SGP (version 1.7-0.0)

baselineSGP: Analyze student data to produce student growth percentiles and coefficient matrices from a baseline (i.e. multiple cohort) norm group

Description

Utility function/exemplar used to produce student growth percentiles using long formatted data like that provided by prepareSGP. Used as part of analyzeSGP for baseline referenced student growth percentile analyses.

Usage

baselineSGP(sgp_object,
        state=NULL,
        years=NULL,
        content_areas=NULL,
        grades=NULL,
	exclude.years=NULL,
	sgp.config=NULL,
	sgp.baseline.config=NULL,
	sgp.baseline.panel.years=NULL,
	sgp.percentiles.baseline.max.order=3,
	return.matrices.only=FALSE,
	calculate.baseline.sgps=TRUE,
	calculate.simex.baseline=NULL,
	goodness.of.fit.print=TRUE,
	parallel.config=NULL,
	SGPt=NULL,
	...)

Arguments

sgp_object

An object of class SGP containing long formatted data in the @Data (from prepareSGP) slot.

state

Acronym indicating state associated with the data for access to embedded knot and boundaries.

years

A vector indicating year(s) in which to produce baseline referenced student growth percentiles.

content_areas

A vector indicating content area in which to produce baseline referenced student growth percentiles.

grades

A vector indicating which grades to calculate baseline referenced student growth percentiles.

exclude.years

A vector indicating which years to exclude from the calculations?

sgp.config

If years, content_areas, and grades are missing, user can directly specify a list containing three vectors: baseline.content.areas, baseline.panel.years, and baseline.grade.sequences. This advanced option is helpful for analysis of non-traditional grade progressions and other special cases. See analyzeSGP for use cases.

sgp.baseline.config

A list containing three vectors: sgp.content.areas, sgp.panel.years, sgp.grade.sequences indicating how baseline student growth percentile analyses are to be conducted. In most cases this value will be calculated by default within the function but can be specified directly for advanced use cases. See source code for more detail on this configuration option.

sgp.baseline.panel.years

A character vector indicating the years to be used for the calculation of baseline SGPs. Default is to use most recent five years of data.

sgp.percentiles.baseline.max.order

Integer indicating the maximum order to calculate baseline student growth percentiles (regardless of maximum coefficient matrix order). Default is 3. To utilize the maximum matrix order, set to NULL.

return.matrices.only

Boolean variable indicating whether the function will only return baseline referenced coefficient matrices. Defaults to FALSE.

calculate.baseline.sgps

Boolean variable indicating whether the function will calculate baseline referenced student growth percentiles from baseline referenced coefficient matrices. Defaults to TRUE.

calculate.simex.baseline

A list including state/csem variable, csem.data.vnames, csem.loss.hoss, simulation.iterations, lambda and extrapolation method. Defaults to NULL, no simex calculations performed. Alternatively, setting the argument to TRUE sets the list up with state=state, lambda=seq(0,2,0.5), simulation.iterations=50, simex.sample.size=25000, extrapolation="linear" and save.matrices=TRUE.

goodness.of.fit.print

Boolean variable indicating whether the function will export goodness of fit plots if baseline referenced student growth percentiles are calculated. Defaults to TRUE.

parallel.config

parallel configuration argument allowing for parallel analysis by 'tau'. Defaults to NULL.

SGPt

Argument supplied to generate time dependent SGPs. Defaults to NULL/FALSE.

...

Arguments to be passed internally to studentGrowthPercentiles for finer control over SGP calculations.

Value

If return.matrices.only is set to TRUE function returns a list containing the baseline referenced coefficient matrices. Otherwise function returns the SGP object provided with the sgp_object argument with the baseline referenced coefficient matrices, growth percentiles, etc. embedded.

See Also

prepareSGP, analyzeSGP, combineSGP

Examples

# NOT RUN {
##  Calculate baseline referenced SGPs 
## (using coefficient matrices embedded in SGPstateData)

Demonstration_SGP <- prepareSGP(sgpData_LONG)
Demonstration_SGP <-  baselineSGP(Demonstration_SGP)


## Calculate baseline referenced coefficient matrices

SGPstateData[["DEMO"]][["Baseline_splineMatrix"]] <- NULL
Demonstration_SGP <- prepareSGP(sgpData_LONG)
DEMO_Baseline_Matrices <- baselineSGP(
				Demonstration_SGP, 
				return.matrices.only=TRUE, 
				calculate.baseline.sgps=FALSE)


## Calculate baseline referenced coefficient matrices and 
## baseline referenced SGPs with 4 years of data

SGPstateData[["DEMO"]][["Baseline_splineMatrix"]] <- NULL

sgpData_LONG_4_YEAR <- subset(sgpData_LONG, YEAR!="2013_2014")

Demonstration_SGP <- prepareSGP(sgpData_LONG_4_YEAR)
Demonstration_SGP <- baselineSGP(Demonstration_SGP)
# }