Thanks Bob
This is great,
The correlation does jump out when I plot it- I am just looking for a
quantified way of testing what I see. If there is a more appropriate test
I'd be happy to learn.
Many thanks
Tania Bird MSc
*"There is a sufficiency in the world for man's need but not for man's
greed" ~ Mahatma Gandhi*
https://www.linkedin.com/in/taniabird
https://taniabird.webs.com
On 26 July 2017 at 12:51, Bob O'Hara <bohara at senckenberg.de> wrote:
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*
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