rmna(response, tvcov=NULL, ccov=NULL)
response
|
An object of class, response (created by
restovec ), containing the response variable information.
|
tvcov
|
An object of class, tvcov (created by tvctomat ),
containing the time-varying covariate information.
|
tccov
|
An object of class, tccov (created by tcctomat ),
containing the time-constant covariate information.
|
rmna
forms an object of class, repeated, from a response object
and possibly time-varying covariate (tvcov), and time-constant
covariate (tccov) objects, removing any response and covariate values
that have NAs.
Such objects can be printed and plotted.
restovec
), and possibly the two
classes of covariate objects (z$ccov and z$tvcov).
Methods are available for extracting the response, the numbers of
observations per individual, the times, the weights, the nesting
variable, and the covariates or their names: response
,
nobs
, times
, weights
, nesting
,
covariates
, and names
.
carma
, elliptic
, gettvc
,
kalcount
, kalseries
, nbkal
,
read.list
, restovec
, tcctomat
,
tvctomat
.y <- matrix(rnorm(20),ncol=5) tt <- c(1,3,6,10,15) print(resp <- restovec(y,times=tt)) x <- c(0,0,1,1) tcc <- tcctomat(x) z <- matrix(rpois(20,5),ncol=5) tvc <- tvctomat(z) print(reps <- rmna(resp, tvcov=tvc, ccov=tcc)) response(reps) response(reps, nind=2:3) times(reps) nobs(reps) weights(reps) covariates(reps) covariates(reps,names="x") covariates(reps,names="z") names(reps) nesting(reps) # because individuals are the only nesting, this is the same as covind(reps) # binomial y <- matrix(rpois(20,5),ncol=5) print(respb <- restovec(y,totals=y+matrix(rpois(20,5),ncol=5),times=tt)) print(repsb <- rmna(respb, tvcov=tvc, ccov=tcc)) response(repsb) # censored data y <- matrix(rweibull(20,2,5),ncol=5) print(respc <- restovec(y,censor=matrix(rbinom(20,1,0.9),ncol=5),times=tt)) print(repsc <- rmna(respc, tvcov=tvc, ccov=tcc)) # if there is no censoring, censor indicator is not printed response(repsc) # nesting clustered within individuals nest <- c(1,1,2,2,2) print(respn <- restovec(y,censor=matrix(rbinom(20,1,0.9),ncol=5), times=tt,nest=nest)) print(repsn <- rmna(respn, tvcov=tvc, ccov=tcc)) response(respn) times(respn) nesting(respn)