nonest.basis {estimability}

R Documentation

Estimability Tools

Description

This documents the functions needed to test estimability of linear functions of regression coefficients.

Usage

nonest.basis(x, ...)
## S3 method for class 'qr'
nonest.basis(x, ...)
## S3 method for class 'matrix'
nonest.basis(x, ...)
## S3 method for class 'lm'
nonest.basis(x, ...)

all.estble

is.estble(x, nbasis, tol = 1e-8)

Arguments

x	For nonest.basis, an object of a class in ‘⁠methods("nonest.basis")⁠’. Or, in is.estble, a numeric vector or matrix for assessing estimability of ‘⁠sum(x * beta)⁠’, where beta is the vector of regression coefficients.
nbasis	Matrix whose columns span the null space of the model matrix. Such a matrix is returned by nonest.basis.
tol	Numeric tolerance for assessing nonestimability. For nonzero x, estimability of \beta'x is assessed by whether or not ||N'x||^2 < \tau ||x'x||^2, where N and \tau denote nbasis and tol, respectively.
...	Additional arguments, currently ignored.

Details

Consider a linear model y = X\beta + E. If X is not of full rank, it is not possible to estimate \beta uniquely. However, X\beta is uniquely estimable, and so is a'X\beta for any conformable vector a. Since a'X comprises a linear combination of the rows of X, it follows that we can estimate any linear function where the coefficients lie in the row space of X. Equivalently, we can check to ensure that the coefficients are orthogonal to the null space of X.

The constant all.estble is simply a 1 x 1 matrix of NA. This specifies a trivial non-estimability basis, and using it as nbasis will cause everything to test as estimable.

Value

When X is not full-rank, the methods for nonest.basis return a basis for the null space of X. The number of rows is equal to the number of regression coefficients (including any NAs); and the number of columns is equal to the rank deficiency of the model matrix. The columns are orthonormal. If the model is full-rank, then nonest.basis returns all.estble. The matrix method uses X itself, the qr method uses the QR decomposition of X, and the lm method recovers the required information from the object.

The function is.estble returns a logical value (or vector, if x is a matrix) that is TRUE if the function is estimable and FALSE if not.

Author(s)

Russell V. Lenth <russell-lenth@uiowa.edu>

References

Monahan, John F. (2008) A Primer on Linear Models, CRC Press. (Chapter 3)

Examples

Run examples

require(estimability)

X <- cbind(rep(1,5), 1:5, 5:1, 2:6)
( nb <- nonest.basis(X) )

# Test estimability of some linear functions for this X matrix
lfs <- rbind(c(1,4,2,5), c(2,3,9,5), c(1,2,2,1), c(0,1,-1,1))
is.estble(lfs, nb)

# Illustration on 'lm' objects:
warp.lm1 <- lm(breaks ~ wool * tension, data = warpbreaks, 
    subset = -(26:38), 
    contrasts = list(wool = "contr.treatment", tension = "contr.treatment"))
zapsmall(nonest.basis(warp.lm1))

warp.lm2 <- update(warp.lm1, 
    contrasts = list(wool = "contr.sum", tension = "contr.helmert"))
zapsmall(nonest.basis(warp.lm2))

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