tidy.maxLik: tidy and glance methods for maxLik objects

Description

These methods return quick summary information about the estimated model, either one parameter per row (tibble), or on a single row (glance). Both require the tibble package to be installed.

Usage

# S3 method for maxim
tidy(x,  ...)
# S3 method for maxLik
tidy(x,  ...)
# S3 method for maxim
glance(x, ...)
# S3 method for maxLik
glance(x, ...)

Value

tidy() for 'maxLik' objects returns a tibble (data frame) with one row per parameter, and with the following columns:

term: The name of the estimated parameter (parameters are sequentially numbered if names missing).
estimate: The estimated parameter.
std.error: The standard error of the estimate.
statistic: The \(z\)-statistic of the estimate.
p.value: The \(p\)-value.

This is essentially the same table as summary-method prints, just in form of a tibble (data frame).

For 'maxim' objects, the corresponding tibble contains

term: The name of the estimated parameter (parameters are sequentially numbered if names missing).
estimate: The estimated parameter.
gradient: The corresponding gradient value.

glance() for 'maxLik' objects returns an one-row tibble with columns:

df: The degrees of freedom of the model.
logLik: The log-likelihood of the model.
AIC: Akaike's Information Criterion for the model.
nobs: The number of observations, if this is available, otherwise NA.

For 'maxim' objects the tibble contains

value: The function value at optimum.
iterations: The number of iterations spent (what exactly is “iteration” depends on the optimizer).
gradient: Norm of gradient at the optimum.
code: optimizer return code (see returnCode).

Arguments

x: object of class 'maxLik' or 'maxim'.
...: Not used.

Author

David Hugh-Jones

Examples

Run this code

## Example with a single parameter
t <- rexp(100, 2)
loglik <- function(theta) log(theta) - theta*t
a <- maxLik(loglik, start=2)
tidy(a)
glance(a)
## Example with a parameter vector
x <- rnorm(100)
loglik <- function(theta) {
   dnorm(x, mean=theta[1], sd=theta[2], log=TRUE)
}
a <- maxLik(loglik, start=c(mu=0, sd=1))
tidy(a)
glance(a)