vgram {fields}  R Documentation 
Computes pairwise squared differences as a function of distance. Returns either raw values or statistics from binning.
vgram(loc, y, id=NULL, d=NULL, lon.lat=FALSE, dmax=NULL, N=NULL, breaks=NULL)
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 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. 
A list with these components.
vgram 

d 

call 

stats 

centers 
Bin centers
See any standard reference on spatial statistics. For example Cressie, Spatial Statistics
vgram.matrix bplot.xy, vgram.matrix
# # 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)