GainIndex function

Calculate the Gain Index (GI) using JAGS

This function computes the Gain Index and other related statistics for educational trials. Gain index provides a proportion of pupils who would not have make good progress without intervention. This function supports flexible configurations for JAGS modeling.

GainIndex(
  data,
  formula,
  random,
  intervention,
  NA.omit = TRUE,
  n.iter = 20000,
  n.chains = 3,
  n.burnin = 10000,
  inits = NULL,
  model.file = NULL,
  alpha = 0.05
)

Arguments

• data: A list containing the data for the JAGS model which must include columns: School, Posttest, Pretest, Intervention. Data should not have any missing values in these columns.
• formula: the model to be analysed is of the form y ~ x1+x2+.... Where y is the outcome variable and Xs are the independent variables. Formula does not need to include Intervention variable.
• random: a string variable specifying the "clustering variable" as contained in the data. See example below.
• intervention: a string variable specifying the "intervention variable" as appearing in the formula and the data. See example below.
• NA.omit: Optional; a logic to check if omitting missing value. If NA.omit = TRUE, results will output the percentage of missing value in the four required columns and then JAGS results. If NA.omit = FALSE, will give a warning "Please handle missing values before using GainIndex()." If not provided, the function uses default TRUE.
• n.iter: Total number of iterations for the MCMC simulation.
• n.chains: Number of chains to run in the MCMC simulation.
• n.burnin: Number of burn-in iterations to be discarded before analysis.
• inits: Optional; a list of initial values for the JAGS model. If NULL, the function generates default initial values.
• model.file: Optional; a custom path to the JAGS model file. If not provided, the function uses default path.
• alpha: significant level, default alpha = 0.05.

Returns

An S3 object containing the following components:

• GI: A data frame containing the Gain Index and its 95% confidence intervals, as well as the Progress Index and its 95% confidence intervals.
• Proportions: A data frame showing the proportion of participants achieving each level of gain (low and high) for both control and intervention groups.
• Timing: A vector with execution time details, including user and elapsed time in seconds.

Examples

######### EXAMPLE ONE: crtData #########
## Not run:

  data(crtData)
  output1 <- GainIndex(data = crtData, formula = Posttest~Prettest, random = "School",n.iter = 200,
                       intervention = "Intervention", NA.omit = T, alpha = 0.05)
  output1

  ########## EXAMPLE TWO: mstData ######
  data(mstData)
  output1 <- GainIndex(data = mstData, formula = Posttest~Prettest, random = "School",n.iter = 200,
                       intervention = "Intervention", NA.omit = T, alpha = 0.05)
  output1
## End(Not run)