kalord {ordinal} | R Documentation |
kalord
is designed to handle repeated measurements models with
time-varying covariates. The distributions have two extra parameters
as compared to the parameterization of the logistic distribution
specified by distribution
. Dependence among observations on a
unit can be through gamma frailties (a type of random effect) or
serial dependence over time.
Nonlinear regression models can be supplied as formulae where
parameters are unknowns in which case factor variables cannot be used
and parameters must be scalars. (See finterp
.)
Marginal, individual and predicted profiles can be plotted using
moprofile
, ioprofile
and
poprofile
.
If the responses on a unit are clustered, not longitudinal, use the frailty dependence.
kalord(response,times=NULL,distribution="multinomial", depend="independence",mu=NULL,ccov=NULL,tvcov=NULL,torder=0, interaction=NULL,preg=NULL,ptvc=NULL,pinitial=1,pdepend=NULL, envir=sys.frame(sys.parent()),optimize=T,print.level=0, ndigit=10,gradtol=0.00001,steptol=0.00001,fscale=1, iterlim=100,typsiz=abs(p),stepmax=10*sqrt(p
response |
A list of two column matrices with responses and
corresponding times for each individual, one matrix or dataframe of
response values, or an object of class, response (created by
restovec ) or repeated (created by
rmna or lvna ). If the repeated
data object contains more than one response variable, give that
object in envir and give the name of the response variable to
be used here. |
times |
When response is a matrix, a vector of possibly unequally
spaced times when they are the same for all individuals or a matrix
of times. Not necessary if equally spaced. Ignored if response has
class, response or repeated . |
distribution |
Specifies the parameterization of the logistic distribution to put in the Pareto distribution. Choices are binary, multinomial, continuation-ratio, and proportional-odds. |
depend |
Type of dependence. Choices are independence ,
Markov , serial , and frailty . |
mu |
A regression function for the location parameter or a
formula beginning with ~, specifying either a linear regression
function in the Wilkinson and Rogers notation or a general function
with named unknown parameters. The regression function must not
contain intercepts. Give the initial estimates in preg or in
ptvc . |
ccov |
A vector or matrix containing time-constant baseline
covariates with one row per individual, a model formula using
vectors of the same size, or an object of class, tccov
(created by tcctomat ). If response has class,
repeated , the covariates must be supplied as a Wilkinson and
Rogers formula unless none are to be used or mu is given. |
tvcov |
A list of matrices with time-varying covariate values,
observed at the event times in response , for each individual
(one column per variable), one matrix or dataframe of such covariate
values, or an object of class, tvcov (created by
tvctomat ). If a time-varying covariate is observed at
arbitrary time, gettvc can be used to find the most
recent values for each response and create a suitable list. If
response has class, repeated , the covariates must be supplied
as a Wilkinson and Rogers formula unless none are to be used or
mu is given. |
torder |
The order of the polynomial in time to be fitted. |
interaction |
Vector of length equal to the number of
time-constant covariates, giving the levels of interactions between
them and the polynomial in time in the linear model . |
preg |
Initial parameter estimates for the regression model:
intercept, one for each covariate in ccov , and torder
plus sum(interaction ). If mu is a formula with unknown
parameters, their estimates must be supplied either in their order
of appearance in the expression or in a named list. |
ptvc |
Initial parameter estimates for the coefficients of the
time-varying covariates, as many as in tvcov . |
pinitial |
An initial estimate for the initial parameter, if set
to NULL this parameter will be fixed at zero. (With
frailty dependence, this is the frailty parameter.) |
pdepend |
An initial estimate for the serial dependence parameter. |
envir |
Environment in which model formulae are to be interpreted
or a data object of class, repeated , tccov , or
tvcov ; the name of the response variable should be given in
response . If response has class repeated , it is
used as the environment. |
optimize |
If set to TRUE then nlm is used
to perform the numerical optimization of the likelihood function,
otherwise if set to FALSE no optimization is performed. |
others |
Arguments controlling nlm . |
A list of classes kalordinal
and recursive
is returned.
P.J. Lindsey
finterp
, gettvc
, ioprofile
,
lvna
, moprofile
,
plot.ordinal
, poprofile
,
restovec
, rmna
, tcctomat
,
tvctomat
.
library(ordinal) # # Binary data # data(cardiac.indiv) y <- restovec(cardiac.indiv[,1:4],type="ordinal") cov <- tcctomat(as.matrix(cardiac.indiv[,5:10])) w <- rmna(y,ccov=cov) rm(cardiac.indiv,y,cov) # Time-constant covariate. kalord(w,distribution="binary",ccov=~age,preg=c(3.9507,-0.0308),pinit=NULL) # Time-varying covariate. kalord(w,distribution="binary",tvcov=~times,preg=1.832,ptvc=0.0573,pinit=NULL) # Time-constant and time-varying covariate. kalord(w,distribution="binary",mu=~age+ren+cop+dia+sex+pmi+times, ptvc=c(3.888,-0.0289,-0.642,-0.366,-0.314,-0.154,-0.114,0.057),pinit=NULL) # Time-constant and time-varying covariate with a frailty dependence. kalord(w,distribution="binary",mu=~age+ren+cop+dia+sex+pmi+times, ptvc=c(4.43391,-0.03128,-0.62439,-0.37596,-0.33064,-0.17095,-0.12216,-0.09096), pinit=0.1196,dep="frailty") rm(w) # # Ordinal data # data(tmi2) y <- restovec(tmi2[,1:4],type="ordinal") cov <- tcctomat(tmi2[,5],name="distance") w <- rmna(y,ccov=cov) rm(tmi2,y,cov) # Continuation-ratio model with time-constant covariate with a serial dependence. kalord(w,distribution="continuation-ratio",ccov=~distance,preg=c(-1.907,7.7,-0.162), pinit=2.55,pdep=0.328,dep="serial") # Proportional-odds model with time-constant covariate with a Markov dependence. kalord(w,distribution="proportional-odds",ccov=~distance,preg=c(-1.89,11.652,-0.199), pinit=3.111,pdep=0.217,dep="Markov") rm(w)