Description Usage Arguments Value Author(s) See Also Examples
Summary method for mvna
objects. The function returns a list
containing the cumulative transition hazards, variance and other
informations.
1 2 3 4 5 6 7 |
object |
An object of class |
level |
Level of the pointwise confidence interval. Default is 0.95. |
var.type |
Which of the "aalen" or "greenwood" variance estimator should be displayed and used to compute the pointwise confidence intervals. Default is "aalen". |
ci.fun |
Which transformation to apply to the confidence intervals. One of "linear", "log", "arcsin". Default is "log". |
... |
Further arguments. |
x |
An object of class |
Returns an object of class mvna
which is a list of data frames
named after the possible transitions. Each data frame contains the
following columns:
time |
Event times at which the cumulative hazards are estimated. |
na |
Estimated cumulative transition hazards. |
var.aalen or var.greenwood |
Variance estimates. The name depends
on the |
lower |
Lower bound of the pointwise confidence interval. |
upper |
Upper bound. |
n.risk |
Number of individuals at risk of experiencing an event just before t. |
n.event |
Number of transitions at time t. |
Arthur Allignol, arthur.allignol@gmail.com
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | data(sir.adm)
## data set transformation
data(sir.adm)
id <- sir.adm$id
from <- sir.adm$pneu
to <- ifelse(sir.adm$status==0,"cens",sir.adm$status+1)
times <- sir.adm$time
dat.sir <- data.frame(id,from,to,time=times)
## Possible transitions
tra <- matrix(ncol=4,nrow=4,FALSE)
tra[1:2,3:4] <- TRUE
na.pneu <- mvna(dat.sir,c("0","1","2","3"),
tra,"cens")
summ.na.pneu <- summary(na.pneu)
## cumulative hazard for 0 -> 2 transition:
summ.na.pneu$"0 2"$na
|
[1] 0.00000000 0.08307692 0.17887524 0.29081554 0.40075212 0.50866579
[7] 0.60377448 0.68584135 0.79092610 0.87107877 0.94341919 1.02212290
[13] 1.09865351 1.18152644 1.28455674 1.34620058 1.44839036 1.52216085
[19] 1.57573228 1.63287513 1.64318441 1.70568441 1.73939228 1.84527463
[25] 1.87230166 1.91455518 1.97516124 2.03967737 2.14494053 2.20376406
[31] 2.28709739 2.31035320 2.40559130 2.45822288 2.48679431 2.48679431
[37] 2.51620607 2.57681213 2.60907020 2.60907020 2.64240353 2.67688629
[43] 2.71260057 2.74963761 2.82656069 2.86822735 2.91170561 2.95716016
[49] 2.95716016 3.00716016 3.05979174 3.05979174 3.17090285 3.17090285
[55] 3.35840285 3.43532593 3.52623502 3.62623502 3.73734613 3.73734613
[61] 3.73734613 3.88020327 4.04686994 4.04686994 4.29686994 4.29686994
[67] 4.29686994 4.29686994 4.79686994 4.79686994 4.79686994 5.79686994
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