Greetings Julians: I have produced a dataframe whose columns are all eltype ‘Float64’ as: Col1 = rand(1:0.01:500,6) Col2 = rand(1:0.01:500,6) Col3 = rand(1:0.01:500,6) Matr = hcat(Col1,Col2,Col3) I normalized the Matrix as: using LinearAlgebra matr_norm = la.normalize(Matr, 1000) Converted to …

That is saying that the second degrees of freedom of the F(q,n-k) test is not positive. n is the number of observations, and k is the number of regressors, including the constant, 3 in your case. So, it seems that your number of observations is 3 or less. What’s the number of rows of the data frame,…

When you create the data frame, the names are x1, x2, and x3: julia> MetroDF = DataFrame(matr_norm, :auto) 6×3 DataFrame Row │ x1 x2 x3 │ Float64 Float64 Float64 ─────┼─────────────────────────────── 1 │ 0.136877 0.223588 0.466813 2 │ 1.0 0.755881 …

Notice how you construct MetroDF, yet use Metro_DF in the regression. Perhaps you constructed Metro_DF another way that makes something (perhaps a component in the F-test?) go to 0.0, which causes the error. Also see the post above by @mcreel .

Thank you – @mcreel I followed your approach, however, am wondering if an imputation I performed to fill missing data records or a renaming, had some impact. The MetroDF is about the same, with the only difference being, the column names and some of the row values mirroring those nearby. I use…

Thank you @amrods – it was a transcription error but the original workspace did not have this error. Do you think the methods I addressed to @mcreel above potentially had some impact? Perhaps the scaling (1000) I used during the normalization step?

@mcreel The original DF (metro) had value set: [image] I converted all columns to float, as a general quality assurance check using the broadcast float function as: metro[!, [1,2,3] = float.(metro[!, [1,2,3]]) From here I applied the imputation I described as: METRO = Impute.interp(metro) |> …

@mcreel – thanks for your explanation here. The Imputation step I applied does not drop any missing values, instead it replaces the records with adjacent values (assuming the observations are based on the same individual). After the imputation, there are 6 rows.

The columns must not be linearly independent with this replacement strategy.

How might you troubleshoot this?

There is a large literature, just search for “rank deficient regression”. There’s no clear best solution to the problem.

Simple Linear Regression: Domain Error with 0.0

New to Julia

mcreel March 30, 2022, 11:19am 6

Sorry, can’t tell from this information. You need to provide a MWE as described by Please read: make it easier to help you

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Simple Linear Regression: Domain Error with 0.0

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