summary the Maximum-Likelihood estimation
Summary the Maximum-Likelihood estimation including standard errors and t-values.
## S3 method for class 'maxLik' summary(object, eigentol=1e-12, ... ) ## S3 method for class 'summary.maxLik' coef(object, ...)
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. |
An object of class 'summary.maxLik' with following components:
type of maximization.
number of iterations.
code of success.
a short message describing the code.
the loglik value in the maximum.
numeric matrix, the first column contains the parameter estimates, the second the standard errors, third t-values and fourth corresponding probabilities.
logical vector, which parameters are treated as constants.
number of free parameters.
information about the constrained optimization.
Passed directly further from maxim-object. NULL if
unconstrained maximization.
Ott Toomet, Arne Henningsen
## 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)
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