Created
September 5, 2017 14:25
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source("common.R", encoding = "utf-8") | |
# ----- KFAS ------ | |
df$RP <- calc_RP(df$RP095, df$AP, .95) | |
z <- calc_Z(RP = df$RP, p = df$AP) | |
df <- mutate(df, z1=z$z1, z2=z$z2) | |
df <- mutate(df, z1E=z1*end, z2E=z2*end) | |
# specify model | |
model3KFAS <- SSModel(logPI ~ SSMtrend(1, Q=NA) + | |
SSMarima(ar = c(.1, .1), Q = 0) + | |
SSMregression(~z1 + z1E + z2 + z2E, Q = diag(rep(NA, 4))), | |
H=NA, data = df, distribution = "gaussian") | |
print(model3KFAS) | |
# estimate unknown parameters | |
update.model <- function(pars, model){ | |
model$H[] <- exp(pars[1]) | |
diag(model$Q[, , 1])[1:6] <- exp(pars[2:7]) | |
model$T[6:7, 6, 1] <- tanh(pars[8:9]) | |
return(model) | |
} | |
fit.model3 <- fitSSM(model = model3KFAS, updatefn = update.model, | |
inits = numeric(9), maxit=1000000) | |
# convergence check (not converged...) | |
fit.model3$optim.out$convergence | |
# log likelihood | |
-fit.model3$optim.out$value | |
# smoothed time-varying parameterss | |
ggplot(data.frame(coef(KFS(fit.model3$model))) %>% mutate(t=1:n()) %>% gather(key=series, value=value, -t)) + | |
geom_line(aes(x=t, y=value, group=series), color="grey") + facet_wrap(~series, scales = "free") + | |
labs(x="week", y="") + theme_bw() | |
# ---- without AR(2) ---- | |
model2 <- SSModel(logPI ~ SSMtrend(1, Q = NA) + SSMregression(~ z1 + z1E + z2 + z2E, Q=diag(rep(NA, 4))), data=df, H = NA) | |
fit.model <- fitSSM(model, inits=numeric(6), method="L-BFGS-B") | |
# convergence check | |
fit.model$optim.out$convergence | |
# log likelihood | |
-fit.model$optim.out$value | |
# smoothed time-varying parameterss | |
ggplot(data.frame(coef(KFS(fit.model$model))) %>% mutate(t=1:n()) %>% gather(key=series, value=value, -t)) + | |
geom_line(aes(x=t, y=value, group=series), color="grey") + facet_wrap(~series, scales = "free") + | |
labs(x="week", y="") + theme_bw() |
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