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, nocenter)
agexact.fit(x, y, strata, offset, init, control, weights, method,
rownames, resid=TRUE, nocenter=NULL)
survfitCI(X, Y, weights, id, cluster, robust, istate,
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, robust,
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, robust, cluster)The arguments to these routines are not guaranteed to stay the same from release to release -- call them at your own risk!