spatialEco (version 1.3-0)

raster.modifed.ttest: Dutilleul moving window bivariate raster correlation

Description

A bivarate raster correlation using Dutilleul's modified t-test

Usage

raster.modifed.ttest(
  x,
  y,
  x.idx = 1,
  y.idx = 1,
  d = "AUTO",
  sub.sample = FALSE,
  type = "hexagon",
  p = 0.1,
  size = NULL
)

Arguments

x

x raster for correlation, SpatialPixelsDataFrame or SpatialGridDataFrame object

y

y raster for correlation, SpatialPixelsDataFrame or SpatialGridDataFrame object

x.idx

Index for the column in the x raster object

y.idx

Index for the column in the y raster object

d

Distance for finding neighbors

sub.sample

Should a sub-sampling approach be employed (TRUE/FALSE)

type

If sub.sample = TRUE, what type of sample (random or hexagon)

p

If sub.sample = TRUE, what proportion of population should be sampled

size

Fixed sample size

Value

A SpatialPixelsDataFrame or SpatialPointsDataFrame with the following attributes:

• corr Correlation

• Fstat The F-statistic calculated as [degrees of freedom * unscaled F-statistic]

• p.value p-value for the test

• moran.x Moran's-I for x

• moran.y Moran's-I for y

References

Clifford, P., S. Richardson, D. Hemon (1989), Assessing the significance of the correlation between two spatial processes. Biometrics 45:123-134. Dutilleul, P. (1993), Modifying the t test for assessing the correlation between two spatial processes. Biometrics 49:305-314.

Examples

# NOT RUN {
library(gstat)                                         
library(sp)                                            
                                                        
data(meuse)                                            
data(meuse.grid)                                       
coordinates(meuse) <- ~x + y                           
coordinates(meuse.grid) <- ~x + y                      
                                                        
# GRID-1 log(copper):                                              
v1 <- variogram(log(copper) ~ 1, meuse)                  
x1 <- fit.variogram(v1, vgm(1, "Sph", 800, 1))           
G1 <- krige(zinc ~ 1, meuse, meuse.grid, x1, nmax = 30)
gridded(G1) <- TRUE                                      
G1@data = as.data.frame(G1@data[,-2])

# GRID-2 log(elev):                                              
v2 <- variogram(log(elev) ~ 1, meuse)                  
x2 <- fit.variogram(v2, vgm(.1, "Sph", 1000, .6))        
G2 <- krige(elev ~ 1, meuse, meuse.grid, x2, nmax = 30)
gridded(G2) <- TRUE    
G2@data <- as.data.frame(G2@data[,-2])
G2@data[,1] <- G2@data[,1]

corr <- raster.modifed.ttest(G1, G2)
  plot(raster::raster(corr,1))
  
corr.rand <- raster.modifed.ttest(G1, G2, sub.sample = TRUE, type = "random")	 
corr.hex <- raster.modifed.ttest(G1, G2, sub.sample = TRUE, d = 500, size = 1000)	
  head(corr.hex@data)
    bubble(corr.hex, "corr") 
# }
# NOT RUN {
# }