R: Smoothed Estimates or One-step-ahead Predictions of States
coef.SSModel
R Documentation
Smoothed Estimates or One-step-ahead Predictions of States
Description
Compute smoothed estimates or one-step-ahead predictions of states of
SSModel object or extract them from output of KFS.
For non-Gaussian models without simulation (nsim = 0),
these are the estimates of conditional modes of
states. For Gaussian models and non-Gaussian models with importance sampling,
these are the estimates of conditional means of states.
Usage
## S3 method for class 'KFS'
coef(object, start = NULL, end = NULL, filtered = FALSE,
states = "all", last = FALSE, ...)
## S3 method for class 'SSModel'
coef(object, start = NULL, end = NULL, filtered = FALSE,
states = "all", last = FALSE, nsim = 0, ...)
Arguments
object
An object of class KFS or SSModel.
start
The start time of the period of interest. Defaults to first time
point of the object.
end
The end time of the period of interest. Defaults to the last time
point of the object.
filtered
Logical, return filtered instead of smoothed estimates of
state vector. Default is FALSE.
states
Which states to extract? Either a numeric vector containing
the indices of the corresponding states, or a character vector defining the
types of the corresponding states. Possible choices are
"all", "level", "slope",
"trend", "regression", "arima", "custom",
"cycle" or "seasonal", where "trend" extracts all states
relating to trend. These can be combined. Default is "all".
last
If TRUE, extract only the last time point as numeric vector
(ignoring start and end). Default is FALSE.
nsim
Only for method for for non-Gaussian model of class SSModel.
The number of independent samples used in importance sampling.
Default is 0, which computes the
approximating Gaussian model by approxSSM and performs the
usual Gaussian filtering/smoothing so that the smoothed state estimates
equals to the conditional mode of p(α[t]|y).
In case of nsim = 0, the mean estimates and their variances are computed using
the Delta method (ignoring the covariance terms).
...
Additional arguments to KFS.
Ignored in method for object of class KFS.
Value
Multivariate time series containing estimates states.
Examples
model <- SSModel(log(drivers) ~ SSMtrend(1, Q = list(1)) +
SSMseasonal(period = 12, sea.type = "trigonometric") +
log(PetrolPrice) + law, data = Seatbelts, H = 1)
coef(model, states = "regression", last = TRUE)
coef(model, start = c(1983, 12), end = c(1984, 2))
out <- KFS(model)
coef(out, states = "regression", last = TRUE)
coef(out, start = c(1983, 12), end = c(1984, 2))
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(KFAS)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/KFAS/coef.SSModel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: coef.SSModel
> ### Title: Smoothed Estimates or One-step-ahead Predictions of States
> ### Aliases: coef.KFS coef.SSModel
>
> ### ** Examples
>
>
> model <- SSModel(log(drivers) ~ SSMtrend(1, Q = list(1)) +
+ SSMseasonal(period = 12, sea.type = "trigonometric") +
+ log(PetrolPrice) + law, data = Seatbelts, H = 1)
>
> coef(model, states = "regression", last = TRUE)
log(PetrolPrice) law
-0.1956752 -0.2332047
> coef(model, start = c(1983, 12), end = c(1984, 2))
log(PetrolPrice) law level sea_trig1 sea_trig*1
Dec 1983 -0.1956752 -0.2332047 6.946033 0.10939698 -0.0696719
Jan 1984 -0.1956752 -0.2332047 6.983564 0.05990461 -0.1150361
Feb 1984 -0.1956752 -0.2332047 6.986484 -0.00563915 -0.1295765
sea_trig2 sea_trig*2 sea_trig3 sea_trig*3 sea_trig4
Dec 1983 0.0602264533 -0.03450591 0.02977791 -0.02020845 0.0228839865
Jan 1984 0.0002302297 -0.06941059 -0.02020845 -0.02977791 -0.0226747351
Feb 1984 -0.0599962235 -0.03490468 -0.02977791 0.02020845 -0.0002092515
sea_trig*4 sea_trig5 sea_trig*5 sea_trig6
Dec 1983 -0.01297045 0.0259741178 0.01532238 -0.007098248
Jan 1984 -0.01333289 -0.0148330544 -0.02625663 0.007098248
Feb 1984 0.02630334 -0.0002825139 0.03015544 -0.007098248
> out <- KFS(model)
> coef(out, states = "regression", last = TRUE)
log(PetrolPrice) law
-0.1956752 -0.2332047
> coef(out, start = c(1983, 12), end = c(1984, 2))
log(PetrolPrice) law level sea_trig1 sea_trig*1
Dec 1983 -0.1956752 -0.2332047 6.946033 0.10939698 -0.0696719
Jan 1984 -0.1956752 -0.2332047 6.983564 0.05990461 -0.1150361
Feb 1984 -0.1956752 -0.2332047 6.986484 -0.00563915 -0.1295765
sea_trig2 sea_trig*2 sea_trig3 sea_trig*3 sea_trig4
Dec 1983 0.0602264533 -0.03450591 0.02977791 -0.02020845 0.0228839865
Jan 1984 0.0002302297 -0.06941059 -0.02020845 -0.02977791 -0.0226747351
Feb 1984 -0.0599962235 -0.03490468 -0.02977791 0.02020845 -0.0002092515
sea_trig*4 sea_trig5 sea_trig*5 sea_trig6
Dec 1983 -0.01297045 0.0259741178 0.01532238 -0.007098248
Jan 1984 -0.01333289 -0.0148330544 -0.02625663 0.007098248
Feb 1984 0.02630334 -0.0002825139 0.03015544 -0.007098248
>
>
>
>
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>
> dev.off()
null device
1
>
providing 
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