for now, set eval=false for articles; will come back to these once al…

…l the computational work is completed
pbreheny · Feb 17, 2024 · 58830d0 · 58830d0
1 parent 47449bd
commit 58830d0
Show file tree

Hide file tree

Showing 4 changed files with 15 additions and 4 deletions.
diff --git a/vignettes/articles/B-choose_k.Rmd b/vignettes/articles/B-choose_k.Rmd
@@ -8,7 +8,8 @@ editor_options:
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  eval = FALSE
 )
 ```
 
@@ -17,6 +18,8 @@ library(plmm)
 library(corrplot)
 ```
 
+**This page is under construction** -- DO NOT follow this like a tutorial yet.
+
 ## Context & motivation
 
 One of the novel features of the `plmm` package is in the way our methods address the relationships among samples (e.g., patients/participants) in the data. Unlike other mixed models packages like `lme4`, our package does not based relationships on a grouping factor. Rather, relationships are described as a matrix with cell values capturing the degree of relationship among each pair of samples. Let $\mathbf{X}$ represent the $n \times p$ design matrix of the data, and let $\tilde{\mathbf{X}}$ be the standardized $\mathbf{X}$ [1]. We call the $n \times n$ relationship matrix $\mathbf{K}$. By default, $\mathbf{K}$ is estimated from the data using the from data using $\mathbf{K} = \frac{1}{p} \tilde{\mathbf{X}}\tilde{\mathbf{X}}^\top$ [2].

diff --git a/vignettes/articles/C-explore_options.Rmd b/vignettes/articles/C-explore_options.Rmd
@@ -5,7 +5,8 @@ title: "Exploring different modeling options"
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  eval = FALSE
 )
 ```
 
@@ -15,6 +16,9 @@ library(data.table)
 library(ggplot2)
 ```
 
+
+**This page is under construction** -- DO NOT follow this like a tutorial yet.
+
 The `plmm()` function has many options, so this article aims to provide an overview (and comparison) of several of these model fitting options. This is a more detailed presentation than what is provided in the "Getting started" vignette.
 
 # Options for K matrix

diff --git a/vignettes/articles/D-deconfounding.Rmd b/vignettes/articles/D-deconfounding.Rmd
@@ -5,7 +5,8 @@ title: "Deconfounding"
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  eval = FALSE
 )
 ```
 
@@ -14,6 +15,8 @@ library(plmm)
 ```
 
 
+**This page is under construction** -- DO NOT follow this like a tutorial yet.
+
 ## Context 
 
 I want to explore how different types of confounding impact PLMMs --- the questions on my mind include: 

diff --git a/vignettes/articles/E-mfdr.Rmd b/vignettes/articles/E-mfdr.Rmd
@@ -7,7 +7,8 @@ editor_options:
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  eval = FALSE
 )
 ```