Internal survival functions
survreg.fit(x, y, weights, offset, init, controlvals, dist, scale = 0,
nstrat = 1, strata, parms = NULL,assign)
survpenal.fit(x, y, weights, offset, init, controlvals, dist, scale = 0,
nstrat = 1, strata, pcols, pattr, assign, parms = NULL)
survdiff.fit(y, x, strat, rho = 0)
match.ratetable(R, ratetable)
# S3 method for ratetable
as.matrix(x, ...)
# S3 method for coxph.penalty
is.na(x)
coxpenal.df(hmat, hinv, fdiag, assign.list, ptype, nvar, pen1,
pen2, sparse)
coxpenal.fit(x, y, strata, offset, init, control, weights, method,
rownames, pcols, pattr, assign)
coxph.wtest(var, b, toler.chol = 1e-09)
agexact.fit(x, y, strata, offset, init, control, weights, method,
rownames, resid=TRUE)
survfitCI(X, Y, weights, id, istate, cluster,
stype=1, ctype=1,
se.fit=TRUE,
conf.int= .95,
conf.type=c('log', 'log-log', 'plain', 'none',
'logit', 'arcsin'),
conf.lower=c('usual', 'peto', 'modified'),
influence=FALSE, start.time, p0, type)
survfitKM(x, y, weights=rep(1,length(x)),
stype=1, ctype=1,
se.fit=TRUE,
conf.int= .95,
conf.type=c('log', 'log-log', 'plain', 'none',
'logit', 'arcsin'),
conf.lower=c('usual', 'peto', 'modified'),
start.time, id, cluster,
influence=FALSE, type)
survfitTurnbull(x, y, weights,
type=c('kaplan-meier', 'fleming-harrington', 'fh2'),
error=c('greenwood', "tsiatis"), se.fit=TRUE,
conf.int= .95,
conf.type=c('log', 'log-log', 'plain', 'none',
'logit', 'arcsin'),
conf.lower=c('usual', 'peto', 'modified'),
start.time)The arguments to these routines are not guaranteed to stay the same from release to release -- call them at your own risk!