Merge pull request #13 from dalejbarr/master

fix broken shiny links

05-linear-mixed-effects-intro.Rmd

@@ -749,5 +749,5 @@ ggplot(sleep2, aes(x = days_deprived, y = Reaction)) +
 
 ## Multi-level app
 
-[Try out the multi-level web app](https://shiny.psy.gla.ac.uk/Dale/multilevel){target="_blank"} to sharpen your understanding of the three different approaches to multi-level modeling.
+[Try out the multi-level web app](https://rstudio-connect.psy.gla.ac.uk/multilevel){target="_blank"} to sharpen your understanding of the three different approaches to multi-level modeling.
 

06-linear-mixed-effects-one.Rmd

@@ -84,7 +84,7 @@ One of the main selling points of the general linear models / regression framewo
 
 Let's consider a situation where you are testing the effect of alcohol consumption on simple reaction time (e.g., press a button as fast as you can after a light appears). To keep it simple, let's assume that you have collected data from 14 participants randomly assigned to perform a set of 10 simple RT trials after one of two interventions: drinking a pint of alcohol (treatment condition) or a placebo drink (placebo condition).  You have 7 participants in each of the two groups. Note that you would need more than this for a real study.
 
-This [web app](https://shiny.psy.gla.ac.uk/Dale/icc){target="_blank"} presents simulated data from such a study. Subjects P01-P07 are from the placebo condition, while subjects T01-T07 are from the treatment condition. Please stop and have a look!
+This [web app](https://rstudio-connect.psy.gla.ac.uk/icc){target="_blank"} presents simulated data from such a study. Subjects P01-P07 are from the placebo condition, while subjects T01-T07 are from the treatment condition. Please stop and have a look!
 
 If we were going to run a t-test on these data, we would first need to calculate subject means, because otherwise the observations are not independent. You could do this as follows. (If you want to run the code below, you can download sample data from the web app above and save it as `independent_samples.csv`).
 

07-crossed-random-factors.Rmd

@@ -231,7 +231,7 @@ For more technical details about convergence problems and what to do, see `?lme4
 
 ## Simulating data with crossed random factors
 
-For these exercises, we will generate simulated data corresponding to an experiment with a single, two-level factor (independent variable) that is within-subjects and between-items.  Let's imagine that the experiment involves lexical decisions to a set of words (e.g., is "PINT" a word or nonword?), and the dependent variable is response time (in milliseconds), and the independent variable is word type (noun vs verb).  We want to treat both subjects and words as random factors (so that we can generalize to the population of events where subjects encounter words).  You can play around with the web app (or [click here to open it in a new window](https://shiny.psy.gla.ac.uk/Dale/crossed){target="_blank"}), which allows you to manipulate the data-generating parameters and see their effect on the data.
+For these exercises, we will generate simulated data corresponding to an experiment with a single, two-level factor (independent variable) that is within-subjects and between-items.  Let's imagine that the experiment involves lexical decisions to a set of words (e.g., is "PINT" a word or nonword?), and the dependent variable is response time (in milliseconds), and the independent variable is word type (noun vs verb).  We want to treat both subjects and words as random factors (so that we can generalize to the population of events where subjects encounter words).  You can play around with the web app (or [click here to open it in a new window](https://rstudio-connect.psy.gla.ac.uk/crossed){target="_blank"}), which allows you to manipulate the data-generating parameters and see their effect on the data.
 
 By now, you should have all the pieces of the puzzle that you need to simulate data from a study with crossed random effects. @Debruine_Barr_2020 provides a more detailed, step-by-step walkthrough of the exercise below.
 

08-generalized-linear-models.Rmd

@@ -151,8 +151,8 @@ $$np(1 - p).$$
 
 The app below allows you to manipulate the intercept and slope of a line in log odds space and to see the projection of the line back into response space. Note the S-shaped ("sigmoidal") shape of the function in the response shape.
 
-```{r logit-app, echo=FALSE, fig.cap="**Logistic regression web app** <https://shiny.psy.gla.ac.uk/Dale/logit>"}
-knitr::include_app("https://shiny.psy.gla.ac.uk/Dale/logit", "800px")
+```{r logit-app, echo=FALSE, fig.cap="**Logistic regression web app** <https://rstudio-connect.psy.gla.ac.uk/logit>"}
+knitr::include_app("https://rstudio-connect.psy.gla.ac.uk/logit", "800px")
 ```
 
 ### Estimating logistic regression models in R

docs/02-correlation-regression.md

@@ -44,18 +44,18 @@ starwars %>%
 ```
 
 ```
-## 
-## Correlation method: 'pearson'
-## Missing treated using: 'pairwise.complete.obs'
+## Correlation computed with
+## • Method: 'pearson'
+## • Missing treated using: 'pairwise.complete.obs'
 ```
 
 ```
 ## # A tibble: 3 × 4
 ##   term       height   mass birth_year
 ##   <chr>       <dbl>  <dbl>      <dbl>
-## 1 height     NA      0.134     -0.400
-## 2 mass        0.134 NA          0.478
-## 3 birth_year -0.400  0.478     NA
+## 1 height     NA      0.131     -0.404
+## 2 mass        0.131 NA          0.478
+## 3 birth_year -0.404  0.478     NA
 ```
 
 You can look up any bivariate correlation at the intersection of any given row or column. So the correlation between `height` and `mass` is .134, which you can find in row 1, column 2 or row 2, column 1; the values are the same. Note that there are only `choose(3, 2)` = 3 unique bivariate relationships, but each appears twice in the table. We might want to show only the unique pairs. We can do this by appending `corrr::shave()` to our pipeline.
@@ -69,18 +69,18 @@ starwars %>%
 ```
 
 ```
-## 
-## Correlation method: 'pearson'
-## Missing treated using: 'pairwise.complete.obs'
+## Correlation computed with
+## • Method: 'pearson'
+## • Missing treated using: 'pairwise.complete.obs'
 ```
 
 ```
 ## # A tibble: 3 × 4
 ##   term       height   mass birth_year
 ##   <chr>       <dbl>  <dbl>      <dbl>
 ## 1 height     NA     NA             NA
-## 2 mass        0.134 NA             NA
-## 3 birth_year -0.400  0.478         NA
+## 2 mass        0.131 NA             NA
+## 3 birth_year -0.404  0.478         NA
 ```
 
 Now we've only got the lower triangle of the correlation matrix, but the `NA` values are ugly and so are the leading zeroes. The **`corrr`** package also provides the `fashion()` function that cleans things up (see `?corrr::fashion` for more options).
@@ -95,9 +95,9 @@ starwars %>%
 ```
 
 ```
-## 
-## Correlation method: 'pearson'
-## Missing treated using: 'pairwise.complete.obs'
+## Correlation computed with
+## • Method: 'pearson'
+## • Missing treated using: 'pairwise.complete.obs'
 ```
 
 ```
@@ -193,16 +193,16 @@ starwars3 %>%
 ```
 
 ```
-## 
-## Correlation method: 'pearson'
-## Missing treated using: 'pairwise.complete.obs'
+## Correlation computed with
+## • Method: 'pearson'
+## • Missing treated using: 'pairwise.complete.obs'
 ```
 
 ```
 ##         term height mass birth_year
 ## 1     height                       
-## 2       mass    .74                
-## 3 birth_year    .45  .24
+## 2       mass    .73                
+## 3 birth_year    .44  .24
 ```
 
 Note that these values are quite different from the ones we started with.
@@ -219,16 +219,16 @@ starwars %>%
 ```
 
 ```
-## 
-## Correlation method: 'spearman'
-## Missing treated using: 'pairwise.complete.obs'
+## Correlation computed with
+## • Method: 'spearman'
+## • Missing treated using: 'pairwise.complete.obs'
 ```
 
 ```
 ##         term height mass birth_year
 ## 1     height                       
-## 2       mass    .75                
-## 3 birth_year    .16  .15
+## 2       mass    .72                
+## 3 birth_year    .15  .15
 ```
 
 Incidentally, if you are generating a report from R Markdown and want your tables to be nicely formatted you can use `knitr::kable()`.
@@ -261,13 +261,13 @@ starwars %>%
   </tr>
   <tr>
    <td style="text-align:left;"> mass </td>
-   <td style="text-align:left;"> .75 </td>
+   <td style="text-align:left;"> .72 </td>
    <td style="text-align:left;">  </td>
    <td style="text-align:left;">  </td>
   </tr>
   <tr>
    <td style="text-align:left;"> birth_year </td>
-   <td style="text-align:left;"> .16 </td>
+   <td style="text-align:left;"> .15 </td>
    <td style="text-align:left;"> .15 </td>
    <td style="text-align:left;">  </td>
   </tr>