derivQfun {CensSpatial} | R Documentation |
Maximum Likelihood Expectation (logQ
function and its derivates)
Description
It computes the logQ
function, its derivates of first and second order and the inverse of the hessian matrix for the SAEM estimated parameters.
Usage
derivQfun(est, fix.nugget = TRUE)
Arguments
est |
object of the class "SAEMSpatialCens". See |
fix.nugget |
(logical) it indicates if the |
Details
The logQ
function refers to the logarithm of the Maximum likelihood conditional expectation, the first and second moments of the truncated normal distribution of censored data are involved in its computation.
Value
Qlogvalue |
value of the |
gradQ |
gradient for the |
HQ |
hessian Matrix for the |
Qinv |
inverse of the negative Hessian matrix for the |
Author(s)
Alejandro Ordonez <<ordonezjosealejandro@gmail.com>>, Victor H. Lachos <<hlachos@ime.unicamp.br>> and Christian E. Galarza <<cgalarza88@gmail.com>>
Maintainer: Alejandro Ordonez <<ordonezjosealejandro@gmail.com>>
References
Diggle, P. & Ribeiro, P. (2007). Model-Based Geostatistics. Springer Series in Statistics.
Gradshtejn, I. S. & Ryzhik, I. M. (1965). Table of integrals, series and products. Academic Press.
See Also
Examples
require(geoR)
data("Missouri")
data=Missouri[1:70,]
data$V3=log((data$V3))
cc=data$V5
y=data$V3
datare1=data
coords=datare1[,1:2]
data1=data.frame(coords,y)
data1=data1[cc==0,]
geodata=as.geodata(data1,y.col=3,coords.col=1:2)
v=variog(geodata)
v1=variofit(v)
cov.ini=c(0,2)
est=SAEMSCL(cc,y,cens.type="left",trend="cte",coords=coords,M=15,perc=0.25,MaxIter=5,pc=0.2,
cov.model="exponential",fix.nugget=TRUE,nugget=2,inits.sigmae=cov.ini[2],inits.phi=cov.ini[1],
search=TRUE,lower=0.00001,upper=50)
d1=derivQfun(est)
d1$QI