#' A function to fit movement models using rStan
#'
#' This function fits standard movement models using rstan to observed movement data. STOPPED HERE
#' @param modelList A list of stanfit models.
#' @export
#' @examples
#'
#'
simMovements <- function(modelList, iter=100, startingContext= c(0,0), names=c("x","y"),formula="a ~ beta_cv * cv", functionList = NULL){
postList <- list(extract(fit.groupOnly))
df.sim <- as.data.frame(t(startingContext))
#for each iteration
for(i in iter){
#for each model
for (j in length(postList)){
postList[[1]]
}
}
#get samples
post<-extract(model.fit)
#set extent
min.x <- min(df.obs[rangePred,]$x)-buffer
min.y <- min(df.obs[rangePred,]$y)-buffer
max.x <- max(df.obs[rangePred,]$x)+buffer
max.y <- max(df.obs[rangePred,]$y)+buffer
#genrate plot
plot(x=one.obs$x,y=one.obs$y, xlim=c(min.x,max.x), ylim=c(min.y,max.y))
for(i in rangePred) {
one.obs <- df.obs[i,]
pred.points <- matrix(,numbDraws,2)
for(j in 1:numbDraws){
predX <- one.obs$x + cos(post$y_pred[j,i])*post$d_pred[i,j]
predY <- one.obs$y + sin(post$y_pred[j,i])*post$d_pred[i,j]
pred.points[j,] <- c(predX,predY)
}
ks.pred <- kde(pred.points, binned = T)
plot(ks.pred,display="slice",cont=contours,add=T)
points(df.obs[i+1,]$x,df.obs[i+1,]$y,col="green")
points(df.obs[i,]$x,df.obs[i,]$y,col="blue")
}
}
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