qacf: Auto correlation plot in ggplot

In barryrowlingson/caramellar: Fit The CAR Space-time Model

Description

Reproduce the stats::acf plots from the base R graphics package in ggplot2.

Usage

qacf(x, conf.level = 0.95, lag.max = NULL, type = c("correlation",
  "covariance", "partial"), show.sig = FALSE)

Arguments

x	vector, or data.frame, to be plotted.
conf.level	confidence intervals for determining 'significant' autocorrelations.
lag.max	how many lags to present. default is the same as the stats::acf plot
type	same options as stats::acf, either a correlation (default), covariance, or partial correlation plot
show.sig	Extension to the stats::acf function. If TRUE the lags are colored to indicate statistically significant correlations different from zero. This option is only used for the corrleation plot.

Value

a ggplot object

Author(s)

Peter DeWitt

Examples

## Not run: 
# Generate a random data set
set.seed(42)
n <- 250
x1 <- x2 <- x3 <- x4 <- vector('numeric', length = n)
x1[1] <- runif(1)
x2[1] <- runif(1)
x3[1] <- runif(1)
x4[1] <- runif(1)

# white noise
Z.1 <- rnorm(n, 0, 1)
Z.2 <- rnorm(n, 0, 2)
Z.3 <- rnorm(n, 0, 5)

for(i in 2:n)
{
x1[i] <- x1[i-1] + Z.1[i] - Z.1[i-1] + x4[i-1] - x2[i-1]
x2[i] <- x2[i-1] - 2 * Z.2[i] + Z.2[i-1] - x4[i-1]
x3[i] <- x3[i-1] + x2[i-1] + 0.2 * Z.3[i] + Z.3[i-1]
x4[i] <- x4[i-1] + runif(1, 0.5, 1.5) * x4[i-1]
}
testdf <- data.frame(x1, x2, x3, x4)

# Base acf plot for one variable
acf(testdf$x1)

# qacf plot for one variable
qacf(testdf$x1)

# more than one variable
acf(testdf)
qacf(testdf)

## End(Not run)

barryrowlingson/caramellar documentation built on May 29, 2019, 4:50 p.m.