depratio {drm} | R Documentation |
Empirical estimates of the dependence ratios
Description
Calculates the observed values of the adjacent dependence ratios
from the data.
Usage
depratio(formula, data, subset, ord = 2, boot.ci = FALSE, n.boot = NULL,
ci.width=0.95)
Arguments
formula |
the syntax is of form y~cluster(id)+Time(time) ,
where id denotes the cluster indicator, and Time
denotes the order along which the adjacent dependence ratios will be
calculated. |
data |
optional data frame containing the variables in the formula |
subset |
an optional vector specifying a subset of observations
from the data |
ord |
order of the dependence ratios to be calculated. The
default is 2 |
boot.ci |
logical argument specifying whether bootstrap
confidence intervals will be calculated for the empirical dependence
ratio estimates |
n.boot |
number of bootstrap replicates |
ci.width |
width of the confidence interval. Default is 0.95 |
Value
An object of class depratio
. Generic functions
print
and plot
are also available.
An object of class depratio
is a list containing at least the
following two components:
tau |
matrix of the observed dependence ratios |
freq |
matrix of the frequencies of events for the numerator of
the observed dependence ratios |
See Also
drm
, cluster
, Time
Examples
## calculate and plot the observed 2nd order dependence ratios
## for the marijuana data:
data(marijuana)
dr.male <- depratio(y~cluster(id)+Time(age), data=marijuana,
subset=sex=="male")
dr.male
plot(dr.male)
## confirm that the 1st order Markov assumption is adequate
## for the madras data:
data(madras)
dr2 <- depratio(symptom~cluster(id)+Time(month), data=madras)
dr3 <- depratio(symptom~cluster(id)+Time(month), ord=3, data=madras)
dr <- rbind(dr2$tau[-length(dr2$tau)]*dr2$tau[-1], dr3$tau)
matplot(1:ncol(dr), t(dr))
[Package
drm version 0.5-8
Index]