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]] _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
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]] > > _______________________________________________ > R-sig-ecology mailing list > [hidden email] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > -- Bob O'Hara NOTE: this email will die at some point, so please update you records to [hidden email] Institutt for matematiske fag NTNU 7491 Trondheim Norway Mobile: +49 1515 888 5440 Journal of Negative Results - EEB: www.jnr-eeb.org _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
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 <[hidden email]> 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* >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> R-sig-ecology mailing list >> [hidden email] >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> >> > > -- > Bob O'Hara > NOTE: this email will die at some point, so please update you records to > [hidden email] > > Institutt for matematiske fag > NTNU > 7491 Trondheim > Norway > > Mobile: +49 1515 888 5440 > Journal of Negative Results - EEB: www.jnr-eeb.org > > _______________________________________________ > R-sig-ecology mailing list > [hidden email] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
Hi,
a compact, practical and well readable introduction to some time series methods can be found in chapter 6 of Kleiber and Zeileis (2008): Applied Economics with R. This book is also well suited for ecologists and builds a fundamental for further reading and understanding. Thomas Am 26.07.2017 um 12:05 schrieb Tania Bird: > 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 <[hidden email]> 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* >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> [hidden email] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >>> >>> >> >> -- >> Bob O'Hara >> NOTE: this email will die at some point, so please update you records to >> [hidden email] >> >> Institutt for matematiske fag >> NTNU >> 7491 Trondheim >> Norway >> >> Mobile: +49 1515 888 5440 >> Journal of Negative Results - EEB: www.jnr-eeb.org >> -- Thomas Petzoldt Technische Universitaet Dresden Faculty of Environmental Sciences Institute of Hydrobiology 01062 Dresden, Germany http://tu-dresden.de/Members/thomas.petzoldt -- limnology and ecological modelling -- _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
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