The post wasn't about this, but I must say I love to see support for Cox models coming to Julia!
Regards Le vendredi 23 janvier 2015 à 14:37 -0800, Ben Kuhn a écrit : > Hi Julia folks, > > > Trying to get a feel for Julia, I decided to write a basic Cox > proportional hazards model. While I was implementing the routines to > calculate the gradient and Hessian of the model's log-likelihood, I > realized that I was making a ton of array "type errors" (dimension > errors) that weren't being caught by Julia's type system because the > array operations that I was using were too overloaded. Here are some > examples of what I mean: > > > - For a 2d array X, trying to get a row with X[i] instead of X[i,:]. > This returns a scalar, but if you try to add it to another row vector > you'll silently get a different row vector than you expected instead > of a failure. > - Reversing the order of y * transpose(y) (for y an array) to get the > scalar product instead of the outer product (similar silent failure as > above). > - Doing y .* z when one side is a row vector and the other side is a > column vector, and forgetting to transpose them, causing an accidental > outer product (via broadcasting) instead of elementwise product. This > one is harder to get a silent failure with but I'm pretty sure I > managed somehow. > > > I caught them all in testing (I think) and am fairly satisfied my code > does the right thing now, but I'd love to know if there are > conventions or tools I can use to limit these errors or catch them > earlier. I'm sure I'm missing a ton of stuff because I'm a Julia > novice (as well as a numerics novice in general). Does anyone have any > pointers? If it helps, the code I wrote is here. (It's correct now, or > at least agrees with R on a small but nontrivial model; the link is to > the function that computes the Hessian of the log-likelihood, which > was unsurprisingly the most error-prone part.) > > > Thanks! > Ben
