Are you looking for the fitted values? Is predict(OLS) what you are
looking for?
*julia> **X = [1;2;3.]*
*3-element Array{Float64,1}:*
* 1.0*
* 2.0*
* 3.0*
*julia> **Y = [1;0;1.]*
*3-element Array{Float64,1}:*
* 1.0*
* 0.0*
* 1.0*
*julia> **data = DataFrame(X=X,Y=Y)*
*3x2 DataFrame*
*|-------|-----|-----|*
*| Row # | X | Y |*
*| 1 | 1.0 | 1.0 |*
*| 2 | 2.0 | 0.0 |*
*| 3 | 3.0 | 1.0 |*
*julia> **OLS = glm(Y~X,data,Normal(),IdentityLink())*
*DataFrameRegressionModel{GeneralizedLinearModel,Float64}:*
*Coefficients:*
* Estimate Std.Error z value Pr(>|z|)*
*(Intercept) 0.666667 1.24722 0.534522 0.5930*
*X -4.16334e-16 0.57735 -7.21111e-16 1.0000*
*julia> **predict(OLS)*
*3-element Array{Float64,1}:*
* 0.666667*
* 0.666667*
* 0.666667*
