confirmatory_mixed_effect: Primary analysis: Mixed-effects logistic regression

In marton-balazs-kovacs/tppr: Shiny app to show real-time results of the TPP project

Description

This function calculates the mixed-effects logistic regression for the primary analysis. A random intercept is placed on the participants to predict the outcome of the guesses in erotic trials. Then the function calculates the Wald confidence interval around the estimate of the odds ratio of success and transforms it to logit probability.

Usage

confirmatory_mixed_effect(processed_data, n_iteration = 1)

Arguments

processed_data	a dataframe containing only erotic and non empty trials
n_iteration	numeric, the nth time the analysis is conducted

Value

A list of three containing the width, the lower and the upper border of the confidence interval of 95

Note

If the mixed-effects logistic regression is run by multiple times during the primary confirmatory analysis the alpha level is corrected by for each iteration.

See Also

Other confirmatory functions: confirmatory_bayes_factor()

Examples

 

# Including only erotic trials
tpp_processed_data <- clean_data(raw_data = example_m0)
# Running the confirmatory analysis
confirmatory_mixed_effect(processed_data = tpp_processed_data, n_iteration = 1)

marton-balazs-kovacs/tppr documentation built on Oct. 27, 2021, 3:04 p.m.