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.