I have been implementing a workflow previously devised ArcGIS 10.2 in R. The process involves a discretization of an integer raster (the cells are counts) through quantile reclassification.
I've discovered that the ArcGIS quantile classify tool produces completely different results to different R packages and GRASS.
For quantile classification where n=6 produces the following breaks(showing break value start points): ArcGIS: 0,1,3,6,11,97
R (classInt package): 0,0,0,0,0,0,97 - which effectively yields a 2 class map rather than the desired 6 class map.
My raster data does include a large proportion of a single value (0 in my case) which must be skewing the allocation of equal number of values into quantile bins.
So I guess ArcGIS is doing something more to determine the breaks. Does anyone know what this is and how I may implement it myself?
FYI, the statistics of the raster are: Min = 0 Max = 97 Mean = 0.601 SD = 3.306
Note I've tried various different R packages and functions including classInt, binr and cut2 (table(cut2(rValues, g=nClassBreaks,levels.mean = TRUE)), and also GRASS (r.reclass). All give same or very similar results to each other. But ArcGIS is doing something wildly different.