Omit 0 lag order in ACF plot How can I omit the zero lag order in an acf plot? See this picture:

generated by
dummy<-c(14,0.004,0.2,1,0.002,-3,-0.042,1.2,-1,1.3,2.1,4,3001,-2,0.3,2,3)
acf(dummy)

The high peak (which is logically 1) is destroying the plot, since the scaling is too big. I would like to omit the high peak at lag order 1, so that the scaling can be reduced to -0.2 up to 0.2 for example, how can I do this?
 A: Another possible solution is as follows:
# Create an "acf" object called z
z <- acf(dummy)
# Check class of the object
class(z)
# View attributes of the "acf" object
attributes(z)
# Use "acf" attribute to view the first 13 elements (1 = lag at 0)
z$acf[1:13]
# Get rid of the first element (i.e. lag 0)
z$acf[2:13]
# Plot the autocorrelation function without lag 0
plot(x$acf[2:13], 
     type="h", 
     main="Autocorrelation Function", 
     xlab="Lag",     
     ylab="ACF", 
     ylim=c(-0.2,0.2), # this sets the y scale to -0.2 to 0.2
     las=1,
     xaxt="n")
abline(h=0)
# Add labels to the x-axis
x <- c(1:12)
y <- c(1:12)
axis(1, at=x, labels=y)

So far, this answers the original question. After running the code you should see a plot like the one shown below.

With regard to adding significance bands to the autocorrelation function, it will first be necessary, in this case, to choose a larger scale on the y axis. Otherwise, the significant bands will be outside the range and we won't be able to see them.
For example:
# Plot the autocorrelation function without lag 0
plot(z$acf[2:13], 
     type="h", 
     main="Autocorrelation Function", 
     xlab="Lag",     
     ylab="ACF", 
     ylim=c(-1,1), # this sets the y scale to -1 to 1
     las=1,
     xaxt="n")
abline(h=0)
# Add labels to the x-axis
x <- c(1:12)
y <- c(1:12)
axis(1, at=x, labels=y)
# Add 5% critical levels
abline(h=c(2/sqrt(17),-2/sqrt(17)),lty=c(2,2))

Since you'd probably prefer to have Bartlett's approximations rather than those 5% critical values, you can do the following:
# Store length of dummy
n <- length(dummy)
# Create a vector to store Bartlett's standard errors
bart.error <- c() 
# Use a loop to calculate Bartlett's standard errors
for (k in 1:n) {
     ends <- k-1
     bart.error[k] <- ((1 + sum((2*z$acf[0:(ends)]^2)))^0.5)*(n^-0.5)
}
# Create upper bound of interval (two standard errors above zero)
upper.bart <- 2*bart.error[1:12]
# Create lower bound of interval (two standard errors below zero)
lower.bart <- 2*-bart.error[1:12]
# Add intervals based on Bartlett's approximations to ACF plot
lines(upper.bart, lty=2, col="red"); lines(lower.bart, lty=2, col="red")

After running the code, you should see something like the plot below. The black dashed lines are the 5% critical values and the red dashed lines is the interval based on Bartlett's standard errors.

I hope this answers all of your questions.
A: Use the Acf function from the forecast package.
A: Use this code:
suppose;

x = rnorm(100) ## A typical white noise process

plot(acf(x,plot=F)[1:20])


A: Set the xlim and ylim. For example:
acf(x,20,xlim(1,20),ylim(-0.2,0.5))
