Package index • aphantasiaReasoningViie

Raw data

survey_data: Participant's questionnaire data

experiment_data: Reasoning experiment data

Data preparation

get_clean_data(): Wrapper function to get clean "analysis-ready" data

filter_random_accuracy_ids(): Filter participants with below random accuracy

filter_manually_identified_ids(): Filter manually identified participant based on various criteria

filter_suspicious_rt_ids() mark_suspicious_rt_ids(): Filter (or mark) participants with suspicious median RTs

filter_trials_on_rt(): Filter outlier trials based on mean response time per participant

factor_categories(): Convert the category column to a factor with contrasts

factor_groups(): Convert the group column to a factor with the desired VVIQ classification

factor_strategies(): Convert strategy columns to factors with optionally ordered levels

factor_chr_vars(): Convert all character variables in a data frame to factors

compute_nieq_scores(): Compute NIEQ scores by combining the frequency and proportion items of each subscale

pivot_strategies_longer(): Get a long format data frame with the strategies gathered in a single column

pivot_terms_longer(): Get a long format data frame with the problem terms in a single column

Clustering

cluster_osivq(): Cluster the OSIVQ data using consensus between various algorithms

add_named_clusters(): Add a column with named cluster assignments to a data frame

summarise_clustering(): Get the size and questionnaire means of clusters

Modelling

build_formula(): Build a formula based on the common model for accuracy and RT modelling

set_ranef_prior(): Create a weakly informative regularizing Gamma prior for the random effects

fit_clm(): Fit a cumulative link model (CLM) using the ordinal package

get_contrast(): Get the pairwise contrasts of variables in a model

get_params(): Get the fixed parameters of a model in a clean format

get_performance(): Get performance indices for a model in a clean format

get_singularity(): Check if the model is singular and print a message

report_contrast(): Get the contrasts of a model and format them for reporting

Visualisation

plot_median_rt_distribution(): Plot the distribution of the median RT across participants

plot_osivq_ternary(): Plot the OSIVQ scores of clusters in a ternary diagram

plot_strategies_barplot(): Plot proportions of strategy use for groups as barplots

plot_strategies_scores(): Plot mean strategy scores for groups

add_significance(): Add significance label and line to a plot

theme_pdf(): Theme for elegant scientific vector figures

save_plot(): Custom ggsave wrapper set with Nature's formatting guidelines

Simulation

simulate_rt_data(): Simulate skewed RT data for the factorial design

simulate_acc_data(): Simulate accuracy data for the factorial design