v 0.2.0

README.md

@@ -108,7 +108,7 @@ Generate DataFrame with random multivariate data. Where:
 
 ## Structures
 
-Struct information see [here](https://github.com/PharmCat/ReplicateBE.jl/blob/master/docs/src/struct.md).
+Struct information see [here](https://pharmcat.github.io/ReplicateBE.jl/build/struct/).
 
 ## Acknowledgments
 

src/randrbeds.jl

@@ -15,13 +15,42 @@ mutable struct RandRBEDS
 end
 
 """
+```julia
     randrbeds(;n=24, sequence=[1,1],
         design = ["T" "R" "T" "R"; "R" "T" "R" "T"],
         inter=[0.5, 0.4, 0.9], intra=[0.1, 0.2],
         intercept = 0, seqcoef = [0.0, 0.0], periodcoef = [0.0, 0.0, 0.0, 0.0], formcoef = [0.0, 0.0],
         dropsubj = 0.0, dropobs::Int = 0, seed::Int = 0)
+```
+
+Random dataset generation for bioequivalence.
+
+#  Parameters
+    * n: number of subjects
+    * sequence: distribution in sequences [1,1] means 1:1, [1,3] - 1:4 etc.
+    * design: desin matrix, each line is a sequence, each column - periods, cell - formulation id
+    * inter: inter-subject variance vector for G matrix (length 3)
+    * intra: intra-subject variance vector for R matrix (length 2)
+    * intercept: intercept effect value
+    * seqcoef: coefficients of sequences (length(sequence) == length(seqcoef) == size(design, 1))
+    * periodcoef: coefficients of periods (length(periodcoef) == size(design, 2))
+    * formcoef: coeficients of formulations
+    * dropobs: number of randomly dropped subjects
+    * seed: seed for random
+
+Multivariate normal disribution:
+
+```math
+f(\\mathbf{x}; \\boldsymbol{\\mu}, \\boldsymbol{V}) = \\frac{1}{(2 \\pi)^{d/2} |\\boldsymbol{V}|^{1/2}}
+\\exp \\left( - \\frac{1}{2} (\\mathbf{x} - \\boldsymbol{\\mu})^T V^{-1} (\\mathbf{x} - \\boldsymbol{\\mu}) \\right)
+```
+
+Where V:
+
+```math
+V_{i} = Z_{i}GZ_i'+R_{i}
+```
 
-Random dataset for bioequivalence.
 """
 function randrbeds(;n=24, sequence=[1,1],
     design = ["T" "R" "T" "R"; "R" "T" "R" "T"],
@@ -32,13 +61,15 @@ function randrbeds(;n=24, sequence=[1,1],
 end
 
 """
+```julia
     randrbeds(n::Int, sequence::Vector,
         design::Matrix,
         θinter::Vector, θintra::Vector,
         intercept::Real, seqcoef::Vector, periodcoef::Vector, formcoef::Vector,
         dropsubj::Float64, dropobs::Int, seed::Int)
+```
 
-Random dataset for bioequivalence.
+Simple interface.
 
 """
 function randrbeds(n::Int, sequence::Vector,

src/rbe.jl

@@ -2,6 +2,7 @@
 # GENARAL REPLICATE BIOEQUIVALENCE STRUCTURE
 #
 """
+```julia
     struct RBE
         model::ModelFrame               #Model frame
         rmodel::ModelFrame              #Random effect model
@@ -27,6 +28,7 @@
         preoptim::Union{Optim.MultivariateOptimizationResults, Nothing}         #Pre-optimization result object
         optim::Optim.MultivariateOptimizationResults                            #Optimization result object
     end
+```
 
 Replicate bioequivalence structure.
 
@@ -72,13 +74,16 @@ Mixed model fitting function for replicate bioequivalence without data preparati
 
 Mixed model in matrix form:
 
-<p align="center">
-``y = X\\beta+Zu+\\epsilon``
-</p>
+```math
+y = X\\beta+Zu+\\epsilon
+```math
+
 
 with covariance matrix for each subject:
 
-``V_{i} = Z_{i}GZ_i'+R_{i}``
+```math
+V_{i} = Z_{i}GZ_i'+R_{i}
+```
 
 """
 function rbe(df; dvar::Symbol,
@@ -262,11 +267,13 @@ end #END OF rbe()
 """
 This function apply following code for each factor before executing:
 
+```julia
     categorical!(df, subject);
     categorical!(df, formulation);
     categorical!(df, period);
     categorical!(df, sequence);
     sort!(df, [subject, formulation, period])
+```
 
 It can takes more time, but can help to avoid some errors like: "ERROR: type ContinuousTerm has no field contrasts".
 """
@@ -314,9 +321,12 @@ end
 
 Returm -2logREML for rbe model
 
-``logREML(\\theta,\\beta) = -\\frac{N-p}{2} - \\frac{1}{2}\\sum_{i=1}^nlog|V_{i}|-``
+```math
+
+logREML(\\theta,\\beta) = -\\frac{N-p}{2} - \\frac{1}{2}\\sum_{i=1}^nlog|V_{i}|-
 
-``-\\frac{1}{2}log|\\sum_{i=1}^nX_i'V_i^{-1}X_i|-\\frac{1}{2}\\sum_{i=1}^n(y_i - X_{i}\\beta)'V_i^{-1}(y_i - X_{i}\\beta)``
+-\\frac{1}{2}log|\\sum_{i=1}^nX_i'V_i^{-1}X_i|-\\frac{1}{2}\\sum_{i=1}^n(y_i - X_{i}\\beta)'V_i^{-1}(y_i - X_{i}\\beta)
+```
 
 """
 function reml2(rbe::RBE)
@@ -338,7 +348,9 @@ end
 
 Return the standard errors for the coefficients of the model.
 
-``se = \\sqrt{LCL'}``
+```math
+se = \\sqrt{LCL'}
+```
 """
 function StatsBase.stderror(rbe::RBE)
     return collect(rbe.fixed.se)
@@ -348,7 +360,9 @@ end
 
 Return model coefficients.
 
-``\\beta = {(\\sum_{i=1}^n X_{i}'V_i^{-1}X_{i})}^{-1}(\\sum_{i=1}^n X_{i}'V_i^{-1}y_{i})``
+```math
+\\beta = {(\\sum_{i=1}^n X_{i}'V_i^{-1}X_{i})}^{-1}(\\sum_{i=1}^n X_{i}'V_i^{-1}y_{i})
+```
 """
 function StatsBase.coef(rbe::RBE)
     return collect(rbe.fixed.est)
@@ -368,6 +382,8 @@ end
 
 Compute confidence intervals for coefficients, with confidence level ```level``` (by default 95%).
 
+# Arguments
+
 ```expci = true```: return exponented CI.
 
 ```inv = true```: return ```-estimate ± t*se```
@@ -376,6 +392,10 @@ Compute confidence intervals for coefficients, with confidence level ```level```
 
 ```df = :df3``` or ```df = :cont```: DF (contain) = N - rank(ZX).
 
+```math
+CI = stimate ± t(alpha, df)*se
+```
+
 """
 function StatsBase.confint(obj::RBE; level::Real=0.95, expci::Bool = false, inv::Bool = false, df = :sat)
     confint(obj, 1 - level; expci = expci, inv = inv, df = df)
@@ -425,7 +445,7 @@ end
 """
     theta(rbe::RBE)
 
-Return theta vector (vector of variation parameters from optimization procedure).
+Return theta (θ) vector (vector of variation parameters from optimization procedure).
 """
 function theta(rbe::RBE)
     return collect(rbe.θ)
@@ -443,7 +463,7 @@ end
 
 Return design information object, where:
 
-
+```julia
     struct Design
         obs::Int          # Number of observations
         subj::Int         # Number of statistica independent subjects
@@ -457,7 +477,7 @@ Return design information object, where:
         df3::Int          # obs  - rankxz      (Contain DF for sequence and period)
         df4::Int          # obs  - rankxz + p
     end
-
+```
 """
 function design(rbe::RBE)::Design
     return rbe.design

src/utils.jl

@@ -4,11 +4,15 @@
 
 Return contrast table for L matrix.
 
-``F = \\frac{\\beta'L'(LCL')^{-1}L\\beta}{rank(LCL')}``
+```math
+F = \\frac{\\beta'L'(LCL')^{-1}L\\beta}{rank(LCL')}
+```
 
 DF for one-dimetion case:
 
-``df = \\frac{2(LCL')^{2}}{g'Ag}``
+```math
+df = \\frac{2(LCL')^{2}}{g'Ag}
+```
 
 where ``A = 2H``
 
@@ -47,25 +51,39 @@ end
 
 Return estimate table for L 1xp matrix.
 
-``estimate = L\\beta``
+```math
+estimate = L\\beta
+```
 
-``se = \\sqrt{LCL'}``
+```math
+se = \\sqrt{LCL'}
+```
 
-``t = estimate/se``
+```math
+t = estimate/se
+```
 
 For ```df = :sat```:
 
-``df = \\frac{2(LCL')^{2}}{g'Ag}``
+```math
+df = \\frac{2(LCL')^{2}}{g'Ag}
+```
 
 where ``A = 2H``
 
 where ``g = \\triangledown _{\\theta}(LC^{-1}L')``
 
 For ```df = :cont``` (contain):
 
-``df = N - rank(ZX)``
+```math
+df = N - rank(ZX)
+```
 
-CI estimate is ``CI = stimate ± t(alpha, df)*se ``
+CI estimate is:
+
+```math
+CI = stimate ± t(alpha, df)*se 
+```
 """
 function estimate(rbe::RBE, L::Matrix; df = :sat, name = "Estimate", memopt = true, alpha = 0.05)
     lcl     = L*rbe.C*L'