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Last data update: 2014.03.03 |
Times series diagnostic plots for a structured set of ARIMA models.DescriptionTimes series diagnostic plots for a structured set of ARIMA models. Usage
tsdiagplot(x,
p.max=2, q.max=p.max,
model=c(p.max, 0, q.max), ## S-Plus
order=c(p.max, 0, q.max), ## R
lag.max=36, gof.lag=lag.max,
armas=arma.loop(x, order=order,
series=deparse(substitute(x)), ...),
diags=diag.arma.loop(armas, x,
lag.max=lag.max,
gof.lag=gof.lag),
ts.diag=rearrange.diag.arma.loop(diags),
lag.units=ts.diag$tspar["frequency"],
lag.lim=range(pretty(ts.diag$acf$lag))*lag.units,
lag.x.at=pretty(ts.diag$acf$lag)*lag.units,
lag.x.labels={tmp <- lag.x.at
tmp[as.integer(tmp)!=tmp] <- ""
tmp},
lag.0=TRUE,
main, lwd=0,
...)
acfplot(rdal, type="acf",
main=paste("ACF of std.resid:", rdal$series,
" model:", rdal$model),
lag.units=rdal$tspar["frequency"],
lag.lim=range(pretty(rdal[[type]]$lag)*lag.units),
lag.x.at=pretty(rdal[[type]]$lag)*lag.units,
lag.x.labels={tmp <- lag.x.at
tmp[as.integer(tmp)!=tmp] <- ""
tmp},
lag.0=TRUE,
xlim=xlim.function(lag.lim/lag.units),
...)
aicsigplot(z, z.name=deparse(substitute(z)), series.name="ts",
model=NULL,
xlab="", ylab=z.name,
main=paste(z.name, series.name, model),
layout=c(1,2), between=list(x=1,y=1), ...)
residplot(rdal,
main=paste("std.resid:", rdal$series,
" model:", rdal$model),
...)
gofplot(rdal,
main=paste("P-value for gof:", rdal$series,
" model:", rdal$model),
lag.units=rdal$tspar["frequency"],
lag.lim=range(pretty(rdal$gof$lag)*lag.units),
lag.x.at=pretty(rdal$gof$lag)*lag.units,
lag.x.labels={tmp <- lag.x.at
tmp[as.integer(tmp)!=tmp] <- ""
tmp},
xlim=xlim.function(lag.lim/lag.units),
pch=16, ...)
Arguments
Value
The other functions return Author(s)Richard M. Heiberger (rmh@temple.edu) References"Displays for Direct Comparison of ARIMA Models" The American Statistician, May 2002, Vol. 56, No. 2, pp. 131-138. Richard M. Heiberger, Temple University, and Paulo Teles, Faculdade de Economia do Porto. Richard M. Heiberger and Burt Holland (2004), Statistical Analysis and Data Display, Springer, ISBN 0-387-40270-5 See Also
Examples
data(tser.mystery.X)
X <- tser.mystery.X
X.dataplot <- tsacfplots(X, lwd=1, pch.seq=16, cex=.7)
X.dataplot
X.loop <- if.R(
s=
arma.loop(X, model=list(order=c(2,0,2)))
,r=
arma.loop(X, order=c(2,0,2))
)
X.dal <- diag.arma.loop(X.loop, x=X)
X.diag <- rearrange.diag.arma.loop(X.dal)
X.diagplot <- tsdiagplot(armas=X.loop, ts.diag=X.diag, lwd=1)
X.diagplot
X.loop
X.loop[["1","1"]]
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(HH)
Loading required package: lattice
Loading required package: grid
Loading required package: latticeExtra
Loading required package: RColorBrewer
Loading required package: multcomp
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data
Loading required package: MASS
Attaching package: 'TH.data'
The following object is masked from 'package:MASS':
geyser
Loading required package: gridExtra
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HH/tsdiagplot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: tsdiagplot
> ### Title: Times series diagnostic plots for a structured set of ARIMA
> ### models.
> ### Aliases: tsdiagplot acfplot aicsigplot residplot gofplot
> ### Keywords: hplot
>
> ### ** Examples
>
> data(tser.mystery.X)
> X <- tser.mystery.X
>
> X.dataplot <- tsacfplots(X, lwd=1, pch.seq=16, cex=.7)
> X.dataplot
>
> X.loop <- if.R(
+ s=
+ arma.loop(X, model=list(order=c(2,0,2)))
+ ,r=
+ arma.loop(X, order=c(2,0,2))
+ )
> X.dal <- diag.arma.loop(X.loop, x=X)
> X.diag <- rearrange.diag.arma.loop(X.dal)
> X.diagplot <- tsdiagplot(armas=X.loop, ts.diag=X.diag, lwd=1)
> X.diagplot
>
> X.loop
$series
[1] "X"
$model
[1] "(p,0,q)"
$sigma2
0 1 2
0 2.714968 1.434707 1.276545
1 1.500114 1.271072 1.262075
2 1.286423 1.260059 1.259868
$aic
0 1 2
0 387.6657 326.6338 317.0996
1 330.9222 316.7015 318.0026
2 317.8579 317.8503 319.8347
$coef
ar1 ar2 ma1 ma2 intercept
(0,0,0) NA NA NA NA NA
(1,0,0) 0.6635554 NA NA NA -0.2744204
(2,0,0) 0.9134671 -0.3721161 NA NA -0.2527722
(0,0,1) NA NA 0.7263795 NA -0.2568172
(1,0,1) 0.4298965 NA 0.5221491 NA -0.2628785
(2,0,1) 0.6213237 -0.1780866 0.3482906 NA -0.2571157
(0,0,2) NA NA 0.9272016 0.32957790 -0.2548033
(1,0,2) 0.2613548 NA 0.7056599 0.17340566 -0.2587636
(2,0,2) 0.5417462 -0.1478790 0.4279958 0.04825652 -0.2572223
$t.coef
ar1 ar2 ma1 ma2 intercept
(0,0,0) NA NA NA NA NA
(1,0,0) 9.0295264 NA NA NA -0.7683601
(2,0,0) 9.9262413 -4.0464280 NA NA -1.0257358
(0,0,1) NA NA 13.203654 NA -1.2471457
(1,0,1) 3.8605613 NA 5.154041 NA -0.8827784
(2,0,1) 2.6785425 -0.9700575 1.526213 NA -0.9525257
(0,0,2) NA NA 10.628021 3.5060232 -1.0062817
(1,0,2) 1.0948883 NA 3.001941 0.8897271 -0.9135335
(2,0,2) 0.7891486 -0.4657525 0.624625 0.1266807 -0.9478341
> X.loop[["1","1"]]
Call:
arima(x = x, order = c(1, 0, 1))
Coefficients:
ar1 ma1 intercept
0.4299 0.5221 -0.2629
s.e. 0.1114 0.1013 0.2978
sigma^2 estimated as 1.271: log likelihood = -154.35, aic = 316.7
>
>
>
>
>
> dev.off()
null device
1
>
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