Create a repeated measures correlation matrix.

Usage

rmcorr_mat(participant, variables, dataset, CI.level = 0.95)

Arguments

participant: A variable giving the subject name/id for each observation.
variables: A character vector indicating the columns of variables to include in the correlation matrix.
dataset: The data frame containing the variables.
CI.level: The level of confidence intervals to use in the rmcorr models.

Value

A list with class "rmcmat" containing the following components.

matrix: the repeated measures correlation matrix
summary: a dataframe showing rmcorr stats for each pair of variables
models: a list of the full rmcorr model for each pair of variables

References

Bakdash, J.Z., & Marusich, L.R. (2017). Repeated Measures Correlation. Frontiers in Psychology, 8, 456. doi:10.3389/fpsyg.2017.00456 .

Bland, J.M., & Altman, D.G. (1995). Calculating correlation coefficients with repeated observations: Part 1 -- correlation within subjects. BMJ, 310, 446, doi:10.1136/bmj.310.6977.446 .

Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd edition), Routledge. ISBN: 9780805822236.

Examples

dist_rmc_mat <- rmcorr_mat(participant = Subject, 
                           variables = c("Blindwalk Away",
                                         "Blindwalk Toward",
                                         "Triangulated BW",
                                         "Verbal",
                                         "Visual matching"),
                           dataset = twedt_dist_measures,
                           CI.level = 0.95)
plot(dist_rmc_mat$models[[2]])