R: Plot Autocovariance and Autocorrelation Functions

plot.acf {stats}

R Documentation

Plot Autocovariance and Autocorrelation Functions

Description

Plot method for objects of class "acf".

Usage

## S3 method for class 'acf':
plot(x, ci = 0.95, type = "h", xlab = "Lag", ylab = NULL,
     ylim = NULL, main = NULL,
     ci.col = "blue", ci.type = c("white", "ma"),
     max.mfrow = 6, ask = Npgs > 1 && dev.interactive(),
     mar = if(nser > 2) c(3,2,2,0.8) else par("mar"),
     oma = if(nser > 2) c(1,1.2,1,1) else par("oma"),
     mgp = if(nser > 2) c(1.5,0.6,0) else par("mgp"),
     xpd = par("xpd"), cex.main = if(nser > 2) 1 else par("cex.main"),
     verbose = getOption("verbose"),
     ...)

Arguments

`x`	an object of class `"acf"`.
`ci`	coverage probability for confidence interval. Plotting of the confidence interval is suppressed if `ci` is zero or negative.
`type`	the type of plot to be drawn, default to histogram like vertical lines.
`xlab`	the x label of the plot.
`ylab`	the y label of the plot.
`ylim`	numeric of length 2 giving the y limits for the plot.
`main`	overall title for the plot.
`ci.col`	colour to plot the confidence interval lines.
`ci.type`	should the confidence limits assume a white noise input or for lag k an MA(k-1) input?
`max.mfrow`	positive integer; for multivariate `x` indicating how many rows and columns of plots should be put on one page, using `par(mfrow = c(m,m))`.
`ask`	logical; if `TRUE`, the user is asked before a new page is started.
`mar, oma, mgp, xpd, cex.main`	graphics parameters as in `par(*)`, by default adjusted to use smaller than default margins for multivariate `x` only.
`verbose`	logical. Should R report extra information on progress?
`...`	graphics parameters to be passed to the plotting routines.

Note

The confidence interval plotted in plot.acf is based on an uncorrelated series and should be treated with appropriate caution. Using ci.type = "ma" may be less potentially misleading.

Examples

z4  <- ts(matrix(rnorm(400), 100, 4), start=c(1961, 1), frequency=12)
z7  <- ts(matrix(rnorm(700), 100, 7), start=c(1961, 1), frequency=12)
acf(z4)
acf(z7, max.mfrow = 7)# squeeze on 1 page
acf(z7) # multi-page