summary.maxLik {maxLik} | R Documentation |
summary the Maximum-Likelihood estimation
Description
Summary the Maximum-Likelihood estimation including standard errors and t-values.
Usage
## S3 method for class 'maxLik'
summary(object, eigentol=1e-12, ... )
## S3 method for class 'summary.maxLik'
coef(object, ...)
Arguments
object |
object of class 'maxLik', or 'summary.maxLik', usually a result from Maximum-Likelihood estimation. |
eigentol |
The standard errors are only calculated if the ratio of the smallest and largest eigenvalue of the Hessian matrix is less than “eigentol”. Otherwise the Hessian is treated as singular. |
... |
currently not used. |
Value
An object of class 'summary.maxLik' with following components:
- type
type of maximization.
- iterations
number of iterations.
- code
code of success.
- message
a short message describing the code.
- loglik
the loglik value in the maximum.
- estimate
numeric matrix, the first column contains the parameter estimates, the second the standard errors, third t-values and fourth corresponding probabilities.
- fixed
logical vector, which parameters are treated as constants.
- NActivePar
number of free parameters.
- constraints
information about the constrained optimization. Passed directly further from
maxim
-object.NULL
if unconstrained maximization.
Author(s)
Ott Toomet, Arne Henningsen
See Also
maxLik
for maximum likelihood estimation,
confint
for confidence intervals, and tidy
and glance
for alternative quick summaries of the ML
results.
Examples
## ML estimation of exponential distribution:
t <- rexp(100, 2)
loglik <- function(theta) log(theta) - theta*t
gradlik <- function(theta) 1/theta - t
hesslik <- function(theta) -100/theta^2
## Estimate with numeric gradient and hessian
a <- maxLik(loglik, start=1, control=list(printLevel=2))
summary(a)
## Estimate with analytic gradient and hessian
a <- maxLik(loglik, gradlik, hesslik, start=1, control=list(printLevel=2))
summary(a)