cohort
variable
is used to define the current qualifying condition for a cohort of
subjects, e.g., y>=q 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
).
cr.setup(y)
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.
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
.
Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu
Berridge DM, Whitehead J: Analysis of failure time data with ordinal categories of response. Stat in Med 10:1703–1710, 1991.
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$subs] lrm(Y ~ cohort + sex) f <- lrm(Y ~ cohort*sex) # saturated model - has to fit all data cells f # In S-PLUS: #Prob(y=0|female): # plogis(-.50078) #Prob(y=0|male): # plogis(-.50078+.11301) #Prob(y=1|y>=1, female): plogis(-.50078+.31845) #Prob(y=1|y>=1, male): plogis(-.50078+.31845+.11301-.07379) combinations <- expand.grid(cohort=levels(cohort), sex=levels(sex)) combinations p <- predict(f, combinations, type="fitted") p p0 <- p[c(1,3)] p1 <- p[c(2,4)] p1.unconditional <- (1 - p0) *p1 p1.unconditional p2.unconditional <- 1 - p0 - p1.unconditional p2.unconditional ## Not run: dd <- datadist(inputdata) # do this on non-replicated data options(datadist='dd') pain.severity <- inputdata$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 ## End(Not run)