cratio: Ordinal Regression with Continuation Ratios

Description

Fits a continuation ratio logit/probit/cloglog/cauchit/... regression model to an ordered (preferably) factor response.

Usage

cratio(link = "logitlink", parallel = FALSE, reverse = FALSE,
    zero = NULL, thresholds = c("unconstrained", "equidistant"),
    Treverse = reverse, Tref = if (Treverse) "M" else 1,
    whitespace = FALSE)

Value

An object of class "vglmff"

(see vglmff-class). The object is used by modelling functions such as vglm,

rrvglm

and vgam.

Arguments

link: Link function applied to the $M$ continuation ratio probabilities. See Links for more choices.
parallel: A logical, or formula specifying which terms have equal/unequal coefficients.
reverse: Logical. By default, the continuation ratios used are $η_{j} = l o g i t (P [Y > j | Y \geq j])$ for $j = 1, \dots, M$ . If reverse is TRUE, then $η_{j} = l o g i t (P [Y < j + 1 | Y \leq j + 1])$ 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,..., $M$ }. The default value means none are modelled as intercept-only terms. See CommonVGAMffArguments for more information.
thresholds, Treverse, Tref: See cumulative for information. These arguments apply to ordinal categorical regression models.
whitespace: See CommonVGAMffArguments for information.

Author

Thomas W. Yee

Warning

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

Boersch-Supan (2021) looks at sparse data and the numerical problems that result; see sratio.

Details

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

There are a number of definitions for the continuation ratio in the literature. To make life easier, in the VGAM package, we use continuation ratios and stopping ratios (see sratio). Stopping ratios deal with quantities such as logitlink(P[Y=j|Y>=j]).

References

See sratio.

Examples

Run this code

pneumo <- transform(pneumo, let = log(exposure.time))
(fit <- vglm(cbind(normal, mild, severe) ~ let,
             cratio(parallel = TRUE), data = pneumo))
coef(fit, matrix = TRUE)
constraints(fit)
predict(fit)
predict(fit, untransform = TRUE)
margeff(fit)

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