R: Extract Gradients Evaluated at each Observation

estfun.maxLik {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, ... )

### Arguments

`x` | an object of class `maxLik`.
`...` | further arguments (currently ignored).

### Value

Matrix of 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 before this method can be used.

This method works only 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

### See Also

`estfun`, `maxLik`.

### Examples

    ## ML estimation of exponential duration model:
    t <- rexp(100, 2)
    loglik <- function(theta) log(theta) - theta*t

    ## Estimate with numeric gradient and hessian
    a <- maxLik(loglik, start=1 )

    # Extract the gradients evaluated at each observation
    library( sandwich )
    head(estfun( a ), 10)

    ## Estimate with analytic gradient.
    ## Note: it returns a vector
    gradlik <- function(theta) 1/theta - t
    b <- maxLik(loglik, gradlik, start=1)
    head(estfun( b ), 10)

[Package maxLik version 1.3-4 Index]