Plot the ACF without the traditional noninformation unit spike at lag 0.
Acf(x, lag.max = NULL, type = c("correlation", "covariance", "partial"),
plot = TRUE, na.action = na.fail, demean = TRUE, ...)# S3 method for 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"), acfLag0 = FALSE, ...)
for acf
, an object of class 'Acf', which inherits
from class 'acf', as described with help('acf', package='stats')
.
for plot.Acf
, NULL
for 'acf': a numeric vector or time series.
for 'plot.acf': an object of class 'acf'.
maximum lag at which to calculate the acf.
coverage probability for confidence interval for 'plot.acf'.
the type of 'acf' or 'plot'
logical. If 'TRUE' the 'acf' function will call 'plot.acf'.
function to be called by 'acf' to handle missing values.
logical: Should the x be replaced by x - mean(x)
before computing
the sums of squares and lagged cross products to produce the 'acf'?
see the help page of acf
: help('acf', package = 'stats')
.
logical: TRUE to plot the traditional noninformation unit spike at lag 0. FALSE to omit that spike, consistent with the style in Tsay (2005).
further arguments passed to 'plot.acf'.
Spencer Graves for the FinTS modification of 'plot.acf'.
These functions are provided to make it easy to plot an autocorrelation function without the noninformative unit spike at lag 0. This is done by calling plot(x, acfLag0 = FALSE, ...). Apart from the 'acfLag0' argument, the rest of the arguments are identical to those for 'acf' and 'plot.acf'.
Ruey Tsay (2005) Analysis of Financial Time Series, 2nd ed. (Wiley)
data(m.ibm2697)
Acf(m.ibm2697)
Acf(m.ibm2697, lag.max=100)
Acf(m.ibm2697, lag.max=100, main='Monthly IBM returns, 1926-1997')
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