tsdiagplot: Times series diagnostic plots for a structured set of ARIMA models.

Description

Times 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

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 for

order

A valid R order for

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. If FALSE, do not plot the correlation at lag=0.

type

"acf" or "pacf"

A matrix constructed as the aic or sigma2 component of the sumamry of a arma.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 to arima or arima.mle. acfplot, aicsigplot residplot, and gofplot send them to xyplot.

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.

Heiberger, Richard M. and Holland, Burt (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://www.springer.com/us/book/9781493921218

Examples

Run this code

# NOT RUN {
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"]]
# }