# brant: Brant Test In brant: Test for Parallel Regression Assumption

## Description

The function calculates the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models to test the parallel regression assumption.

## Usage

 `1` ```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

 ```1 2 3 4 5``` ```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) ```

### Example output

```Call:
MASS::polr(formula = Smoke ~ Sex + Height, data = data, Hess = TRUE)

Coefficients:
Value Std. Error t value
SexMale 0.09478     0.4808  0.1971
Height  0.02785     0.0243  1.1458

Intercepts:
Value  Std. Error t value
Never|Occas 6.3113 4.0564     1.5559
Occas|Regul 6.9224 4.0619     1.7042
Regul|Heavy 7.8802 4.0732     1.9347

Residual Deviance: 287.0276
AIC: 297.0276
(29 observations deleted due to missingness)
--------------------------------------------
Test for	X2	df	probability
--------------------------------------------
Omnibus		3.28	4	0.51
SexMale		1.58	2	0.45
Height		0.02	2	0.99
--------------------------------------------

H0: Parallel Regression Assumption holds
```

brant documentation built on Oct. 23, 2020, 7:51 p.m.