# How can I substitute NaN in a GLM model with zeroes?

**URL:** <https://discourse.julialang.org/t/how-can-i-substitute-nan-in-a-glm-model-with-zeroes/102038>\
**Category:** Statistics\
**Tags:** regression, glm, linear-regression, modelling\
**Created:** [July 24, 2023, 7:44pm UTC](https://discourse.julialang.org/t/how-can-i-substitute-nan-in-a-glm-model-with-zeroes/102038 "2023-07-24T19:44:03Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![bertulli](https://avatars.discourse-cdn.com/v4/letter/b/ecc23a/32.png) [@bertulli](https://discourse.julialang.org/u/bertulli)\
**Post date:** [July 24, 2023, 7:44pm UTC](https://discourse.julialang.org/t/how-can-i-substitute-nan-in-a-glm-model-with-zeroes/102038/1 "2023-07-24T19:44:03Z")

</div>

Hi all!

I have a linear model, created with `GLM.jl`. Since I correlated some variables with a categorical one, I have lots of `NaN` as coefficients, when the data are provided for only some of the categorical values:

```julia
julia> model
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

Base power mean (W) ~ 1 + mnemonic + APSR (s flag) + Is conditional + Dest reg == source reg + Barrel shift amount + Has barrel shift + Has immediate operand + mnemonic & Binary weight + Barrel shift amount & Has barrel shift + mnemonic & APSR (s flag) + mnemonic & Is conditional + mnemonic & Dest reg == source reg + mnemonic & Barrel shift amount + mnemonic & Has barrel shift + mnemonic & Has immediate operand + mnemonic & Barrel shift amount & Has barrel shift + mnemonic & Binary weight & APSR (s flag) + mnemonic & Binary weight & Is conditional + mnemonic & Binary weight & Dest reg == source reg + mnemonic & Binary weight & Barrel shift amount + mnemonic & Binary weight & Has barrel shift + mnemonic & Binary weight & Has immediate operand + mnemonic & Binary weight & Barrel shift amount & Has barrel shift

Coefficients:
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
                                                                                  Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.0804049 0.0305935 2.63 0.0086 0.0204363 0.140373
mnemonic: add -0.0209991 0.0306201 -0.69 0.4929 -0.0810198 0.0390215
mnemonic: and 0.000384739 0.0309979 0.01 0.9901 -0.0603766 0.0611461
mnemonic: asr -5.10902e-6 0.0385573 -0.00 0.9999 -0.0755842 0.0755739
mnemonic: b 0.00127954 0.00981586 0.13 0.8963 -0.0179612 0.0205203
mnemonic: bfc 0.0 NaN NaN NaN NaN NaN
mnemonic: bfi 0.0 NaN NaN NaN NaN NaN
mnemonic: bic 0.0 NaN NaN NaN NaN NaN

```

This means that, when I do a prediction with variables for which the model is not trained for, I got an error. **Is there a way I can substitute each `NaN` with `0.0`** , so that the model doesn’t throw error anymore, but just ignores the missing coefficients?

Thanks!