Confidence Intervals for Maximum Likelihood Estimates

Source: R/generics.R

Computes a confidence interval for one or more parameters in a unvariateML object.

# S3 method for univariateML
confint(object, parm = NULL, level = 0.95,
  Nreps = 1000, ...)

Arguments

object

An object of class univariateML.
parm

Vector of strings; the parameters to calculate a confidence interval for. Each parameter must be a member of names(object).
level

The confidence level.
Nreps

Number of bootstrap iterations. Passed to bootstrapml.
...

Additional arguments passed to bootstrapml.

Value

A matrix or vector with columns giving lower and upper confidence limits for each parameter in parm.

Details

confint.univariateML is a wrapper for bootstrapml that computes confidence intervals for the main parameters of object. The main parameters of object are the members of names(object). For instance, the main parameters of an object obtained from mlnorm are mean and sd. The confidence intervals are parametric bootstrap percentile intervals with limits (1-level)/2 and 1 - (1-level).

See also

confint for the generic function and bootstrapml for the function used to calculate the confidence intervals.

Examples

object <- mlinvgauss(airquality$Wind)
confint(object) # 95% confidence interval for mean and shape
#>            2.5%    97.5%
#> mean   9.302435 10.60839
#> shape 45.028074 70.80907
confint(object, "mean") # 95% confidence interval for the mean parameter
#>     2.5%    97.5% 
#>  9.33726 10.65172 
# confint(object, "variance") # Fails since 'variance isn't a main parameter.