swh:1:snp:dfbb8ae2fb3632e8a8608d675a49ab1f110b7c6d
Tip revision: 8eab500c3dad2103092ff68706417414fe53e16b authored by Doug Nychka on 22 September 2009, 20:23:49 UTC
version 6.01
version 6.01
Tip revision: 8eab500
CO2.Rd
\name{CO2}
\alias{CO2}
\docType{data}
\title{Simulated global CO2 observations}
\description{
This is an example of moderately large spatial data set and consist of
simulated CO2 concentrations.
}
\usage{data(CO2)}
\format{
The format is a list with two components:
\itemize{
\item lon.lat: 26633x2 matrix of the longitude/latitude locations.
These are a subset of a larger lon/lat grid (see example below).
\item y: 26633 CO2 concentrations in parts per million.
}
}
\details{
This data was provided by Dorit Hammerling and Randy Kawa as a
test example for the spatial analysis of remotely sensed (i.e. satellite) and
irregular observations.
}
\examples{
data(CO2)
#
# A quick look at the observations with world map
quilt.plot( CO2$lon.lat, CO2$y)
world( add=TRUE)
# Note high concentrations in Borneo (biomass burning), Amazonia and
# ... Michigan (???).
# spatial smoothing using the wendland compactly supported covariance
# see help( fastTps) for details
# First smooth using locations and Euclidean distances
# note taper is in units of degrees
out<-fastTps( CO2$lon.lat, CO2$y, theta=4, lambda=2.0)
#summary of fit note about 7300 degrees of freedom
# associated with fitted surface
print( out)
# image plot on a grid (this takes a while)
surface( out, type="I", nx=300, ny=150)
# smooth with respect to great circle distance
out2<-fastTps( CO2$lon.lat, CO2$y, lon.lat=TRUE,lambda=1.5, theta=4*68)
print(out2)
#surface( out2, type="I", nx=300, ny=150)
# these data are actually subsampled from a grid.
# create the image object that holds the data
#
# Note: this code below would not work if some marginal lons or lats
# from the parent grid are missing in the obs.
x<- unique( CO2$lon.lat[,1])
y<- unique( CO2$lon.lat[,2])
m<- length( x)
n<- length(y)
z<- matrix( NA, nrow=m, ncol=n)
ind<- cbind( match( CO2$lon.lat[,1], x), match( CO2$lon.lat[,2], y))
z[ind] <- CO2$y
# look at gridded object.
image.plot(x,y, z)
# to predict _exactly_ on this grid for the second fit;
# (this take a while)
look<- predict.surface( out2, grid.list=list( x=x, y=y))
image.plot(look)
}
\keyword{datasets}