lexpit-class: 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.