MAGMA.R (version 1.0.3)

initial_unbalance: 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,
  covariates_ordinal = NULL,
  covariates_nominal = NULL
)

Value

A numeric vector of length 4 containing the balance criteria for the unmatched sample.

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.

covariates_ordinal

A character vector listing the names of all ordinal covariates of interest.

covariates_nominal

A character vector listing the names of all nominal covariates of interest.

Author

Julian Urban

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.

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. (tools:::Rd_expr_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