nbkal {repeated} | R Documentation |
nbkal
fits a negative binomial regression with Kalman update
over time. The variance is proportional to the mean function, whereas,
for kalcount
with exponential intensity, it is
a quadratic function of the mean.
Marginal and individual profiles can be plotted using
mprofile
and iprofile
and
residuals with plot.residuals
.
nbkal(response, times, mu, preg, pdepend, kalman=TRUE, print.level=0, ndigit=10, gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100, 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 or lvna ). |
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. |
mu |
The mean function. |
preg |
The initial parameter estimates for the mean function. |
pdepend |
The estimates for the dependence parameters, either one or three. |
kalman |
If TRUE, fits the kalman update model, otherwise, a standard negative binomial distribution. |
others |
Arguments controlling nlm . |
A list of classes nbkal
and recursive
is returned.
P. Lambert and J.K. Lindsey
Lambert, P. (1996) Applied Statistics 45, 31-38.
Lambert, P. (1996) Biometrics 52, 50-55.
gar
, gnlmm
,
gnlr
, iprofile
kalcount
, mprofile
,
read.list
, rmna
,
restovec
, tcctomat
,
tvctomat
.
y <- matrix(rnbinom(20,5,0.5), ncol=5) times <- matrix(rep(seq(10,50,by=10),4), ncol=5, byrow=TRUE) y0 <- matrix(rep(rnbinom(5,5,0.5),4), ncol=5, byrow=TRUE) mu <- function(p) p[1]*log(y0)+(times<30)*p[2]* (times-30)+(times>30)*p[3]*(times-30) nbkal(y, preg=c(1.3,0.008,-0.05), times=times, pdep=1.2, mu=mu)