Design update some features

src/Survey.jl

@@ -27,6 +27,7 @@ include("show.jl")
 
 export load_data
 export AbstractSurveyDesign, SimpleRandomSample, StratifiedSample
+export SurveyDesign
 export svydesign
 export svyglm
 export svyby
@@ -37,6 +38,7 @@ export @formula
 export svyplot
 export svyhist, sturges, freedman_diaconis
 export svyboxplot
+export ht_svytotal, ht_svymean
 export
     #families
     Normal,

src/SurveyDesign.jl

@@ -14,7 +14,7 @@ abstract type AbstractSurveyDesign end
 """
     SimpleRandomSample <: AbstractSurveyDesign
 
-Survey design sampled by simple random sampling.
+    Survey design sampled by simple random sampling.
 """
 struct SimpleRandomSample <: AbstractSurveyDesign
     data::AbstractDataFrame
@@ -53,10 +53,11 @@ struct SimpleRandomSample <: AbstractSurveyDesign
                 if !all(p -> p == first(probs), probs)
                     error("all probability weights must be equal for Simple Random Sample")
                 end
-                weights = 1 ./ probs
+                weights = 1 / probs
             end
             # estimate population size
-            popsize = round(sampsize .* first(weights)) |> UInt
+            popsize = round(sampsize * first(weights)) |> UInt
+            # @show popsize, sampsize # Check this Line TODO
             if sampsize > popsize
                 error("sample size cannot be greater than population size")
             end
@@ -70,13 +71,13 @@ struct SimpleRandomSample <: AbstractSurveyDesign
             error("either population size or frequency/probability weights must be specified")
         end
         # set sampling fraction
-        sampfraction = sampsize ./ popsize
+        sampfraction = sampsize / popsize
         # set fpc
-        fpc = ignorefpc ? 1 : 1 .- (sampsize ./ popsize)
+        fpc = ignorefpc ? 1 : 1 - (sampsize / popsize)
         # add columns for frequency and probability weights to `data`
         data[!, :weights] = weights
         if isnothing(probs)
-            probs = 1 ./ data[!, :weights] 
+            probs = 1 ./ data[!, :weights]
         end
         data[!, :probs] = probs
 
@@ -87,19 +88,19 @@ end
 """
     StratifiedSample <: AbstractSurveyDesign
 
-Survey design sampled by stratification.
+    Survey design sampled by stratification.
 """
 struct StratifiedSample <: AbstractSurveyDesign
     data::AbstractDataFrame
     strata::Symbol
-    sampsize::Vector{Union{Nothing,Float64}}
-    popsize::Vector{Union{Nothing,Float64}}
+    sampsize::Union{Nothing,Vector{Real}}
+    popsize::Union{Nothing,Vector{Real}}
     sampfraction::Vector{Real}
     fpc::Vector{Real}
     ignorefpc::Bool
     function StratifiedSample(data::AbstractDataFrame, strata::Symbol;
         popsize=nothing,
-        sampsize= transform(groupby(data,strata), nrow => :counts ).counts ,
+        sampsize=transform(groupby(data, strata), nrow => :counts).counts,
         weights=nothing,
         probs=nothing,
         ignorefpc=false
@@ -133,7 +134,7 @@ struct StratifiedSample <: AbstractSurveyDesign
         elseif typeof(popsize) <: Vector{<:Real}
             # TODO: change `elseif` condition
             weights = popsize ./ sampsize # expansion estimator
-            # TODO: add probability weights
+        # TODO: add probability weights
         else
             error("either population size or frequency/probability weights must be specified")
         end
@@ -156,20 +157,21 @@ end
 """
     ClusterSample <: AbstractSurveyDesign
 
-Survey design sampled by clustering.
+    One stage clusters, Primary Sampling Units (PSU) chosen with SRS. No stratification.
+    Assuming every individual is in one and only one cluster, and the subsampling probabilities
+    for any given cluster do not depend on which other clusters were sampled. (Lumley pg41, para2)
 """
 struct ClusterSample <: AbstractSurveyDesign
     data::AbstractDataFrame
-    strata::Symbol
-    sampsize::Vector{Union{Nothing,Float64}}
-    popsize::Vector{Union{Nothing,Float64}}
+    clusters::Union{Symbol,Nothing}
+    sampsize::Union{Nothing,Vector{Real}}
+    popsize::Union{Nothing,Vector{Real}}
     sampfraction::Vector{Real}
     fpc::Vector{Real}
     ignorefpc::Bool
-    # TODO: change entire struct body
-    function GeneralSample(data::AbstractDataFrame, strata::Symbol;
+    function ClusterSample(data::AbstractDataFrame, strata::Symbol;
         popsize=nothing,
-        sampsize= transform(groupby(data,strata), nrow => :counts ).counts ,
+        sampsize=transform(groupby(data, strata), nrow => :counts).counts,
         weights=nothing,
         probs=nothing,
         ignorefpc=false
@@ -181,8 +183,10 @@ struct ClusterSample <: AbstractSurveyDesign
             probs = data[!, probs]
         end
 
-        if ignorefpc # && (isnothing(popsize) || isnothing(weights) || isnothing(probs))
-            @warn "Assuming equal weights"
+        if ignorefpc
+            # TODO: change what happens if `ignorepfc == true` or if the user only
+            # specifies `data`
+            @warn "assuming equal weights"
             weights = ones(nrow(data))
         end
 
@@ -201,7 +205,7 @@ struct ClusterSample <: AbstractSurveyDesign
         elseif typeof(popsize) <: Vector{<:Real}
             # TODO: change `elseif` condition
             weights = popsize ./ sampsize # expansion estimator
-            # TODO: add probability weights
+        # TODO: add probability weights
         else
             error("either population size or frequency/probability weights must be specified")
         end
@@ -220,3 +224,111 @@ struct ClusterSample <: AbstractSurveyDesign
         new(data, strata, sampsize, popsize, sampfraction, fpc, ignorefpc)
     end
 end
+
+
+"""
+    SurveyDesign <: AbstractSurveyDesign
+
+    Survey design with arbitrary design weights
+
+    clusters: can be passed as `Symbol`, Vector{Symbol}, Vector{Real} or Nothing
+    strata: can be passed as `Symbol`, Vector{Symbol}, Vector{Real} or Nothing
+    sampsize: can be passed as `Symbol`, Vector{Symbol}, Vector{Real} or Nothing
+    popsize: can be passed as `Symbol`, Vector{Symbol}, Vector{Real} or Nothing
+"""
+struct SurveyDesign <: AbstractSurveyDesign
+    data::AbstractDataFrame
+    clusters::Union{Symbol,Vector{Symbol},Nothing}
+    strata::Union{Symbol,Vector{Symbol},Nothing}
+    sampsize::Union{Real,Vector{Real},Nothing}
+    popsize::Union{Real,Vector{Real},Nothing}
+    sampfraction::Union{Real,Vector{Real}}
+    fpc::Union{Real,Vector{Real}}
+    ignorefpc::Bool
+    # TODO: struct body still work in progress
+    function SurveyDesign(data::AbstractDataFrame;
+        clusters=nothing,
+        strata=nothing,
+        popsize=nothing,
+        sampsize=nothing,
+        weights=nothing,
+        probs=nothing,
+        ignorefpc=false
+    )
+        if isnothing(sampsize)
+            if isnothing(strata)
+                sampsize = nrow(data)
+            else
+                sampsize = transform(groupby(data, strata), nrow => :counts).counts
+            end
+
+        end
+
+        if isa(weights, Symbol)
+            weights = data[!, weights]
+        end
+        if isa(probs, Symbol)
+            probs = data[!, probs]
+        end
+        if isa(popsize, Symbol)
+            popsize = data[!, popsize]
+        end
+
+        # TODO: Check below, may not be correct for all designs
+        if ignorefpc # && (isnothing(popsize) || isnothing(weights) || isnothing(probs))
+            @warn "Assuming equal weights"
+            weights = ones(nrow(data))
+        end    
+
+        # TODO: Do the other case where clusters are given
+        if isnothing(clusters)
+            # set population size if it is not given; `weights` and `sampsize` must be given
+            if isnothing(popsize)
+                # TODO: add probability weights if `weights` is not `nothing`
+                if typeof(probs) <: Vector{<:Real}
+                    weights = 1 ./ probs
+                end
+                # estimate population size
+                popsize = sampsize .* weights
+
+                if sampsize > popsize
+                    error("sample size cannot be greater than population size")
+                end
+            elseif typeof(popsize) <: Vector{<:Real}
+                # TODO: change `elseif` condition
+                weights = popsize ./ sampsize # expansion estimator
+            # TODO: add probability weights
+            else
+                error("either population size or frequency/probability weights must be specified")
+            end
+            # TODO: for now support SRS within SurveyDesign. Later, just call SimpleRandomSample??
+            if typeof(sampsize) <: Real && typeof(popsize) <: Vector{<:Real} # Eg when SRS
+                if !all(y -> y == first(popsize), popsize) # SRS by definition is equi-weighted
+                    error("Simple Random Sample must be equi-weighted. Different sampling weights detected in vectors")
+                end
+                popsize = first(popsize) |> Real
+            end
+
+            # set sampling fraction
+            sampfraction = sampsize ./ popsize
+            # set fpc
+            fpc = ignorefpc ? 1 : 1 .- (sampsize ./ popsize)
+            # add columns for frequency and probability weights to `data`
+            if !isnothing(probs)
+                data[!, :probs] = probs
+                data[!, :weights] = 1 ./ data[!, :probs]
+            else
+                data[!, :weights] = weights
+                data[!, :probs] = 1 ./ data[!, :weights]
+            end
+            # @show clusters, strata, sampsize,popsize, sampfraction, fpc, ignorefpc
+            new(data, clusters, strata, sampsize, popsize, sampfraction, fpc, ignorefpc)
+        elseif isa(clusters,Symbol)
+            # One Cluster sampling - PSU chosen with SRS,
+            print("One stage cluster design with PSU SRS")
+        elseif typeof(clusters) <: Vector{Symbol}
+            # TODO "Multistage cluster designs"
+            print("TODO: Multistage cluster design with PSU SRS")
+        end
+    end
+end

src/ht.jl

@@ -1,17 +1,43 @@
 # TODO: add docstrings
-function HorwitzThompson_HartleyRaoApproximation(y::AbstractVector, design::AbstractSurveyDesign, HT_total)
+"""
+    Hartley Rao Approximation of the variance of the Horvitz-Thompson Estimator.
+    Avoids exhaustively calculating joint (inclusion) probabilities πᵢⱼ of the sampling scheme. 
+"""
+function HT_HartleyRaoVarApprox(y::AbstractVector, design::AbstractSurveyDesign, HT_total)
     # TODO: change function name
-    # TODO: change variable name (gets mixed up with the constant pi)
-    pi = design.data.probs
-    hartley_rao_var = sum((1 .- ((design.sampsize .- 1) ./ design.sampsize) .* pi) .*
+    πᵢ = design.data.probs
+    hartley_rao_var = sum((1 .- ((design.sampsize .- 1) ./ design.sampsize) .* πᵢ) .*
                           ((y .* design.data.weights) .- (HT_total ./ design.sampsize)) .^ 2)
-    return sqrt(hartley_rao_var)
+    return hartley_rao_var
 end
 
-# TODO: add docstrings
-function ht_calc(x::Symbol, design)
-    # TODO: change function name
+"""
+    Horvitz-Thompson Estimator of Population Total
+    For arbitrary sampling probabilities
+"""
+function ht_svytotal(x::Symbol, design)
     total = wsum(Float32.(design.data[!, x]), design.data.weights)
-    se = HorwitzThompson_HartleyRaoApproximation(design.data[!, x], design, total)
-    return DataFrame(total = total, se = se)
+    var_total = HT_HartleyRaoVarApprox(design.data[!, x], design, total)
+    return DataFrame(total = total, se = sqrt(var_total))
 end
+
+# TODO: add docstrings
+"""
+    Horvitz-Thompson Estimator of Population Mean
+    Scales the Population Total by the relevant
+"""
+function ht_svymean(x::Symbol, design)
+    total_df = ht_svytotal(x, design)
+    total = total_df.total[1]
+    var_total = total_df.se[1]
+
+    mean = 1.0 ./ sum(design.popsize) .* total
+    # @show total_df, total, var_total, mean
+    #1.0 ./ (sum(design.popsize).^2) .* 
+
+    se = sqrt( var_total )
+
+    @show design.popsize
+
+    return DataFrame(mean = mean, se = se)
+end

src/poststratify.jl

@@ -0,0 +1,3 @@
+"""
+    Postratification functions
+"""

src/sampling.jl

@@ -0,0 +1,3 @@
+"""
+    Sampling functions using various schemes
+"""

src/show.jl

@@ -33,6 +33,23 @@ function Base.show(io::IO, ::MIME"text/plain", design::AbstractSurveyDesign)
     printinfo(io, "ignorefpc", string(design.ignorefpc); newline=false)
 end
 
+"`show` method for printing information about a survey design"
+function Base.show(io::IO, ::MIME"text/plain", design::SurveyDesign)
+    type = typeof(design)
+    printstyled(io, "$type:\n"; bold=true)
+    printstyled(io, "data: "; bold=true)
+    println(io, size(design.data, 1), "x", size(design.data, 2), " DataFrame")
+    println(io, "clusters: ", design.clusters)
+    println(io, "strata: ", design.strata)
+    printinfo(io, "weights", makeshort(design.data.weights))
+    printinfo(io, "probs", makeshort(design.data.probs))
+    printinfo(io, "popsize", makeshort(design.popsize))
+    printinfo(io, "sampsize", makeshort(design.sampsize))
+    printinfo(io, "fpc", makeshort(design.data.fpc))
+    printinfo(io, "sampfraction", makeshort(design.sampfraction))
+    printinfo(io, "ignorefpc", string(design.ignorefpc); newline=false)
+end
+
 "Print information about a survey design initialized using `svydesign`."
 function Base.show(io::IO, ::MIME"text/plain", design::svydesign)
     printstyled(io, "Survey Design:\n"; bold=true)

src/svyratio.jl

@@ -0,0 +1,14 @@
+"""
+    Ratio estimators and Subpopulation estimates.
+    Domain Estimation is special case of subpopulation estimate.
+"""
+
+function ratio(x::Symbol,y::Symbol,design::AbstractSurveyDesign)
+    num = design.data[!,x]
+    denom = design.data[!,y]
+    # fill(A,)
+    # A == B
+
+    mean = num / denom
+    return DataFrame(mean = mean)
+end