Supplemental-Material-B-Power-Simulation.Rmd

---
title: "Supplemental Information B: Power Simulations"
subtitle: "Supplemental Material for 'Need Fulfillment During Intergroup Contact: Three Experience Sampling Studies'"
author:
- ██████ ██████████^1^
- ██████████ ████████^1^
- █████ █████████^1^
- █████ ██ █████^1^
- ███ ███████^1^
- ^1^██████████ ██ █████████, Department of Psychology
- "Author Information:"
- "Correspondence concerning this article should be addressed to ██████ ██████████, ██████████ ██ ██████████, █████████ ██ ██████████, █████ ██████████ ███, ████ ██ █████████ ████████████████. E-mail: █████████████████"
- 'The main manuscript is available at <a href="https://www.doi.org/ToBePublished" target="_blank">doi.org/ToBePublished</a>'
- 'The data repository for this manuscript is available at <a href="https://osf.io/pr9zs/?view_only=1ea47bb646694632a764dead807ef970" target="_blank">osf.io/pr9zs/?view_only=1ea47bb646694632a764dead807ef970</a>'
- 'The GitHub repository for this manuscript is available at <a href="https://github.com/maskedForPeerReview" target="_blank">github.com/maskedForPeerReview</a>'
date: "Last updated: `r format(Sys.time(), '%d %B, %Y')`"
output: 
  bookdown::html_document2:
    fig_caption: yes
    md_extensions: +footnotes
    code_folding: hide
    mathjax: default
    theme: yeti
    toc: yes
    toc_float: yes
    number_sections: false
    css: style.css
    includes:
      in_header: "_includes/head-custom-rmd.html" 
editor_options:
  chunk_output_type: console
bibliography: references.bib
csl: apa.csl
header-includes:
   - \usepackage{amsmath, nccmath}
---

<style type="text/css">
.main-container {
  max-width: 1300px;
  margin-left: auto;
  margin-right: auto;
}
.table {
  margin-left:auto; 
  margin-right:auto;
}
</style>


```{r setup, include=FALSE}
# R Studio Clean-Up
cat("\014") # clear console
# rm(list=ls()) # clear workspace - use restart R instead [cmd/alt + shift + F10]
gc() # garbage collector

# Install and Load Packages
# !IMPORTANT!
# BEFORE FIRST RENDER:
# To install all relevant packages please run "renv::restore()" (or renv::init() and then initiate from lockfile) in the console before the first use to ensure that all packages are using the correct version.
# to store the packages in a contained library within the project folder: renv::settings$use.cache(FALSE) and add 'RENV_CONFIG_SANDBOX_ENABLED = FALSE' to an '.Renviron' file
lib <- c(
  "rmarkdown",
  "knitr",
  "remedy",
  "bookdown",
  "brms",
  "psych",
  "ggplot2",
  "ggthemes",
  # "haven",
  "RColorBrewer",
  # "plotly",
  "gridExtra",
  "ggpattern",
  "ggridges",
  "binom",
  "iterators",
  "pbkrtest",
  "plotrix",
  "RLRsim",
  "stats",
  "methods",
  "utils",
  "graphics",
  "grDevices",
  "car",
  "testthat",
  "simr",
  "data.table",
  "lme4",
  "lmerTest",
  # "nlme",
  "jtools",
  # "gtsummary",
  "sessioninfo",
  # "tibble",
  "pander",
  # "devtools",
  # "mada",
  #"tidyr",
  #"tidyverse",
  "plyr",
  "dplyr",
  # "Hmisc",
  "kableExtra",
  # "papaja",
  "stringr"#,
  # "stringi",
  # "reshape2",
  # "lubridate",
  # "purrr",
  # "metafor"
)
invisible(lapply(lib, library, character.only = TRUE))
rm(lib)

# Load Custom Packages
source("./scripts/functions/fun.panel.R")
source("./scripts/functions/themes.R")
# source("./scripts/functions/binaryCor.R")
# source("./scripts/functions/MlCorMat.R")
# source("./scripts/functions/MlTbl.R")
# source("./scripts/functions/metaLmer.R")

# Markdown Options
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file()) # set working directory
knitr::opts_knit$get("root.dir") # check working directory
options(
  scipen = 999,
  digits = 4,
  width = 400
) # removes scientific quotation
# knitr::opts_chunk$set(echo = TRUE, cache = F, cache.path = rprojroot::find_rstudio_root_file('cache/')) # cache settings
knitr::knit_hooks$set(
  error = function(x, options) {
    paste('\n\n<div class="alert alert-danger">',
      gsub("##", "\n", gsub("^##\ Error", "**Error**", x)),
      "</div>",
      sep = "\n"
    )
  },
  warning = function(x, options) {
    paste('\n\n<div class="alert alert-warning">',
      gsub("##", "\n", gsub("^##\ Warning:", "**Warning**", x)),
      "</div>",
      sep = "\n"
    )
  },
  message = function(x, options) {
    paste('\n\n<div class="alert alert-info">',
      gsub("##", "\n", x),
      "</div>",
      sep = "\n"
    )
  }
)
htmltools::tagList(rmarkdown::html_dependency_font_awesome())

# Global Chunk Options
knitr::opts_chunk$set(
  fig.width = 12,
  fig.height = 8,
  fig.path = "Figures/",
  echo = TRUE,
  cache = TRUE,
  warning = FALSE,
  message = FALSE
)

# set ggplot theme
theme_set(theme_Publication())
```

<br/>

<i class="fas fa-exclamation-circle"></i> Note. Boxplots display the interquartile range (IQR, center box), and the whiskers extend 1.5*IQR from the lower and upper hinge. The white point indicates the mean and the white center line indicates the median.   

<br/>

# Data Import  

```{r}
#| label: import data

load("data/wrangled.RData")
```

# Extract Effects for Simulation

## Prepare Data

```{r}
#| label: data prep

# TBD
```


# ML Regression

Run multilevel regression to extract the effect sizes and covariance matrices

```{r}
#| label: run regression 

# run regresion 
lmer_quality_contemporary <-
  lmer(
    quality_overall_1 ~ 
      keymotive_fulfillemt_1_cwc + (1 + keymotive_fulfillemt_1_cwc | PID),
    data = dtWorkerSupp$workerOutWithinBetween
  )
lmer_attitude_contemporary <-
  lmer(
    thermometerDutch_1 ~ 
      keymotive_fulfillemt_1_cwc + (1 + keymotive_fulfillemt_1_cwc | PID),
    data = dtWorkerSupp$workerOutWithinBetween
  )
lmer_attitude_need_quality_contemporary <-
  lmer(
    thermometerDutch_1 ~
      keymotive_fulfillemt_1_cwc + quality_overall_1_cwc + (1 + keymotive_fulfillemt_1_cwc + quality_overall_1_cwc | PID),
    data = dtWorkerSupp$workerOutWithinBetween
  )







```


For the main analyses we have four fixed effects of interest. (1) The effect of need fulfillment on interaction quality: _b_ = `r round(fixef(lmer_quality_contemporary)['keymotive_fulfillemt_1_cwc'], 3)`, (2) the effect of need fulfillment on outgroup attitudes: _b_ = `r round(fixef(lmer_attitude_contemporary)['keymotive_fulfillemt_1_cwc'], 3)`, (3) the effect of need fulfillment on outgroup attitudes while controlling for interaction quality: _b_ = `r round(fixef(lmer_attitude_need_quality_contemporary)['keymotive_fulfillemt_1_cwc'], 3)`, as well as (4) the effect of interaction quality on outgroup attitudes while controlling for need fulfillment: _b_ = `r round(fixef(lmer_attitude_need_quality_contemporary)['quality_overall_1_cwc'], 3)`.


# Power Simulation Parameters

```{r}
#| label: simulation setup
nsim <- 1000
progress <- FALSE
n_ppt <- 250
n_measure <- 180 # 3 months with 2 measurements per day

# extrapolate data:
sim_n_quality <- extend(lmer_quality_contemporary, along="PID", n = n_ppt)
sim_n_attitude <- extend(lmer_attitude_contemporary, along="PID", n = n_ppt)
sim_n_attitude_full <- extend(lmer_attitude_need_quality_contemporary, along="PID", n = n_ppt)

# extrapolate data:
sim_t_quality <- extend(lmer_quality_contemporary, within="PID", n = n_measure)
sim_t_attitude <- extend(lmer_attitude_contemporary, within="PID", n = n_measure)
sim_t_attitude_full <- extend(lmer_attitude_need_quality_contemporary, within="PID", n = n_measure)
```

# Power Simulation Participant Numbers

Simulate different participant numbers for all fixed effects.

## Power of `Quality ~ NeedFulfill` for different numbers of participants

```{r}
#| label: participant simulation Quality NeedFulfill
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_n_quality_need = powerCurve(
  fit = sim_n_quality,
  test = simr::fixed("keymotive_fulfillemt_1_cwc", "t"),
  along = "PID",
  progress = progress,
  breaks = seq(1, 12, 1),
  nsim = nsim
)
plot(pc_n_quality_need) +
  title("Quality given Need Fulfillment [participant numbers]")
```

## Power of `Attitude ~ NeedFulfill` for different numbers of participants

```{r}
#| label: participant simulation Attitude NeedFulfill
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_n_attitude_need = powerCurve(
  fit = sim_n_attitude,
  test = simr::fixed("keymotive_fulfillemt_1_cwc", "t"),
  along = "PID",
  progress = progress,
  breaks = seq(1, 45, 1),
  nsim = nsim
)
plot(pc_n_attitude_need) +
  title("Attitudes given Need Fulfillment [participant numbers]")
```

## Power of `Attitude ~ NeedFulfill + Quality` for different numbers of participants

we expect this to be super small. Keep in mind that the _b_ = `r round(fixef(lmer_attitude_need_quality_contemporary)["keymotive_fulfillemt_1_cwc"],3)`. So even with a large number of participants, we would not expect these effects to be meaningful.

```{r}
#| label: participant simulation Attitude NeedFulfill given Quality
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_n_attitude_need = powerCurve(
  fit = sim_n_attitude_full,
  test = simr::fixed("keymotive_fulfillemt_1_cwc", "t"),
  along = "PID",
  progress = progress,
  breaks = seq(10, 250, 10),
  nsim = nsim
)
plot(pc_n_attitude_need) +
  title("Attitudes given Need Fulfillment (controling for Quality) [participant numbers]")
```

## Power of `Attitude ~ Quality` given NeedFulfill for different numbers of participants

```{r}
#| label: participant simulation Attitude Qaulity given NeedFulfill
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_n_attitude_full_quality = powerCurve(
  fit = sim_t_attitude_full,
  test = simr::fixed("quality_overall_1_cwc", "t"),
  along = "PID",
  progress = progress,
  breaks = c(seq(1, 25, 1), seq(30, 100, 10)),
  nsim = nsim
)
plot(pc_n_attitude_full_quality) +
  title("Attitudes given Quality (controlling for Need Fulfillment) [participant numbers]")
```


# Power Simulation Measurement Numbers

Simulate different number of within person measurements.

## Power of `Quality ~ NeedFulfill` for different numbers of measurements

```{r}
#| label: measurement simulation Quality NeedFulfill
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_t_quality_need = powerCurve(
  fit = sim_t_quality,
  test = simr::fixed("keymotive_fulfillemt_1_cwc", "t"),
  within = "PID",
  progress = progress,
  breaks = seq(1, 20, 1),
  nsim = nsim
)
plot(pc_t_quality_need) +
  title("Quality given Need Fulfillment [measurement numbers]")
```

## Power of `Attitude ~ NeedFulfill` for different numbers of measurements

```{r}
#| label: measurement simulation Attitude NeedFulfill
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_t_attitude_need = powerCurve(
  fit = sim_t_attitude,
  test = simr::fixed("keymotive_fulfillemt_1_cwc", "t"),
  within = "PID",
  progress = progress,
  breaks = seq(1, 25, 1),
  nsim = nsim
)
plot(pc_t_attitude_need) +
  title("Attitudes given Need Fulfillment [measurement numbers]")
```

## Power of `Attitude ~ NeedFulfill` given Quality for different numbers of measurements

we expect this to be super small. Keep in mind that the _b_ = `r round(fixef(lmer_attitude_need_quality_contemporary)["keymotive_fulfillemt_1_cwc"],3)`. So even with a large number of measurements, we would not expect these effects to be meaningful.

```{r}
#| label: measurement simulation Attitude NeedFulfill given Quality
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_t_attitude_full_need = powerCurve(
  fit = sim_t_attitude_full,
  test = simr::fixed("keymotive_fulfillemt_1_cwc", "t"),
  within = "PID",
  progress = progress,
  breaks = seq(10, 180, 10),
  nsim = nsim
)
plot(pc_t_attitude_full_need) +
  title("Attitudes given Need Fulfillment (controlling for Quality) [measurement numbers]")
```


## Power of `Attitude ~ Quality` given NeedFulfill for different numbers of measurements

```{r}
#| label: measurement simulation Attitude Qaulity given NeedFulfill
#| warning: FALSE
#| error: FALSE

# Run and plot simulations
pc_t_attitude_full_quality = powerCurve(
  fit = sim_t_attitude_full,
  test = simr::fixed("quality_overall_1_cwc", "t"),
  within = "PID",
  progress = progress,
  breaks = seq(1, 15, 1),
  nsim = nsim
)
plot(pc_t_attitude_full_quality) +
  title("Attitudes given Quality (controlling for Need Fulfillment) [measurement numbers]")
```



# Export data and plots

```{r}
#| label: export

# save simulations, because this takes for freakin' ever.      
save(list = ls(pattern = "plt."), 
     file = "data/S1_Workers/processed/PowerSimPlots.RData")

#load("PowerSimPlots.RData")
```

# Software Information  

The full session information with all relevant system information and all loaded and installed packages is available in the collapsible section below.  

<details>
  <summary>System Info</summary>
  
  \renewcommand{\arraystretch}{0.8} <!-- decrease line spacing for the table -->
```{r Reproducibility-SessionInfo-R-environment, echo=FALSE, message=FALSE, warning=FALSE, fig.align="center", out.width='100%', results='asis'}
df_session_platform <- devtools::session_info()$platform %>%
  unlist(.) %>%
  as.data.frame(.) %>%
  rownames_to_column(.)

colnames(df_session_platform) <- c("Setting", "Value")

kbl(
  df_session_platform,
  booktabs = T,
  align = "l",
  caption = "R environment session info for reproducibility of results" # complete caption for main document
) %>%
  kable_classic(
    full_width = F,
    lightable_options = "hover",
    html_font = "Cambria"
  )
```
  \renewcommand{\arraystretch}{1} <!-- reset row height/line spacing -->
 </details>
 <br>
 <details>
  <summary>Package Info</summary>

\renewcommand{\arraystretch}{0.6} <!-- decrease line spacing for the table -->
```{r Reproducibility-SessionInfo-R-packages, echo=FALSE, message=FALSE, warning=FALSE, fig.align="center", out.width='100%', results='asis'}
df_session_packages <- devtools::session_info()$packages %>%
  as.data.frame(.) %>%
  filter(attached == TRUE) %>%
  dplyr::select(loadedversion, date, source) %>%
  rownames_to_column()

colnames(df_session_packages) <- c("Package", "Loaded version", "Date", "Source")

kbl(
  df_session_packages,
  booktabs = T,
  align = "l",
  caption = "Package info for reproducibility of results" # complete caption for main document
) %>%
  kable_classic(
    full_width = F,
    lightable_options = "hover",
    html_font = "Cambria"
  )
```
\renewcommand{\arraystretch}{1} <!-- reset row height/line spacing -->
</details>
<br>
<details>
  <summary>Full Session Info (including loaded but unattached packages --- for troubleshooting only)</summary>
    `r pander(sessionInfo(), compact = FALSE)`
</details>


--------------------------------------------------------------------

</br>