eot: EOT analysis of a predictor and (optionally) a response...

Description Usage Arguments Details Value References Examples

Description

Calculate a given number of EOT modes either internally or between RasterStacks.

Usage

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eot(pred, resp = NULL, n = 1, standardised = TRUE, write.out = FALSE,
  path.out = ".", names.out = NULL, reduce.both = FALSE, type = c("rsq",
  "ioa"), print.console = TRUE, ...)

Arguments

pred

a ratser stack used as predictor

resp

a RasterStack used as response. If resp is NULL, pred is used as resp

n

the number of EOT modes to calculate

standardised

logical. If FALSE the calculated r-squared values will be multiplied by the variance

write.out

logical. If TRUE results will be written to disk using path.out

path.out

the file path for writing results if write.out is TRUE. Defaults to current working directory

names.out

optional prefix to be used for naming of results if write.out is TRUE

reduce.both

logical. If TRUE both pred and resp are reduced after each iteration. If FALSE only resp is reduced

type

the type of the link function. Defaults to 'rsq' as in original proposed method from Dool2000. If set to 'ioa' index of agreement is used instead

print.console

logical. If TRUE some details about the calculation process will be output to the console

...

not used at the moment

Details

For a detailed description of the EOT algorithm and the mathematics behind it, see the References section. In brief, the algorithm works as follows: First, the temporal profiles of each pixel xp of the predictor domain are regressed against the profiles of all pixels xr in the response domain (in case of only a single field xr = xp - 1). The calculated coefficients of determination are summed up and the pixel with the highest sum is identified as the 'base point' of the first/leading mode. The temporal profile at this base point is the first/leading EOT. Then, the residuals from the regression are taken to be the basis for the calculation of the next EOT, thus ensuring orthogonality of the identified teleconnections. This procedure is repeated until a predefined amount of n EOTs is calculated. In general, Reot implements a 'brute force' spatial data mining approach to identify locations of enhanced potential to explain spatio-temporal variability within the same or another geographic field.

Value

a list of n EOTs. Each EOT is also a list with the following components:

All *.predictor fields are also returned for the *.response domain, even if predictor and response domain are equal. This is due to that fact, that if not both fields are reduced after the first EOT is found, these RasterLayers will differ.

References

Empirical Orthogonal Teleconnections
H. M. van den Dool, S. Saha, A. Johansson (2000)
Journal of Climate, Volume 13, Issue 8, pp. 1421-1435
http://journals.ametsoc.org/doi/abs/10.1175/1520-0442%282000%29013%3C1421%3AEOT%3E2.0.CO%3B2

Empirical methods in short-term climate prediction
H. M. van den Dool (2007)
Oxford University Press, Oxford, New York
http://www.oup.com/uk/catalogue/?ci=9780199202782

Examples

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### EXAMPLE I:
### a single field
data(vdendool)

# claculate 2 leading modes
modes <- eot(pred = vdendool, resp = NULL, n = 2, reduce.both = FALSE,
             standardised = FALSE, print.console = TRUE)

plotEot(modes, eot = 1, show.eot.loc = TRUE)
plotEot(modes, eot = 2, show.eot.loc = TRUE)

environmentalinformatics-marburg/Reot documentation built on May 16, 2019, 7:50 a.m.