Compute Bayes Factors for Each Participant and Summarize Results

Description

This function splits a dataset by participants, fits linear models for each participant, computes Bayes Factors (BFs) using the bain package, and summarizes the results.

Usage

BF_for_everyone(.df, .participant, formula, hypothesis)

Arguments

Value

A list containing:

GPBF

A matrix of the geometric product, evidence rate, and stability rate for each hypothesis.

BFs

A matrix of the Bayes Factors for each participant and hypothesis.

BF_summary

A summary matrix of the mean, median, standard deviation, minimum, and maximum of the Bayes Factors for each hypothesis.

N

The number of participants.

bain_res

A list of bain results for each participant.

Plot

A ggplot2 object visualizing the distribution of Bayes Factors by hypothesis.

References

Klaassen, F. (2020). Combining Evidence Over Multiple Individual Analyses. In R. van de Schoot & M. Miočević (Eds.), Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners (1st ed., pp. 13). Routledge. doi:10.4324/9780429273872-11

Examples

 # Create data
 cars2 <- cars
 cars2[["parts"]] <- rep(1:10, each = 5)

 # Run analysis
 res <- BF_for_everyone(.df = cars2, .participant = "parts",
                        formula = "dist ~ speed", hypothesis = "speed > 0")

 # View GPBF results
 res$GPBF

Shiny Application for Setting Up and Running Bayes Factors for Each Participant

Description

This function launches a 'shiny' application that allows users to set up and run a Bayes Factors analysis for each participant using the BF_for_everyone function.

Usage

BFfe()

Details

The app allows users to upload a CSV file, specify the formula for the linear model, define hypotheses, and select the participant variable. The app then runs the analysis and displays the results, including a summary of Bayes Factors, geometric product of Bayes Factors, and individual participant results.

Value

Launches a Shiny application in the user's default web browser.

References

Klaassen, F. (2020). Combining Evidence Over Multiple Individual Analyses. In R. van de Schoot & M. Miočević (Eds.), Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners (1st ed., pp. 13). Routledge. doi:10.4324/9780429273872-11

Examples

# To run the Shiny app
if(interactive()){
  BFfe()
}

RepliCrisis Shiny App

Description

The RepliCrisis Shiny app is an interactive game based on the process for evaluating replication studies presented in Hoijtink et. al. (2009). Users can conduct original studies, process the results, and attempt to replicate the studies while adjusting parameters to see how changes affect the outcome.

Usage

RepliCrisis()

Value

An interactive Shiny app.

References

Hoijtink, H., Mulder, J., van Lissa, C., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological methods, 24(5), 539–556. doi:10.1037/met0000201

Examples

if(interactive()){
RepliCrisis()
}

Deal Cards to a RepliCrisis Grid

Description

This function deals cards from a shuffled deck and arranges them into a matrix suitable for the RepliCrisis game grid.

Usage

deal_cards_to_rc_grid(
  deck = mmcards::i_deck(deck = mmcards::shuffle_deck(), i_path = "www", i_names =
    c("2_of_clubs", "2_of_diamonds", "2_of_hearts", "2_of_spades", "3_of_clubs",
    "3_of_diamonds", "3_of_hearts", "3_of_spades", "4_of_clubs", "4_of_diamonds",
    "4_of_hearts", "4_of_spades", "5_of_clubs", "5_of_diamonds", "5_of_hearts",
    "5_of_spades", "6_of_clubs", "6_of_diamonds", "6_of_hearts", "6_of_spades",
    "7_of_clubs", "7_of_diamonds", "7_of_hearts", "7_of_spades", "8_of_clubs",
    "8_of_diamonds", "8_of_hearts", "8_of_spades", 
     "9_of_clubs", "9_of_diamonds",
    "9_of_hearts", "9_of_spades", "10_of_clubs", "10_of_diamonds", "10_of_hearts",
    "10_of_spades", "jack_of_clubs", "jack_of_diamonds", "jack_of_hearts",
    "jack_of_spades", "queen_of_clubs", "queen_of_diamonds", "queen_of_hearts",
    "queen_of_spades", "king_of_clubs", "king_of_diamonds", "king_of_hearts",
    "king_of_spades", "ace_of_clubs", "ace_of_diamonds", "ace_of_hearts",
    "ace_of_spades")),
  n
)

Arguments

Details

The function first checks if there are enough cards in the deck to deal the required number of pairs. If not, it stops with an error. Then, it deals 2*n cards from the provided deck, reshaping them into a 2-row matrix where each column represents a pair of cards.

If no deck is provided, the function will shuffle a standard deck using functions from the mmcards package. The default deck includes all standard 52 playing cards.

The grid of cards will be used by the generate_study_data function to generate data for n groups where the values for each group are simulated from a normal distribution with mean and standard deviation defined by the values in the card pair.

Value

A matrix with two rows and n columns, representing the dealt cards arranged into pairs.

Examples

# Deal a grid with 3 card pairs
grid <- deal_cards_to_rc_grid(n = 3)

Generate Null Hypothesis from Pairwise t-test Results

Description

This function generates a null hypothesis statement (Ho) from the results of pairwise t-tests. If pairwise t-tests are not available, it uses the hypothesis from the original study results.

Usage

generate_Ho_from_pairwise_t(original_study_results)

Arguments

Details

The function checks if the pairwise_t element is present in the original_study_results list. If present, it extracts the p-values and the corresponding column and row names to identify all unique variables involved in the pairwise comparisons.

The unique variables are prefixed with "ColLabs" to denote the column labels from the original dataset. These variables are then concatenated with an equality sign to form the null hypothesis statement, which assumes no difference between any of the groups.

If the pairwise_t element is not present, indicating that no pairwise comparisons were made, the function returns the hypothesis generated by the original study results.

Value

A string representing the null hypothesis for the study.

Examples

Ho <- generate_Ho_from_pairwise_t(
          original_study_results = process_original_study(
                                   df = generate_study_data(
                                   x = deal_cards_to_rc_grid(n = 3),
                                   sample_size = 30),
                                   alpha = 0.05))

Generate Descriptive Statistics for Study Groups

Description

This internal helper function generates descriptive statistics for each group within the original study data. It calculates the sample size, mean, standard deviation, median, median absolute deviation, minimum, maximum, skewness, and kurtosis for the values associated with each group.

Usage

generate_descriptives(df, group_var)

Arguments

Details

The function iterates over each unique group found in the group_var column of the data frame df. For each group, it computes the descriptive statistics using base R functions and functions from the psych and e1071 packages for skewness and kurtosis, respectively.

The results are returned as a data frame where each row corresponds to a group and each column to a descriptive statistic.

Note

As this is an internal function intended for use within process_original_study function of the package.

Generate Study Data for RepliCrisis

Description

This function simulates data for the Replication Crisis study by drawing samples from normal distributions defined by the card values.

Usage

generate_study_data(x, sample_size)

Arguments

Details

The function expects a matrix x generated from deal_cards_to_rc_grid() where the first row contains mean values and the second row contains standard deviation values. It then generates sample_size number of normal random values for each group, using the respective mean and standard deviation. The resulting data frame has two columns: one for the group labels and one for the generated values.

The group labels are factors with levels corresponding to the column numbers prefixed by 'Col'. The generated values are numeric and simulate the data that would be collected in the study. The function uses the rnorm function from the stats package for generating random samples.

Value

A data frame containing the simulated study data. Each row corresponds to a single sample and includes the group label and the sampled value.

Examples

study_data <- generate_study_data(x = deal_cards_to_rc_grid(n = 3),
                                  sample_size = 30)

Interpretation of Replication Study Results

Description

This function interprets the results of a replication study by applying Bayes factor (BF) and posterior model probabilities (PMPb) thresholds. It checks against predefined thresholds to determine if there is strong evidence for the hypotheses derived from the original study.

Usage

interpret_replication_results(
  replication_results,
  bf_threshold = 3,
  pmpb_threshold = 0.8
)

Arguments

Details

The function first checks for the presence of a secondary hypothesis (H2) in the analysis results. If H2 is present, it will prioritize its interpretation; otherwise, it defaults to interpreting H1. Interpretation is based on whether the BF.c and PMPb values exceed their respective thresholds.

A 'win' result means there is strong evidence for the hypothesis, while a 'lose' indicates the evidence is inconclusive or not strong enough.

The function includes a disclaimer about the use of threshold values for hypothesis testing and recommends consulting the cited literature for a comprehensive understanding of Bayesian factors and informative hypothesis testing.

Value

A list with elements 'interpretation' providing the interpretative message, 'result' indicating a 'win' or 'lose' based on the interpretation, and 'disclaimer' providing a contextual disclaimer.

Note

The thresholds used in this function are for educational purposes within the context of a game and should not be taken as rigid rules for hypothesis testing in practice.

Examples

# Original study
os_deck <- deal_cards_to_rc_grid(n = 3)
original_study_data <- generate_study_data(os_deck, sample_size = 100)
original_study_results <- process_original_study(original_study_data)

# Replication study
rs_deck <- deal_cards_to_rc_grid(n = 3)
replication_data <- generate_study_data(rs_deck, sample_size = 100)
replication_results <- process_replication_study(replication_data,
                                                 original_study_results)

interpret_replication_results(replication_results)

Fit Statistical Models for MMI Bain Processing

Description

This function provides a unified interface to fit different statistical models supported by the 'bain' package.

Usage

mmib_model(
  formula = NULL,
  column_names = NULL,
  model = NULL,
  data,
  engine = c("lm", "t_test", "lavaan"),
  ...
)

Arguments

Details

The mmib_model() function provides a simple interface to fit various statistical models, which can be subsequently processed by the bain::bain() function. It ensures that only one of formula, column_names, or model is provided, checks the validity of the provided data, and selects the appropriate statistical method based on the engine parameter.

Value

Returns an object of the type associated with the engine selected (lm, htest, or lavaan object).

See Also

bain for processing the models fit with mmib_model.

Examples

data(mtcars)

# Fit linear model
mod1 <- mmib_model(mpg ~ wt + qsec, data = mtcars, engine = "lm")

mmibain Shiny App

Description

The user can upload CSV data; choose a model engine (lm, t_test, lavaan); specify the formula, variables, or model; and provide additional arguments. Once the model is fitted, the app allows for setting up hypotheses for evaluation. Upon running the analysis, it displays the results of the Bayesian Informative Hypotheses Evaluation.

Usage

mmibain()

Details

This function launches a Shiny app that facilitates a user-friendly interface for setting up and running a Bayesian Informative Hypotheses Evaluation using the bain package.

The app's UI consists of a sidebar for user inputs and a main panel for displaying available variables, model terms, and analysis results. The app relies on the bain package for analysis.

Value

This function returns a running instance of the Shiny app. Interact with the app through the browser or the RStudio Viewer pane.

UI Components

• Data upload (CSV format).

• Engine selection (lm, t_test, lavaan).

• Model input based on chosen engine.

• Additional arguments for statistical model function.

• Action button to fit the model.

• Hypotheses input.

• Fraction input for the bain fraction parameter.

• Option to evaluate hypotheses with respect to standardized regression coefficients.

• Confidence interval input.

• Seed input for reproducibility.

• Action button to run the Bayesian Informative Hypotheses Evaluation.

References

Hoijtink, H., Mulder, J., van Lissa, C., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological methods, 24(5), 539–556. doi:10.1037/met0000201

See Also

bain

Examples

if(interactive()){
  mmibain()
}

Process Original Study Data for Analysis

Description

This function processes the original study data by performing ANOVA, post-hoc t-tests, and checking assumptions such as normality of residuals and homogeneity of variances.

Usage

process_original_study(df, alpha = 0.05)

Arguments

Details

The function starts by fitting an ANOVA model to the data to check for overall significance. If significant differences are found, it proceeds with pairwise t-tests to explore differences between individual conditions.

The function then checks for normality of residuals using the Shapiro-Wilk test and for homogeneity of variances using Levene's test. It constructs a hypothesis string based on the results of the pairwise t-tests.

A directed graph is created to represent the relationships between the conditions. The graph is then simplified to reflect the most direct relationships, which are used to construct the final hypothesis string.

Descriptive statistics are generated for each condition using the generate_descriptives() internal function.

The function returns a list with the following components:

• hypothesis: A string representing the simplified relationships between conditions.

• pairwise_t: The result of pairwise t-tests, if performed.

• fit_summary: The summary of the ANOVA model.

• descriptives: Descriptive statistics for each condition.

• fit: The ANOVA model object.

• shapiro_test: The result of the Shapiro-Wilk normality test.

• levene_test: The result of Levene's test for homogeneity of variances.

Value

A list containing elements for hypothesis, pairwise t-tests (if applicable), fit summary, descriptives, fit object, Shapiro-Wilk normality test result, and Levene's test result.

Examples

results <- process_original_study(df = generate_study_data(
                                       x = deal_cards_to_rc_grid(n = 3),
                                       sample_size = 30),
                                  alpha = 0.05)

Process Replication Study Data

Description

This function processes the data from a replication study by comparing it with the results from an original study, using the bain package for Bayesian inference.

Usage

process_replication_study(replication_data, original_study_results)

Arguments

Details

The function begins by extracting the original hypothesis (Horiginal) from the original_study_results. It then generates the null hypothesis (Ho) using the generate_Ho_from_pairwise_t function.

A hypothesis string for the Bayesian analysis is prepared by comparing Ho and Horiginal. If they are the same, only Horiginal is used; otherwise, both are included in the Bayesian analysis via bain.

An ANOVA model is fitted to the replication data, which is then passed to the bain function along with the hypothesis string. The Bayesian analysis results are returned along with any message regarding the comparison of hypotheses.

Value

A list containing the results from the Bayesian analysis (bain_results) and a message indicating if the null hypothesis and the original hypothesis were the same (message).

Examples

replication_results <- process_replication_study(
  replication_data = generate_study_data(
    x = deal_cards_to_rc_grid(n = 3),
    sample_size = 30
  ),
  original_study_results = process_original_study(
    df = generate_study_data(
      x = deal_cards_to_rc_grid(n = 3),
      sample_size = 30
    ),
    alpha = 0.05
  )
)

Render Card Grid for Shiny App

Description

This internal helper function is used to render a grid of card images in the RepliCrisis Shiny application. It takes a matrix representing the card grid and converts it into a Shiny UI layout with card images.

Usage

render_card_grid(new_card_grid)

Arguments

Details

The function iterates over each row of the new_card_grid matrix to render the card images using Shiny's renderImage function. It then constructs a UI layout using fluidRow and column to display the images in a grid format suitable for a Shiny application.

The images are sourced from the mmibain package and are assumed to be PNG files that are formatted and named in a way that is compatible with the system.file function used to locate them.

Value

Returns a Shiny UI object that contains a rendered grid of card images.

Note

This function is an internal helper specifically designed for the RepliCrisis Shiny application. It relies on the structure of the new_card_grid being consistent with expected input and the mmibain package for card images.

See Also

renderImage, fluidRow, and column for details on the Shiny functions used to render images and construct the UI layout.

Convert a Character String to a List

Description

This internal function converts a character string representing R arguments into a list of arguments. It is primarily used to facilitate the passing of additional arguments from the Shiny UI to internal functions within the app.

Usage

str2list(arg_str)

Arguments

Value

A list containing the arguments represented by arg_str. If arg_str is not a valid representation of R arguments, the function will throw an error.

Card Matrix Swapping Function

Description

This function performs swapping operations within a card matrix to rearrange cards. It can swap entire columns, swap cards within a single column, or swap cards within a single row. It is designed to be used on a replication study card grid before generating replication study data.

Usage

swapper(cards_matrix, swap_cols = NULL, swap_in_col = NULL, swap_in_row = NULL)

Arguments

Details

The swapper function can be used to rearrange cards in a card matrix. It allows for three types of swaps:

• Swapping two columns: Specify two columns to swap their entire content.

• Swapping within a column: Reverse the order of two cards in the same column.

• Swapping within a row: Swap two cards within the same row.

The function keeps track of the swapping history to prevent multiple swaps within the same column or row, as these are restricted operations. After a swap operation, the function updates the class of the cards_matrix to include "swapper" to track its history.

Value

A matrix of the same dimensions as cards_matrix with the specified swaps performed.

Examples

swapper(cards_matrix = deal_cards_to_rc_grid(n = 3), swap_cols = c(1,2))