| 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)