Compute asymptotic confidence intervals for STAR linear regression

For a linear regression model within the STAR framework, compute (asymptotic) confidence intervals for a regression coefficient of interest. Confidence intervals are computed by inverting the likelihood ratio test and profiling the log-likelihood.

Usage

# S3 method for lmstar
confint(object, parm, level = 0.95, ...)

Arguments

object

Object of class "lmstar" as output by lm_star

parm

a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.

level

confidence level; default is 0.95

...

Ignored

Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in

Examples

#Simulate data with count-valued response y:
sim_dat = simulate_nb_lm(n = 100, p = 2)
y = sim_dat$y; X = sim_dat$X[,-1] # remove intercept

# Select a transformation:
transformation = 'np'

#Estimate model
fit = lm_star(y~X, transformation = transformation)

#Confidence interval for all parameters
confint(fit)
#>                  2.5 %    97.5 %
#> (Intercept) -0.1651785 0.1805371
#> X            0.3098616 0.6679353