I am performing some simulations where a statistical model is used first to simulate a new response then to fit the response then to extract quantities of interest. The current code is function parametricbootstrap(N::Integer, m::LinearMixedModel{T}, rng::AbstractRNG=Random.GLOBAL_RNG, prop…

Thanks to all. I ended up using a thunk as suggested by @tkf and it works fine. """ parametricbootstrap(rng::AbstractRNG, nsamp::Integer, m::LinearMixedModel, properties=(:objective, :σ, :β, :θ); β = m.β, σ = m.σ, θ = m.θ) Perform `nsamp` parametric bootstrap replication fits of `m`, …

I don’t know if this is necessarily better but I like to use ntuple(_ -> f(x), N) for this

Note that the ntulple solution will be type-unstable (and therefore potentially slower) if the value of N is not known to the compiler. So if N is a literal or a constant, it’s probably ok, but if N is a variable then the OneTo(N) approach is better.

I think it’s fine, but for long expressions like this, I find that having 1:N at the very end reduces readability – it’s off the screen in your code above for example. There are several other ways of doing this, one idiom that I often use is: val = 1:N .|>_-> NamedTuple{props, ptype}(...) However…

Don’t bother with OneTo in this case; using 1:N is surely going to give you exactly the same performance. There is also the foreach function which you can use if you want to discard your results. Since you’re already broadcasting in this case, you may be able to structure your broadcast such that …

The usage of the comprehension syntax looks fine to me. But if you want to try something else: val = map(1:N) do _ NamedTuple{props, ptype}(...) end [image] mbauman: you may be able to structure your broadcast such that it does the repetition for you In many cases, this could be a grea…

I didn’t know this. Thank you!

[image] tkf: In many cases, this could be a great solution. But I think this is not good for the case here because the computation involves side-effect (presumably due to rng ). I suppose it’s better to use broadcasting only for “pure” functions? I love writing things like A .= rand.(). Whil…

I typically use comprehension as well but what’s the difference between 1:N and Base.OneTo(N)?

Base.OneTo(N) statically encodes the fact that it’s starting at 1 within the type itself. This was needed to eek the very last bit of performance out of array iteration and axes computations and it now distinguishes one-based arrays from offset arrays. We now have very powerful constant propagatio…

There are many ways to write this but I personally use for _ = 1:n; ...; end since it’s considerably easier on the compiler than idioms that use closures.

Is there an idiom for "do this N times"?

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Is there an idiom for "do this N times"?

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