blm-class {blm} | R Documentation |

## 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.`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*version 2022.0.0.1 Index]