I was doing some principal component analysis on my macbook running Microsoft R 3.3.0 when I got some strange results. Double checking with a colleague, I've realised that the output of the SVD function was different from what I may get by using vanilla R. 

This is the reproducible result, please load the file (~78 Mb) [here](https://dl.dropboxusercontent.com/u/1249990/Cx.rda) 

With Microsoft R 3.3.0 (x86\_64-apple-darwin14.5.0) I get:

```
>> sv <- svd(Cx)
>> print(sv$d[1:10])

 [1] 122.73664 104.45759  90.52001  87.21890  81.28256  74.33418      73.29427  66.26472  63.51379
[10]  55.20763

```

Instead on a vanilla R (both with R 3.3 and R 3.3.1 on two different linux machines):

```
>> sv <- svd(Cx)
>> print(sv$d[1:10])

 [1] 122.73664  34.67177  18.50610  14.04483   8.35690   6.80784   6.14566
 [8]   3.91788   3.76016   2.66381

```

This is not happening with all the data, if I create some random matrix and I apply svd on that, I get the same results. So, it looks like a sort of numerical instability, isn't it? 

UPDATE: I've tried to compute the SVD on the same matrix (`Cx`) on the same machine (macbook) with the same version of R by using the `svd` package and finally I get the "right" numbers. Then it seems due to the svd implementation used by Microsoft R Open. 

UPDATE: The behaviour happens also on MRO 3.3.1