summary.maxLik {maxLik} | R Documentation |
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 |
nonzero print limit on the range of the absolute values of the hessian. Specifically, define: absEig <- eigen(hessian(object), symmetric=TRUE)[['values']] Then compute and print t values, p values, etc. only if min(absEig) > (eigentol * max(absEig)). |
... |
currently not used. |
summary.maxLik
returns an
object of class 'summary.maxLik' with following components:
type |
type of maximisation. |
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 |
coef.summary.maxLik
returns the matrix of estimated values,
standard errors, and t- and p-values.
Ott Toomet otoomet@ut.ee, Arne Henningsen
## ML estimation of exponential duration model: 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, print.level=2) summary(a) ## Estimate with analytic gradient and hessian a <- maxLik(loglik, gradlik, hesslik, start=1) summary(a)