initial_unbalance {MAGMA.R} | R Documentation |
initial_unbalance
Description
This function computes all four balance criteria of 'MAGMA.R,' namely Pillai's Trace, d-ratiO, mean g, and adjusted d-ratio for the unmatched data set. This enables comparison of initial unbalance with the balance after matching.
Usage
initial_unbalance(Data, group, covariates, verbose = TRUE)
Arguments
Data |
A data frame containing at least the grouping variable and all covariates of interest. |
group |
A character specifying the name of your grouping variable in data. Note that MAGMA can only match your data for a maximum of 4 groups. For matching over two grouping variables (e.g., 2x2 design) is possible by specifying group as a character vector with a length of two. In this case each or the two grouping variables can only have two levels. |
covariates |
A character vector listing the names of all binary and metric covariates of interest. |
verbose |
TRUE or FALSE indicating whether matching information should be printed to the console. |
Details
This function computes all four Balance criteria of 'MAGMA.R', namely Pillai's Trace, d-ratio, mean g, and adjusted d-ratio for the overall samples. Missing data for Pillai's Trace are excluded listwise, while for the other balance criteria pairwise exclusion is applied.
Value
A numeric vector of length 4 containing the balance criteria for the unmatched sample.
Author(s)
Julian Urban
References
Pastore, M., Loro, P.A.D., Mingione, M., Calcagni, A. (2022). overlapping: Estimation of Overlapping in Empirical Distributions. R package version 2.1, (https://CRAN.R-project.org/package=overlapping).
Revelle, W. (2023). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.3.6, (https://CRAN.R-project.org/package=psych)
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1-48. (doi:10.18637/jss.v036.i03)
Fisher, Z., Tipton, E., Zhipeng, H. (2023). robumeta: Robust Variance Meta-Regression. R package version 2.1, (https://CRAN.R-project.org/package=robumeta).
Examples
# Defining covariates for balance estimation
covariates_vector <- c("GPA_school", "IQ_score", "Motivation", "parents_academic", "gender")
# Computing initial unbalance using the data set 'MAGMA_sim_data'
# Computing initial unbalance for the variable 'gifted_support' (received
# giftedness support yes or no)
unbalance_gifted <- initial_unbalance(Data = MAGMA_sim_data,
group = "gifted_support",
covariates = covariates_vector)
unbalance_gifted
# Computing initial unbalance using the data set 'MAGMA_sim_data'
# Computing initial unbalance for the variable 'teacher_ability_rating'
# (ability rated from teacher as below average, average, or above average)
unbalance_tar <- initial_unbalance(Data = MAGMA_sim_data,
group = "teacher_ability_rating",
covariates = covariates_vector)
unbalance_tar
# Computing initial unbalance using the data set 'MAGMA_sim_data'
# Computing initial unbalance for the variables 'gifted_support' (received
# giftedness support yes or no) and 'enrichment' (participated in enrichment
# or not)
unbalance_2x2 <- initial_unbalance(Data = MAGMA_sim_data,
group = c("gifted_support", "enrichment"),
covariates = covariates_vector)
unbalance_2x2