The problem with the Kolmogorov-Smirnov is you have discretized data. If you use the usual null distribution, it needs continuous data. Binning it makes the test conservative, often to an surprising degree.

However, you can still do a Kolmogorov-Smirnov, by performing a permutation test on the actual discrete CDFs.

There are a variety of ways to get your data into R. If it was me, I'd put just the data (depth bin, and the 4 sets of counts) into a sheet by itself, write as a .csv file and read that in, then use R to turn that back into 4 sets of individual binned depth measurements so that it's suitable for a permutation/randomization test (you can actually do it direct from the bin-counts, but it's conceptually much easier this way).

Whether the K-S is particularly suitable depends on what the alternatives of interest are.

It looks to me like you might need some form of GLM, but the interval censoring (caused by binning) will be a nuisance. (You could treat it as ordered categorical, perhaps.)

The problem with the Kolmogorov-Smirnov is you have discretized data. If you use the usual null distribution, it needs continuous data. Binning it makes the test conservative, often to a surprising degree.

However, you can still do a Kolmogorov-Smirnov, by performing a permutation test on the actual discrete CDFs.

There are a variety of ways to get your data into R. If it was me, I'd put just the data (depth bin, and the 4 sets of counts) into a sheet by itself, write as a .csv file and read that in, then use R to turn that back into 4 sets of individual binned depth measurements so that it's suitable for a permutation/randomization test (you can actually do it direct from the bin-counts, but it's conceptually much easier this way).

Whether the K-S is particularly suitable depends on what the alternatives of interest are.

It looks to me like you might need some form of GLM, but the interval censoring (caused by binning) will be a nuisance. (You could treat it as ordered categorical, perhaps.)

The problem with the Kolmogorov-Smirnov is you have discretized data. If you use the usual null distribution, it needs continuous data. Binning it makes the test conservative, often to an surprising degree.

However, you can still do a Kolmogorov-Smirnov, by performing a permutation test on the actual discrete CDFs.

There are a variety of ways to get your data into R. If it was me, I'd put just the data (depth bin, and the 4 sets of counts) into a sheet by itself, write as a .csv file and read that in, then use R to turn that back into 4 sets of individual binned depth measurements so that it's suitable for a permutation/randomization test (you can actually do it direct from the bin-counts, but it's conceptually much easier this way).

Whether the K-S is particularly suitable depends on what the alternatives of interest are.

The problem with the Kolmogorov-Smirnov is you have discretized data. If you use the usual null distribution, it needs continuous data. Binning it makes the test conservative, often to an surprising degree.

However, you can still do a Kolmogorov-Smirnov, by performing a permutation test on the actual discrete CDFs.

There are a variety of ways to get your data into R. If it was me, I'd put just the data (depth bin, and the 4 sets of counts) into a sheet by itself, write as a .csv file and read that in, then use R to turn that back into 4 sets of individual binned depth measurements so that it's suitable for a permutation/randomization test (you can actually do it direct from the bin-counts, but it's conceptually much easier this way).

Whether the K-S is particularly suitable depends on what the alternatives of interest are.

It looks to me like you might need some form of GLM, but the interval censoring (caused by binning) will be a nuisance. (You could treat it as ordered categorical, perhaps.)