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Parametrize by array type instead of eltype #56

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Feb 24, 2014
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Parametrize by array type instead of eltype #56

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1 change: 1 addition & 0 deletions src/GLM.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,7 @@ module GLM
wrkresp # working response

typealias FP FloatingPoint
typealias FPStoredVector{T<:FloatingPoint} StoredArray{T,1}

abstract ModResp # model response

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40 changes: 20 additions & 20 deletions src/glmfit.jl
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Original file line number Diff line number Diff line change
@@ -1,18 +1,18 @@
type GlmResp{T<:FP} <: ModResp # response in a glm model
y::AbstractVector{T} # response
type GlmResp{V<:FPStoredVector} <: ModResp # response in a glm model
y::V # response
d::UnivariateDistribution
l::Link
devresid::AbstractVector{T} # (squared) deviance residuals
eta::AbstractVector{T} # linear predictor
mu::AbstractVector{T} # mean response
mueta::AbstractVector{T} # derivative of mu w.r.t. eta
offset::AbstractVector{T} # offset added to linear predictor (usually 0)
var::AbstractVector{T} # (unweighted) variance at current mu
wts::AbstractVector{T} # prior weights
wrkresid::AbstractVector{T} # working residuals
function GlmResp(y::AbstractVector{T}, d::UnivariateDistribution, l::Link,
eta::AbstractVector{T}, mu::AbstractVector{T},
off::AbstractVector{T}, wts::AbstractVector{T})
devresid::V # (squared) deviance residuals
eta::V # linear predictor
mu::V # mean response
mueta::V # derivative of mu w.r.t. eta
offset::V # offset added to linear predictor (usually 0)
var::V # (unweighted) variance at current mu
wts::V # prior weights
wrkresid::V # working residuals
function GlmResp(y::V, d::UnivariateDistribution, l::Link,
eta::V, mu::V,
off::V, wts::V)
if isa(d, Binomial)
for yy in y; 0. <= yy <= 1. || error("$yy in y is not in [0,1]"); end
else
Expand Down Expand Up @@ -42,14 +42,14 @@ linkinv!(r::GlmResp) = linkinv!(r.l, r.mu, r.eta)
# evaluate the mueta vector (derivative of mu w.r.t. eta) from the linear predictor (eta)
mueta!(r::GlmResp) = mueta!(r.l, r.mueta, r.eta)

function updatemu!{T<:FP}(r::GlmResp{T}, linPr::Vector{T})
function updatemu!{T<:FPStoredVector}(r::GlmResp{T}, linPr::T)
n = length(linPr)
length(r.offset) == n ? map!(Add(), r.eta, linPr, r.offset) : copy!(r.eta, linPr)
linkinv!(r); mueta!(r); var!(r); wrkresid!(r); devresid!(r)
sum(r.devresid)
end

updatemu!{T<:FP}(r::GlmResp{T}, linPr) = updatemu!(r, convert(typeof(r.y),vec(linPr)))
updatemu!{T<:FPStoredVector}(r::GlmResp{T}, linPr) = updatemu!(r, convert(T,vec(linPr)))

var!(r::GlmResp) = var!(r.d, r.var, r.mu)

Expand All @@ -60,9 +60,9 @@ function wrkresp(r::GlmResp)
map(Add(), r.eta, r.wrkresid)
end

function wrkwt{T<:FP}(r::GlmResp{T})
length(r.wts) == 0 && return [(r.mueta[i] * r.mueta[i]/r.var[i])::T for i in 1:length(r.var)]
[(r.wts[i] * r.mueta[i] * r.mueta[i]/r.var[i])::T for i in 1:length(r.var)]
function wrkwt(r::GlmResp)
length(r.wts) == 0 && return [r.mueta[i] * r.mueta[i]/r.var[i] for i in 1:length(r.var)]
[r.wts[i] * r.mueta[i] * r.mueta[i]/r.var[i] for i in 1:length(r.var)]
end

type GlmMod <: LinPredModel
Expand Down Expand Up @@ -126,10 +126,10 @@ function glm(f::Formula, df::AbstractDataFrame, d::UnivariateDistribution, l::Li
end
n = length(y); lw = length(wts)
lw == 0 || lw == n || error("length(wts) = $lw should be 0 or $n")
w = lw == 0 ? ones(T,n) : (T <: Float64 ? copy(wts) : convert(Vector{T}, wts))
w = lw == 0 ? ones(T,n) : (T <: Float64 ? copy(wts) : convert(typeof(y), wts))
mu = mustart(d, y, w)
off = T <: Float64 ? copy(offset) : convert(Vector{T}, offset)
rr = GlmResp{T}(y, d, l, linkfun!(l, similar(mu), mu), mu, off, w)
rr = GlmResp{typeof(y)}(y, d, l, linkfun!(l, similar(mu), mu), mu, off, w)
res = GlmMod(mf, rr, DensePredChol(mm), f, false)
dofit ? fit(res) : res
end
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18 changes: 9 additions & 9 deletions src/lm.jl
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Original file line number Diff line number Diff line change
@@ -1,23 +1,23 @@
type LmResp{T<:FP} <: ModResp # response in a linear model
mu::Vector{T} # mean response
offset::Vector{T} # offset added to linear predictor (may have length 0)
wts::Vector{T} # prior weights (may have length 0)
y::Vector{T} # response
function LmResp(mu::Vector{T}, off::Vector{T}, wts::Vector{T}, y::Vector{T})
type LmResp{V<:FPStoredVector} <: ModResp # response in a linear model
mu::V # mean response
offset::V # offset added to linear predictor (may have length 0)
wts::V # prior weights (may have length 0)
y::V # response
function LmResp(mu::V, off::V, wts::V, y::V)
n = length(y); length(mu) == n || error("mismatched lengths of mu and y")
ll = length(off); ll == 0 || ll == n || error("length of offset is $ll, must be $n or 0")
ll = length(wts); ll == 0 || ll == n || error("length of wts is $ll, must be $n or 0")
new(mu,off,wts,y)
end
end
LmResp{T<:FP}(y::Vector{T}) = LmResp{T}(zeros(T,length(y)), T[], T[], y)
LmResp{V<:FPStoredVector}(y::V) = LmResp{V}(fill!(similar(y), zero(eltype(V))), similar(y, 0), similar(y, 0), y)

function updatemu!{T<:FP}(r::LmResp{T}, linPr::Vector{T})
function updatemu!{V<:FPStoredVector}(r::LmResp{V}, linPr::V)
n = length(linPr); length(r.y) == n || error("length(linPr) is $n, should be $(length(r.y))")
length(r.offset) == 0 ? copy!(r.mu, linPr) : map!(Add(), r.mu, linPr, r.offset)
deviance(r)
end
updatemu!{T<:FP}(r::LmResp{T}, linPr) = updatemu!(r, convert(Vector{T},vec(linPr)))
updatemu!{V<:FPStoredVector}(r::LmResp{V}, linPr) = updatemu!(r, convert(V,vec(linPr)))

type WtResid <: Functor{3} end
evaluate{T<:FP}(::WtResid,wt::T,y::T,mu::T) = (y - mu)*sqrt(wt)
Expand Down