fields (version 1.2)

vgram: Finds a traditional or robust variogram for spatial data.

Description

Computes pairwise squared differences as a function of distance. Returns either raw values or statistics from binning.

Usage

vgram(loc, y, id=NULL, d=NULL, lon.lat=FALSE, dmax=NULL, N=NULL, breaks=NULL)

Arguments

loc

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

y

Value of the field at locations

id

A 2 column matrix that specifies which variogram differnces to find. If omitted all possible pairing are found. This can 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.

Value

• name= vgram> variogram values

  name= vgram> variogram values

  name= d> pairwise distances

  name= call> calling string

  name= 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.

  name= centers> Bin centers

References

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

See Also

vgram.matrix bplot.xy, vgram.matrix

Examples

#
# 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$d, look$vgram)
#lines(look$centers, look$stats["mean",], col=4)

brk<- seq( 0, 250,,25)
 
## or some boxplot bin summaries

bplot.xy( look$d, sqrt(look$vgram), breaks=brk,ylab="sqrt(VG)")
lines(look$centers, look$stats["mean",], col=4)