R: Standard Errors of Linear Parameter Combinations in gnm...

se.gnm {gnm} | R Documentation

## Standard Errors of Linear Parameter Combinations in gnm Models

### Description

Computes approximate standard errors for (a selection of) individual parameters or one or more linear combinations of the parameters in a `gnm` (generalized nonlinear model) object. By default, a check is made first on the estimability of each specified combination.

### Usage

    ## S3 method for class 'gnm'
    se(object, estimate = NULL, checkEstimability = TRUE,
           Vcov = NULL, dispersion = NULL, ...)

### Arguments

`object` | a model object of class `"gnm"`.
`estimate` | (optional) specifies parameters or linear combinations of parameters for which to find standard errors. In the first case either a character vector of names, a numeric vector of indices or `"[?]"` to select from a Tk dialog. In the second case coefficients given as a vector or the rows of a matrix, such that `NROW(estimate)` is equal to `length(coef(object))`. If `NULL`, standard errors are returned for all (non-eliminated) parameters in the model.
`checkEstimability` | logical: should the estimability of all specified combinations be checked?
`Vcov` | either NULL, or a matrix
`dispersion` | either NULL, or a positive number
`...` | possible further arguments for `checkEstimable`.

### Value

A data frame with two columns:

`Estimate` | The estimated parameter combinations
`Std. Error` | Their estimated standard errors

If available, the column names of `coefMatrix` will be used to name the rows.

### Note

In the case where `estimate` is a numeric vector, `se` will assume that indices have been specified if all the values of `estimate` are in `seq(length(coef(object))`.

Where both `Vcov` and `dispersion` are supplied, the variance-covariance matrix of estimated model coefficients is taken to be `Vcov * dispersion`.

### Author(s)

David Firth and Heather Turner

### See Also

`gnm`, `getContrasts`, `checkEstimable`, `ofInterest`

### Examples

    set.seed(1)

    ## Fit the "UNIDIFF" mobility model across education levels
    unidiff <- gnm(Freq ~ educ*orig + educ*dest +
                   Mult(Exp(educ), orig:dest),
                   ofInterest = "[.]educ", family = poisson,
                   data = yaish,  subset = (dest != 7))
    ## Deviance is 200.3

    ## Get estimate and se for the contrast between educ4 and educ5 in the
    ## UNIDIFF multiplier
    mycontrast <- numeric(length(coef(unidiff)))
    mycontrast[ofInterest(unidiff)[4:5]] <- c(1, -1)
    se(unidiff, mycontrast)

    ## Get all of the contrasts with educ5 in the UNIDIFF multipliers
    getContrasts(unidiff, rev(ofInterest(unidiff)))

[Package gnm version 1.1-2 Index]