kalcount(response, times=NULL, origin=0, intensity="exponential", depend="independence", update="Markov", mu=NULL, shape=NULL, density=F, ccov=NULL, tvcov=NULL, preg=NULL, ptvc=NULL, pbirth=NULL, pintercept=NULL, pshape=1, pinitial=1, pdepend=NULL, envir=sys.frame(sys.parent()), print.level=0, ndigit=10, gradtol=0.00001, steptol=0.00001, iterlim=100, fscale=1, typsiz=abs(p), stepmax=10*sqrt(p%*%p))
response
|
A list of two column matrices with counts and
corresponding times for each individual, one matrix or dataframe of
counts, or an object of class, response (created by
restovec ) or repeated (created by rmna ).
The time origin is taken to be zero and the given times to be the ends
of periods (since the previous time given) in which the counts occurred.
|
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 .
|
origin
| If the time origin is to be before the start of observations, a positive constant to be added to all times. |
intensity
| The form of function to be put in the Pareto distribution. Choices are exponential, Weibull, gamma, log normal, log logistic, log Cauchy, log Student, and gen(eralized) logistic. |
depend
| Type of dependence. Choices are independence, frailty, and serial. |
update
|
Type of for serial dependence. Choices are Markov, serial,
event, cumulated, count, and kalman. With frailty dependence,
weighting by length of observation time may be specified by setting
update to time .
|
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 (a log link is assumed) or a general function with named unknown parameters. If there are only time-constant covariates, give the initial estimates in preg; if any covariates are time-varying, give all initial estimates in ptvc. |
shape
| A regression function for the shape 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. It must yield one value per observation. |
density
|
If TRUE, the density of the function specified in
intensity is used instead of the intensity.
|
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 in the time periods 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 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.
|
preg
|
Initial parameter estimates for the regression model:
intercept plus one for each covariate in ccov . If a location
function (mu) is supplied that contains time-varying covariates, all
initial estimates must be given in ptvc. 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 . If a location
function (mu) is supplied that contains time-varying covariates, all
initial estimates must be given here.
|
pbirth
| If supplied, this is the initial estimate for the coefficient of the birth model. |
pintercept
| The initial estimate of the intercept for the generalized logistic intensity. |
pshape
|
An initial estimate for the shape parameter of the
intensity function (except exponential intensity). If shape is
a function or formula, the corresponding initial estimates. If
shape 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.
|
pinitial
| An initial estimate for the initial parameter. (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 .
If response has class repeated , it is used as the
environment.
|
others
|
Arguments controlling nlm .
|
kalcount
is designed to handle repeated measurements models
with time-varying covariates. The distributions have two extra
parameters as compared to the functions specified by intensity
and are generally longer tailed than those distributions. Dependence
among observations on a unit can be through frailty (a type of random
effect) or serial dependence over time.
Here, the variance, with exponential intensity, is a quadratic
function of the mean, whereas, for nbkal
, it is
proportional to the mean function.
If the counts on a unit are clustered, not longitudinal, use the failty dependence with the default exponential intensity.
Nonlinear regression models can be supplied as formulae where
parameters are unknowns. Factor variables cannot be used and
parameters must be scalars. (See finterp
.)
Marginal and individual profiles can be plotted using
profile
and iprofile
and
residuals with plot.residuals
.
kalcount
and recursive
is returned.carma
, elliptic
, finterp
,
gar
, gettvc
, gnlmm
,
gnlr
, iprofile
, kalseries
,
kalsurv
, nbkal
, profile
,
read.list
, restovec
, rmna
,
tcctomat
, tvctomat
.treat <- c(0,0,1,1) tr <- tcctomat(treat) dose <- # matrix(rpois(20,10),ncol=5) matrix(c(9,13,16,7,12,6,9,10,11,9,10,10,7,9,9,9,8,10,15,4), ncol=5,byrow=T) dd <- tvctomat(dose) y <- # matrix(rpois(20,1+3*rep(treat,5)),ncol=5) restovec(matrix(c(1,1,1,1,0,1,0,1,0,5,3,3,4,1,4,4,2,3,2,5), ncol=5,byrow=T)) reps <- rmna(y, ccov=tr, tvcov=dd) # # log normal intensity, independence model kalcount(y, intensity="log normal", dep="independence", preg=1, pshape=0.1) # random effects kalcount(y, intensity="log normal", dep="frailty", pdep=0.1, preg=1, psh=0.1) # serial dependence kalcount(y, intensity="log normal", dep="serial", pinitial=0.1, preg=1, pdep=0.01, psh=0.1) # add time-constant variable kalcount(y, intensity="log normal", pinitial=0.1, psh=0.1, preg=c(1,0), ccov=treat) # or equivalently kalcount(y, intensity="log normal", mu=~treat, pinitial=0.1, psh=0.1, preg=c(1,0)) # or kalcount(y, intensity="log normal", mu=~b0+b1*treat, pinitial=0.1, psh=0.1, preg=c(1,0), envir=reps) # add time-varying variable kalcount(y, intensity="log normal", pinitial=0.1, psh=0.1, preg=c(1,0), ccov=treat, ptvc=0, tvc=dose) # or equivalently, from the environment kalcount(y, intensity="log normal", mu=~b0+b1*rep(treat,rep(5,4))+b2*as.vector(t(dose)), pinitial=0.1, psh=0.1, ptvc=c(1,0,0)) # or from the reps data object kalcount(y, intensity="log normal", mu=~b0+b1*treat+b2*dose, pinitial=0.1, psh=0.1, ptvc=c(1,0,0), envir=reps)