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Cumulative distribution functions allow you to answer the questions, what percent of my sample is less than or greater than a value. For example I work with sage-brush cover frequently. With a cumulative distribution function I can answer the question, what proportion of my plots with sagebrush have greater than 90% cover.

A little snippet that helps subset raster extents.

A collection of tools and documentation on remote sensing.

Below is a method to use the raster package extract() function to get a subet of rasterBrick values. To be specific, I need to extract all raster values that are within a polygon boundary. In the past I have used crop(), mask() and then the getValues() functions from the raster package to subset data values within a polygon. But that method returns a data frame with a ton of NA values (anything outside of the crop area in the raster is an NA). This is fine most of the time but the current project that I am working on requires almost all of the memory on my computer. I'm working with extremely large rasters (2Gb). Removing the NA values after the crop(), mask(), and getValues() process crashes my computer. So I need a more effecient process.

Resources to understand and run random forests in r.

A quick little snippet for making a polygon with coordinates out of thin air in r.