Do we have anything like r’s na.rm attribute in Julia for Turing.jl for ignoring missing values? I have a data with missing values which is causing issue during sampling.

Don’t know `Turing.jl`

, but the julia way to “ignore” missing values is to “skip” them:

```
julia> x = [1,missing,3]
3-element Vector{Union{Missing, Int64}}:
1
missing
3
julia> sum(x)
missing
julia> sum(skipmissing(x))
4
```

2 Likes

This I know but I’m looking for something like this as shown below.

```
for i in 1:n
for j in 1:size(data[i])[2]
data[i][:,j] ~ MvNormal(predicted[i] , σ)
end
end
```

Thanks for reply though

How valid is this ?

```
for j in 1:size(data)[2]
for k in 1:size(data)[1]
if ismissing(data[k,j]) == true
continue
end
data[k,j] ~ Normal(predicted[k] , σ*(data[k,j] .+10^-5))
end
end
```

Basically yes, but the more idiomatic way may be with a guard like this:

```
for j in axes(data, 2)
for k in axes(data, 1)
ismissing(data[k, j]) && continue
data[k,j] ~ Normal(predicted[k] , σ*(data[k,j] .+10^-5))
end
end
```