README.md

In bentaylor1/kalmanST: Kalman Filtering for Spatiotemporal Processes

kalmanST

Warning: package under development!!

rm(list=ls())

library(devtools) library(kalmanST) library(spatsurv) library(sp) load_all("kalmanST")

source("optifix.R")

set.seed(1)

N <- 50 T <- 100

ids <- rep(1:N,T) uqids <- 1:N times <- rep(1:T,each=N) xcoords <- runif(N) ycoords <- runif(N)

COVMODEL <- ExponentialCovFct()

u <- as.vector(as.matrix(dist(cbind(xcoords,ycoords))))

a <- 0.8 sigma_S <- 5 phi_S <- 0.1 sigma_Y <- 3

Sigma <- matrix(EvalCov(COVMODEL,u=u,parameters=c(sigma_S,phi_S)),N,N) cholS <- t(chol(Sigma))

S <- matrix(cholS%%rnorm(N),N,1) for(i in 2:T){ S <- cbind(S,aS[,i-1]+sqrt(1-a^2)cholS%%rnorm(N)) } S <- as.vector(S)

age <- runif(NT,20,70) sex <- sample(c("Male","Female"),NT,replace=TRUE)

outcome <- age0.5 + 10 outcome[sex=="Female"] <- outcome[sex=="Female"] - 10 outcome <- outcome + S + rnorm(NT,0,sigma_Y)

dat <- data.frame(ID=ids,times=times,xcoord=rep(xcoords,T),ycoord=rep(ycoords,T),age=age,sex=sex,outcome=outcome) coordinates(dat) =~ xcoord + ycoord

dat <- dat[-sample(1:nrow(dat),floor(N*T/20))]

dat$outcome[(NT-20N):(N*T)] <- NA

out <- kffit_stationary_STGP( form=outcome ~ age + sex, param=paramSpec(a=a,sigma_S=sigma_S,phi_S=phi_S,sigma_Y=sigma_Y), pid="ID", tid="times", tmax=T, data=dat, cov.model=COVMODEL)

out <- kffit_stationary_STGP( form="outcome", param=paramSpec(a=a,sigma_S=sigma_S,phi_S=phi_S,sigma_Y=sigma_Y), pid="ID", tid="times", tmax=T, data=dat, cov.model=COVMODEL)

predictPlots(out)

fun <- kffit_stationary_STGP( form=outcome ~ age + sex, param=paramSpec(a=a,sigma_S=sigma_S,phi_S=phi_S,sigma_Y=sigma_Y), pid="ID", tid="times", tmax=T, data=dat, cov.model=COVMODEL, returnFunction=TRUE)

pars <- KFparest(fun) cat("\n\n") print(pars)

bentaylor1/kalmanST documentation built on May 12, 2019, 2:09 p.m.