HH (version 2.2-17)

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=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, ...)

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

tsacfplots, arma.loop

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"]]