prepareSGP
, analyzeSGP
and combineSGP
.summarizeSGP(sgp_object,
state,
years,
content_areas,
sgp.summaries=list(MEDIAN_SGP="median_na(SGP)",
MEDIAN_SGP_COUNT="num_non_missing(SGP)",
PERCENT_AT_ABOVE_PROFICIENT="percent_in_category(ACHIEVEMENT_LEVEL, list(c('Proficient', 'Advanced')), list(c('Unsatisfactory', 'Partially Proficient', 'Proficient', 'Advanced')))",
PERCENT_AT_ABOVE_PROFICIENT_COUNT="num_non_missing(ACHIEVEMENT_LEVEL)"),
summary.groups=list(institution=c("STATE", "DISTRICT_NUMBER", "SCHOOL_NUMBER"),
content="CONTENT_AREA",
time="YEAR",
institution_level="GRADE",
demographic=c("GENDER", "ETHNICITY", "FREE_REDUCED_LUNCH_STATUS", "ELL_STATUS", "IEP_STATUS", "GIFTED_AND_TALENTED_PROGRAM_STATUS", "CATCH_UP_KEEP_UP_STATUS"),
institution_inclusion=list(STATE="STATE_ENROLLMENT_STATUS", DISTRICT_NUMBER="DISTRICT_ENROLLMENT_STATUS", SCHOOL_NUMBER="SCHOOL_ENROLLMENT_STATUS")),
confidence.interval.groups=list(TYPE="Bootstrap",
VARIABLES=c("SGP"),
QUANTILES=c(0.025, 0.975),
GROUPS=list(institution="SCHOOL_NUMBER",
content="CONTENT_AREA",
time="YEAR",
institution_level= NULL,
demographic=NULL,
institution_inclusion=list(STATE=NULL, DISTRICT_NUMBER=NULL, SCHOOL_NUMBER="SCHOOL_ENROLLMENT_STATUS"))))
Student
slot. If summaries of student growth percentiles are requested, those quantities must first be produced (possibly by first using analyzeSGP
stateData
.summary.group
argument. The default summaries include the group level MEDIAN_SGP
, MEDIAN_SGP_COUNT
(the number of students used to compute institution
(e.g. state, districts and schools), content area
, time
, institution_level
(usually grade), de
TYPE
is either Bootstrap
(default) or CSEM
(experimental). VARIALBES
indicates the variables on which to calculate confidenSummary
slot of the SGP data object. Each institution
has a slot in the Summary
list.foreach
package to parallel process summary tables of student data. Currently, it is the user's responsibility to register a parallel back end of their choice before running the summarizeSGP
function. The proper choice may be dependent upon the user's operating system, software and system memory capacity. Please see the foreach
documentation for details. If no parallel back end is specified, the function will process the summary tables sequentially.prepareSGP
, analyzeSGP
, combineSGP
## summarizeSGP is Step 4 of 5 of abcSGP
DEMO_Data <- summarizeSGP(DEMO_Data, state="DEMO")
Run the code above in your browser using DataLab