StructTS with 2 seasons
Thanks!
Following Prof. Ripley's reply I inserted the following lines into
StructTS (makeBSM)
if (nf < 3)
{ ind <- 2L }
else
{ ind <- 3:nf
T[cbind(ind + 1L, ind)] <- 1
}
This worked and gave me the same variance estimates as the dlm package.
I also see that the StructTS function was updated and can handle 2
seasons now.
Thanks for the help!
Birgit
I have replreplied that StructI have replaced t to the help from
Giovanni Petris and Brian Ripley
From: Giovanni Petris <gpetris at uark.edu>
To: Birgit Erni <Birgit.Erni at uct.ac.za>
CC: <r-help at r-project.org>
Date: 30 November 2010 05:40 PM
Subject: Re: [R] StructTS with 2 seasons
Hi Birgit,
You are right, StructTS does not seem to be able to handle a time
series
with frequency 2 (and after a quick glance at the code I suspect a
frequency 3 may not work either).
There are contributed packages that you can use to fit (and smooth,
filter, forecast) structural time series and more general state space
models. Among them I would look at dlm, KFAS, dse, sspir.
HTH,
Giovanni
(author of 'dlm')
On Tue, 2010-11-30 at 10:40 +0200, Birgit Erni wrote:
Dear All, I am trying to fit a structural time series model using the StructTS function (package stats) with only 2 seasons (summer and winter).
More
than 2 seasons work fine but with 2 seasons I get this error:
fit <- StructTS(y.ts, type="BSM")
Error in T[cbind(ind + 1L, ind)] <- 1 : subscript out of bounds I have looked at Prof. Ripley's 2002 RNews article but cannot see
why
theoretically it should not be possible to have only 2 seasons. I
have
also looked at the StructTS code, and the problem is with the
transition
matrix in makeBSM, but I am not sure whether I should overwrite this
or
whether my thinking is wrong. I attach code that simulates data for the basic structural model
(with
time-varying trend and season), the top line s determines the number
of
seasons in a year (3 and 4 work fine, 2 gives the above error), and
at
the end tries to fit the model. I am using R version 2.11.1, Windows
XP.
# ----- Basic structural model -----
s <- 2 # frequency or period
mu0 <- 6
beta0 <- 1
it0 <- 0
it.states <- sample(1:6,s)
mu <- numeric() # mean
beta <- numeric() # slope
it <- numeric() # seasonal state
mu[1] <- mu0 + beta0 + rnorm(1,0,2)
beta[1] <- beta0 + rnorm(1,0,0.5)
it[1] <- - sum(it.states[2:s]) + rnorm(1,0,1)
it.states <- c(it[1],it.states[2:s])
for (i in 2:30)
{ mu[i] <- mu[i-1] + beta[i-1] + rnorm(1,0,2)
beta[i] <- beta[i-1] + rnorm(1,0,2)
it[i] <- - sum(it.states[1:(s-1)]) + rnorm(1,0,1)
it.states <- c(it[i],it.states[1:(s-1)])
}
y <- numeric()
for (i in 1:30)
{ y[i] <- mu[i] + it[i] + rnorm(1,0,3) }
par(mfrow=c(2,2))
plot(y,type="b")
plot(mu)
plot(beta)
plot(it)
y.ts <- ts(y,start=c(1977,1),frequency=s)
y.ts
fit <- StructTS(y.ts, type="BSM")
fit
# ------------------------------------------------------
Thank you.
Birgit Erni
Department of Statistical Sciences
University of Cape Town, South Africa
###
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Giovanni Petris <GPetris at uark.edu> Associate Professor Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/ ### UNIVERSITY OF CAPE TOWN This e-mail is subject to the UCT ICT policies and e-mai...{{dropped:13}}