quilt.plot {fields} | R Documentation |

Given a vector of z values associated with 2-d locations this function produces an image-like plot where the locations are discretized to a grid and the z values are coded as a color level from a color scale.

quilt.plot(x, y, z, nx = 64, ny = 64, grid = NULL, add.legend=TRUE,add=FALSE, nlevel=64, col = tim.colors(nlevel), nrow=NULL, ncol=NULL,FUN = NULL, plot=TRUE, ...)

`x` |
A vector of the x coordinates of the locations -or- a a 2 column matrix of the x-y coordinates. |

`y` |
A vector of the y coordinates -or- if the locations are passed in x the z vector |

`z` |
Values of the variable to be plotted. |

`nlevel` |
Number of color levels. |

`nx` |
Number of grid boxes in x if a grid is not specified. |

`ny` |
Number of grid boxes in y. |

`nrow` |
Depreciated, same as nx. |

`ncol` |
Depreciated same as ny. |

`grid` |
A grid in the form of a |

`add.legend` |
If TRUE a legend color strip is added |

`add` |
If FALSE add to existing plot. |

`col` |
Color scale for the image, the default is tim.colors – a pleasing spectrum. |

`plot` |
If FALSE just returns the image object instead of plotting it. |

`FUN` |
The function to apply to values that are common to a grid box. The default is to find the mean. (see |

`...` |
arguments to be passed to the image.plot function |

This function combines the discretization to an image by the function
`as.image`

and is then graphed by `image.plot`

.
Locations that fall into the same grid box will have their z values
averaged.

A similar function exists in the lattice package and produces good looking plots. The advantage of this fields version is that it uses the standard R graphics functions and is written in R code. Also, the aggregation to average values for z values in the same grid box allows for different choices of grids. If two locations are very close, separating them could result in very small boxes.

As always, legend placement is never completely automatic. Place the
legend independently for more control, perhaps using `image.plot`

in tandem with `split.screen`

or enlarging the plot margin
See `help(image.plot)`

for examples of this function and these
strategies.

D.Nychka

as.image, image.plot, lattice, persp, drape.plot

data( ozone2) # plot 16 day of ozone data set quilt.plot( ozone2$lon.lat, ozone2$y[16,]) US( add=TRUE, col="grey", lwd=2) # # and ... if you are fussy # do it again # quilt.plot( ozone2$lon.lat, ozone2$y[16,],add=TRUE) # to draw over the state boundaries. # ### adding a common legend strip "by hand" ## and a custom color table coltab<- two.colors( 256, middle="grey50" ) par( oma=c( 0,0,0,5)) # save some room for the legend set.panel(2,2) zr<- range( ozone2$y, na.rm=TRUE) for( k in 1:4){ quilt.plot( ozone2$lon.lat, ozone2$y[15+k,], add.legend=FALSE, zlim=zr, col=coltab, nx=40, ny=40) US( add=TRUE) } par( oma=c(0,0,0,1)) image.plot(zlim=zr,legend.only=TRUE, col=coltab) # may have to adjust number of spaces in oma to make this work.

[Package *fields* version 8.4-1 Index]