My DataFrame looks like this
poly-d bias var irreducible_error mse
Float64 Float64 Float64 Float64 Float64
0.0 0.186675 0.00042362 0.01 0.197262
1.0 0.0411247 0.000571736 0.01 0.051418
2.0 0.0434971 0.000984634 0.01 0.0545198
3.0 1.85323e-5 0.00144489 0.01 0.011316
4.0 4.88342e-5 0.00140199 0.01 0.0110883
5.0 1.6811e-6 0.00220646 0.01 0.0121978
6.0 1.05011e-5 0.00213953 0.01 0.0122632
7.0 4.17582e-9 0.00250119 0.01 0.0119988
8.0 0.000105478 0.00314533 0.01 0.0135444
9.0 4.51571e-6 0.00291042 0.01 0.0127425
10.0 1.07721e-6 0.00381774 0.01 0.0142986
11.0 5.78187e-6 0.00398581 0.01 0.0147937
12.0 7.96598e-7 0.00461939 0.01 0.0134817
13.0 2.95272e-5 0.00490143 0.01 0.0136078
14.0 1.03758e-5 0.00558545 0.01 0.0160345
15.0 1.36473e-6 0.00655103 0.01 0.0157278
16.0 3.65874e-6 0.00706143 0.01 0.0185338
17.0 4.49153e-6 0.00653606 0.01 0.0156871
18.0 2.79525e-6 0.00774455 0.01 0.0178697
19.0 3.0369e-5 0.00919628 0.01 0.018715
I take the sum of the columns the Julia way like this
sum(Matrix(res[:,["bias","var","irreducible_error"]]),dims=2)
I'd like to assign this output as a new column mse_explained.
It seems like this should work
res[:mse_explained] = sum(Matrix(res[:,["bias","var","irreducible_error"]]),dims=2)
But there's a DimensionMismatch. I checked and sum(Matrix(res[:,["bias","var","irreducible_error"]]),dims=2) is a 20x1 array so I'm not sure why Julia is having a hard time.
I tried this as well which didn't work
res[:mse_explained] .= sum(Matrix(res[:,["bias","var","irreducible_error"]]),dims=2)