The thing with julia is that most of the language is written in julia, so getting answers means just reading more julia code. So in GLM/src/lm.jl there is a function coeftable(..) that generates the above table. Taking the calculation from there, you get:
[ccdf(FDist(1,df_residual(lm1.model)),abs2(fval)) for fval in coef(lm1)./stderr(lm1)] which gives the Pr(>|t|) column: 0.551595 3.40919e-7 And as for how it is calculated - the formula shows it uses the F distribution. On Tuesday, October 14, 2014 12:21:17 PM UTC+3, [email protected] wrote: > > In below example of GLM, I want to get the Pr(>|t|) value 3.4e-7. How can I > get it? > Also, how this p-value be calculated? By F test or by Chisq test? I can > choose the test type in R but I can not choose in Julia. > > julia> using GLM, RDatasets > julia> form = dataset("datasets","Formaldehyde")6x2 > DataFrame|-------|------|--------|| Row # | Carb | OptDen || 1 | 0.1 | > 0.086 || 2 | 0.3 | 0.269 || 3 | 0.5 | 0.446 || 4 | 0.6 | > 0.538 || 5 | 0.7 | 0.626 || 6 | 0.9 | 0.782 | > julia> lm1 = fit(LinearModel, OptDen ~ Carb, form)Formula: OptDen ~ Carb > Coefficients: > Estimate Std.Error t value Pr(>|t|)(Intercept) 0.00508571 > 0.00783368 0.649211 0.5516Carb 0.876286 0.0135345 64.7444 > 3.4e-7 > >
