| plotBMAforecast {ensembleBMA} | R Documentation |
Produces contour, image, or perspective plot of a BMA forecast using prediction on a grid.
plotBMAforecast( forecast, longitude, latitude, nGrid = 65,
type = c("image", "contour", "persp"), ...,
interpolate = FALSE, span = 0.75, map = NULL)
forecast |
Numeric vector of forecasts. |
longitude |
Numeric vector giving the longitude of each forecast location. |
latitude |
Numeric vector giving the latitude of each forecast location. |
nGrid |
Number of grid points for loess interpolation.
(Binning and interpolation are done on an nGrid by nGrid grid).
|
type |
A character string indicating the desired plot type.
Should be one of either "contour", "image", or
"persp".
|
... |
Additional arguments to be passed to the plotting method. |
interpolate |
A logical variable indicating whether or not a loess
fit should be used to interpolate the data to points on
a grid. The default is to determine grid values by binning,
rather than interpolation.
|
span |
Smoothing parameter for loess (used only when
interpolate = TRUE).
The default value is 0.75, which is
the default for loess.
|
map |
A logical value indicating whether or not to include
a map outline. The default is to include an outline
if type = "image" and the fields library
is loaded.
|
If the fields library is loaded, a legend (and optionally
a map outline) will be included in image plots.
An image, contour, or perspective plot of the forecast.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA: An R Package for Probabilistic Forecasting
using Ensembles and Bayesian Model Averaging,
Technical Report No. 516, Department of Statistics, University of
Washington, August 2007.
data(srft)
labels <- c("CMCG","ETA","GASP","GFS","JMA","NGPS","TCWB","UKMO")
srftData <- ensembleData( forecasts = srft[,labels],
dates = srft$date, observations = srft$obs,
latitude = srft$lat, longitude = srft$lon)
## Not run:
bmaFit <- ensembleBMA( srftData, date = "2004012900",
model = "normal",trainingRule = list(length = 25, lag = 2))
bmaForc <- quantileForecastBMA( bmaFit, srftData, date = "2004021400",
quantiles = c(.1, .5. .9))
obs <- srftData$date == "2004012900"
lat <- srftData$latitude[obs]
lon <- srftData$longitude[obs]
plotBMAforecast( bmaForc[,"0.5"], lat, lon,
type = "contour", interpolate = TRUE)
title("Median Forecast")
plotBMAforecast( srftData$obs[obs], lat, lon,
type = "contour", interpolate = TRUE)
title("Observed Surface Temperature")
data(srftGrid)
memberLabels <- c("CMCG","ETA","GASP","GFS","JMA","NGPS","TCWB","UKMO")
srftGridData <- ensembleData(forecasts = srftGrid[,memberLabels],
latitude = srftGrid[,"latitude"], longitude = srftGrid[,"longitude"])
gridForc <- quantileForecastBMA( bmaFit, srftGridData,
date = "2004021400", quantiles = c( .1, .5, .9))
library(fields)
plotBMAforecast(gridForc[,"0.5"],lon=srftGridData$lon,
lat=srftGridData$lat,type="image",col=rev(rainbow(100,start=0,end=0.85)))
title("Median Grid Forecast for Surface Temperature", cex = 0.5)
probFreeze <- cdfBMA( bmaFit, srftGridData, date = "2004021400",
value = 273.15)
plotBMAforecast(probFreeze, lon=srftGridData$lon, lat=srftGridData$lat,
type="image",col=gray((32:0)/32))
title("Probability of Freezing", cex = 0.5)
## End(Not run)