Description Usage Arguments Value Author(s) See Also Examples
Objects of class, response
, contain response values, and possibly
the corresponding times, binomial totals, nesting categories, censor
indicators, and/or units of precision/Jacobian. Objects of class,
tccov
, contain timeconstant or interindividual, baseline
covariates. Objects of class, tvcov
, contain timevarying or
intraindividual covariates. Objects of class, repeated
,
contain a response
object and possibly tccov
and
tvcov
objects.
In formula and functions, the key words, times
can be used to
refer to the response times from the data object as a covariate,
individuals
to the index for individuals as a factor covariate,
and nesting
the index for nesting as a factor covariate. The
latter two only work for W&R notation.
The following methods are available for accessing the contents of such data objects.
as.data.frame
: places all of the variables in the data object
in one dataframe, extending timeconstant covariates to the length of
the others unless the object has class, tccov
. Binomial and
censored response variables have two columns, respectively ‘yes’ and
‘no’ and response and censoring indicator, with the name given to the
response.
as.matrix
: places all of the variables in the data object
in one matrix, extending timeconstant covariates to the length of
the others unless the object has class, tccov
. If any
covariates are factor variables (instead of the corresponding sets of
indicator variables), the matrix will be character instead of numeric.
covariates
: extracts covariate matrices from a data object (for
formulae and functions, possibly for selected individuals. See
covariates.formulafn
).
covind
: gives the indexing of the response by individual (that
is, the nesting indicator for observations within individuals). It can
be used to expand timeconstant covariates to the size of the repeated
measurements response.
delta
: extracts the units of measurement vector and Jacobian of
any transformation of the response, possibly for selected individuals.
Note that, if the unit of measurement/Jacobian is available in the
response
object, this is automatically included in the
calculation of the likelihood function in all library model functions.
units
: prints the variable names and their description
and returns the latter.
formula
: gives the formula used to create the timeconstant
covariate matrix of a data object (for formulae and functions, see
formula.formulafn
).
names
: extracts the names of the response and/or covariates.
nesting
: gives the coding variable(s) for individuals (same as
covind
) and also for nesting within individuals if available,
possibly for selected individuals.
nobs
: gives the number of observations per individual.
plot
: plots the variables in the data object in various ways.
For repeated
objects, name
can be a response or a
timevarying covariate.
print
: prints summary information about the variables in a data object.
response
: extracts the response vector, possibly for selected
individuals. If there are censored observations, this is a twocolumn
matrix, with the censor indicator in the second column. For binomial
data, it is a twocolumn matrix with "positive" (y) and "negative"
(totalsy) frequencies.
resptype
: extracts the type of each response.
times
: extracts the times vector, possibly for selected
individuals.
transform
: transforms variables. For example,
transform(z, y=fcn1(y), times=fcn2(times))
where fcn1
and fcn2
are transformation functions. When the response is
transformed, the Jacobian is automatically calculated. New response
variables and covariates can be created in this way, if the left hand
side is a new name (ynew=fcn3(y)
), as well as replacing an old
variable with the transformed one. If the transformation reverses the
order of the responses, use its negative to keep the ordering and have
a positive Jacobian; for example, ry=1/y
. For repeated
objects, only the response and the times can be transformed.
units
: prints the variable names and their units of measurement
and returns the latter.
weights
: extracts the weight vector, possibly for selected
individuals.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  as.data.frame(x, ...)
as.matrix(x, ...)
covariates(z, ...)
covind(z, ...)
delta(z, ...)
## S3 method for class 'tccov'
formula(x, ...)
## S3 method for class 'repeated'
formula(x, ...)
## S3 method for class 'tccov'
names(x, ...)
## S3 method for class 'repeated'
names(x, ...)
nesting(z, ...)
nobs(z, ...)
## S3 method for class 'response'
plot(x, name=NULL, nind=NULL, nest=1, ccov=NULL, add=FALSE, lty=NULL, pch=NULL,
main=NULL, ylim=NULL, xlim=NULL, xlab=NULL, ylab=NULL, ...)
## S3 method for class 'repeated'
plot(x, name=NULL, nind=NULL, nest=1, ccov=NULL, add=FALSE, lty=NULL, pch=NULL,
main=NULL, ylim=NULL, xlim=NULL, xlab=NULL, ylab=NULL, ...)
## S3 method for class 'tccov'
print(x, ...)
## S3 method for class 'repeated'
print(x, nindmax=50, ...)
response(z, ...)
resptype(z, ...)
times(z, ...)
## S3 method for class 'response'
transform(`_data`, times=NULL, units=NULL, ...)
## S3 method for class 'repeated'
transform(`_data`, times=NULL, ...)
units(x, ...)
weights(object, ...)

x,z 
A 
times 
The function, when the times are to be transformed. 
names 
The names of the response variable(s) or covariate(s). 
nind 
The numbers of individuals to be used. (For plotting,
cannot be used simultaneously with 
ccov 
For plotting: If a vector of values for the timeconstant
covariates is supplied, only individuals having that set of values
will have profiles plotted. These values must be in the order in which
the covariates appear when the data object is printed. For factor
variables, the codes must be given. If the name of a covariate is
supplied, a set of graphs is plotted, one for each covariate value,
showing profiles of all individuals having that value. (The covariate
can have a maximum of six values.) Cannot be used simultaneously with

nest 
For plotting: nesting category to plot. 
add 
For plotting: add to previous plot. 
nindmax 
For printing a 
name,lty,pch,main,ylim,xlim,xlab,ylab 
See base plot. 
_data,units,object 
TBD. 
... 
Arguments to other methods 
These methods extract information stored in response
,
tccov
, tvcov
, and repeated
data objects created
respectively by restovec
, tcctomat
,
tvctomat
, and rmna
.
Note that if a vector of binomial totals or a censoring indicator is
present, this is extract as the second column of the matrix produced
by the response
method.
J.K. Lindsey
restovec
, rmna
,
tcctomat
, tvctomat
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76  # set up some data and create the objects
#
y < matrix(rnorm(20),ncol=5)
tt < c(1,3,6,10,15)
print(resp < restovec(y, times=tt, units="m", type="duration"))
x < c(0,0,1,1)
x2 < as.factor(c("a","b","a","b"))
tcc < tcctomat(data.frame(x=x,x2=x2))
z < matrix(rpois(20,5),ncol=5)
tvc < tvctomat(z)
print(reps < rmna(resp, tvcov=tvc, ccov=tcc))
#
plot(resp)
plot(reps)
plot(reps, nind=1:2)
plot(reps, ccov=c(0,1))
plot(reps, ccov="x2")
plot(reps, name="z", nind=3:4, pch=1:2)
plot(reps, name="z", ccov="x2")
#
response(resp)
response(transform(resp, y=1/y))
response(reps)
response(reps, nind=2:3)
response(transform(reps,y=1/y))
#
times(resp)
times(transform(resp,times=times6))
times(reps)
#
delta(resp)
delta(reps)
delta(transform(reps,y=1/y))
delta(transform(reps,y=1/y), nind=3)
#
nobs(resp)
nobs(tvc)
nobs(reps)
#
units(resp)
units(reps)
#
resptype(resp)
resptype(reps)
#
weights(resp)
weights(reps)
#
covariates(tcc)
covariates(tcc, nind=2:3)
covariates(tvc)
covariates(tvc, nind=3)
covariates(reps)
covariates(reps, nind=3)
covariates(reps,names="x")
covariates(reps,names="z")
#
names(tcc)
names(tvc)
names(reps)
#
nesting(resp)
nesting(reps)
#
# because individuals are the only nesting, this is the same as
covind(resp)
covind(reps)
#
as.data.frame(resp)
as.data.frame(tcc)
as.data.frame(tvc)
as.data.frame(reps)
#
# use in glm
rm(y,x,z)
glm(y~x+z, data=reps)

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