Hernior: Recovery from Elective Herniorrhaphy

Description

A data set on measures of post-operative recovery of 32 patients undergoing an elective herniorrhaphy operation, in relation to pre-operative measures.

Usage

data(Hernior)

Arguments

Format

A data frame with 32 observations on the following 9 variables.

age: patient age
sex: patient sex, a factor with levels f m
pstat: physical status (ignoring that associated with the operation). A 1-5 scale, with 1=perfect health, 5=very poor health.
build: body build, a 1-5 scale, with 1=emaciated, 2=thin, 3=average, 4=fat, 5=obese.
cardiac: preoperative complications with heart, 1-4 scale, with 1=none, 2=mild, 3=moderate, 4=severe.
resp: preoperative complications with respiration, 1-4 scale, with 1=none, 2=mild, 3=moderate, 4=severe.
leave: condition upon leaving the recovery room, a 1-4 scale, with 1=routine recovery, 2=intensive care for observation overnight, 3=intensive care, with moderate care required, 4=intensive care, with moderate care required.
los: length of stay in hospital after operation (days)
nurse: level of nursing required one week after operation, a 1-5 scale, with 1=intense, 2=heavy, 3=moderate, 4=light, 5=none (?); see Details

Details

leave, nurse and los are outcome measures; the remaining variables are potential predictors of recovery status.

The variable nurse is recorded as 1-4, with remaining (20) entries entered as "-" in both sources. It is not clear whether this means "none" or NA. The former interpretation was used in constructing the R data frame, so nurse==5 for these observations. Using Hernior$nurse[Hernior$nurse==5] <- NA would change to the other interpretation, but render nurse useless in a multivariate analysis.

The ordinal predictors could instead be treated as factors, and there are also potential interactions to be explored.

References

Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J. and Ostrowski, E. (1994), A Handbook of Small Data Sets, Number 484, 390-391.

Examples

Run this code

# NOT RUN {
str(Hernior)
Hern.mod <- lm(cbind(leave, nurse, los) ~ 
               age + sex +  pstat +  build + cardiac + resp, data=Hernior)
Anova(Hern.mod, test="Roy") # actually, all tests are identical
# test overall regression
linearHypothesis(Hern.mod, c("age", "sexm", "pstat", "build", "cardiac", "resp"))
# joint test of age, sex & caridac
linearHypothesis(Hern.mod, c("age", "sexm", "cardiac"))

clr <- c("red", "darkgray", "blue", "darkgreen", "magenta", "brown", "black")
heplot(Hern.mod, col=clr)
pairs(Hern.mod, col=clr)

## Enhancing the pairs plot ...
# create better variable labels
vlab <- c("LeaveCondition\n(leave)", "NursingCare\n(nurse)", "LengthOfStay\n(los)")
# Add ellipse to test all 5 regressors simultaneously
hyp <- list("Regr" = c("age", "sexm", "pstat", "build", "cardiac", "resp"))
pairs(Hern.mod, hypotheses=hyp, col=clr, var.labels=vlab)

## Views in canonical space for the various predictors
if (require(candisc)) {
	Hern.canL <- candiscList(Hern.mod)
	plot(Hern.canL, term="age")
	plot(Hern.canL, term="sex")
	plot(Hern.canL, term="pstat")  # physical status
}

# }

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