Description Usage Arguments Value Author(s) References See Also Examples
This function conducts an Empirical Orthogonal Teleconnection analysis (EOT) of a raster time series predictor using "remote" package especially designed to identify locations of enhanced potential and explain spatio-temporal variability within the same geographic field.
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rasterts |
Input raster time series as |
rastermask |
Either a |
nu |
Numeric. Defines the number of EOTs to return. Defaults to return the first 2 EOT modes. |
gapfill |
Character. Defines the algorithm to be used to interpolate pixels with incomplete temporal profiles
Accepts argument supported as method in function |
predictor |
Character. Defines the predictor components to export from those available in Value section of |
... |
Additional arguments to be passed through to function |
Object of class EOTstack-class
containing the following components:
eot | EOT temporal profiles corresponding to base point coordinates as xts object |
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total_variance | Numeric. Total explained variance of input raster time series by the entire set of computed EOTs | |
explained_variance | Numeric vector. Percentage of variance explained by each EOT mode with respect to the total variance of input raster time series | |
r_predictor | RasterBrick. Correlation coefficients between the base point and each pixel of the predictor domain as RasterBrick object (only exported if predictor = "all" or predictor = "r_predictor" ) |
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rsq_predictor | RasterBrick. Coefficient of determination between the base point and each pixel of the predictor domain as RasterBrick object (only exported if predictor = "all" or predictor = "rsq_predictor" ) |
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rsq_sums_predictor | RasterBrick. Sums of correlation coefficients between the base point and each pixel of the predictor domain as RasterBrick object (only exported if predictor = "all" or predictor = "rsq_sums_predictor" ) |
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int_predictor | RasterBrick. Intercept of the regression equation for each pixel of the predictor domain as RasterBrick object (only exported if predictor = "all" or predictor = "int_predictor" ) |
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slp_predictor | RasterBrick. Slope of the regression equation for each pixel of the predictor domain as RasterBrick object (only exported if predictor = "all" or predictor = "slp_predictor" ) |
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r_predictor | RasterBrick. The significance (p-value) of the the regression equation for each pixel of the predictor domain as RasterBrick object (only exported if predictor = "all" or predictor = "p_predictor" ) |
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Federico Filipponi
van den Dool H.M., Saha S., Johansson A. (2000). Empirical Orthogonal Teleconnections. 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
eot
, rtsa.eof
, rtsa.gapfill
, EOTstack
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Not run:
## create raster time series using the 'pacificSST' data from 'remote' package
require(remote)
data(pacificSST)
pacificSST[which(getValues(pacificSST == 0))] <- NA # set NA values
# create rts object
rasterts <- rts(pacificSST, seq(as.Date('1982-01-15'), as.Date('2010-12-15'), 'months'))
## generate raster mask
rmk <- pacificSST[[1]] # create raster mask
values(rmk) <- 1 # set raster mask values
rmk[which(is.na(getValues(pacificSST[[1]])))] <- 0 # set raster mask values
## compute EOT
# compute the first 2 EOTs
eot_result <- rtsa.eot(rasterts=rasterts, rastermask=rmk, nu=2)
# compute the first 2 EOTs and export only the compontents 'r_predict' and 'p_predictor'
eot_rp <- rtsa.eot(rasterts=rasterts, rastermask="compute", nu=2, predictor=c("r_predictor", "p_predictor"))
# compute the first 2 EOTs using the index of agreement
eot_ioa <- rtsa.eot(rasterts=rasterts, rastermask=rmk, nu=2, type="ioa", predictor="all", verbose=T)
## End(Not run)
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