suppose I have variables y, x, and weights w, eg
x = randn(100)
σ = 0.1
w = rand(length(x))
y = x + σ*randn(length(x))./w
and I want to estimate
1. the coefficient and the intercept (something around 0, and 1)
2. and some simple estimate for the standard deviation of the error term
(eg something around 0.1).
What's the recommended way of doing this in Julia? GLM allows me to fit
the model, but I could not figure out how to do it with weights, and I
have a hard time extracting residuals etc to calculate an estimator for
sigma (maybe I am doing it wrong, but residuals is not supported for the
fit object).
Julia 0.5-rc2, using latest DataFrames, last released GLM.
To be clear, this does what I want:
function myfit(y, X, w)
W = Diagonal(sqrt(w))
WX = W*X
Wy = W*y
β = WX \ Wy
ϵ = Wy-WX*β
(β, sqrt(dot(ϵ, ϵ)/(size(X, 1)-size(X, 2))))
end
myfit(y, hcat(ones(length(x)), x), w)
but I want to learn the "standard" way of doing this.
Best,
Tamas
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