spatialEco (version 1.3-0)

class.comparison: Class comparison between two nominal rasters

Description

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

Usage

class.comparison(
  x,
  y,
  x.idx = 1,
  y.idx = 1,
  d = "AUTO",
  stat = "kappa",
  sub.sample = FALSE,
  type = "hexagon",
  p = 0.1,
  size = NULL
)

Arguments

x

First raster for comparison, SpatialPixelsDataFrame or SpatialGridDataFrame object

y

Second raster for comparison, 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, the default "AUTO" will derive a distance

stat

Statistic to use in comparison ("kappa", "t.test", "both")

sub.sample

Should a subsampling approach be employed (FALSE/TRUE)

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

If sub.sample = TRUE, alternate to proportion of population (p), using fixed sample size

Value

A SpatialPixelsDataFrame or SpatialPointsDataFrame with the following attributes:

• x x variable used to derive Kappa (d)

• y y variable used to derive Kappa (d)

• kappa Kappa (d) statistic

• t.test Paired t.test statistic (if stat = "t.test" or "both")

• p.value p-value of the paired t.test statistic (if stat = "t.test" or "both")

References

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20:37-46

Examples

# NOT RUN {
 library(sp)                                            
 library(raster)
                                                                                               
 data(meuse.grid)
 r1 <- sp::SpatialPixelsDataFrame(points = meuse.grid[c("x", "y")], data = meuse.grid)
   r1@data$class1 <- round(runif(nrow(r1), 1,5),0)
 r2 <- sp::SpatialPixelsDataFrame(points = meuse.grid[c("x", "y")], data = meuse.grid) 
r2@data$class2 <- round(runif(nrow(r2), 1,5),0)

 d <- class.comparison(r1, r2, x.idx = 8, y.idx = 8, stat="both")
   par(mfrow=c(2,2))
     plot(raster(d, layer=3), main="Kappa")
  plot(raster(d, layer=4), main="t.test")
  plot(raster(d, layer=5), main="t.test p-value")

 # Hexagonal sampling	  
 d.hex <- class.comparison(r1, r2, x.idx = 8, y.idx = 8, stat = "both",
                           sub.sample = TRUE, d = 500, size = 1000)
   sp::bubble(d.hex, "kappa")
    d.hex <- sp.na.omit(d.hex, col.name = "t.test")
  sp::bubble(d.hex, "t.test")

 # Random sampling		
 d.rand <- class.comparison(r1, r2, x.idx = 8, y.idx = 8, stat = "both", 
                            sub.sample = TRUE, type = "random")	
   sp::bubble(d.rand, "kappa")

# }
# NOT RUN {
# }