prepareSGP, SGP data analysis, analyzeSGP,
data combining, combineSGP, data summarization, summarizeSGP, and data visualization visualizeSGP. Calculating and using
student growth percentiles is as easy as ABC.abcSGP(sgp_object,
state=NULL,
steps=c("prepareSGP", "analyzeSGP", "combineSGP", "summarizeSGP", "visualizeSGP"),
years=NULL,
content_areas=NULL,
grades=NULL,
prepareSGP.var.names=NULL,
sgp.percentiles=TRUE,
sgp.projections=TRUE,
sgp.projections.lagged=TRUE,
sgp.percentiles.baseline=TRUE,
sgp.projections.baseline=TRUE,
sgp.projections.lagged.baseline=TRUE,
simulate.sgps=TRUE,
parallel.config=NULL,
save.intermediate.results=FALSE,
sgPlot.demo.report=FALSE,
sgp.summaries=NULL,
summary.groups=NULL,
confidence.interval.groups=NULL,
plot.types=c("bubblePlot", "studentGrowthPlot", "growthAchievementPlot"))sgpData_LONG for an exemplar. NOTE: Data preparation must be meticulous to utilize this enhanced functionality.prepareSGP, analyzeSGP, combineSGP, prepareSGP for more details. Defaults to NULL.FabcSGP be saved after each of prepareSGP, analyzeSGP,
combineSGPsummary.group argument. Default is NULL allowing the summarizeSGP
function to produce the list of summaries institution, content, time, institution_type,
institution_level, demographic, and institutisummary.groups argument indicating which groups to provide confidence intervals for.
See documentation for summarizeSGP and visualizeSGP indicating the types of plots to produce. Currently supported plots include bubblePlots,
Student slot as a data.table keyed using VALID_CASE, CONTENT_AREA,
YEAR, ID, SGP results including student growth percentile and student growth projections/trajectories in the SGP slot, and summary results in the
Summary slot.prepareSGP, analyzeSGP, combineSGP, summarizeSGP,
studentGrowthPercentiles, and studentGrowthProjections## Runs all 5 steps
Demonstration_SGP <- abcSGP(sgp_object=sgpData_LONG, state="DEMO")
## Or letting the function detect the state
Demonstration_SGP <- sgpData_LONG
Demonstration_SGP <- abcSGP(Demonstration_SGP)
###
### Example uses of the parallel.config argument
###
Demonstration_SGP <- sgpData_LONG
Demonstration_SGP <- abcSGP(Demonstration_SGP,
parallel.config=list(
BACKEND="PARALLEL", TYPE="SOCK",
WORKERS=list(
PERCENTILES=8, BASELINE_PERCENTILES=8, PROJECTIONS=7, LAGGED_PROJECTIONS=6,
SUMMARY=8,
GA_PLOTS=8, SG_PLOTS=8)
)
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