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, ...)

sgp_object | An object of class |
---|---|

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 |

sgp.baseline.config | A list containing three vectors: |

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 |

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.

`prepareSGP`

, `analyzeSGP`

, `combineSGP`

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) })