---
title: "VPT - Difference of Means"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{vpt_diff_of_means}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(splithalfr)
```
This vignette describes a scoring method similar to [Mogg and Bradley (1999)](https://doi.org/10.1080/026999399379050);
difference of mean reaction times (RTs) between conditions with probe-at-test and probe-at-control, for correct responses, after removing RTs below 200 ms and above 520 ms, on Visual Probe Task data.

<br />

# Dataset
Load the included VPT dataset and inspect its documentation.
```
data("ds_vpt", package = "splithalfr")
?ds_vpt
```
## Relevant variables
The columns used in this example are:

* `UserID`, which identifies participants
* `block_type`, in order to select assessment blocks only
* `patt`, in order to compare trials in which the probe is at the test or at the control stimulus
* `response`, in order to select correct responses only
* `rt`, in order to drop RTs outside of the range [200, 520] and calculate means per level of patt
* `thor`, which is the horizontal position of test stimulus
* `keep`, which is whether probe was superimposed on the stimuli or replaced stimuli

## Data preparation
Only select trials from assessment blocks
```
ds_vpt <- subset(ds_vpt, block_type == "assess")
```

## Counterbalancing
The variables `patt`, `thor`, and `keep` were counterbalanced. Below we illustrate this for the first participant.
```
ds_1 <- subset(ds_vpt, UserID == 1)
table(ds_1$patt, ds_1$thor, ds_1$keep)
```

<br />

# Scoring the VPT

## Scoring function

The scoring function calculates the score of a single participant as follows:

1. select only correct responses
2. drop responses with RTs outside of the range [200, 520]
3. calculate the mean RT of remaining responses

```
fn_score <- function (ds) {
  ds_keep <- ds[ds$response == 1 & ds$rt >= 200 & ds$rt <= 520, ]
  rt_yes <- mean(ds_keep[ds_keep$patt == "yes", ]$rt)
  rt_no <- mean(ds_keep[ds_keep$patt == "no", ]$rt)
  return (rt_no - rt_yes)
}
```

## Scoring a single participant
Let's calculate the VPT score for the participant with UserID 23. NB - This score has also been calculated manually via Excel in the splithalfr repository.
```
fn_score(subset(ds_vpt, UserID == 23))
```

## Scoring all participants
To calculate the VPT score for each participant, we will use R's native `by` function and convert the result to a data frame.
```
scores <- by(
  ds_vpt,
  ds_vpt$UserID,
  fn_score
)
data.frame(
  UserID = names(scores),
  score = as.vector(scores)
)
```

<br />

# Estimating split-half reliability

## Calculating split scores
To calculate split-half scores for each participant, use the function `by_split`. The first three arguments of this function are the same as for `by`. An additional set of arguments allow you to specify how to split the data and how often. In this vignette we will calculate scores of 1000 permutated splits. The trial properties `patt`, `thor` and `keep` were counterbalanced in the VPT design. We will stratify splits by these trial properties. See the vignette on splitting methods for more ways to split the data.

The `by_split` function returns a data frame with the following columns:

* `participant`, which identifies participants
* `replication`, which counts replications
* `score_1` and `score_2`, which are the scores calculated for each of the split datasets

*Calculating the split scores may take a while. By default, `by_split` uses all available CPU cores, but no progress bar is displayed. Setting `ncores = 1` will display a progress bar, but processing will be slower.*

```
split_scores <- by_split(
  ds_vpt,
  ds_vpt$UserID,
  fn_score,
  replications = 1000,
  stratification = paste(ds_vpt$patt, ds_vpt$thor, ds_vpt$keep)
)
```

## Calculating reliability coefficients
Next, the output of `by_split` can be analyzed in order to estimate reliability. By default, functions are provided that calculate Spearman-Brown adjusted Pearson correlations (`spearman_brown`), Flanagan-Rulon (`flanagan_rulon`), Angoff-Feldt (`angoff_feldt`), and Intraclass Correlation (`short_icc`) coefficients. Each of these coefficient functions can be used with `split_coef` to calculate the corresponding coefficients per split, which can then be plotted or averaged, for instance via a simple `mean`. 

```
# Spearman-Brown adjusted Pearson correlations per replication
coefs <- split_coefs(split_scores, spearman_brown)
# Distribution of coefficients
hist(coefs)
# Mean of coefficients
mean(coefs)
```

## Calculating bootstrapped confidence intervals for population reliability coefficient
Finally, we can estimate the Calculate bootstrapped confidence intervals for the value of the reliability coefficient in the population by bootstrapping participants. For this, we'll need to repeatedly sample participants from the population, calculate a collection of reliability coefficients between the split scores of that sample of participants, and average those coefficients together. Hence, the call to `split_ci` below, takes (1) the split scores produced by calling `by_split` (`split_scores`), (2) the reliability coefficient we used above (`spearman_brown`), and (3) the method for averaging coefficients we used above (`mean`).

*The bootstrap can take even longer than the split, and doesn't show any progress bar, but it also uses all available CPU cores by default.*

```
# Conduct a bootstrap (of participants)
bootstrap_result <- split_ci(split_scores, spearman_brown, mean)
# Report confidence intervals
library(boot)
print(boot.ci(bootstrap_result, type="bca"))
```