# Regression with variables in array

**URL:** <https://discourse.julialang.org/t/regression-with-variables-in-array/92280>\
**Category:** Statistics\
**Created:** [December 28, 2022, 8:30pm UTC](https://discourse.julialang.org/t/regression-with-variables-in-array/92280 "2022-12-28T20:30:23Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![Agnes\_H](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/agnes_h/32/35286_2.png) [@Agnes\_H](https://discourse.julialang.org/u/Agnes_H)\
**Post date:** [December 28, 2022, 8:30pm UTC](https://discourse.julialang.org/t/regression-with-variables-in-array/92280/1 "2022-12-28T20:30:23Z")

</div>

I apologize for reheating this old topic, but how do I deal with this when both variables are dependent on a list? I tried using term, but was unable to. The only way I was able to receive meaningful output was by using eval.

I am trying to use **RegressionTables.jl** to turn it into format that I am able to export, but to no avail so far.

```julia
for i in list
		for j in list_2
			calc = (reg(DF1, @eval @formula($i ~ $j)))
			print(calc)
		end
	end

```

---

<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [December 29, 2022, 5:35pm UTC](https://discourse.julialang.org/t/regression-with-variables-in-array/92280/2 "2022-12-29T17:35:24Z")

</div>

It’s not clear to me what `list` and `list_2` are, and the fact that you are calling `reg` suggests that you are using some additional package (although as long as it relies on StatsModels you’ll be fine).

The case when both right hand side and left hand side variables come from some sort of iterator is a straightforward extension of what I posted above:

```julia
julia> using DataFrames, GLM

julia> df = DataFrame([:y1, :y2, :x1, :x2, :x3] .=> eachcol(rand(3, 5)))
3×5 DataFrame
 Row │ y1 y2 x1 x2 x3       
     │ Float64 Float64 Float64 Float64 Float64  
─────┼───────────────────────────────────────────────────
   1 │ 0.406401 0.974452 0.57843 0.553727 0.816638
   2 │ 0.429666 0.873084 0.808852 0.0404561 0.728702
   3 │ 0.998661 0.865624 0.269401 0.6146 0.630912

julia> rhs = ["y1", "y2"]; lhs = ["x1", "x2", "x3"];

julia> for y ∈ rhs
           for x ∈ lhs
               println(lm(term(y) ~ term(x), df))
           end
       end
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y1 ~ 1 + x1

Coefficients:
────────────────────────────────────────────────────────────────────────
                Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────
(Intercept) 1.22034 0.336678 3.62 0.1714 -3.05755 5.49824
x1 -1.10239 0.566025 -1.95 0.3020 -8.29441 6.08964
────────────────────────────────────────────────────────────────────────
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y1 ~ 1 + x2

Coefficients:
───────────────────────────────────────────────────────────────────────
                Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
───────────────────────────────────────────────────────────────────────
(Intercept) 0.374734 0.423848 0.88 0.5391 -5.01076 5.76023
x2 0.587803 0.886367 0.66 0.6272 -10.6746 11.8502
───────────────────────────────────────────────────────────────────────
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y1 ~ 1 + x3

Coefficients:
────────────────────────────────────────────────────────────────────────
                Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────
(Intercept) 2.96036 1.16501 2.54 0.2387 -11.8425 17.7632
x3 -3.23784 1.59728 -2.03 0.2918 -23.5332 17.0575
────────────────────────────────────────────────────────────────────────
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y2 ~ 1 + x1

Coefficients:
────────────────────────────────────────────────────────────────────────
                 Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────
(Intercept) 0.886426 0.132183 6.71 0.0942 -0.793119 2.56597
x1 0.0325252 0.222227 0.15 0.9075 -2.79114 2.85619
────────────────────────────────────────────────────────────────────────
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y2 ~ 1 + x2

Coefficients:
─────────────────────────────────────────────────────────────────────────
                 Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
─────────────────────────────────────────────────────────────────────────
(Intercept) 0.876624 0.0860848 10.18 0.0623 -0.217188 1.97044
x2 0.0689036 0.180024 0.38 0.7673 -2.21852 2.35632
─────────────────────────────────────────────────────────────────────────
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y2 ~ 1 + x3

Coefficients:
───────────────────────────────────────────────────────────────────────
                Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
───────────────────────────────────────────────────────────────────────
(Intercept) 0.4862 0.225802 2.15 0.2768 -2.38289 3.35529
x3 0.576478 0.309584 1.86 0.3137 -3.35716 4.51012
───────────────────────────────────────────────────────────────────────

```

As a general rule of thumb, if you find yourself using `@eval` you’re likely doing it wrong.

---

<div class="post-metadata">

**Author:** ![Agnes\_H](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/agnes_h/32/35286_2.png) [@Agnes\_H](https://discourse.julialang.org/u/Agnes_H)\
**Post date:** [December 30, 2022, 10:51am UTC](https://discourse.julialang.org/t/regression-with-variables-in-array/92280/3 "2022-12-30T10:51:38Z")

</div>

Basically I have a dataframe with two blocks of data that should be iterated with each other individually.

`list` and `list_2` are referring to those two blocks. `reg` is a command used in `RegressionTables.jl` which is based on `GLM.jl`.

Thank you very much, the performance is so much better with your solution.