ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
Produces a ggplot object of their equivalent Acf, Pacf, Ccf, taperedacf and taperedpacf functions.
## S3 method for class 'acf'
autoplot(object, ci = 0.95, ...)
ggAcf(
x,
lag.max = NULL,
type = c("correlation", "covariance", "partial"),
plot = TRUE,
na.action = na.contiguous,
demean = TRUE,
...
)
ggPacf(
x,
lag.max = NULL,
plot = TRUE,
na.action = na.contiguous,
demean = TRUE,
...
)
ggCcf(
x,
y,
lag.max = NULL,
type = c("correlation", "covariance"),
plot = TRUE,
na.action = na.contiguous,
...
)
## S3 method for class 'mpacf'
autoplot(object, ...)
ggtaperedacf(
x,
lag.max = NULL,
type = c("correlation", "partial"),
plot = TRUE,
calc.ci = TRUE,
level = 95,
nsim = 100,
...
)
ggtaperedpacf(x, ...)object |
Object of class “ |
ci |
coverage probability for confidence interval. Plotting of the confidence interval is suppressed if ci is zero or negative. |
... |
Other plotting parameters to affect the plot. |
x |
a univariate or multivariate (not Ccf) numeric time series object or a numeric vector or matrix. |
lag.max |
maximum lag at which to calculate the acf. |
type |
character string giving the type of acf to be computed. Allowed
values are " |
plot |
logical. If |
na.action |
function to handle missing values. Default is
|
demean |
Should covariances be about the sample means? |
y |
a univariate numeric time series object or a numeric vector. |
calc.ci |
If |
level |
Percentage level used for the confidence intervals. |
nsim |
The number of bootstrap samples used in estimating the confidence intervals. |
If autoplot is given an acf or mpacf object, then an
appropriate ggplot object will be created.
ggtaperedpacf
A ggplot object.
Mitchell O'Hara-Wild
plot.acf, Acf,
acf, taperedacf
library(ggplot2) ggAcf(wineind) wineind %>% Acf(plot=FALSE) %>% autoplot ## Not run: wineind %>% taperedacf(plot=FALSE) %>% autoplot ggtaperedacf(wineind) ggtaperedpacf(wineind) ## End(Not run) ggCcf(mdeaths, fdeaths)
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