| Title: | Empirical Orthogonal Teleconnections in R |
|---|---|
| Description: | Empirical orthogonal teleconnections in R. 'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time. |
| Authors: | Tim Appelhans, Florian Detsch, Thomas Nauss |
| Maintainer: | Tim Appelhans <[email protected]> |
| License: | GPL (>= 3) | file LICENSE |
| Version: | 1.2.1 |
| Built: | 2024-11-13 04:28:23 UTC |
| Source: | https://github.com/cran/remote |
R EMpirical Orthogonal TEleconnections
A collection of functions to facilitate empirical orthogonal teleconnection analysis. Some handy functions for preprocessing, such as deseasoning, denoising, lagging are readily available for ease of usage.
Tim Appelhans, Florian Detsch
Maintainer: Tim Appelhans [email protected]
Empirical Orthogonal Teleconnections
H. M. van den Dool, S. Saha, A. Johansson (2000)
Journal of Climate, Volume 13, Issue 8 (April 2000) pp. 1421 - 1435
Empirical methods in short-term climate prediction
H. M. van den Dool (2007)
Oxford University Press, Oxford, New York (2007)
remote is built upon Raster* classes from the raster-package.
Please see their documentation for data preparation etc.
The function creates an anomaly RasterStack either based on the
overall mean of the original stack, or a supplied reference RasterLayer.
For the creation of seasonal anomalies use deseason.
anomalize(x, reference = NULL, ...)anomalize(x, reference = NULL, ...)
x |
a RasterStack |
reference |
an optional RasterLayer to be used as the reference |
... |
additional arguments passed to |
an anomaly RasterStack
data(australiaGPCP) aus_anom <- anomalize(australiaGPCP) opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[10]], main = "original") plot(aus_anom[[10]], main = "anomalized") par(opar)data(australiaGPCP) aus_anom <- anomalize(australiaGPCP) opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[10]], main = "original") plot(aus_anom[[10]], main = "anomalized") par(opar)
Monthly Gridded Precipitation Climatology Project precipitation data for Australia from 1982/01 to 2010/12
a RasterBrick with the following attributes
dimensions : 12, 20, 240, 348 (nrow, ncol, ncell, nlayers)
resolution : 2.5, 2.5 (x, y)
extent : 110, 160, -40, -10 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
Monthly Gridded Precipitation Climatology Project precipitation data for Australia from 1982/01 to 2010/12
The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979 - Present)
Adler et al. (2003)
Journal of Hydrometeorology, Volume 4, Issue 6, pp. 1147 - 1167
http://dx.doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2
The function calculates the (optionally standardised) space-time variance of a RasterStack or RasterBrick.
calcVar(x, standardised = FALSE, ...)calcVar(x, standardised = FALSE, ...)
x |
a RasterStack or RasterBrick |
standardised |
logical. |
... |
currently not used |
the mean (optionally standardised) space-time variance.
data("pacificSST") calcVar(pacificSST)data("pacificSST") calcVar(pacificSST)
Create a weighted covariance matrix
covWeight(m, weights, ...)covWeight(m, weights, ...)
m |
a matrix (e.g. as returned by |
weights |
a numeric vector of weights. For lat/lon data this
can be produced with |
... |
additional arguments passed to |
see cov.wt
The function cuts a specified number of layers off a RrasterStack in order to create lagged RasterStacks.
cutStack(x, tail = TRUE, n = NULL)cutStack(x, tail = TRUE, n = NULL)
x |
a RasterStack |
tail |
logical. If |
n |
the number of layers to take away. |
a RasterStack shortened by n layers either from the
beginning or the end, depending on the specification of tail
data(australiaGPCP) # 6 layers from the beginning cutStack(australiaGPCP, tail = FALSE, n = 6) # 8 layers from the end cutStack(australiaGPCP, tail = TRUE, n = 8)data(australiaGPCP) # 6 layers from the beginning cutStack(australiaGPCP, tail = FALSE, n = 6) # 8 layers from the end cutStack(australiaGPCP, tail = TRUE, n = 8)
Convert degrees to radians
deg2rad(deg)deg2rad(deg)
deg |
vector of degrees to be converted to radians |
data(vdendool) ## latitude in degrees degrees <- coordinates(vdendool)[, 2] head(degrees) ## latitude in radians radians <- deg2rad(coordinates(vdendool)[, 2]) head(radians)data(vdendool) ## latitude in degrees degrees <- coordinates(vdendool)[, 2] head(degrees) ## latitude in radians radians <- deg2rad(coordinates(vdendool)[, 2]) head(radians)
Filter noise from a RasterStack by decomposing into principal components and subsequent reconstruction using only a subset of components
denoise(x, k = NULL, expl.var = NULL, weighted = TRUE, use.cpp = TRUE, verbose = TRUE, ...)denoise(x, k = NULL, expl.var = NULL, weighted = TRUE, use.cpp = TRUE, verbose = TRUE, ...)
x |
RasterStack to be filtered |
k |
number of components to be kept for reconstruction
(ignored if |
expl.var |
minimum amount of variance to be kept after reconstruction
(should be set to NULL or omitted if |
weighted |
logical. If |
use.cpp |
logical. Determines whether to use Rcpp
functionality, defaults to |
verbose |
logical. If |
... |
additional arguments passed to |
a denoised RasterStack
data("vdendool") vdd_dns <- denoise(vdendool, expl.var = 0.8) opar <- par(mfrow = c(1,2)) plot(vdendool[[1]], main = "original") plot(vdd_dns[[1]], main = "denoised") par(opar)data("vdendool") vdd_dns <- denoise(vdendool, expl.var = 0.8) opar <- par(mfrow = c(1,2)) plot(vdendool[[1]], main = "original") plot(vdd_dns[[1]], main = "denoised") par(opar)
The function calculates anomalies of a RasterStack by supplying a
suitable seasonal window. E. g. to create monthly anomalies of a
raster stack of 12 layers per year, use cycle.window = 12.
## S4 method for signature 'RasterStackBrick' deseason(x, cycle.window = 12L, use.cpp = FALSE, filename = "", ...) ## S4 method for signature 'numeric' deseason(x, cycle.window = 12L)## S4 method for signature 'RasterStackBrick' deseason(x, cycle.window = 12L, use.cpp = FALSE, filename = "", ...) ## S4 method for signature 'numeric' deseason(x, cycle.window = 12L)
x |
An object of class 'RasterStack' (or 'RasterBrick') or, alternatively, a 'numeric' time series. |
cycle.window |
Integer. The window for the creation of the anomalies. |
use.cpp |
Logical. Determines whether or not to use Rcpp
functionality, defaults to |
filename |
|
... |
Additional arguments passed on to |
If x is a 'RasterStack' (or 'RasterBrick') object, a
deseasoned 'RasterStack'; else a deseasoned 'numeric' vector.
data("australiaGPCP") aus_dsn <- deseason(australiaGPCP, 12) opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[1]], main = "original") plot(aus_dsn[[1]], main = "deseasoned") par(opar)data("australiaGPCP") aus_dsn <- deseason(australiaGPCP, 12) opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[1]], main = "original") plot(aus_dsn[[1]], main = "deseasoned") par(opar)
Calculate a given number of EOT modes either internally or between RasterStacks.
## S4 method for signature 'RasterStackBrick' eot(x, y = NULL, n = 1, standardised = TRUE, write.out = FALSE, path.out = ".", prefix = "remote", reduce.both = FALSE, type = c("rsq", "ioa"), verbose = TRUE, ...)## S4 method for signature 'RasterStackBrick' eot(x, y = NULL, n = 1, standardised = TRUE, write.out = FALSE, path.out = ".", prefix = "remote", reduce.both = FALSE, type = c("rsq", "ioa"), verbose = TRUE, ...)
x |
a RasterStack used as predictor |
y |
a RasterStack used as response. If |
n |
the number of EOT modes to calculate |
standardised |
logical. If |
write.out |
logical. If |
path.out |
the file path for writing results if |
prefix |
optional prefix to be used for naming of results if
|
reduce.both |
logical. If |
type |
the type of the link function. Defaults to |
verbose |
logical. If |
... |
not used at the moment |
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. 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, remote 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.
if n = 1 an EotMode, if n > 1 an EotStack of n
EotModes. Each EotMode has the following components:
mode - the number of the identified mode (1 - n)
eot - the EOT (time series) at the identified base point.
Note, this is a simple numeric vector, not of class ts
coords_bp - the coordinates of the identified base point
cell_bp - the cell number of the indeified base point
cum_exp_var - the (cumulative) explained variance of the considered EOT
r_predictor - the RasterLayer of the correlation coefficients between the base point and each pixel of the predictor domain
rsq_predictor - as above but for the coefficient of determination
rsq_sums_predictor - as above but for the sums of coefficient of determination
int_predictor - the RasterLayer of the intercept of the regression equation for each pixel of the predictor domain
slp_predictor - same as above but for the slope of the regression equation for each pixel of the predictor domain
p_predictor - the RasterLayer of the significance (p-value) of the the regression equation for each pixel of the predictor domain
resid_predictor - the RasterBrick of the reduced data for the predictor domain
Apart from rsq_sums_predictor, 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.
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
https://global.oup.com/academic/product/empirical-methods-in-short-term-climate-prediction-9780199202782?cc=de&lang=en&
### EXAMPLE I ### a single field data(vdendool) ## claculate 2 leading modes nh_modes <- eot(x = vdendool, y = NULL, n = 2, standardised = FALSE, verbose = TRUE) plot(nh_modes, y = 1, show.bp = TRUE) plot(nh_modes, y = 2, show.bp = TRUE)### EXAMPLE I ### a single field data(vdendool) ## claculate 2 leading modes nh_modes <- eot(x = vdendool, y = NULL, n = 2, standardised = FALSE, verbose = TRUE) plot(nh_modes, y = 1, show.bp = TRUE) plot(nh_modes, y = 2, show.bp = TRUE)
EotCycle() calculates a single EOT and is controlled by the main eot() function
EotCycle(x, y, n = 1, standardised, orig.var, write.out, path.out, prefix, type, verbose, ...)EotCycle(x, y, n = 1, standardised, orig.var, write.out, path.out, prefix, type, verbose, ...)
x |
a ratser stack used as predictor |
y |
a RasterStack used as response. If |
n |
the number of EOT modes to calculate |
standardised |
logical. If |
orig.var |
original variance of the response domain |
write.out |
logical. If |
path.out |
the file path for writing results if |
prefix |
optional prefix to be used for naming of results if
|
type |
the type of the link function. Defaults to |
verbose |
logical. If |
... |
not used at the moment |
Class EotMode
modethe number of the identified mode
namethe name of the mode
eotthe EOT (time series) at the identified base point. Note, this is a simple numeric vector
coords_bpthe coordinates of the identified base point
cell_bpthe cell number of the indeified base point
cum_exp_varthe cumulative explained variance of the considered EOT mode
r_predictorthe RasterLayer of the correlation coefficients between the base point and each pixel of the predictor domain
rsq_predictoras above but for the coefficient of determination of the predictor domain
rsq_sums_predictoras above but for the sums of coefficient of determination of the predictor domain
int_predictorthe RasterLayer of the intercept of the regression equation for each pixel of the predictor domain
slp_predictorsame as above but for the slope of the regression equation for each pixel of the predictor domain
p_predictorthe RasterLayer of the significance (p-value) of the the regression equation for each pixel of the predictor domain
resid_predictorthe RasterBrick of the reduced data for the predictor domain
r_responsethe RasterLayer of the correlation coefficients between the base point and each pixel of the response domain
rsq_responseas above but for the coefficient of determination of the response domain
int_responsethe RasterLayer of the intercept of the regression equation for each pixel of the response domain
slp_responseas above but for the slope of the regression equation for each pixel of the response domain
p_responsesame the RasterLayer of the significance (p-value) of the the regression equation for each pixel of the response domain
resid_responsethe RasterBrick of the reduced data for the response domain
Class EotStack
modesa list containing the individual 'EotMode's of the 'EotStack'
namesthe names of the modes
The function performs geographic weighting of non-projected long/lat
data. By default it uses the cosine of latitude to compensate for the
area distortion, though the user can supply other functions via f.
geoWeight(x, f = function(x) cos(x), ...)geoWeight(x, f = function(x) cos(x), ...)
x |
a Raster* object |
f |
a function to be used to the weighting.
Defaults to |
... |
additional arguments to be passed to f |
a weighted Raster* object
data(vdendool) wgtd <- geoWeight(vdendool) opar <- par(mfrow = c(1,2)) plot(vdendool[[1]], main = "original") plot(wgtd[[1]], main = "weighted") par(opar)data(vdendool) wgtd <- geoWeight(vdendool) opar <- par(mfrow = c(1,2)) plot(vdendool[[1]], main = "original") plot(wgtd[[1]], main = "weighted") par(opar)
Calculate weights using the cosine of latitude to compensate for area distortion of non-projected lat/lon data
getWeights(x, f = function(x) cos(x), ...)getWeights(x, f = function(x) cos(x), ...)
x |
a Raster* object |
f |
a function to be used to the weighting.
Defaults to |
... |
additional arguments to be passed to f |
a numeric vector of weights
data("australiaGPCP") wghts <- getWeights(australiaGPCP) wghts_rst <- australiaGPCP[[1]] wghts_rst[] <- wghts opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[1]], main = "data") plot(wghts_rst, main = "weights") par(opar)data("australiaGPCP") wghts <- getWeights(australiaGPCP) wghts_rst <- australiaGPCP[[1]] wghts_rst[] <- wghts opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[1]], main = "data") plot(wghts_rst, main = "weights") par(opar)
The function is used to produce two lagged RasterStacks. The second is cut from the beginning, the first from the tail to ensure equal output lengths (provided that input lengths were equal).
lagalize(x, y, lag = NULL, freq = 12, ...)lagalize(x, y, lag = NULL, freq = 12, ...)
x |
a RasterStack (to be cut from tail) |
y |
a RasterStack (to be cut from beginning) |
lag |
the desired lag (in the native frequency of the RasterStack) |
freq |
the frequency of the RasterStacks |
... |
currently not used |
a list with the two RasterStacks lagged by lag
data(pacificSST) data(australiaGPCP) # lag GPCP by 4 months lagged <- lagalize(pacificSST, australiaGPCP, lag = 4, freq = 12) lagged[[1]][[1]] #check names to see date of layer lagged[[2]][[1]] #check names to see date of layerdata(pacificSST) data(australiaGPCP) # lag GPCP by 4 months lagged <- lagalize(pacificSST, australiaGPCP, lag = 4, freq = 12) lagged[[1]][[1]] #check names to see date of layer lagged[[2]][[1]] #check names to see date of layer
Calculate long-term means from an input 'RasterStack' (or 'RasterBrick')
object. Ideally, the number of input layers should be divisable by the
supplied cycle.window. For instance, if x consists of monthly
layers, cycle.window should be a multiple of 12.
longtermMeans(x, cycle.window = 12L)longtermMeans(x, cycle.window = 12L)
x |
A 'RasterStack' (or 'RasterBrick') object. |
cycle.window |
'integer'. See |
If cycle.window equals nlayers(x) (which obviously doesn't make
much sense), a 'RasterLayer' object; else a 'RasterStack' object.
Florian Detsch
data("australiaGPCP") longtermMeans(australiaGPCP)data("australiaGPCP") longtermMeans(australiaGPCP)
Get or set names of Eot* objects
## S4 method for signature 'EotStack' names(x) ## S4 replacement method for signature 'EotStack' names(x) <- value ## S4 method for signature 'EotMode' names(x) ## S4 replacement method for signature 'EotMode' names(x) <- value## S4 method for signature 'EotStack' names(x) ## S4 replacement method for signature 'EotStack' names(x) <- value ## S4 method for signature 'EotMode' names(x) ## S4 replacement method for signature 'EotMode' names(x) <- value
x |
a EotMode or EotStack |
value |
name to be assigned |
if x is a EotStack, the names of all mdoes,
if x is a EotMode, the name the respective mode
data(vdendool) nh_modes <- eot(vdendool, n = 2) ## mode names names(nh_modes) names(nh_modes) <- c("vdendool1", "vdendool2") names(nh_modes) names(nh_modes[[2]])data(vdendool) nh_modes <- eot(vdendool, n = 2) ## mode names names(nh_modes) names(nh_modes) <- c("vdendool1", "vdendool2") names(nh_modes) names(nh_modes[[2]])
Number of modes of an EotStack
## S4 method for signature 'EotStack' nmodes(x)## S4 method for signature 'EotStack' nmodes(x)
x |
an EotStack |
retrieves the number of modes of an EotStack
integer
data(vdendool) nh_modes <- eot(vdendool, n = 2) nmodes(nh_modes)data(vdendool) nh_modes <- eot(vdendool, n = 2) nmodes(nh_modes)
The function identifies the number of modes needed to explain a certain amount of variance within the response field.
## S4 method for signature 'EotStack' nXplain(x, var = 0.9)## S4 method for signature 'EotStack' nXplain(x, var = 0.9)
x |
an EotStack |
var |
the minimum amount of variance to be explained by the modes |
an integer denoting the number of EOTs needed to explain var
This is a post-hoc function. It needs an EotStack
created as returned by eot. Depending on the potency
of the identified EOTs, it may be necessary to compute a high number of
modes in order to be able to explain a large enough part of the variance.
data(vdendool) nh_modes <- eot(x = vdendool, y = NULL, n = 3, standardised = FALSE, verbose = TRUE) ### How many modes are needed to explain 25% of variance? nXplain(nh_modes, 0.25)data(vdendool) nh_modes <- eot(x = vdendool, y = NULL, n = 3, standardised = FALSE, verbose = TRUE) ### How many modes are needed to explain 25% of variance? nXplain(nh_modes, 0.25)
Monthly NOAA sea surface temperatures for the tropical Pacific Ocean from 1982/01 to 2010/12
a RasterBrick with the following attributes
dimensions : 30, 140, 4200, 348 (nrow, ncol, ncell, nlayers)
resolution : 1, 1 (x, y)
extent : 150, 290, -15, 15 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
Monthly NOAA sea surface temperatures for the tropical Pacific Ocean from 1982/01 to 2010/12
Daily High-Resolution-Blended Analyses for Sea Surface Temperature
Reynolds et al. (2007)
Journal of Climate, Volume 20, Issue 22, pp. 5473 - 5496
http://dx.doi.org/10.1175/2007JCLI1824.1
This is the standard plotting routine for the results of eot.
Three panels will be drawn i) the predictor domain, ii) the response
domain, iii) the time series at the identified base point
## S4 method for signature 'EotMode,ANY' plot(x, y, pred.prm = "rsq", resp.prm = "r", show.bp = FALSE, anomalies = TRUE, add.map = TRUE, ts.vec = NULL, arrange = c("wide", "long"), clr = NULL, locations = FALSE, ...) ## S4 method for signature 'EotStack,ANY' plot(x, y, pred.prm = "rsq", resp.prm = "r", show.bp = FALSE, anomalies = TRUE, add.map = TRUE, ts.vec = NULL, arrange = c("wide", "long"), clr = NULL, locations = FALSE, ...)## S4 method for signature 'EotMode,ANY' plot(x, y, pred.prm = "rsq", resp.prm = "r", show.bp = FALSE, anomalies = TRUE, add.map = TRUE, ts.vec = NULL, arrange = c("wide", "long"), clr = NULL, locations = FALSE, ...) ## S4 method for signature 'EotStack,ANY' plot(x, y, pred.prm = "rsq", resp.prm = "r", show.bp = FALSE, anomalies = TRUE, add.map = TRUE, ts.vec = NULL, arrange = c("wide", "long"), clr = NULL, locations = FALSE, ...)
x |
either an object of EotMode or EotStack as returned by |
y |
integer or character of the mode to be plotted (e.g. 2 or "mode_2") |
pred.prm |
the parameter of the predictor to be plotted. |
resp.prm |
the parameter of the response to be plotted. |
show.bp |
logical. If |
anomalies |
logical. If |
add.map |
logical. If |
ts.vec |
an (optional) time series vector of the considered EOT calculation to be shown as the x-axis in the time series plot |
arrange |
whether the final plot should be arranged in "wide" or "long" format |
clr |
an (optional) color palette for displaying of the predictor and response fields |
locations |
logical. If x is an EotStack, set this to TRUE to produce a map showing the locations of all modes. Ignored if x is an EotMode |
... |
further arguments to be passed to |
x = EotStack,y = ANY: EotStack
data(vdendool) ## claculate 2 leading modes nh_modes <- eot(x = vdendool, y = NULL, n = 2, standardised = FALSE, verbose = TRUE) ## default settings plot(nh_modes, y = 1) # is equivalent to ## Not run: plot(nh_modes[[1]]) plot(nh_modes, y = 2) # shows variance explained by mode 2 only plot(nh_modes[[2]]) # shows cumulative variance explained by modes 1 & 2 ## showing the loction of the mode plot(nh_modes, y = 1, show.bp = TRUE) ## changing parameters plot(nh_modes, y = 1, show.bp = TRUE, pred.prm = "r", resp.prm = "p") ## change plot arrangement plot(nh_modes, y = 1, show.bp = TRUE, arrange = "long") ## plot locations of all base points plot(nh_modes, locations = TRUE) ## End(Not run)data(vdendool) ## claculate 2 leading modes nh_modes <- eot(x = vdendool, y = NULL, n = 2, standardised = FALSE, verbose = TRUE) ## default settings plot(nh_modes, y = 1) # is equivalent to ## Not run: plot(nh_modes[[1]]) plot(nh_modes, y = 2) # shows variance explained by mode 2 only plot(nh_modes[[2]]) # shows cumulative variance explained by modes 1 & 2 ## showing the loction of the mode plot(nh_modes, y = 1, show.bp = TRUE) ## changing parameters plot(nh_modes, y = 1, show.bp = TRUE, pred.prm = "r", resp.prm = "p") ## change plot arrangement plot(nh_modes, y = 1, show.bp = TRUE, arrange = "long") ## plot locations of all base points plot(nh_modes, locations = TRUE) ## End(Not run)
Make spatial predictions using the fitted model returned by eot.
A (user-defined) set of n modes will be used to model the outcome
using the identified link functions of the respective modes which are
added together to produce the final prediction.
## S4 method for signature 'EotStack' predict(object, newdata, n = 1, ...) ## S4 method for signature 'EotMode' predict(object, newdata, n = 1, ...)## S4 method for signature 'EotStack' predict(object, newdata, n = 1, ...) ## S4 method for signature 'EotMode' predict(object, newdata, n = 1, ...)
object |
an Eot* object |
newdata |
the data to be used as predictor |
n |
the number of modes to be used for the prediction.
See |
... |
further arguments to be passed to calc |
a RasterStack of nlayers(newdata)
EotMode: EotMode
### not very useful, but highlights the workflow data(pacificSST) data(australiaGPCP) ## train data using eot() train <- eot(x = pacificSST[[1:10]], y = australiaGPCP[[1:10]], n = 1) ## predict using identified model pred <- predict(train, newdata = pacificSST[[11:20]], n = 1) ## compare results opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[13]], main = "original", zlim = c(0, 10)) plot(pred[[3]], main = "predicted", zlim = c(0, 10)) par(opar)### not very useful, but highlights the workflow data(pacificSST) data(australiaGPCP) ## train data using eot() train <- eot(x = pacificSST[[1:10]], y = australiaGPCP[[1:10]], n = 1) ## predict using identified model pred <- predict(train, newdata = pacificSST[[11:20]], n = 1) ## compare results opar <- par(mfrow = c(1,2)) plot(australiaGPCP[[13]], main = "original", zlim = c(0, 10)) plot(pred[[3]], main = "predicted", zlim = c(0, 10)) par(opar)
Extract a set of modes from an EotStack
## S4 method for signature 'EotStack' subset(x, subset, drop = FALSE, ...) ## S4 method for signature 'EotStack,ANY,ANY' x[[i]]## S4 method for signature 'EotStack' subset(x, subset, drop = FALSE, ...) ## S4 method for signature 'EotStack,ANY,ANY' x[[i]]
x |
EotStack to be subset |
subset |
integer or character. The modes to ectract (either by integer or by their names) |
drop |
if |
... |
currently not used |
i |
number of EotMode to be subset |
an Eot* object
data(vdendool) nh_modes <- eot(x = vdendool, y = NULL, n = 3, standardised = FALSE, verbose = TRUE) subs <- subset(nh_modes, 2:3) # is the same as subs <- nh_modes[[2:3]] ## effect of 'drop=FALSE' when selecting a single layer subs <- subset(nh_modes, 2) class(subs) subs <- subset(nh_modes, 2, drop = TRUE) class(subs)data(vdendool) nh_modes <- eot(x = vdendool, y = NULL, n = 3, standardised = FALSE, verbose = TRUE) subs <- subset(nh_modes, 2:3) # is the same as subs <- nh_modes[[2:3]] ## effect of 'drop=FALSE' when selecting a single layer subs <- subset(nh_modes, 2) class(subs) subs <- subset(nh_modes, 2, drop = TRUE) class(subs)
NCEP/NCAR reanalysis data of mean seasonal (DJF) 700 mb geopotential heights from 1948 to 1998
a RasterBrick with the following attributes
dimensions : 14, 36, 504, 50 (nrow, ncol, ncell, nlayers)
resolution : 10, 4.931507 (x, y)
extent : -180, 180, 20.9589, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
NCEP/NCAR reanalysis data of mean seasonal (DJF) 700 mb geopotential heights from 1948 to 1998
http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.pressure.html
Original Source: NOAA National Center for Environmental Prediction
The NCEP/NCAR 40-year reanalysis project
Kalnay et al. (1996)
Bulletin of the American Meteorological Society, Volume 77, Issue 3, pp 437 - 471
http://journals.ametsoc.org/doi/abs/10.1175/1520-0477(1996)077%3C0437%3ATNYRP%3E2.0.CO%3B2
Write Eot* objects to disk. This is merely a wrapper around writeRaster so see respective help section for details.
## S4 method for signature 'EotMode' writeEot(x, path.out = ".", prefix = "remote", overwrite = TRUE, ...) ## S4 method for signature 'EotStack' writeEot(x, path.out = ".", prefix, ...)## S4 method for signature 'EotMode' writeEot(x, path.out = ".", prefix = "remote", overwrite = TRUE, ...) ## S4 method for signature 'EotStack' writeEot(x, path.out = ".", prefix, ...)
x |
an Eot* object |
path.out |
the path to the folder to write the files to |
prefix |
a prefix to be added to the file names (see Details) |
overwrite |
see writeRaster.
Defaults to |
... |
further arguments passed to writeRaster |
writeEot will write the results of either an EotMode or an EotStack
to disk. For each mode the following files will be written:
pred_r - the RasterLayer of the correlation coefficients between the base point and each pixel of the predictor domain
pred_rsq - as above but for the coefficient of determination
pred_rsq_sums - as above but for the sums of coefficient of determination
pred_int - the RasterLayer of the intercept of the regression equation for each pixel of the predictor domain
pred_slp - same as above but for the slope of the regression equation for each pixel of the predictor domain
pred_p - the RasterLayer of the significance (p-value) of the the regression equation for each pixel of the predictor domain
pred_resid - the RasterBrick of the reduced data for the predictor domain
Apart from pred_rsq_sums, all these files are also created for the response domain as resp_*. These will be pasted together with the prefix & the respective mode so that the file names will look like, e.g.:
prefix_mode_n_pred_r.grd
for the RasterLayer of the predictor correlation coefficient of mode n using the standard raster file type (.grd).
EotStack: EotStack
data(vdendool) nh_modes <- eot(x = vdendool, y = NULL, n = 2, standardised = FALSE, verbose = TRUE) ## write the complete EotStack writeEot(nh_modes, prefix = "vdendool") ## write only one EotMode writeEot(nh_modes[[2]], prefix = "vdendool")data(vdendool) nh_modes <- eot(x = vdendool, y = NULL, n = 2, standardised = FALSE, verbose = TRUE) ## write the complete EotStack writeEot(nh_modes, prefix = "vdendool") ## write only one EotMode writeEot(nh_modes[[2]], prefix = "vdendool")