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#' Automatic Calculation of optimal Initial Parameters
#'
#' Implemented according to Engelhardt et al. 2017.
#'
#' The function can be replaced by an user defined version if necessary.
#'
#' @param VARIANCE standard error of the observed stat dynamics (per time point)
#' @param N number of system states
#' @param BETA_LAMDBA mcmc tuning parameter (weighting of observed states)
#' @param alphainit mcmc tuning parameter (weighting of observed states)
#' @param betainit mcmc tuning parameter (weighting of observed states)
#' @param ROH mcmc tuning parameter
#' @param R mcmc tuning parameter
#'
#' @return A list of optimal initial parameters; i.e. R, Roh, Alpha, Beta, Tau, Lambda1, Lambda2
#'
#'
#'
SETTINGS <- function(VARIANCE,N,BETA_LAMDBA,alphainit,betainit,R=c(1000,1000),ROH=c(10,10)){
if (length(alphainit)!=N) alphainit = rep(1,N)
if (length(betainit)!=N) {betainit = rep(1,N)}
CONTAINER <- VARIANCE[,2]
for (i in 3:length(VARIANCE[1,]-1)){
CONTAINER= c(CONTAINER,VARIANCE[,i])
}
PHI <- MASS::fitdistr(1/CONTAINER, "gamma")
ALPHA <- rep(1,N)*PHI[[1]][1]*alphainit
BETA <- rep(1,N)*(PHI[[1]][2])*BETA_LAMDBA*betainit
R[2] <- 1000
ROH[2] <- 10
R[1] <- 1000
ROH[1] <- 10
TAU <- rep(1,N)
LAMBDA1 <- rep(1,N)
LAMBDA2 <- 1
LIST <- list(R,ROH,ALPHA,BETA,TAU,LAMBDA1,LAMBDA2)
names(LIST) <- c("R","ROH","ALPHA","BETA","TAU","LAMBDA1","LAMBDA2")
return(LIST)
}
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