# cr.setup

##### Continuation Ratio Ordinal Logistic Setup

Creates several new variables which help set up a dataset with an
ordinal response variable \(y\) for use in fitting a forward continuation
ratio (CR) model. The CR model can be fitted with binary logistic
regression if each input observation is replicated the proper
number of times according to the \(y\) value, a new binary \(y\)
is computed that has at most one \(y=1\) per subject,
and if a `cohort`

variable is used to define the current
qualifying condition for a cohort of subjects, e.g., \(y\geq 2\).
`cr.setup`

creates the needed auxilliary variables. See
`predab.resample`

and `validate.lrm`

for information about
validating CR models (e.g., using the bootstrap to sample with
replacement from the original subjects instead of the records used in
the fit, validating the model separately for user-specified values of
`cohort`

).

- Keywords
- models, regression, category

##### Usage

`cr.setup(y)`

##### Arguments

- y
a character, numeric,

`category`

, or`factor`

vector containing values of the response variable. For`category`

or`factor`

variables, the`levels`

of the variable are assumed to be listed in an ordinal way.

##### Value

a list with components `y, cohort, subs, reps`

. `y`

is a new binary
variable that is to be used in the binary logistic fit. `cohort`

is
a `factor`

vector specifying which cohort condition currently applies.
`subs`

is a vector of subscripts that can be used to replicate other
variables the same way `y`

was replicated. `reps`

specifies how many
times each original observation was replicated. `y, cohort, subs`

are
all the same length and are longer than the original `y`

vector.
`reps`

is the same length as the original `y`

vector.
The `subs`

vector is suitable for passing to `validate.lrm`

or `calibrate`

,
which pass this vector under the name `cluster`

on to `predab.resample`

so that bootstrapping can be
done by sampling with replacement from the original subjects rather than
from the individual records created by `cr.setup`

.

##### References

Berridge DM, Whitehead J: Analysis of failure time data with ordinal categories of response. Stat in Med 10:1703--1710, 1991.

##### See Also

##### Examples

```
# NOT RUN {
y <- c(NA, 10, 21, 32, 32)
cr.setup(y)
set.seed(171)
y <- sample(0:2, 100, rep=TRUE)
sex <- sample(c("f","m"),100,rep=TRUE)
sex <- factor(sex)
table(sex, y)
options(digits=5)
tapply(y==0, sex, mean)
tapply(y==1, sex, mean)
tapply(y==2, sex, mean)
cohort <- y>=1
tapply(y[cohort]==1, sex[cohort], mean)
u <- cr.setup(y)
Y <- u$y
cohort <- u$cohort
sex <- sex[u$subs]
lrm(Y ~ cohort + sex)
f <- lrm(Y ~ cohort*sex) # saturated model - has to fit all data cells
f
#Prob(y=0|female):
# plogis(-.50078)
#Prob(y=0|male):
# plogis(-.50078+.11301)
#Prob(y=1|y>=1, female):
plogis(-.50078+.31845)
#Prob(y=1|y>=1, male):
plogis(-.50078+.31845+.11301-.07379)
combinations <- expand.grid(cohort=levels(cohort), sex=levels(sex))
combinations
p <- predict(f, combinations, type="fitted")
p
p0 <- p[c(1,3)]
p1 <- p[c(2,4)]
p1.unconditional <- (1 - p0) *p1
p1.unconditional
p2.unconditional <- 1 - p0 - p1.unconditional
p2.unconditional
# }
# NOT RUN {
dd <- datadist(inputdata) # do this on non-replicated data
options(datadist='dd')
pain.severity <- inputdata$pain.severity
u <- cr.setup(pain.severity)
# inputdata frame has age, sex with pain.severity
attach(inputdata[u$subs,]) # replicate age, sex
# If age, sex already available, could do age <- age[u$subs] etc., or
# age <- rep(age, u$reps), etc.
y <- u$y
cohort <- u$cohort
dd <- datadist(dd, cohort) # add to dd
f <- lrm(y ~ cohort + age*sex) # ordinary cont. ratio model
g <- lrm(y ~ cohort*sex + age, x=TRUE,y=TRUE) # allow unequal slopes for
# sex across cutoffs
cal <- calibrate(g, cluster=u$subs, subset=cohort=='all')
# subs makes bootstrap sample the correct units, subset causes
# Predicted Prob(pain.severity=0) to be checked for calibration
# }
```

*Documentation reproduced from package rms, version 5.1-4, License: GPL (>= 2)*