lnalik {lnar} | R Documentation |
Estimates the log-likelihood of the LNA approximation.
lnalik(cout, nthetas, mydata, syssize = NA, relerr = 1e-09, abserr = 1e-09, method = 0, dfunction)
cout |
The parsed model. |
nthetas |
The vector of the parameters. |
mydata |
Either a matrix or a data frame of the data to be evaluated. The first column is assumed to correspond to the time of each observation. |
syssize |
Optional, a scalar indicating the system size. |
relerr |
Optional, a scalar indicating the relative error for the ODE solver. |
abserr |
Optional, a scalar indicating the absolute error for the ODE solver. |
method |
Optional, a scalar with possible options:
|
dfunction |
The compiled model. |
See Giagos (2010) for a discussion on the Restarting and the Non Restarting method.
Returns the estimated log-likelihood.
Vasileios Giagos
Giagos, V.: 2010, Inference for auto-regulatory genetic networks using diffusion process approximations, Thesis, Lancaster University, 2010.
## Not run: require(lnar) tt <- matrix(c(1,-1,0,0,1,-1),nrow=2,ncol=3,byrow=TRUE) rfun <- c("con1 * Prey","con2 * Prey * Predator","con3 * Predator") thetas <- paste("con",1:3,sep="") species <- c("Prey","Predator") cout <- parsemod(tt,rfun,thetas,species) mydata<-c(0.0, 5000.0, 3000, 1, 5989, 2992, 2, 7165, 3107, 3, 8534, 3306,4, 10041, 3709, 5, 11624, 4265, 6, 13306, 5181, 7, 14741, 6492,8, 15867, 8337, 9, 16025, 10981) mydata2 <- matrix(mydata,10,3,byrow=TRUE)#Example dataset compmod(cout,"derivs") #Our initial values nthetas<-c(.25,.20,0.125) print(derivs(mydata[1],c(mydata[2],mydata[3], c(0,0,0,0,0)),rep(0,7),nthetas)) (l1<-lnalik(cout,nthetas=nthetas, mydata=mydata2, method=1, relerr=1e-9, abserr=1e-9, dfunction=derivs) ) nthetas2<-c(.25,.20/8000,0.125) (l2<-lnalik(cout,nthetas=nthetas2, mydata=mydata2, method=0, relerr=1e-9, abserr=1e-9, dfunction=derivs) ) ## End(Not run)