CO2 {fields} | R Documentation |
This is an example of moderately large spatial data set and consist of simulated CO2 concentrations.
data(CO2)
The format is a list with two components:
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.
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)