Linear hypothesis test
p = linhyptest(beta,COVB,c,H,dfe)
[p,t,r] = linhyptest(...)
p = linhyptest(beta,COVB,c,H,dfe) returns the p value p of a hypothesis test on a vector of parameters. beta is a vector of k parameter estimates. COVB is the k-by-k estimated covariance matrix of the parameter estimates. c and H specify the null hypothesis in the form H*b = c, where b is the vector of unknown parameters estimated by beta. dfe is the degrees of freedom for the COVB estimate, or Inf if COVB is known rather than estimated.
beta is required. The remaining arguments have default values:
COVB = eye(k)
c = zeros(k,1)
H = eye(K)
dfe = Inf
If H is omitted, c must have k elements and it specifies the null hypothesis values for the entire parameter vector.
Note: The following functions return outputs suitable for use as the COVB input argument to linhyptest: nlinfit, coxphfit, glmfit, mnrfit, regstats, robustfit. nlinfit returns COVB directly; the other functions return COVB in stats.covb.
[p,t,r] = linhyptest(...) also returns the test statistic t and the rank r of the hypothesis matrix H. If dfe is Inf or is not given, t*r is a chi-square statistic with r degrees of freedom . If dfe is specified as a finite value, t is an F statistic with r and dfe degrees of freedom.
linhyptest performs a test based on an asymptotic normal distribution for the parameter estimates. It can be used after any estimation procedure for which the parameter covariances are available, such as regstats or glmfit. For linear regression, the p-values are exact. For other procedures, the p-values are approximate, and may be less accurate than other procedures such as those based on a likelihood ratio.
Fit a multiple linear model to the data in hald.mat:
load hald stats = regstats(heat,ingredients,'linear'); beta = stats.beta beta = 62.4054 1.5511 0.5102 0.1019 -0.1441
Perform an F-test that the last two coefficients are both 0:
SIGMA = stats.covb; dfe = stats.fstat.dfe; H = [0 0 0 1 0;0 0 0 0 1]; c = [0;0]; [p,F] = linhyptest(beta,SIGMA,c,H,dfe) p = 0.4668 F = 0.8391