vgram {fields} | R Documentation |

`vgram`

computes pairwise squared differences as a function of distance.
Returns an S3 object of class "vgram" with either raw values or statistics from
binning. `crossCoVGram`

is the same as `vgram`

but differences are
taken across different variables rather than the same variable.

`plot.vgram`

and `boxplotVGram`

create lineplots and boxplots of
vgram objects output by the `vgram`

function. `boxplotVGram`

plots
the base R boxplot function, and plots estimates of the mean over the boxplot.

The `getVGMean`

function returns the bin centers and means of the `vgram`

object based on the bin breaks provided by the user.

vgram(loc, y, id = NULL, d = NULL, lon.lat = FALSE, dmax = NULL, N = NULL, breaks = NULL, type=c("variogram", "covariogram", "correlogram")) crossCoVGram(loc1, loc2, y1, y2, id = NULL, d = NULL, lon.lat = FALSE, dmax = NULL, N = NULL, breaks = NULL, type=c("cross-covariogram", "cross-correlogram")) boxplotVGram(x, N=10, breaks = pretty(x$d, N, eps.correct = 1), plot=TRUE, plot.args, ...) ## S3 method for class 'vgram' plot(x, N=10, breaks = pretty(x$d, N, eps.correct = 1), add=FALSE, ...) getVGMean(x, N = 10, breaks = pretty(x$d, N, eps.correct = 1))

`loc` |
Matrix where each row is the coordinates of an observed point of the field |

`y` |
Value of the field at locations |

`loc1` |
Matrix where each row is the coordinates of an observed point of field 1 |

`loc2` |
Matrix where each row is the coordinates of an observed point of field 2 |

`y1` |
Value of field 1 at locations |

`y2` |
Value of field 2 at locations |

`id` |
A 2 column matrix that specifies which variogram differnces to find. If omitted all possible pairing are found. This can used if the data has an additional covariate that determines proximity, for example a time window. |

`d` |
Distances among pairs indexed by id. If not included distances from from directly from loc. |

`lon.lat ` |
If true, locations are assumed to be longitudes and latitudes and distances found are great circle distances (in miles see rdist.earth). Default is FALSE. |

`dmax` |
Maximum distance to compute variogram. |

`N` |
Number of bins to use. |

`breaks` |
Bin boundaries for binning variogram values. Need not be equally spaced but must be ordered. |

`x` |
An object of class "vgram" (an object returned by |

`add` |
If |

`plot` |
If |

`plot.args` |
Additional arguments to be passed to |

`type` |
One of "variogram", "covariogram", "correlogram", "cross-covariogram", and
"cross-correlogram". |

`...` |
Additional argument passed to |

`vgram`

and `crossCoVGram`

return a "vgram" object containing the
following values:

`vgram` |
Variogram or covariogram values |

`d` |
Pairwise distances |

`call` |
Calling string |

`stats` |
Matrix of statistics for values in each bin. Rows are the summaries returned by the stats function or describe. If not either breaks or N arguments are not supplied then this component is not computed. |

`centers` |
Bin centers. |

If `boxplotVGram`

is called with `plot=FALSE`

, it returns a
list with the same components as returned by `bplot.xy`

See any standard reference on spatial statistics. For example Cressie, Spatial Statistics

John Paige, Doug Nychka

# # compute variogram for the midwest ozone field day 16 # (BTW this looks a bit strange!) # data( ozone2) good<- !is.na(ozone2$y[16,]) x<- ozone2$lon.lat[good,] y<- ozone2$y[16,good] look<-vgram( x,y, N=15, lon.lat=TRUE) # locations are in lon/lat so use right #distance # take a look: plot(look, pch=19) #lines(look$centers, look$stats["mean",], col=4) brk<- seq( 0, 250,,25) ## or some boxplot bin summaries boxplotVGram(look, breaks=brk, plot.args=list(type="o")) plot(look, add=TRUE, breaks=brk, col=4) # # compute equivalent covariogram, but leave out the boxplots # look<-vgram( x,y, N=15, lon.lat=TRUE, type="covariogram") plot(look, breaks=brk, col=4) # # compute equivalent cross-covariogram of the data with itself #(it should look almost exactly the same as the covariogram of #the original data, except with a few more points in the #smallest distance boxplot and points are double counted) # look = crossCoVGram(x, x, y, y, N=15, lon.lat=TRUE, type="cross-covariogram") plot(look, breaks=brk, col=4)

[Package *fields* version 8.4-1 Index]