Extractor Methods for htobit Objects

Methods for extracting information from fitted htobit objects.

# S3 method for htobit
coef(object, model = c("full", "location", "scale"), …)
# S3 method for htobit
vcov(object, model = c("full", "location", "scale"), …)

# S3 method for htobit
terms(x, model = c("location", "scale", "full"), …)
# S3 method for htobit
model.matrix(object, model = c("location", "scale"), …)

Arguments

object, x	an object of class "htobit".
model	character indicating (sub)model for which information should be extracted.
…	currently not used.

Details

In addition to the methods above, a set of standard extractor functions for "htobit" objects is available, see htobit for an overview.

See also

htobit

Examples

## heteroscedastic tobit model for budget share of alcohol
data("AlcoholTobacco", package = "htobit")
AlcoholTobacco$persons <- with(AlcoholTobacco, adults + oldkids + youngkids)
ma <- htobit(alcohol ~ age + log(expenditure) + persons | age + log(expenditure) + persons,
  data = AlcoholTobacco)

## extract coefficients
coef(ma)
#>              (Intercept)                      age         log(expenditure) 
#>             -0.071762027              0.002381390              0.006243513 
#>                  persons      (scale)_(Intercept)              (scale)_age 
#>             -0.001763290              0.175709476              0.064390807 
#> (scale)_log(expenditure)          (scale)_persons 
#>             -0.277921386             -0.110616284 
coef(ma, model = "location")
#>      (Intercept)              age log(expenditure)          persons 
#>     -0.071762027      0.002381390      0.006243513     -0.001763290 
coef(ma, model = "scale")
#>      (Intercept)              age log(expenditure)          persons 
#>       0.17570948       0.06439081      -0.27792139      -0.11061628 

## corresponding model matrices
head(model.matrix(ma, model = "location"))
#>   (Intercept) age log(expenditure) persons
#> 1           1   2         14.19054       3
#> 2           1   3         13.90857       3
#> 3           1   2         13.97461       1
#> 4           1   2         13.76281       3
#> 5           1   2         13.80800       3
#> 6           1   2         14.00313       5
head(model.matrix(ma, model = "scale"))
#>   (Intercept) age log(expenditure) persons
#> 1           1   2         14.19054       3
#> 2           1   3         13.90857       3
#> 3           1   2         13.97461       1
#> 4           1   2         13.76281       3
#> 5           1   2         13.80800       3
#> 6           1   2         14.00313       5