VGAM (version 1.1-6)

# acat: Ordinal Regression with Adjacent Categories Probabilities

## Description

Fits an adjacent categories regression model to an ordered (preferably) factor response.

## Usage

acat(link = "loglink", parallel = FALSE, reverse = FALSE,
zero = NULL, whitespace = FALSE)

## Arguments

Link function applied to the ratios of the adjacent categories probabilities. See Links for more choices.

parallel

A logical, or formula specifying which terms have equal/unequal coefficients.

reverse

Logical. By default, the linear/additive predictors used are $$\eta_j = \log(P[Y=j+1]/P[Y=j])$$ for $$j=1,\ldots,M$$. If reverse is TRUE then $$\eta_j = \log(P[Y=j]/P[Y=j+1])$$ will be used.

zero

An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The values must be from the set {1,2,…,$$M$$}.

whitespace

See CommonVGAMffArguments for information.

## Value

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.

## Warning

No check is made to verify that the response is ordinal if the response is a matrix; see ordered.

## Details

In this help file the response $$Y$$ is assumed to be a factor with ordered values $$1,2,\ldots,M+1$$, so that $$M$$ is the number of linear/additive predictors $$\eta_j$$.

By default, the log link is used because the ratio of two probabilities is positive.

## References

Agresti, A. (2013). Categorical Data Analysis, 3rd ed. Hoboken, NJ, USA: Wiley.

Simonoff, J. S. (2003). Analyzing Categorical Data, New York: Springer-Verlag.

Yee, T. W. (2010). The VGAM package for categorical data analysis. Journal of Statistical Software, 32, 1--34. 10.18637/jss.v032.i10.

cumulative, cratio, sratio, multinomial, margeff, pneumo.

## Examples

Run this code
# NOT RUN {
pneumo <- transform(pneumo, let = log(exposure.time))
(fit <- vglm(cbind(normal, mild, severe) ~ let, acat, data = pneumo))
coef(fit, matrix = TRUE)
constraints(fit)
model.matrix(fit)
# }


Run the code above in your browser using DataCamp Workspace