Nothing
spm_dcm_priors <-
function(DCM){
a0 <- ifelse (DCM$a!=0,1,0)
b0 <- ifelse (DCM$b!=0,1,0)
c0 <- ifelse (DCM$c!=0,1,0)
p <- 1e-3
b <- 1
s <- 3.0902 #fixed, but never used?
q <- qchisq((1-p),DCM$n*(DCM$n-1))
q <- DCM$n/((DCM$n - 1)*q)
#Intrinsic connections
a0 <- matrix(a0,ncol=DCM$n,byrow=TRUE)
diag(a0) <- 0
dim(b0) <- c(DCM$n,DCM$n,DCM$m)
for (i in 1:DCM$m){
b0[,,i] <- t(b0[,,i])*q
}
pC <- diag(c(1/16, a0*q, b0, c0)) # Covariance neuronal state
#Expectations
Ae <- diag(-1,DCM$n)
Be <- ifelse (DCM$b!=0,0,0)
Ce <- ifelse (DCM$c!=0,0,0)
pE <- cbind(c(log(b),Ae,Be,Ce)) # Expectation neuron state
qE <- DCM$h # Expectation hemod. state
qC<- matrix(c(1.4837866e-2,-1.2823236e-4,-4.0541540e-4,3.4889257e-4,-3.4493093e-4,0,
-1.2823236e-4,1.8370175e-3,-7.9879877e-4,4.2061309e-4,-1.1577819e-5,0,
-4.0541540e-4,-7.9879877e-4,5.1387416e-2,2.0607856e-3,1.7781387e-3,0,
3.4889257e-4,4.2061309e-4,2.0607856e-3,2.0564973e-4,6.6429753e-5,0,
-3.4493093e-4,-1.1577819e-5,1.7781387e-3,6.6429753e-5,6.9002931e-5,0,
0,0,0,0,0,3.1250000e-2),ncol=6)
qC <- qC%x%diag(1,DCM$n)
qE <- qE%x%cbind(rep(1,DCM$n))
pE <- rbind(pE,qE)
pC <- as.matrix(bdiag(pC,qC))
p<-list()
p$pC<-pC
p$pE<-pE
p$qC<-qC
p$qE<-qE
p
}
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