| gradient {maxLik} | R Documentation |
Extract Gradients Evaluated at each Observation
Description
Extract the gradients of the log-likelihood function evaluated
at each observation (‘Empirical Estimating Function’,
see estfun).
Usage
## S3 method for class 'maxLik'
estfun(x, ...)
## S3 method for class 'maxim'
gradient(x, ...)
Arguments
x |
an object inheriting from class |
... |
further arguments (currently ignored). |
Value
gradient |
vector, objective function gradient at estimated maximum (or the last calculated value if the estimation did not converge.) |
estfun |
matrix, observation-wise log-likelihood gradients at the estimated parameter value evaluated at each observation. Observations in rows, parameters in columns. |
Warnings
The sandwich package must be loaded in order to use estfun.
estfun only works if the observaton-specific gradient information
was available for the estimation. This is the case of the
observation-specific gradient was supplied (see the grad
argument for maxLik), or the log-likelihood function
returns a vector of observation-specific values.
Author(s)
Arne Henningsen, Ott Toomet
See Also
Examples
## ML estimation of exponential duration model:
t <- rexp(10, 2)
loglik <- function(theta) log(theta) - theta*t
## Estimate with numeric gradient and hessian
a <- maxLik(loglik, start=1 )
gradient(a)
# Extract the gradients evaluated at each observation
library( sandwich )
estfun( a )
## Estimate with analytic gradient.
## Note: it returns a vector
gradlik <- function(theta) 1/theta - t
b <- maxLik(loglik, gradlik, start=1)
gradient(a)
estfun( b )