blm-class {blm}

Class "blm"

Description

Class for binomial linear regression (BLM).

Objects from the Class

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

Slots

coef:

vector of fitted coefficients

vcov:

matrix of variance-covariate estimates for coef

formula:

model formula

df.residual:

residual degrees of freedom

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

Methods

show

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

print

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

summary

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

coef

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

logLik

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

model.formula

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

resid

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

vcov

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

predict

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

confint

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

See Also

blm, constrOptim