WIDA_IN_SGP_Baseline_2020_B_Growth_Percentiles.R

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###   WIDA Indiana Learning Loss Analyses -- 2020 Baseline Growth Percentiles    ###
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###   Load packages
require(SGP)

###   Load data and remove years that will not be used.
load("Data/WIDA_IN_SGP_LONG_Data.Rdata")

### Test for BASELINE related variable in LONG data and NULL out if they exist
if (length(tmp.names <- grep("BASELINE|SS", names(WIDA_IN_SGP_LONG_Data))) > 0) {
		WIDA_IN_SGP_LONG_Data[,eval(tmp.names):=NULL]
}

###   Add single-cohort baseline matrices to SGPstateData
SGPstateData <- SGPmatrices::addBaselineMatrices("WIDA_IN", "2021")

#####
###   Run BASELINE SGP analysis - create new WIDA_IN_SGP object with historical data
#####

###   Temporarily set names of prior scores from sequential/cohort analyses
data.table::setnames(WIDA_IN_SGP_LONG_Data,
	c("SCALE_SCORE_PRIOR", "SCALE_SCORE_PRIOR_STANDARDIZED"),
	c("SS_PRIOR_COHORT", "SS_PRIOR_STD_COHORT"))

SGPstateData[["WIDA_IN"]][["Assessment_Program_Information"]][["CSEM"]] <- NULL

WIDA_IN_SGP <- abcSGP(
        sgp_object = WIDA_IN_SGP_LONG_Data,
        steps = c("prepareSGP", "analyzeSGP", "combineSGP", "outputSGP"),
        sgp.percentiles = FALSE,
        sgp.projections = FALSE,
        sgp.projections.lagged = FALSE,
        sgp.percentiles.baseline = TRUE,  #  Skip year SGPs for 2021 comparisons
        sgp.projections.baseline = FALSE, #  Calculated in next step
        sgp.projections.lagged.baseline = FALSE,
        save.intermediate.results = FALSE,
        parallel.config = list(
					BACKEND = "PARALLEL",
					WORKERS=list(BASELINE_PERCENTILES=8))
)

###   Re-set and rename prior scores (one set for sequential/cohort, another for skip-year/baseline)
data.table::setnames(WIDA_IN_SGP@Data,
  c("SCALE_SCORE_PRIOR", "SCALE_SCORE_PRIOR_STANDARDIZED", "SS_PRIOR_COHORT", "SS_PRIOR_STD_COHORT"),
  c("SCALE_SCORE_PRIOR_BASELINE", "SCALE_SCORE_PRIOR_STANDARDIZED_BASELINE", "SCALE_SCORE_PRIOR", "SCALE_SCORE_PRIOR_STANDARDIZED"))

###   Save results
save(WIDA_IN_SGP, file="Data/WIDA_IN_SGP.Rdata")