Extract Gradients Evaluated at each Observation
Extract the gradients of the log-likelihood function evaluated
at each observation (‘Empirical Estimating Function’,
see estfun).
## S3 method for class 'maxLik' estfun(x, ...) ## S3 method for class 'maxim' gradient(x, ...)
x |
an object inheriting from class |
... |
further arguments (currently ignored). |
|
vector, objective function gradient at estimated maximum (or the last calculated value if the estimation did not converge.) |
|
matrix, observation-wise log-likelihood gradients at the estimated parameter value evaluated at each observation. Observations in rows, parameters in columns. |
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.
Arne Henningsen, Ott Toomet
## 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 )
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