brant: Brant Test
In benjaminschlegel/brant: Test for Parallel Regression Assumption
brant R Documentation
Brant Test
Description
The function calculates the brant test for ordinal logit models to test the parallel regression assumption.
Usage
brant(model,by.var=F)
Arguments
model
the polr-Object generated with polr()
by.var
OPTIONAL if set to true, the tests are made for each variable instead of each coefficient. Default: FALSE.
Details
The function calculates the brant test for parallel regression assumption. The brant test was published by Brant (1990). The function works with models generated with the function polr() from the package 'MASS'.
Value
The output is the brant test, which shows if the parallel assumption holds or not.
Author(s)
Benjamin Schlegel, kontakt@benjaminschlegel.ch
References
Brant, R. (1990) Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46, 1171–1178.
Examples
data = MASS::survey
data$Smoke = ordered(MASS::survey$Smoke,levels=c("Never","Occas","Regul","Heavy"))
model1 = MASS::polr(Smoke ~ Sex + Height, data=data, Hess=TRUE)
summary(model1)
brant(model1)
benjaminschlegel/brant documentation built on April 23, 2024, 8:33 a.m.
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| brant | R Documentation |
Brant Test
Description
The function calculates the brant test for ordinal logit models to test the parallel regression assumption.
Usage
brant(model,by.var=F)
Arguments
model |
the polr-Object generated with polr() |
by.var |
OPTIONAL if set to true, the tests are made for each variable instead of each coefficient. Default: FALSE. |
Details
The function calculates the brant test for parallel regression assumption. The brant test was published by Brant (1990). The function works with models generated with the function polr() from the package 'MASS'.
Value
The output is the brant test, which shows if the parallel assumption holds or not.
Author(s)
Benjamin Schlegel, kontakt@benjaminschlegel.ch
References
Brant, R. (1990) Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46, 1171–1178.
Examples
data = MASS::survey
data$Smoke = ordered(MASS::survey$Smoke,levels=c("Never","Occas","Regul","Heavy"))
model1 = MASS::polr(Smoke ~ Sex + Height, data=data, Hess=TRUE)
summary(model1)
brant(model1)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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