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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

`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` for `arima.mle`.
`order`	A valid R `order` for `arima`. The additional argument `seasonal` 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. If `FALSE`, do not plot the correlation at lag=0.
`type`	`"acf"` or `"pacf"`
`z`	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.

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.

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

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"]]

HH

Statistical Analysis and Data Display: Heiberger and Holland

v3.1-43

GPL (>= 2)

Authors

Richard M. Heiberger

Initial release

2020-11-27