cmcre {repeated} | R Documentation |
cmcre
fits a two-state Markov process in continuous time,
possibly with one or two random effects and/or one covariate.
cmcre(response, covariate=NULL, parameters, pcov=NULL, gradient=FALSE, hessian=FALSE, print.level=0, ndigit=10, gradtol=0.00001, steptol=0.00001, iterlim=100, fscale=1, typsiz=abs(parameters), stepmax=parameters)
response |
A six-column matrix. Column 1: subject identification (subjects can occupy several rows); column 2: time gap between events; columns 3-6: transition matrix frequencies. |
covariate |
An optional vector of length equal to the number of
rows of response upon which the equilibrium probability may
depend. |
parameters |
Initial parameter estimates. The number of them determines the model fitted (minimum 2, yielding an ordinary Markov process). 1: beta1=log(-log(equilibrium probability)); 2: beta2=log(sum of transition intensities); 3: log(tau1)=log(random effect variance for equilibrium probability); 4: log(tau2)=log(random effect variance for sum of transition intensities). |
pcov |
Initial parameter estimate for the covariate influencing the equilibrium probability: exp(-exp(beta1+beta*covariate)). |
gradient |
If TRUE, analytic gradient is used (with accompanying loss of speed). |
hessian |
If TRUE, analytic hessian is used (with accompanying loss of speed). |
others |
Arguments controlling nlm . |
A list of class cmcre
is returned.
R.J. Cook and J.K. Lindsey
Cook, R.J. (1999) A mixed model for two-state Markov processes under panel observations. Biometrics 55, 915-920.
# 12 subjects observed at intervals of 7 days y <- matrix(c(1,7,1,2,3,5, 2,7,10,2,2,0, 3,7,7,0,1,1, 4,7,2,1,0,7, 5,7,1,1,1,11, 6,7,5,4,4,1, 7,7,1,1,1,8, 8,7,2,3,4,2, 9,7,9,0,0,0, 10,7,0,1,2,8, 11,7,8,2,2,1, 12,7,9,2,2,1),ncol=6, byrow=TRUE) # ordinary Markov process cmcre(y, par=c(-0.2,-1)) # random effect for the equilibrium probability cmcre(y, par=c(-0.1,-2,-0.8)) # random effects for the equilibrium probability and sum of transition # intensities cmcre(y, par=c(-0.1,-1.4,-0.5,-1))