start changing parse

Author: mmkim1210

README.md

@@ -64,4 +64,9 @@ fit!(model)
 ## See also
 - J. Kim, J. Shen, A. Wang, D.V. Mehrotra, S. Ko, J.J. Zhou, and H. Zhou: **VCSEL: Prioritizing SNP-set by penalized variance component selection** (2021) ([link](http://doi.org/10.1214/21-aoas1491))
 - L. Hu, W. Lu, J. Zhou, and H. Zhou: **MM algorithms for variance component estimation and selection in logistic linear mixed models** (2019) ([link](http://doi.org/10.5705/ss.202017.0220))
-- J.J. Zhou, T. Hu, D. Qiao, M.H. Cho, and H. Zhou: **Boosting gene mapping power and efficiency with efficient exact variance component tests of single nucleotide polymorphism sets** (2016) ([link](http://doi.org/10.1534/genetics.116.190454))
+- J.J. Zhou, T. Hu, D. Qiao, M.H. Cho, and H. Zhou: **Boosting gene mapping power and efficiency with efficient exact variance component tests of single nucleotide polymorphism sets** (2016) ([link](http://doi.org/10.1534/genetics.116.190454))
+
+## Contributors
+<a href="https://github.com/Hua-Zhou/MultiResponseVarianceComponentModels.jl/graphs/contributors">
+    <img src="https://contrib.rocks/image?repo=Hua-Zhou/MultiResponseVarianceComponentModels.jl" />
+</a>

src/parse.jl

@@ -1,29 +1,29 @@
 """
     lrt(model1::VCModel, model0::VCModel)
 
-Perform a variation of the likelihood ratio test for univariate variance components models as in
-Molenberghs and Verbeke 2007 with model1 and model0 being the full and nested models, respectively.
+Perform a variation of the likelihood ratio test for univariate variance components models
+as in Molenberghs and Verbeke 2007 with `model1` and `model0` being the full and nested
+models, respectively.
 """
 function lrt(
     model1 :: VCModel,
     model0 :: VCModel
     )
     df = length(model1.V) - length(model0.V)
-    @assert df > 0
-    @assert size(model0.Σ[1], 1) == 1
-    @assert size(model1.Σ[1], 1) == 1
+    @assert df > 0 "models should be nested"
+    @assert size(model1.Σ[1], 1) == 1 && size(model0.Σ[1], 1) == 1 "models should be univariate"
     lrt = 2 * (model1.logl[1] - model0.logl[1])
     coefs = [2.0^-df * binomial(df, i) for i in 1:df]
     ps = [ccdf(Chisq(i), lrt) for i in 1:df]
-    sum(coefs .* ps)
+    dot(coefs, ps)
 end
 
 """
     h2(model::VCModel)
 
-Calculate heritability estimates and their standard errors, assuming that all variance components
-capture genetic effects except the last term. Also return total heritability from sum of individual
-contributions and its standard error.
+Calculate heritability estimates and their standard errors, assuming that all variance
+components capture genetic effects except the last term. Also return total heritability from
+sum of individual contributions and its standard error.
 """
 function h2(model::VCModel)
     m, d = length(model.Σ), size(model.Σ[1], 1)
@@ -52,16 +52,6 @@ function h2(model::VCModel)
     h2s, ses
 end
 
-function findvar(d::Int)
-    s, r = ◺(d), d
-    idx = ones(Int, d)
-    for j in 2:length(idx)
-        idx[j] = idx[j - 1] + r
-        r -= 1
-    end
-    idx
-end
-
 """
     rg(model::VCModel)
 
@@ -94,3 +84,13 @@ function rg(model::VCModel)
     [copytri!(ses[i], 'L') for i in 1:m]
     rgs, ses
 end
+
+function findvar(d::Int)
+    s, r = ◺(d), d
+    idx = ones(Int, d)
+    for j in 2:length(idx)
+        idx[j] = idx[j - 1] + r
+        r -= 1
+    end
+    idx
+end