using ifelse() in Nimble models

[Brook Milligan]

I have a model that uses a vector X of constants, some of which are NA. In the model, I calculate a deterministic function like:

x[1:n] <- ifelse(is.na(X[1:n]),A[1:n],B[1:n])

When X is passed to nimble as a constant, it complains with:

R function 'is.na' has arguments that cannot be evaluated in the code ‘is.na(X[1:n])'. Either the function must be a nimbleFunction or values for the following inputs must be specified as constants in the model: X[1:n].

The second part of this is misleadingly wrong, because X actually is passed as a constant. Passing as data yields the same.

Note that A and B above are expressions involving random variables and therefore cannot be calculated outside the nimble model.

How should this be handled? Is it possible?

Thanks for your help.

Cheers,
Brook

[Daniel Turek]

Brook, it's true the error message is misleading.  The issue is stemming from the use of is.na() - even evaluated on X passed as a contestant - inside the ifelse() function, which also requires constant arguments.

There are certainly other ways of accomplishing what you're after, perhaps even more elegant, but below is a sketch of one idea.  If this doesn't work, I'm sure we can find other approaches.

Cheers,

Daniel

library(nimble)

X <- c(2, 4, 7, NA, 8, NA, 10)

NAs <- which(is.na(X))
n_NAs <- length(NAs)

nonNAs <- which(!is.na(X))
n_nonNAs <- length(nonNAs)

code <- nimbleCode({
    ## placeholders for definitinos of A and B:
    A[1:10] <- 1:10
    B[1:10] <- 11:20
    ##
    for(i in 1:n_NAs)
        x[NAs[i]] <- A[NAs[i]]
    for(i in 1:n_nonNAs)
        x[nonNAs[i]] <- B[nonNAs[i]]
})

constants <- list(n_NAs = n_NAs, NAs = NAs,
                  n_nonNAs = n_nonNAs, nonNAs = nonNAs)

Rmodel <- nimbleModel(code, constants)

Rmodel$A

 [1]  1  2  3  4  5  6  7  8  9 10

Rmodel$B
 [1] 11 12 13 14 15 16 17 18 19 20

Rmodel$x

[1] 11 12 13  4 15  6 17

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