spatialEco (version 1.3-2)

cross.tab: Class comparison between two nominal rasters

Description

Compares two categorical rasters using Cohen's Kappa (d) or paired t-test statistic(s)

Usage

cross.tab(x, y, values = NULL, labs = NULL, pct = FALSE, ...)

Arguments

x

rasterLayer class object

y

rasterLayer class object to compare to x

values

Expected values in both rasters

labs

Labels associated with values argument

pct

(TRUE/FALSE) return proportions rather than counts

...

Additional arguments

Value

a table with the cross tabulated counts

References

Pontius Jr, R.G., Shusas, E., McEachern, M. (2004). Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment 101(2):251-268.

See Also

raster::crosstab

Examples

# NOT RUN {
 library(sp)
 library(raster)
   data(meuse.grid)
 
 r1 <- sp::SpatialPixelsDataFrame(points = meuse.grid[c("x", "y")], 
                                  data = meuse.grid)
 lulc2010 <- raster(r1)
   na.idx <- which(!is.na(lulc2010[]))
     lulc2010[na.idx] <- sample(1:5, length(na.idx), replace=TRUE)
  
 lulc2020 <- raster(lulc2010)
   lulc2020[na.idx] <- sample(1:5, length(na.idx), replace=TRUE)
 
 ( v = sort(unique(c(lulc2010[], lulc2020[]))) )
 l = c("water","urban","forest",
       "ag","barren")

 cross.tab(lulc2010, lulc2020) 
 cross.tab(lulc2010, lulc2020, values = v, labs = l)
 cross.tab(lulc2010, lulc2020, values = v, labs = l, pct=TRUE)

# Create asymmetrical classes 
lulc2020[na.idx] <- sample(c(1,2,4,5), length(na.idx), replace=TRUE)

cross.tab(lulc2010, lulc2020, values = v, labs = l, pct=TRUE)

# }