cendiff {NADA} | R Documentation |
Tests if there is a difference between two or more empirical cumulative distribution functions (ECDF) using the G-rho family of tests, or for a single curve against a known alternative.
This function shares the same arguments as survdiff
.
See survdiff
for more info.
cendiff(formula, rho=1, ...)
formula |
a formula expression as for other ECDF models, of the form
Cen(obs, censored) ~ predictors . For a one-sample test, the
predictors must consist of a single offset(sp) term, where
sp is a vector giving the survival probability of each subject.
For a k-sample test, each unique combination of predictors defines a
subgroup. A strata term may be used to produce a stratified test.
To cause missing values in the predictors to be treated as a separate
group, rather than being omitted, use the strata function with
its na.group=T argument.
|
rho |
a scalar parameter that controls the type of test. See Method below. |
... |
additional items to pass to survdiff . Note
|
a list with components:
n |
the number of subjects in each group. |
obs |
the weighted observed number of events in each group. If there are strata, this will be a matrix with one column per stratum. |
exp |
the weighted expected number of events in each group. If there are strata, this will be a matrix with one column per stratum. |
chisq |
the chisquare statistic for a test of equality. |
var |
the variance matrix of the test. |
strata |
optionally, the number of subjects contained in each stratum. |
This function implements the G-rho family of Harrington and Fleming
(1982), with weights on each death of S(t)^rho,
where S is the Kaplan-Meier estimate of survival.
With rho = 0
this is the log-rank or Mantel-Haenszel test, and
with rho = 1
it is equivalent to the Peto & Peto modification
of the Gehan-Wilcoxon test. The default is rho = 1
, or the
Peto & Peto test.
If the right hand side of the formula consists only of an offset
term, then a one sample test is done. To cause missing values in
the predictors to be treated as a separate group, rather than being
omitted, use the factor
function with its exclude
argument.
Lopaka(Rob) Lee <rclee@usgs.gov>
Helsel, Dennis R. (2005). Nondectects and Data Analysis; Statistics for censored environmental data. John Wiley and Sons, USA, NJ.
Harrington, D. P. and Fleming, T. R. (1982). A class of rank test procedures for censored survival data. Biometrika 69, 553-566.
# Contrived: are there diffs between instrument methods? obs = c(0.5, 0.5, 1.0, 1.5, 5.0, 10, 100) censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) instrument = as.factor(c('ICP', 'ICP', 'ICP', 'AA', 'AA', 'AA', 'AA')) cendiff(Cen(obs, censored)~instrument)