backPain: Data on Back Pain Prognosis, from Anderson (1984)

In gnm: Generalized Nonlinear Models

Data on Back Pain Prognosis, from Anderson (1984)

Description

Data from a study of patients suffering from back pain. Prognostic variables were recorded at presentation and progress was categorised three weeks after treatment.

Usage

backPain

Format

A data frame with 101 observations on the following 4 variables.

x1

length of previous attack.

x2

pain change.

x3

lordosis.

pain

an ordered factor describing the progress of each patient with levels worse < same < slight.improvement < moderate.improvement < marked.improvement < complete.relief.

Source

https://ideas.repec.org/c/boc/bocode/s419001.html

References

Anderson, J. A. (1984) Regression and Ordered Categorical Variables. J. R. Statist. Soc. B, 46(1), 1-30.

Examples

set.seed(1)
summary(backPain)

### Re-express as count data
backPainLong <- expandCategorical(backPain, "pain")

### Fit models described in Table 5 of Anderson (1984)

### Logistic family models
noRelationship <- gnm(count ~ pain, eliminate = id,
                      family = "poisson", data = backPainLong)

## stereotype model
oneDimensional <- update(noRelationship,
                         ~ . + Mult(pain, x1 + x2 + x3))

## multinomial logistic
threeDimensional <- update(noRelationship, ~ . + pain:(x1 + x2 + x3))

### Models to determine distinguishability in stereotype model
## constrain scale of category-specific multipliers
oneDimensional <- update(noRelationship,
                         ~ . + Mult(pain, offset(x1) + x2 + x3))
## obtain identifiable contrasts; id possibly indistinguishable slopes
getContrasts(oneDimensional, pickCoef(oneDimensional, "[.]pain"))

## Not run: 
## (this part not needed for package testing)
## fit simpler models and compare 
.pain <- backPainLong$pain

levels(.pain)[2:3] <- paste(levels(.pain)[2:3], collapse = " | ")
fiveGroups <- update(noRelationship,
                     ~ . + Mult(.pain, x1 + x2 + x3))

levels(.pain)[4:5] <- paste(levels(.pain)[4:5], collapse = " | ")
fourGroups <- update(fiveGroups)

levels(.pain)[2:3] <- paste(levels(.pain)[2:3], collapse = " | ")
threeGroups <- update(fourGroups)

### Grouped continuous model, aka proportional odds model
library(MASS)
sixCategories <- polr(pain ~ x1 + x2 + x3, data = backPain)

### Obtain number of parameters and log-likelihoods for equivalent
### multinomial models as presented in Anderson (1984)
logLikMultinom <- function(model, size){
    object <- get(model)
    if (inherits(object, "gnm")) {
        l <- sum(object$y * log(object$fitted/size))
        c(nParameters = object$rank - nlevels(object$eliminate),
          logLikelihood = l)
    }
    else
        c(nParameters = object$edf, logLikelihood = -deviance(object)/2)
}
size <- tapply(backPainLong$count, backPainLong$id, sum)[backPainLong$id]
models <- c("threeDimensional", "oneDimensional", "noRelationship",
            "fiveGroups", "fourGroups", "threeGroups", "sixCategories")
t(sapply(models, logLikMultinom, size))

## End(Not run)

gnm documentation built on Sept. 16, 2023, 5:06 p.m.