This online book is a reprint of:

Bernabeu, P. (2022). Language and sensorimotor simulation in conceptual processing: Multilevel analysis and statistical power. Lancaster University. https://doi.org/10.17635/lancaster/thesis/1795

Materials: https://osf.io/vyb8k

Setup code

General parameters are set in this section.

Code

options(knitr.duplicate.label = 'allow')

# General knitr options
knitr::opts_chunk$set(cache = FALSE, message = FALSE, warning = FALSE, 
                      error = FALSE, echo = TRUE, collapse = TRUE,
                      fig.align = 'center', dev = 'CairoPDF', 
                      knitr.graphics.auto_pdf = TRUE, dpi = 72, 
                      out.width = '100%')

library(rmdfiltr)     # Adjust format of citations to match APA
library(knitr)        # Document rendering
library(kableExtra)   # Tables
library(dplyr)        # Data wrangling

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:kableExtra':
## 
##     group_rows

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Code

library(reshape2)     # Data wrangling
library(formattable)  # Format numbers
library(kableExtra)   # Table formatting (e.g., `pack_rows()`)
library(stringr)      # Text processing
library(car)          # Analysis

## Loading required package: carData

## 
## Attaching package: 'car'

## The following object is masked from 'package:dplyr':
## 
##     recode

Code

library(lmerTest)     # Analysis

## Loading required package: lme4

## Loading required package: Matrix

## 
## Attaching package: 'lmerTest'

## The following object is masked from 'package:lme4':
## 
##     lmer

## The following object is masked from 'package:stats':
## 
##     step

Code

library(simr)         # Analysis

## 
## Attaching package: 'simr'

## The following object is masked from 'package:lme4':
## 
##     getData

## The following object is masked from 'package:stringr':
## 
##     fixed

Code

library(ggplot2)      # Plots
library(ggridges)     # Plots
library(ggtext)       # Plots
library(GGally)       # Correlation plots

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

Code

library(sjPlot)       # Model plots

## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!

Code

library(RColorBrewer) # Colours in plots
library(patchwork)    # Combination of plots

## 
## Attaching package: 'patchwork'

## The following object is masked from 'package:formattable':
## 
##     area

Code

library(Cairo)        # Allows use of special characters such as dashes in plots
library(magick)       # Image rendering

## Linking to ImageMagick 6.9.12.3
## Enabled features: cairo, freetype, fftw, ghostscript, heic, lcms, pango, raw, rsvg, webp
## Disabled features: fontconfig, x11

Code

library(tikzDevice)   # Image rendering

Code


# Read in all custom functions
setwd('R_functions')
sapply(list.files(), source, echo = FALSE)
setwd('../')

# Load main data sets and models. These objects are loaded directly, rather than 
# being run on the go, to keep the knitting of the manuscript reasonably fast. 
# Crucially, however, all the objects can be reproduced from the appropriate R 
# scripts in the current project. 

# Study 2.2.1: Semantic priming

# Code for data set below in 'semanticpriming/data' folder
semanticpriming = read.csv('semanticpriming/data/final_dataset/semanticpriming.csv')

# Code for models below in 'semanticpriming/frequentist_analysis' folder
semanticpriming_lmerTest =   # Primary model object
  readRDS('semanticpriming/frequentist_analysis/results/semanticpriming_lmerTest.rds')
KR_summary_semanticpriming_lmerTest =   # Model with Kenward-Roger p values
  readRDS('semanticpriming/frequentist_analysis/results/KR_summary_semanticpriming_lmerTest.rds')
confint_semanticpriming_lmerTest =   # Confidence intervals
  readRDS('semanticpriming/frequentist_analysis/results/confint_semanticpriming_lmerTest.rds')


# Subset of the semantic priming study that included vision-based similarity.
# Code for data set below in 'semanticpriming/data' folder
semanticpriming_with_visualsimilarity = 
  read.csv('semanticpriming/data/subset_with_visualsimilarity/semanticpriming_with_visualsimilarity.csv')

# Code for models below in 'semanticpriming/semanticpriming_with_visualsimilarity' folder
semanticpriming_with_visualsimilarity_lmerTest =   # Primary model object
  readRDS('semanticpriming/analysis_with_visualsimilarity/results/semanticpriming_with_visualsimilarity_lmerTest.rds')
KR_summary_semanticpriming_with_visualsimilarity_lmerTest =   # Model with Kenward-Roger p values
  readRDS('semanticpriming/analysis_with_visualsimilarity/results/KR_summary_semanticpriming_with_visualsimilarity_lmerTest.rds')
confint_semanticpriming_with_visualsimilarity_lmerTest =   # Confidence intervals
  readRDS('semanticpriming/analysis_with_visualsimilarity/results/confint_semanticpriming_with_visualsimilarity_lmerTest.rds')

# Code for models below in 'semanticpriming/bayesian_analysis' folder
semanticpriming_summary_informativepriors_exgaussian = 
  readRDS('semanticpriming/bayesian_analysis/results/semanticpriming_summary_informativepriors_exgaussian.rds')
semanticpriming_summary_weaklyinformativepriors_exgaussian = 
  readRDS('semanticpriming/bayesian_analysis/results/semanticpriming_summary_weaklyinformativepriors_exgaussian.rds')
semanticpriming_summary_diffusepriors_exgaussian = 
  readRDS('semanticpriming/bayesian_analysis/results/semanticpriming_summary_diffusepriors_exgaussian.rds')


# Study 2.2: Semantic decision
# Code for data set below in 'semanticdecision/data' folder
semanticdecision = read.csv('semanticdecision/data/final_dataset/semanticdecision.csv')

# Code for models below in 'semanticdecision/frequentist_analysis' folder
semanticdecision_lmerTest =   # Primary model object
  readRDS('semanticdecision/frequentist_analysis/results/semanticdecision_lmerTest.rds')
KR_summary_semanticdecision_lmerTest =   # Model with Kenward-Roger p values
  readRDS('semanticdecision/frequentist_analysis/results/KR_summary_semanticdecision_lmerTest.rds')
confint_semanticdecision_lmerTest =   # Confidence intervals
  readRDS('semanticdecision/frequentist_analysis/results/confint_semanticdecision_lmerTest.rds')

# Code for models below in 'semanticdecision/bayesian_analysis' folder
semanticdecision_summary_informativepriors_exgaussian = 
  readRDS('semanticdecision/bayesian_analysis/results/semanticdecision_summary_informativepriors_exgaussian.rds')
semanticdecision_summary_weaklyinformativepriors_exgaussian = 
  readRDS('semanticdecision/bayesian_analysis/results/semanticdecision_summary_weaklyinformativepriors_exgaussian.rds')
semanticdecision_summary_diffusepriors_exgaussian = 
  readRDS('semanticdecision/bayesian_analysis/results/semanticdecision_summary_diffusepriors_exgaussian.rds')


# Study 2.3: Lexical decision
# Code for data set below in 'lexicaldecision/data' folder
lexicaldecision = read.csv('lexicaldecision/data/final_dataset/lexicaldecision.csv')

# Code for models below in 'lexicaldecision/frequentist_analysis' folder
lexicaldecision_lmerTest =   # Primary model object
  readRDS('lexicaldecision/frequentist_analysis/results/lexicaldecision_lmerTest.rds')
KR_summary_lexicaldecision_lmerTest =   # Model with Kenward-Roger p values
  readRDS('lexicaldecision/frequentist_analysis/results/KR_summary_lexicaldecision_lmerTest.rds')
confint_lexicaldecision_lmerTest =   # Confidence intervals
  readRDS('lexicaldecision/frequentist_analysis/results/confint_lexicaldecision_lmerTest.rds')

# Code for models below in 'lexicaldecision/bayesian_analysis' folder
lexicaldecision_summary_informativepriors_exgaussian = 
  readRDS('lexicaldecision/bayesian_analysis/results/lexicaldecision_summary_informativepriors_exgaussian.rds')
lexicaldecision_summary_weaklyinformativepriors_exgaussian = 
  readRDS('lexicaldecision/bayesian_analysis/results/lexicaldecision_summary_weaklyinformativepriors_exgaussian.rds')
lexicaldecision_summary_diffusepriors_exgaussian = 
  readRDS('lexicaldecision/bayesian_analysis/results/lexicaldecision_summary_diffusepriors_exgaussian.rds')

Pablo Bernabeu, 2022. Licence: CC BY 4.0.
Thesis: https://doi.org/10.17635/lancaster/thesis/1795.

Online book created using the R package bookdown.