tsdiagplot
From HH v2.1-12
by Richard Heiberger
Times series diagnostic plots for a structured set of ARIMA models.
Times series diagnostic plots for a structured set of ARIMA models.
- Keywords
- hplot
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=if.R(
s=arma.loop(x, model=model,
series=deparse(substitute(x)), ...),
r=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
- x
- Time series vector.
- p.max, q.max
- Maximum number of AR and MA arguments to use in the series of ARIMA models.
- model
- A valid S-Plus
model
forarima.mle
. - order
- A valid R
order
forarima
. The additional argumentseasonal
may also be used. - lag.max
- Maximum lag for the acf and pacf plots.
- gof.lag
- Maximum lag for the gof plots.
- armas
- An
arma.loop
object. - diags
- An
diag.arma.loop
object. - ts.diag, rdal
- A list constructed as a rearranged
diag.arma.loop
object. - lag.units
- Units for time series, defaults to
frequency(x)
- lag.lim
- scaling for
xlim
in acf and pacf plots. - lag.x.at, lag.x.labels
- Location of ticks and labels for the acf and pacf plots.
- lag.0
- Logical. If
TRUE
, then plot the correlation (identically 1) at lag=0. IfFALSE
, do not plot the correlation at lag=0. - type
"acf"
or"pacf"
- z
- A matrix constructed as the
aic
orsigma2
component of the sumamry of aarma.loop
object. - z.name
"aic"
or"sigma2"
- series.name
- Character string describing the time series.
- xlab, ylab, layout, between, pch, xlim, main, lwd
- Standard trellis arguments.
- ...
- Additional arguments.
tsdiagplot
sends them toarima
orarima.mle
.acfplot
,aicsigplot
residplot
, andgofplot
send them toxyplot
.
Value
tsdiagplot
returns a"tsdiagplot"
object which is a list of"trellis"
objects. It is printed with its own print method. The other functions return"trellis"
objects.
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
X <- as.rts(scan(hh("datasets/tser.mystery.X.dat")))
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"]]
Community examples
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