docs

docs/src/details.md

@@ -63,6 +63,15 @@ V_{i} = Z_{i} G Z_i'+ W^{- \frac{1}{2}}_i R_{i} W^{- \frac{1}{2}}_i
 where ```W``` - diagonal matrix of weights.
 
 
+#### Multiple random and repeated effects
+
+If model include multiple effects ( with n random and m repeated effects) final V will be:
+
+```math
+V_{i} = Z_{i, 1} G_{1} Z_{i, 1}' + ... + Z_{i, n} G_{1} Z_{i, n}'+ W^{- \frac{1}{2}}_i ( R_{i, 1} + ... + R_{i, m}) W^{- \frac{1}{2}}_i
+```
+
+
 ##### Initial step
 
 Initial (first) step before optimization may be done:

docs/src/instanduse.md

@@ -63,8 +63,8 @@ Define `random` and `repreated` effects with [`Metida.VarEffect`](@ref) using [`
 right side is a effect itself. [`Metida.HeterogeneousCompoundSymmetry`](@ref) and [`Metida.Diag`](@ref) (Diagonal) in example bellow is a model of variance-covariance structure. See also [`Metida.@lmmformula`](@ref) macro.
 
 !!! note
-  In some cases levels of repeated effect should not be equal inside each level of subject or model will not have any sense. For example, it is assumed that usually CSH or UN (Unstructured) using with levels of repeated effect is different inside each level of subject.
-  Metida does not check this!
+
+    In some cases levels of repeated effect should not be equal inside each level of subject or model will not have any sense. For example, it is assumed that usually CSH or UN (Unstructured) using with levels of repeated effect is different inside each level of subject. Metida does not check this!
 
 
 ```@example lmmexample
@@ -73,6 +73,15 @@ random = VarEffect(@covstr(formulation|subject), CSH),
 repeated = VarEffect(@covstr(formulation|subject), DIAG));
 ```
 
+Also [`Metida.@lmmformula`](@ref) macro can be used:
+
+```julia
+lmm = LMM(@lmmformula(var~sequence+period+formulation,
+    random = formulation|subject:CSH,
+    repeated = formulation|subject:DIAG),
+    df)
+```
+
 #### Step 3: Fit
 
 Just fit the model.
@@ -87,6 +96,23 @@ fit!(lmm)
 lmm.log
 ```
 
+#### Confidence intervals for coefficients
+
+
+```@example lmmexample
+confint(lmm)
+```
+
+!!! note
+
+    Satterthwaite approximation for the denominator degrees of freedom used by default.
+
+#### StatsBsae API
+
+StatsBsae API implemented: [`Metida.islinear`](@ref), [`Metida.confint`](@ref), [`Metida.coef`](@ref), [`Metida.coefnames`](@ref), [`Metida.dof_residual`](@ref), [`Metida.dof`](@ref), [`Metida.loglikelihood`](@ref), [`Metida.aic`](@ref), [`Metida.bic`](@ref), [`Metida.aicc`](@ref),  [`Metida.isfitted`](@ref), [`Metida.vcov`](@ref), [`Metida.stderror`](@ref), [`Metida.modelmatrix`](@ref), [`Metida.response`](@ref), [`Metida.crossmodelmatrix`](@ref), [`Metida.coeftable`](@ref), [`Metida.responsename`](@ref)
+
+
+
 ##### Type III Tests of Fixed Effects
 
 !!! warning

src/lmm.jl

@@ -23,7 +23,7 @@ Make Linear-Mixed Model object.
 
 `random`: vector of random effects or single random effect
 
-`repeated`: is a repeated effect (only one)
+`repeated`: is a repeated effect or vector
 
 `wts`: regression weights (residuals).