| 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 |
Variogram 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. |
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)