se: 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

se(model, estimate = "all", checkEstimability = TRUE, ...)

Arguments

model

a model object of class "gnm"

estimate

specifies non-eliminated parameters or linear combinations of parameters for which to find standard errors. In the first case either "all", a character vector of names, a numeric vector of indices or "pick" to select

checkEstimability

logical: should the estimability of all specified combinations be checked?

...

possible further arguments for checkEstimable

Value

A data frame with two columns:
estimateThe estimated parameter combinations
SETheir estimated standard errors
and row names the same as the column names (if any) of coefMatrix.

Examples

Run this code

data(yaish)
set.seed(1)

## Fit the "UNIDIFF" mobility model across education levels
unidiff <- gnm(Freq ~ educ*orig + educ*dest +
               Mult(Exp(-1 + educ), -1 + orig:dest), 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
educ4.pos <- grep("Mult.*educ4", names(coef(unidiff)))
mycontrast <- rep(0, length(coef(unidiff)))
mycontrast[educ4.pos] <- 1
mycontrast[educ4.pos + 1] <- -1
se(unidiff, mycontrast)

## Get all of the contrasts with educ5 in the UNIDIFF multipliers
getContrasts(unidiff, rev(grep("Mult.*educ", names(coef(unidiff)))))

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