Merge pull request #43 from PharmCat/dev

v0.2.7
  - setkelauto!
  - getkelauto
  - applyelimrange!
  - getval improove
  - Mean CI fix
  - code fix
  - experimental tests
  - DataSet{Descriptive} show changes
  - improove coverage
  - test update

Project.toml

@@ -1,7 +1,7 @@
 authors = ["Vladimir Arnautov ([email protected])"]
 name = "ClinicalTrialUtilities"
 uuid = "535c2557-d7d0-564d-8ff9-4ae146c18cfe"
-version = "0.2.6"
+version = "0.2.7"
 
 [deps]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

README.md

@@ -117,15 +117,15 @@ ci = meanci(30, 10, 30, alpha = 0.05, method=:norm)
 #### NCA
 ```
 using CSV, DataFrames, ClinicalTrialUtilities
-pkdata2 = CSV.File("pkdata.csv") |> DataFrame
-pkds    = pkimport(pkdata2, [:Subject, :Formulation]; time = :Time, conc = :Concentration)
+pkdatapath = joinpath(dirname(pathof(ClinicalTrialUtilities)))*"\\..\\test\\csv\\pkdata2.csv"
+pkdata  = CSV.File(pkdatapath) |> DataFrame
+pkds    = pkimport(pkdata, [:Subject, :Formulation]; time = :Time, conc = :Concentration)
 pk      = nca!(pkds)
 ncadf   = DataFrame(pk; unst = true)
 ds      = ClinicalTrialUtilities.descriptive(ncadf, stats = [:n, :mean, :sd], sort = [:Formulation])
 dsdf    = ClinicalTrialUtilities.DataFrame(ds; unst = true)
 
 ```
-
 #### Randomization
 ```
 using DataFrames, ClinicalTrialUtilities

cange.log

@@ -1,3 +1,15 @@
+v0.2.7
+  - setkelauto!
+  - getkelauto
+  - applyelimrange!
+  - getval improove
+  - Mean CI fix
+  - code fix
+  - experimental tests
+  - DataSet{Descriptive} show changes
+  - improove coverage
+  - test update
+
 v0.2.6
 
   - findfirst

docs/Project.toml

@@ -1,7 +1,12 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [compat]
 
-Documenter = "0.23"
-
+Documenter = "0.23, 0.24"
+DataFrames = "0.19, 0.20"
+CSV = "0.5, 0.6"
+Plots = "1.2, 1.3"

docs/make.jl

@@ -1,4 +1,4 @@
-using Documenter, ClinicalTrialUtilities
+using Documenter, ClinicalTrialUtilities, DataFrames, CSV, Plots
 
 makedocs(
     modules = [ClinicalTrialUtilities],

docs/src/ci.md

@@ -38,11 +38,13 @@ ClinicalTrialUtilities.diffmeanci
 
 Table cell map:
 
-|         | outcome 1 | outcome 2 |
+
+| group   | outcome 1 | outcome 2 |
 |---------|-----------|-----------|
 | group 1 |     a     |     b     |
 | group 2 |     c     |     d     |
 
+
 ### diffcmhci
 ```@docs
 ClinicalTrialUtilities.diffcmhci

docs/src/pkplot.md

@@ -4,3 +4,29 @@
 ```@docs
 ClinicalTrialUtilities.pkplot
 ```
+
+#### Examples
+
+
+```@example 1
+using ClinicalTrialUtilities, DataFrames, CSV, Plots
+pkdatapath = joinpath(dirname(pathof(ClinicalTrialUtilities)))*"\\..\\test\\csv\\pkdata2.csv"
+pkdata     = CSV.File(pkdatapath) |> DataFrame
+pkds       = ClinicalTrialUtilities.pkimport(pkdata, [:Subject, :Formulation]; conc = :Concentration, time = :Time)
+plot1      = pkplot(pkds[1], legend = false);
+#savefig("pkplot1.svg"); # hide
+plot2      = pkplot(pkds;  pagesort = [:Formulation], typesort = [:Subject])[1];
+#savefig("pkplot2.svg"); nothing # hide
+```
+
+Plot for subject:
+
+```@example 1
+plot1
+```
+
+First plot for DataSet:
+
+```@example 1
+plot2
+```

src/ClinicalTrialUtilities.jl

@@ -123,7 +123,10 @@ ElimRange,
 DoseTime,
 LimitRule,
 applyncarule!,
+applyelimrange!,
 keldata,
+setkelauto!,
+getkelauto,
 # Simulation
 ctsim,
 #Plots

src/ci.jl

@@ -9,16 +9,16 @@
 # DescTools https://CRAN.R-project.org/package=DescTools
 # metafor by Wolfgang Viechtbauer https://cran.r-project.org/package=metafor
 
-struct ConfInt{T <: Real}
-    lower::T
-    upper::T
-    estimate::T
-    alpha::T
-    function ConfInt(lower::T, upper::T, estimate::T) where T <: Real
-        new{T}(lower, upper, estimate, NaN)::ConfInt
+struct ConfInt
+    lower::Real
+    upper::Real
+    estimate::Real
+    alpha::Real
+    function ConfInt(lower, upper, estimate)
+        new(lower, upper, estimate, NaN)::ConfInt
     end
-    function ConfInt(lower::T, upper::T, estimate::T, alpha::T) where T <: Real
-        new{T}(lower, upper, estimate, alpha)::ConfInt
+    function ConfInt(lower, upper, estimate, alpha)
+        new(lower, upper, estimate, alpha)::ConfInt
     end
 end
 
@@ -29,8 +29,10 @@ function getindex(a::ConfInt, b::Int)
         return a.upper
     elseif b == 3
         return a.estimate
+    elseif b == 4
+        return a.alpha
     else
-        throw(ArgumentError("Index should be in 1:3"))
+        throw(ArgumentError("Index should be in 1:4"))
     end
 end
 
@@ -61,10 +63,10 @@ function StatsBase.confint(param::RiskRatio{P}; level = 0.95, method = :default)
     rrpropci(param.a.x, param.a.n, param.b.x, param.b.n; alpha = 1 - level, method = method)
 end
 function StatsBase.confint(param::Mean; level = 0.95, method = :default)::ConfInt
-    meanci(param.m, param.s, param.n; alpha = 1 - level, method = method)
+    meanci(param.m, param.sd^2, param.n; alpha = 1 - level, method = method)
 end
 function StatsBase.confint(param::DiffMean{true}; level = 0.95, method = :default)::ConfInt where T <: Mean
-    diffmeanci(param.a.m, param.a.s, param.a.n, param.b.m, param.b.s, param.b.n; alpha = 1 - level, method = method)
+    diffmeanci(param.a.m, param.a.sd^2, param.a.n, param.b.m, param.b.sd^2, param.b.n; alpha = 1 - level, method = method)
 end
 """
     propci(x::Int, n::Int; alpha=0.05, method = :default)::ConfInt
@@ -205,30 +207,40 @@ function orpropci(x1::Int, n1::Int, x2::Int, n2::Int; alpha::Real = 0.05, method
     end
 end
 """
-    meanci(m::Real, s::Real, n::Int; alpha::Real = 0.05,
+    meanci(m::Real, σ²::Real, n::Int; alpha::Real = 0.05,
         method=:default)::ConfInt
 
-Confidence interval for mean.
+Confidence interval for mean, where:
+
+m  - mean;
+σ² - variance;
+n  - observation number.
 
 # Computation methods:
 
 - :norm - Normal distribution (default);
 - :tdist - T Distribution.
 
 """
-function meanci(m::Real, s::Real, n::Int; alpha::Real = 0.05, method=:default)::ConfInt
+function meanci(m::Real, σ²::Real, n::Int; alpha::Real = 0.05, method=:default)::ConfInt
         if method==:norm || method == :default
-            return meannormci(m, s, n, alpha)
+            return meannormci(m, σ², n, alpha)
         elseif method == :tdist
-            return meantdistci(m, s, n, alpha)
+            return meantdistci(m, σ², n, alpha)
         end
 end
 """
-    diffmeanci(m1::Real, s1::Real, n1::Real, m2::Real, s2::Real, n2::Real;
+    diffmeanci(m1::Real, σ²1::Real, n1::Real, m2::Real, σ²2::Real, n2::Real;
         alpha::Real = 0.05, method::Symbol = :default)::ConfInt
 
 Confidence interval for mead difference.
 
+m1, m2  - mean;
+σ²1, σ²2 - variance;
+n1, n2  - observation number.
+
+# Computation methods:
+
 - :ev - equal variance (default);
 - :uv - unequal variance with Welch-Satterthwaite df correction.
 """
@@ -675,19 +687,19 @@ end
     #-------------------------------MEANS---------------------------------------
 
     #Normal
-    function meannormci(m::Real, s::Real, n::Real, alpha::Real)::ConfInt
-        e = quantile(ZDIST, 1-alpha/2)*sqrt(s/n)
+    function meannormci(m::Real, σ²::Real, n::Real, alpha::Real)::ConfInt
+        e = quantile(ZDIST, 1-alpha/2)*sqrt(σ²/n)
         return ConfInt(m-e, m+e, float(m), alpha)
     end
     #T Distribution
-    function meantdistci(m::Real, s::Real, n::Real, alpha::Real)::ConfInt
-        e = quantile(TDist(n-1), 1-alpha/2)*sqrt(s/n)
+    function meantdistci(m::Real, σ²::Real, n::Real, alpha::Real)::ConfInt
+        e = quantile(TDist(n-1), 1-alpha/2)*sqrt(σ²/n)
         return ConfInt(m-e, m+e, float(m), alpha)
     end
     #mean diff equal var
-    function meandiffev(m1::Real, s1::Real, n1::Real, m2::Real, s2::Real, n2::Real, alpha::Real)::ConfInt
+    function meandiffev(m1::Real, σ²1::Real, n1::Real, m2::Real, σ²2::Real, n2::Real, alpha::Real)::ConfInt
         diff   = float(m1 - m2)
-        stddev = sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))
+        stddev = sqrt(((n1 - 1) * σ²1 + (n2 - 1) * σ²2) / (n1 + n2 - 2))
         stderr = stddev * sqrt(1/n1 + 1/n2)
         d      = stderr*quantile(TDist(n1+n2-2), 1-alpha/2)
         return ConfInt(diff-d, diff+d, diff, alpha)
@@ -698,10 +710,10 @@ end
     #mean diff unequal var
     #Two sample t-test (unequal variance)
     #Welch-Satterthwaite df
-    function meandiffuv(m1::Real, s1::Real, n1::Real, m2::Real, s2::Real, n2::Real, alpha::Real)::ConfInt
+    function meandiffuv(m1::Real, σ²1::Real, n1::Real, m2::Real, σ²2::Real, n2::Real, alpha::Real)::ConfInt
         diff   = float(m1 - m2)
-        v      = (s1/n1+s2/n2)^2/(s1^2/n1^2/(n1-1)+s2^2/n2^2/(n2-1))
-        stderr = sqrt(s1/n1 + s2/n2)
+        v      = (σ²1 / n1 + σ²2 / n2)^2 /( σ²1^2 / n1^2 / (n1 - 1) + σ²2^2 / n2^2 / (n2 - 1))
+        stderr = sqrt(σ²1 / n1 + σ²2 / n2)
         d      = stderr*quantile(TDist(v), 1-alpha/2)
         return ConfInt(diff-d, diff+d, diff, alpha)
     end
@@ -725,8 +737,10 @@ function meanratiot(m1::Real, s1::Real, n1::Real, m2::Real, s2::Real, n2::Real,
     d = sqrt((cov^2 - s1 * s2) * t0^4 + (m1^2 * s2 - 2 * m1 * m2 * cov + m2^2 * s1) * t0^2)
     return ((a - d)/b, (a + d)/b, m2/m1, alpha)
 end
+#=
 function meanratiof(m1::Real, s1::Real, n1::Real, m2::Real, s2::Real, n2::Real, cov::Real, alpha::Real)
 end
+=#
 
     #Cochran–Mantel–Haenszel confidence intervals
     #p275, Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology (3rd ed.). Philadelphia: Lippincott Williams & Wilkins.

src/descriptives.jl

@@ -28,15 +28,89 @@ function Base.show(io::IO, obj::Descriptive)
         println(io, String(spv), "___", "____________")
     end
 end
+
+
 function Base.getindex(a::Descriptive, s::Symbol)::Real
     return a.result[s]
 end
+
+function getkeylist(obj::DataSet)
+    keylist = Vector{Any}(undef, 0)
+    for i = 1:length(obj)
+        if length(obj[i].sort) > 0
+            for k in keys(obj[i].sort)
+                if k ∉ keylist push!(keylist, k) end
+            end
+        end
+    end
+    keylist
+end
+
 function Base.show(io::IO, obj::DataSet{Descriptive})
     println(io, "Descriptive statistics set")
+    if length(obj) == 0
+        println(io, "Empty")
+        return
+    end
+    keylist     = getkeylist(obj)
+    strkeylist  = string.(keylist)
+    maxkeyvars  = Vector{Int}(undef, length(keylist))
+
+    strvars     = Vector{String}(undef, length(obj))
+    keyvalmx    = Matrix{String}(undef, length(obj), length(keylist))
+
+    for i = 1:length(obj)
+        strvars[i] = string(obj[i].var)
+        for kl = 1:length(keylist)
+            if keylist[kl] in keys(obj[i].sort)
+                keyvalmx[i, kl] = string(obj[i].sort[keylist[kl]])*" "
+            else
+                keyvalmx[i, kl] = "."
+            end
+        end
+    end
+
+    maxstrvars  = max(10, maximum(length.(strvars)))
+
+    for i = 1:length(keylist)
+        maxkeyvars[i] = max(length(strkeylist[i]), maximum(length.(keyvalmx[:, i]))) + 2
+        if length(strkeylist[i]) < maxkeyvars[i]
+            for ps = 1: length(strkeylist[i]) - maxkeyvars[i]
+                strkeylist[i] = strkeylist[i]*" "
+            end
+        end
+    end
+
+    print(io, "N ")
+    #=
+    print(io, "Variabels ")
+    =#
+    for i = 1:length(strkeylist)
+        print(io, strkeylist[i])
+        if length(strkeylist[i]) < maxkeyvars[i]
+            for s = 1:(maxkeyvars[i] - length(strkeylist[i])) print(io, " ") end
+        end
+    end
+
+    println(io, " ")
+
+
     for i = 1:length(obj.data)
-        print(io, i, " | ", string(obj.data[i].var))
-        if obj.data[i].sort !== nothing println(io, " | ", string(obj.data[i].sort)) end
-        println(io, "____________________")
+        print(io, "$i ")
+        #=
+        print(io, strvars[i])
+        if length(strvars[i]) < maxstrvars
+            for s = 1:(maxstrvars - length(strvars[i])) print(io, " ") end
+        end
+        =#
+        for c = 1:length(keylist)
+            print(io, keyvalmx[i, c])
+            if length(keyvalmx[i, c]) < maxkeyvars[c]
+                for s = 1:(maxkeyvars[c] - length(keyvalmx[i, c])) print(io, " ") end
+            end
+        end
+
+        println(io, " ")
     end
 end
 """