version 0.5-0.0

DESCRIPTION

@@ -2,8 +2,8 @@ Package: SGP
 Type: Package
 Title: An R Package for the Calculation and Visualization of Student
         Growth Percentiles.
-Version: 0.4-5.0
-Date: 2011-8-15
+Version: 0.5-0.0
+Date: 2011-8-21
 Authors@R: c(person("Damian", "Betebenner",
         email="dbetebenner@nciea.org", role=c("aut", "cre")),
         person("Adam", "Van Iwaarden",
@@ -24,10 +24,10 @@ Author: Damian W. Betebenner <dbetebenner@nciea.org> and Adam Van
         (Wisconsin), Dr. Philip Olsen (Wisconsin), Nick Stroud
         (Wisconsin).
 Maintainer: Damian W. Betebenner <dbetebenner@nciea.org>
-Depends: R (>= 2.10), data.table (>= 1.6.4), foreach, grid, methods,
-        plyr, quantreg, splines
-Suggests: boot, colorspace, multicore, gridBase, pdf2, randomNames (>=
-        0.0-3), sn, snow
+Depends: R (>= 2.12), boot, colorspace, data.table (>= 1.6.4), foreach,
+        grid, gridBase, methods, plyr, randomNames (>= 0.0-3),
+        quantreg, splines
+Suggests: multicore, pdf2, sn, snow
 Description: Functions to calculate student growth percentiles and
         percentile growth projections/trajectories for students using
         large scale, longitudinal assessment data.  Functions use
@@ -46,5 +46,5 @@ LazyLoad: Yes
 LazyData: Yes
 License: CC BY-SA 3.0 US | CC BY-NC-SA 3.0 + file LICENSE
 Repository: CRAN
-Packaged: 2011-08-18 10:18:51 UTC; damian
-Date/Publication: 2011-08-18 15:08:26
+Packaged: 2011-08-25 10:47:19 UTC; damian
+Date/Publication: 2011-08-25 12:16:41

MD5

@@ -1,32 +1,32 @@
-0f4ebdecdb2f8fec3f2f3da1e8f8fee3 *DESCRIPTION
+a07488d46d595050015ba7f6ade02b51 *DESCRIPTION
 f13dd79196887d319fc552a321596f5d *LICENSE
 2acd54f86ecaf1488fa47a7ad3a3f49d *NAMESPACE
 4770bddf43fa9040357f5a84aee7e13f *R/SGP-class.R
 fe2a0515e352bf1d69eea3645defe65b *R/abcSGP.R
-5ba42cd22e470b44afad714de8d22f34 *R/analyzeSGP.R
+2f97644f69283c0fa7da2bc93dec88bc *R/analyzeSGP.R
 136500ff1dbffe820fde4165583a5b2d *R/baselineSGP.R
 8e7afd2aaab8374cfa6150e7fbbdfec5 *R/baselineSGPshort.R
 7baf9b39b630894f43caaee3acc79520 *R/bubblePlot.R
 c939d8cd30b5b5ef473db5a6854b6866 *R/bubblePlot_Styles.R
 17a3676bb6e01584369922b83b733597 *R/combineSGP.R
-eb1c7a4f0f58f375f4470ab41d26f613 *R/growthAchievementPlot.R
+d913159d6fe49168b189b8ebc189d07c *R/growthAchievementPlot.R
 8d83869a3c802442950897120a4a8376 *R/prepareSGP.R
 72528dca680f84d1c4c58e2c1caea80b *R/splineMatrix-class.R
-0a4b3c90172464e3768129768aced7fc *R/studentGrowthPercentiles.R
-048dd0abe817e930e2a45b9e1878d12b *R/studentGrowthPlot.R
+bbfd424afb33ddd38b25c5b04b7b1f7b *R/studentGrowthPercentiles.R
+a9d2dd99cdb9a1b20a9cb8cbbd28ad62 *R/studentGrowthPlot.R
 24eab2a893e9dc44a244c1a92321387d *R/studentGrowthProjections.R
 221801d124179c909bac9eba37dacaff *R/summarizeSGP.R
 184959e6bf8f6a00c0e84330c632b5d4 *R/viewSummaryGroups.R
-3b35d84e58ca772c4bcaa7cd193fafaf *R/visualizeSGP.R
+dfd4df5351e148c7c07292f78e2ebc5e *R/visualizeSGP.R
 a092804f4de2803ad00a10f64cf83ab1 *data/datalist
 255bf6a57db438a329d71fcf7204350a *data/sgpData.rda
 21d89541d9f187fbdec83776f03a9cd9 *data/sgpData_LONG.rda
-f4c4ffd2c3f019d771130e69de8961e3 *data/stateData.rda
-09189dfad34d7b5a555a23035895133e *inst/CITATION
-7a2d6fcd36d95f8b709a52fc022495f7 *inst/NEWS
+a5adb1fe33466ecc7dd9469844d2a211 *data/stateData.rda
+f260dd88fe05be3ebd3846424ca30015 *inst/CITATION
+9a72a19101c9bdd4e39fe41329595e65 *inst/NEWS
 99ea0f9ec9684dddedfce27e4d440cee *inst/doc/sgpPrimer.pdf
 10672fbc73b8db70ad8e859399e7bb7f *man/SGP-class.Rd
-95edf2d6148105845ec37448c9b420fb *man/SGP-package.Rd
+f27589e51af4f43163bca9731cfc95ec *man/SGP-package.Rd
 add4dbd8ce6509ccab1070d02b7e3324 *man/abcSGP.Rd
 cdeb50b3410a0973cf94a0092958313e *man/analyzeSGP.Rd
 1e6f7492fb4218f2f72c4421613b3103 *man/baselineSGP.Rd
@@ -40,7 +40,7 @@ cfea569ebc3bd9fe259200b2fd4a4839 *man/prepareSGP.Rd
 dab644a0a5ecdb689bcc357377930f84 *man/sgpData_LONG.Rd
 839c4aae2859556e7c4062f8efb41ca6 *man/splineMatrix-class.Rd
 03c6df7e61865d1835d45bc4130cd790 *man/stateData.Rd
-c5b2e2e7a9c8783de4ab2b32dc7f88ea *man/studentGrowthPercentiles.Rd
+2f8955f728838f351fd1741acd268669 *man/studentGrowthPercentiles.Rd
 fe5eec09af8a9f3522f730f9018790a2 *man/studentGrowthPlot.Rd
 93c330edba29e13d68ad3be90f39ea5a *man/studentGrowthProjections.Rd
 cf221f9b92bb17863b28bf32f105d1f7 *man/summarizeSGP.Rd

R/analyzeSGP.R

@@ -31,12 +31,11 @@ function(sgp_object,
 
 	.get.config <- function(content_area, year, grades) {
 		tmp.data <- sgp_object@Data[J("VALID_CASE", content_area), c("YEAR", "GRADE"), with=FALSE]
-		.sgp.panel.years <- sort(unique(tmp.data$YEAR))[1:which(sort(unique(tmp.data$YEAR)) == year)]
+		tmp.unique.years <- sort(unique(tmp.data$YEAR))
+		.sgp.panel.years <- tmp.unique.years[1:which(tmp.unique.years == year)]
 		.sgp.content.areas <- rep(content_area, length(.sgp.panel.years))
-		.sgp.grade.sequences <- lapply(grades[-1], function(x) tail(grades[grades <= x], length(unique(tmp.data$YEAR))-1))
-		for (g in seq_along(.sgp.grade.sequences)) {
-			.sgp.grade.sequences[[g]]<-.sgp.grade.sequences[[g]][tail(.sgp.grade.sequences[[g]],1)-.sgp.grade.sequences[[g]] <= length(.sgp.panel.years)-1]
-		}
+		tmp.sgp.grade.sequences <- lapply(grades[-1], function(x) tail(grades[grades <= x], length(tmp.unique.years)))
+		.sgp.grade.sequences <- sapply(tmp.sgp.grade.sequences, function(x) x[(tail(x,1)-x) <= length(.sgp.panel.years)-1])
 		list(sgp.content.areas=.sgp.content.areas, sgp.panel.years=.sgp.panel.years, sgp.grade.sequences=.sgp.grade.sequences) 
 	}
 
@@ -63,6 +62,9 @@ function(sgp_object,
 			sgp.vnames <- c("ID", paste("GRADE", sgp.iter[["sgp.panel.years"]], sep="."), 
 				paste("SCALE_SCORE", sgp.iter[["sgp.panel.years"]], sep="."))
 			if (simulate.sgps) {
+				if (!exists("calculate.confidence.intervals")) {
+					calculate.confidence.intervals <- state
+				}
 				for (k in sgp.iter[["sgp.grade.sequences"]]) {
 					tmp_sgp_object <- studentGrowthPercentiles(
 						panel.data=tmp_sgp_object,
@@ -71,10 +73,7 @@ function(sgp_object,
 						growth.levels=state,
 						panel.data.vnames=sgp.vnames,
 						grade.progression=k,
-						calculate.confidence.intervals=list(state=state,  
-							confidence.quantiles=c(0.16,0.84),
-							simulation.iterations=100, 
-							distribution="Normal", round=1),
+						calculate.confidence.intervals=calculate.confidence.intervals,  
 						...)
 				} ## END k loop
 			} else {
@@ -290,11 +289,8 @@ function(sgp_object,
 
 			if (toupper(parallel.config[["TYPE"]]) == "FOREACH") {
 				require(foreach)
-				if (!is.null(parallel.config[["WORKERS"]])) {
-					cores <- parallel.config[["WORKERS"]]
-				} else cores = getOption("cores")
 				foreach.options <-parallel.config[["OPTIONS"]] # works fine if NULL
-				sgp_object@SGP <- foreach(sgp.iter=iter(sgp.config), .packages="SGP", .combine=".mergeSGP", .inorder=FALSE,
+				sgp_object@SGP <- foreach(sgp.iter=iter(sgp.config), .packages="SGP", .combine=".mergeSGP", .inorder=FALSE, .export="sgp_object",
 					.options.multicore=foreach.options, .options.mpi= foreach.options, .options.redis= foreach.options, .options.smp= foreach.options) %dopar% {
 						return(.analyzeSGP_Internal(sgp.iter))
 				}

R/growthAchievementPlot.R

@@ -101,8 +101,8 @@
 
       create.long.cutscores <- function(state, content_area, year) {
         number.achievement.level.regions <- length(stateData[[state]][["Student_Report_Information"]][["Achievement_Level_Labels"]])
+        my.content_area <- get.my.cutscore.year(state, content_area, year)
         if (!content_area %in% names(stateData[[state]][["Student_Report_Information"]][["Transformed_Achievement_Level_Cutscores"]])) {
-           my.content_area <- get.my.cutscore.year(state, content_area, year)
            tmp.grades <- as.numeric(matrix(unlist(strsplit(names(stateData[[state]][["Achievement"]][["Cutscores"]][[my.content_area]]), "_")),
                                           ncol=2, byrow=TRUE)[,2])
            tmp.cutscores <- matrix(unlist(stateData[[state]][["Achievement"]][["Cutscores"]][[my.content_area]]),