correlating three time-series in R
You can pass the columns to ccf() directly: df <- data.frame(x=rnorm(6), y=rnorm(6)) ccf(df$x, df$y) print(ccf(df$x, df$y)) You should probably also check the time series task view: <https://cran.r-project.org/web/views/TimeSeries.html>, in particular the zoo package, to see what can be done with irregular time series. But with 6 data points I'd be surprised if you have the power to detect anything that doesn't jump out when you simply plot the data. Bob
On 26/07/17 11:07, Tania Bird wrote:
I have three data sets of abundances through time for plants, insects and reptiles. There are 6 samples over a ten year period (all taxa sampled at the same time). I recognise this is a small data set for time series. I would like to correlate the time series to see if a) increases in abundance of one taxon are correlated to another, and b) to see if the correlation between plants:insects is greater than plants:reptiles. I thought to use the cross-correlation function in R e.g. ccf(insects, reptiles) Currently the data is in one dataframe with time as one column and abundance of each taxa is the next three columns. How do I convert the data to a time.series format as given in the R example? How can I compare the two ccf outputs? Thanks Tania Tania Bird MSc *"There is a sufficiency in the world for man's need but not for man's greed" ~ Mahatma Gandhi* [[alternative HTML version deleted]]
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