acfToDf: Computes temporal autocorrelation in a vector, and returns a...

In virtualPollen: Simulating Pollen Curves from Virtual Taxa with Different Life and Niche Traits

Computes temporal autocorrelation in a vector, and returns a dataframe for easy plotting.

Description

It reads a vector representing a time series, applies acf for a given number of lags

Usage

acfToDf(
  x = NULL,
  lag.max = 100,
  length.out = 10
  )

Arguments

x	numeric vector. Must represent a variable sampled at regular times.
lag.max	integer, number of lags over which to compute temporal autocorrelation.
length.out	integer, total number of lags to consider for plotting. Should be a subset of lag.max.

Details

This function computes temporal autocorrelation of a given vector using acf, and returns a dataframe ready for easy plotting with plotAcf.

Value

A dataframe with the columns: #'

• lag: numeric, lag in the time units of x with a maximum determined by lag.max, and a number of unique values determined by length.out

• acf: Pearson correlation index returned by the acf for a given number of lags for the given lag.

• ci.max: Maximum value of the confidence interval of acf.

• ci.min: Minimum value of the confidence interval of acf.

Author(s)

Blas M. Benito <[e-mail]>

See Also

acf, plotAcf

Examples

#getting a driver
data(driverA)

#computing temporal autocorrelations
x.df <- acfToDf(
  x = driverA,
  lag.max = 1000,
  length.out = 100
)
str(x.df)

#plotting output
plotAcf(x.df)

virtualPollen documentation built on March 18, 2022, 6:16 p.m.