wald.test | R Documentation |
Computes a Wald chi-squared test for 1 or more coefficients, given their variance-covariance matrix.
wald.test(Sigma, b, Terms = NULL, L = NULL, H0 = NULL, df = NULL, verbose = FALSE) ## S3 method for class 'wald.test' print(x, digits = 2, ...)
Sigma |
A var-cov matrix, usually extracted from one of the fitting functions (e.g., |
b |
A vector of coefficients with var-cov matrix |
Terms |
An optional integer vector specifying which coefficients should be jointly tested, using a Wald
chi-squared or F test. Its elements correspond to the columns or rows of the var-cov
matrix given in |
L |
An optional matrix conformable to |
H0 |
A numeric vector giving the null hypothesis for the test. It must be as long as |
df |
A numeric vector giving the degrees of freedom to be used in an F test, i.e. the degrees of freedom
of the residuals of the model from which |
verbose |
A logical scalar controlling the amount of output information. The default is |
x |
Object of class “wald.test” |
digits |
Number of decimal places for displaying test results. Default to 2. |
... |
Additional arguments to |
The key assumption is that the coefficients asymptotically follow a (multivariate) normal distribution with mean =
model coefficients and variance = their var-cov matrix.
One (and only one) of Terms
or L
must be given. When L
is given, it must have the same number of
columns as the length of b
, and the same number of rows as the number of linear combinations of coefficients.
When df
is given, the chi-squared Wald statistic is divided by m
= the number of
linear combinations of coefficients to be tested (i.e., length(Terms)
or nrow(L)
). Under the null
hypothesis H0
, this new statistic follows an F(m, df) distribution.
An object of class wald.test
, printed with print.wald.test
.
Diggle, P.J., Liang, K.-Y., Zeger, S.L., 1994. Analysis of longitudinal data. Oxford, Clarendon Press, 253 p.
Draper, N.R., Smith, H., 1998. Applied Regression Analysis. New York, John Wiley & Sons, Inc., 706 p.
vcov
data(orob2) fm <- quasibin(cbind(y, n - y) ~ seed * root, data = orob2) # Wald test for the effect of root wald.test(b = coef(fm), Sigma = vcov(fm), Terms = 3:4)
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