bothsidesmodel {msos}R Documentation

Calculate the least squares estimates

Description

This function fits the model using least squares. It takes an optional pattern matrix P as in (6.51), which specifies which βij\beta _{ij}'s are zero.

Usage

bothsidesmodel(x, y, z = diag(qq), pattern = matrix(1, nrow = p, ncol = l))

Arguments

x

An N×PN \times P design matrix.

y

The N×QN \times Q matrix of observations.

z

A Q×LQ \times L design matrix

pattern

An optional N×PN \times P matrix of 0's and 1's indicating which elements of β\beta are allowed to be nonzero.

Value

A list with the following components:

Beta

The least-squares estimate of β\beta.

SE

The P×LP \times L matrix with the ijijth element being the standard error of β^ij\hat{\beta}_ij.

T

The P×LP \times L matrix with the ijijth element being the tt-statistic based on β^ij\hat{\beta}_{ij}.

Covbeta

The estimated covariance matrix of the β^ij\hat{\beta}_{ij}'s.

df

A pp-dimensional vector of the degrees of freedom for the tt-statistics, where the jjth component contains the degrees of freedom for the jjth column of β^\hat{\beta}.

Sigmaz

The Q×QQ \times Q matrix Σ^z\hat{\Sigma}_z.

Cx

The Q×QQ \times Q residual sum of squares and crossproducts matrix.

See Also

bothsidesmodel.chisquare, bothsidesmodel.df, bothsidesmodel.hotelling, bothsidesmodel.lrt, and bothsidesmodel.mle.

Examples

# Mouth Size Example from 6.4.1
data(mouths)
x <- cbind(1, mouths[, 5])
y <- mouths[, 1:4]
z <- cbind(c(1, 1, 1, 1), c(-3, -1, 1, 3), c(1, -1, -1, 1), c(-1, 3, -3, 1))
bothsidesmodel(x, y, z)

[Package msos version 1.2.0 Index]