glm.scoretest {statmod} | R Documentation |
Computes score test statistics (z-statistics) for adding covariates to a generalized linear model.
glm.scoretest(fit, x2, dispersion=NULL)
fit |
generalized linear model fit object, of class |
x2 |
vector or matrix with each column a covariate to be added. |
dispersion |
the dispersion for the generalized linear model family. |
Rao's score statistic. Is the locally most powerful test for testing vs a one-sided alternative. Asympotically equivalent to likelihood ratio tests, but convenient for one-sided tests.
This function computes a score test statistics for adding each covariate individually.
The dispersion parameter is treated as for summary.glm
.
If NULL
, the Pearson estimator is used, except for the binomial and Poisson
families, for which the dispersion is one.
numeric vector containing the z-statistics, one for each covariate.
Gordon Smyth
Lovison, G (2005). On Rao score and Pearson $X^2$ statistics in generalized linear models. Statistical Papers, 46, 555-574.
Pregibon, D (1982). Score tests in GLIM with applications. In GLIM82: Proceedings of the International Conference on Generalized Linear Models, R Gilchrist (ed.), Lecture Notes in Statistics, Volume 14, Springer, New York, pages 87-97.
Smyth, G. K. (2003). Pearson's goodness of fit statistic as a score test statistic. In: Science and Statistics: A Festschrift for Terry Speed, D. R. Goldstein (ed.), IMS Lecture Notes - Monograph Series, Volume 40, Institute of Mathematical Statistics, Beachwood, Ohio, pages 115-126. http://www.statsci.org/smyth/pubs/goodness.pdf
# Pearson's chisquare test for independence # in a contingency table is a score test. # First the usual test y <- c(20,40,40,30) chisq.test(matrix(y,2,2),correct=FALSE) # Now same test using glm.scoretest a <- gl(2,1,4) b <- gl(2,2,4) fit <- glm(y~a+b,family=poisson) x2 <- c(0,0,0,1) z <- glm.scoretest(fit,x2) z^2