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Last data update: 2014.03.03

R: Modified score test for generalized linear models

mdscore

R Documentation

Modified score test for generalized linear models

Description

Computes the modified score test based for the coefficients of a generalized linear model.

Usage

mdscore(model = model, X1 = X1, phi = NULL)

Arguments

`model`	an object that stores the results of `glm` fit of the model under the null hypothesis.
`X1`	the matrix with the columns of the model matrix X that correspond to the coefficients being specified in the null hypothesis.
`phi`	the precision parameter.

Details

The object fit.model is obtained using the usual options passed to the glm function.

Value

The function mdscore() returns the following list of values:

`Sr`	the value of the score statistic.
`Srcor`	the value of the modified score statistic.
`coef`	a vector with the coefficients A1 , A2 and A3.
`n`	the total sample size.
`df`	the number of degrees of freedom of the chi–squared approximations for the tests.
`phi`	the precision parameter used in the computations

Author(s)

Antonio Hermes M. da Silva-Junior hermes@ccet.ufrn.br

Damiao N. da Silva damiao@ccet.ufrn.br

References

Cordeiro GM, Ferrari SLP (1991). A Modified Score Test Statistic Having chi-squared Distribution to Order n–1 . Biometrika, 78(3), 573–582.

Cordeiro GM, Ferrari SLP, Paula GA (1993). Improved Score Tests for Generalized Linear Models. Journal of the Royal Statistical Society B, 55(3), 661–674.

Cribari-Neto F, Ferrari SLP (1995). Second Order Asymptotics for Score Tests in Generalised Linear Models. Biometrika, 82(2), 426–432.

da Silva-Junior AHM, da Silva DN, Ferrari SLP (2014). mdscore: An R Package to Compute Improved Score Tests in Generalized Linear Models. Journal of Statistical Software, 61(2), 1-16., http://www.jstatsoft.org/v61/c02/

Examples

data(strength)
fitf <- glm(y ~ cut * lot, data = strength,family = inverse.gaussian("inverse"))
summary(fitf)
X <- model.matrix(fitf, data = strength)
fit0 <- glm(y ~ cut + lot, data = strength, family = inverse.gaussian("inverse"))
mdscore(fit0, X1=X[, 7:10])