rtmb_tips

tips for (new) RTMB users

requirements for RTMB functions

• the objective function must be differentiable with respect to parameters (no if(), abs(), round(), min(), max() depending on parameters)
• have to implement exotic probability distributions yourself: anything that descends into C(++)/Fortran code will probably fail
• aliases to base functions made before call to library(RTMB) might not work
• use of <-[ (see here) etc.
  • specifically, if you use the c() function, or if you use the diag<- function (which sets the diagonal of a matrix) or the [<- function (which assigns values within a matrix), you need to add e.g. ADoverload("[<-") to the beginning of your function
• You can only "grow" empty vectors if initialized with numeric(0), not c() or NULL (i.e. x <- c(); x[1] <- 2 throws an error, but x <- numeric(0); x[1] <- 2 does work), see here. In any case it is better practice to preallocate instead of growing vectors, e.g. x <- numeric(1); x[1] <- 2 (see chapter 2 of the R Inferno).
• for matrix exponentials, you should use Matrix::expm() rather than expm::expm()
• RTMB is pickier than R about matrix types. You may need to use some combination of drop() and as.matrix() to convert matrices with dimension 1 in some direction (or Matrix matrices) back to vectors
• [[-indexing may be much faster than [-indexing: see here (and later messages in that thread)
• if you use cat() or print() to print out numeric values, the results may not make sense (you'll see a printout of RTMB's internal representation of autodiff-augmented numbers ...)

if transitioning from TMB

• RTMB uses %*% (as in base R), not * (as in C++) for matrix/matrix and matrix/vector multiplication

more general points

• as in C++ code, you probably don't have to worry as much about non-vectorized code (e.g. for loops) being very slow relative to vectorized codes: see vectorization_comp.R for the code behind this example:

Unit: microseconds
         expr   min    lq  mean median    uq  max neval cld
         vect   2.8   3.2   4.3    3.5   3.9  282 10000 a
    vect_RTMB  12.7  13.4  15.0   13.8  14.8  135 10000  b 
      forloop 223.0 240.1 245.0  244.2 248.7  382 10000   c
 forloop_RTMB  12.6  13.5  15.7   13.9  14.8 3835 10000  b 

• data handling (see here, here) (and very similar arguments from 2004 about MLE fitting machinery taking a data argument)
• have to handle prediction, tests, diagnostics, etc. etc. yourself (although the broom.mixed package does have a rudimentary tidy method for TMB objects (e.g. class(TMBobj) <- c("TMB", class(TMBobj)); broom.mixed::tidy(TMBobj))
• if you do something clever where you define your objective function in a different environment from where you call MakeADFun, you can use assign(..., environment(objective_function)) to make sure that the objective function can see any objects it needs to know about ...