lmfitSaws: Linear model function to output extra objects for saws

In saws: Small-Sample Adjustments for Wald Tests Using Sandwich Estimators

Linear model function to output extra objects for saws

Description

This is a very basic linear model function. It outputs only the objects needed for input into saws.

Usage

lmfitSaws(x,y)

Arguments

x	design matrix
y	response vector

Details

The saws function requires three inputs, the parameter estimates (coefficients), u, and omega. The value u is the K by p matrix of estimating equations evaluated at the coefficient, where each row is an independent estimating equation. For the linear model u[i,] = x[i,] * residual[i]. The value omega is a K by p by p array, where omega[i,,] is the derivative of the ith estimating equation with respect to the parameter vector. For the linear model omega[i,,]= t(Xi)

Value

A list with the following elements

coefficients	p by 1 coefficient vector
u	K by p matrix of estimating equations
omega	K by p by p array, see details

Author(s)

M.P. Fay

References

Fay and Graubard (2001). Small-Sample Adjustments for Wald-Type Tests Using Sandwich Estimators. Biometrics 57: 1198-1206. (for copy see /inst/doc/ directory

See Also

link{lm}

Examples

set.seed(1)
n<-20
x1<-rnorm(n)
x2<-factor(c(rep("a",n/2),rep("b",n/2)))
y<-rnorm(n,x1)
out<-lmfitSaws(model.matrix(~x1*x2),y)
saws(out)

saws documentation built on June 24, 2022, 1:07 a.m.