Coef Method for mixpoissonreg Objects.

Extract model coefficients of fitted mixed Poisson regression models. The parameters arguments allows one to chose if all coefficients should be extracted, with parameters = 'all'; if the coefficients of the mean-related parameters should be extracted, with parameters = 'mean'; if the coefficients of the precision-related parameters should be extracted, with parameters = 'precision'.

# S3 method for mixpoissonreg
coef(object, parameters = c("all", "mean", "precision"), ...)

Arguments

object	object of class "mixpoissonreg" containing results from the fitted model.
parameters	a string to determine which coefficients should be extracted: 'all' extracts all coefficients, 'mean' extracts the coefficients of the mean parameters and 'precision' extracts coefficients of the precision parameters.
...	further arguments passed to or from other methods.

Value

A vector containing the coefficients of a mixpoissonreg object.

See also

vcov.mixpoissonreg

Examples

# \donttest{
data("Attendance", package = "mixpoissonreg")

daysabs_fit <- mixpoissonreg(daysabs ~ gender + math +
prog | gender + math + prog, data = Attendance)
coef(daysabs_fit)
#>              (Intercept)               gendermale                     math 
#>              2.746122950             -0.245112763             -0.006617207 
#>             progAcademic           progVocational    (Intercept).precision 
#>             -0.425983017             -1.269755359              1.414226542 
#>     gendermale.precision           math.precision   progAcademic.precision 
#>             -0.208397158             -0.005122980             -1.084417764 
#> progVocational.precision 
#>             -1.422051450 
coef(daysabs_fit, parameters = "precision")
#>    (Intercept)     gendermale           math   progAcademic progVocational 
#>     1.41422654    -0.20839716    -0.00512298    -1.08441776    -1.42205145 
# }

daysabs_prog <- mixpoissonreg(daysabs ~ prog, data = Attendance)
coef(daysabs_prog)
#>           (Intercept)          progAcademic        progVocational 
#>              2.365560             -0.429104             -1.382397 
#> (Intercept).precision 
#>              1.005579