formula for linear component

formula.expit:

model formula for expit component

df.residual:

residual degrees of freedom

p:

number of linear parameters

q:

number of expit parameters

data:

data frame used in fitting, after applying na.action

which.kept:

vector of index of values in original data source that were used in the model fitting

y:

response vector for fitted model

weights:

vector of weights used in model fitting

strata:

stratification factor for weighted regression.

converged:

logical message about convergence status at the end of algorithm

par.init:

initial parameter values for optimization algorithm

loglik

value of log-likelihood (normalized for weighted likelihood) under full model

loglik.null

value of log-likelihood (normalized for weighted likelihood) under null model

barrier.value

value of the barrier function at the optimum

control.lexpit

list with control parameters for optimization algorithm

Methods

show

signature(object = "lexpit"): Display point estimates of lexpit object.

print

signature(x = "lexpit",...): Display point estimates of lexpit object.

summary

signature(object = "lexpit",...): List of estimates and convergence information.

coef

signature(object = "lexpit"): Extractor for fitted coefficients.

logLik

signature(object = "lexpit"): Extractor for log-likelihood of lexpit model.

model.formula

signature(object = "lexpit"): Extractor for formula of lexpit object.

vcov

signature(object = "lexpit"): Extractor for variance-covariance based on Taylor series large-sample Hessian approximation with the pseudo-likelihood of the constrained optimization.

resid

signature(object = "lexpit"): Extractor for residuals.

predict

signature(object = "lexpit"): Returns vector of linear predictors for each subject of the fitted model.

confint

signature(object = "lexpit", parm, level = 0.95,...): Returns confidence interval (at a given level) for the specified regression parameters.

See Also

lexpit, constrOptim

blm documentation built on Sept. 12, 2022, 9:05 a.m.