CO2 {fields} | R Documentation

## 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:

- lon.lat: 26633x2 matrix of the longitude/latitude locations. These are a subset of a larger lon/lat grid (see example below).
- 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)

[Package fields version 6.01 Index]