Nothing
R/gap.R
In EdSurvey: Analysis of NCES Education Survey and Assessment Data
Defines functions
addALPrefix
getALNames
gmatchAttr
pt2
dofCompute
parseVars
print.gapList
print.gap
calLinkingError
calLinkingErrorPISA
gapHelper
gap
Documented in
gap
print.gap
print.gapList
#' @title Gap Analysis
#'
#' @description Compares the average levels of a variable between two groups
#' that potentially share members.
#'
#' @param variable a character indicating the variable to be compared,
#' potentially with a subject scale or subscale
#' @param data an \code{edsurvey.data.frame}, a \code{light.edsurvey.data.frame}, or an \code{edsurvey.data.frame.list}
#' @param groupA an expression or character expression that defines a condition for the subset.
#' This subset will be compared to \code{groupB}. If not specified, it will define
#' a whole sample as in \code{data}.
#' @param groupB an expression or character expression that defines a condition for the subset.
#' This subset will be compared to \code{groupA}. If not specified, it will define
#' a whole sample as in \code{data}. If set to \code{NULL}, estimates for the second group
#' will be dropped.
#' @param percentiles a numeric vector. The \code{gap} function calculates the
#' mean when this
#' argument is omitted or set to \code{NULL}. Otherwise,
#' the gap at the percentile given is calculated.
#' @param achievementLevel the achievement level(s) at which percentages
#' should be calculated
#' @param achievementDiscrete a logical indicating if the achievement level
#' specified in the \code{achievementLevel}
#' argument should be interpreted as discrete
#' so that
#' just the percentage in that particular achievement
#' level
#' will be included. Defaults to \code{FALSE}
#' so that
#' the percentage at or above that achievement level
#' will be
#' included in the percentage.
#' @param stDev a logical, set to \code{TRUE} to calculate the gap in standard deviations.
#' @param targetLevel a character string. When specified, calculates the gap in
#' the percentage of students at
#' \code{targetLevel} in the \code{variable} argument. This is useful for
#' comparing the gap in the percentage of students at a
#' survey response level.
#' @param weightVar a character indicating the weight variable to use.
#' See Details.
#' @param jrrIMax a numeric value; when using the jackknife variance estimation method, the default estimation option, \code{jrrIMax=1}, uses the
#' sampling variance from the first plausible value as the component for sampling variance estimation. The \code{Vjrr}
#' term, or sampeling variance term, can be estimated with any number of plausible values, and values larger than the number of
#' plausible values on the survey (including \code{Inf}) will result in all plausible values being used.
#' Higher values of \code{jrrIMax} lead to longer computing times and more accurate variance estimates.
#' @param varMethod deprecated parameter, \code{gap} always uses the jackknife variance estimation
#' @param dropOmittedLevels a logical value. When set to the default value of
#' \code{TRUE}, drops those levels of
#' all factor variables.
#' Use \code{print} on an \code{edsurvey.data.frame}
#' to see the omitted levels.
#' @param defaultConditions a logical value. When set to the default value
#' of \code{TRUE}, uses the default
#' conditions stored in \code{edsurvey.data.frame}
#' to subset the data.
#' Use \code{print} on an \code{edsurvey.data.frame}
#' to see the default conditions.
#' @param recode a list of lists to recode variables. Defaults to \code{NULL}.
#' Can be set as
#' \code{recode} \code{=} \code{list(var1} \code{=}
#' \code{list(from} \code{=} \code{c("a",} \code{"b",}
#' \code{"c"),} \code{to} \code{=} \code{"d"))}.
#' @param referenceDataIndex a numeric used only when the \code{data} argument is an
#' \code{edsurvey.data.frame.list},
#' indicating which dataset is the reference
#' dataset that other datasets are compared with.
#' Defaults to 1.
#' @param returnVarEstInputs a logical value; set to \code{TRUE} to return the
#' inputs to the jackknife and imputation variance
#' estimates which allows for the
#' computation
#' of covariances between estimates.
#' @param returnSimpleDoF a logical value set to \code{TRUE} to return the degrees
#' of freedom for some statistics (see Value
#' section) that do not have a
#' \emph{t}-test; useful primarily for further computation
#' @param returnSimpleN a logical value set to \code{TRUE} to add the count
#' (\emph{n}-size) of observations included in groups A and B
#' in the percentage object
#' @param returnNumberOfPSU a logical value set to \code{TRUE} to return the number of
#' PSUs used in the calculation
#' @param noCov set the covariances to zero in result
#' @param pctMethod a character that is one of \code{unbiased} or \code{simple}.
#' See the help for \code{\link{percentile}} for more information.
#' @param includeLinkingError a logical value set to \code{TRUE} to include the
#' linking error in variance estimation.
#' Standard errors (e.g., \code{diffAAse}, \code{diffBBse},
#' and \code{diffABABse}) and \emph{p}-values (e.g., \code{diffAApValue},
#' \code{diffBBpValue}, and \code{diffABABpValue}) would be adjusted for
#' comparisons between digitally based assessments (DBA) and
#' paper-based assessments (PBA) data.
#' This option is supported only for NAEP data.
#'
#' @param omittedLevels this argument is deprecated. Use \code{dropOmittedLevels}.
#'
#' @details This function calculates the gap between \code{groupA} and \code{groupB} (which
#' may be omitted to indicate the full sample). The gap is
#' calculated for one of four statistics:
#' \describe{
#' \item{the gap in means}{The mean score gap (in the score
#' variable) identified in the \code{variable} argument.
#' This is the default. The means and their standard errors are
#' calculated using the methods
#' described in the \code{\link{lm.sdf}} function documentation.}
#' \item{the gap in percentiles}{The gap between respondents at
#' the percentiles specified in the \code{percentiles} argument.
#' This is returned when the \code{percentiles} argument is
#' defined. The mean and standard error are computed as described in the
#' \code{\link{percentile}} function documentation.}
#' \item{the gap in achievement levels}{The gap in the percentage of
#' students at (when \code{achievementDiscrete} is \code{TRUE}) or at
#' or above (when \code{achievementDiscrete} is \code{FALSE}) a
#' particular achievement level. This is used when the
#' \code{achievementLevel} argument is defined. The mean and standard error
#' are calculated as described in the \code{\link{achievementLevels}}
#' function documentation.}
#' \item{the gap in a survey response}{The gap in the percentage of
#' respondents responding at \code{targetLevel} to
#' \code{variable}. This is used when \code{targetLevel} is
#' defined. The mean and standard deviation are calculated as described in
#' the \code{\link{edsurveyTable}} function documentation.}
#' }
#'
#' @return
#' The return type depends on if the class of the \code{data} argument is an
#' \code{edsurvey.data.frame} or an \code{edsurvey.data.frame.list}. Both
#' include the call (called \code{call}), a list called \code{labels},
#' an object named \code{percentage}
#' that shows the percentage in \code{groupA} and \code{groupB}, and an object
#' that shows the gap called \code{results}.
#'
#' The labels include the following elements:
#' \item{definition}{the definitions of the groups}
#' \item{nFullData}{the \emph{n}-size for the full dataset (before applying the definition)}
#' \item{nUsed}{the \emph{n}-size for the data after the group is subsetted and other
#' restrictions (such as omitted values) are applied}
#' \item{nPSU}{the number of PSUs used in calculation--only returned when
#' \code{returnNumberOfPSU} \code{=} \code{TRUE}}
#'
#' The percentages are computed according to the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}
#' in the section
#' \dQuote{Estimation of Weighted Percentages When Plausible Values Are Not Present.}
#' The standard errors are calculated according to
#' \dQuote{Estimation of the Standard Error of Weighted Percentages When Plausible Values Are Not Present, Using the Jackknife Method.}
#' Standard errors of differences are calculated as the square root of the typical
#' variance formula
#' \deqn{Var(A-B) = Var(A) + Var(B) - 2 Cov(A,B)}
#' where the covariance term is calculated as described in the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}
#' in the section
#' \dQuote{Estimation of Covariances.} These degrees of freedom are available only
#' with the jackknife variance estimation. The degrees of freedom used for hypothesis testing
#' are always set to the number of jackknife replicates in the data.
#'
#' \strong{the data argument is an edsurvey.data.frame}
#' When the \code{data} argument is an \code{edsurvey.data.frame},
#' \code{gap} returns an S3 object of class \code{gap}.
#'
#' The \code{percentage} object is a numeric vector with the following elements:
#' \item{pctA}{the percentage of respondents in \code{groupA} compared with the whole sample in \code{data}}
#' \item{pctAse}{the standard error on the percentage of respondents in
#' \code{groupA}}
#' \item{dofA}{degrees of freedom appropriate for a \emph{t}-test involving \code{pctA}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{pctB}{the percentage of respondents in \code{groupB}.}
#' \item{pctBse}{the standard error on the percentage of respondents in
#' \code{groupB}}
#' \item{dofB}{degrees of freedom appropriate for a \emph{t}-test involving \code{pctA}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{diffAB}{the value of \code{pctA} minus \code{pctB}}
#' \item{covAB}{the covariance of \code{pctA} and \code{pctB}; used in
#' calculating \code{diffABse}.}
#' \item{diffABse}{the standard error of \code{pctA}
#' minus \code{pctB}}
#' \item{diffABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAB}
#' is zero}
#' \item{dofAB}{degrees of freedom used in calculating
#' \code{diffABpValue}}
#'
#' The \code{results} object is a numeric data frame with the following elements:
#' \item{estimateA}{the mean estimate of \code{groupA} (or the percentage estimate
#' if \code{achievementLevel} or \code{targetLevel} is specified)}
#' \item{estimateAse}{the standard error of \code{estimateA}}
#' \item{dofA}{degrees of freedom appropriate for a \emph{t}-test involving \code{meanA}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{estimateB}{the mean estimate of \code{groupB} (or the percentage estimate
#' if \code{achievementLevel} or \code{targetLevel} is specified)}
#' \item{estimateBse}{the standard error of \code{estimateB}}
#' \item{dofB}{degrees of freedom appropriate for a \emph{t}-test involving \code{meanB}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{diffAB}{the value of \code{estimateA} minus \code{estimateB}}
#' \item{covAB}{the covariance of \code{estimateA} and \code{estimateB}. Used in
#' calculating \code{diffABse}.}
#' \item{diffABse}{the standard error of \code{diffAB}}
#' \item{diffABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAB}
#' is zero.}
#' \item{dofAB}{degrees of freedom used for the \emph{t}-test on \code{diffAB}}
#'
#' If the gap was in achievement levels or percentiles and more
#' than one percentile or achievement level is requested,
#' then an additional column
#' labeled \code{percentiles} or \code{achievementLevel} is included
#' in the \code{results} object.
#'
#' When \code{results} has a single row and when \code{returnVarEstInputs}
#' is \code{TRUE}, the additional elements \code{varEstInputs} and
#' \code{pctVarEstInputs} also are returned. These can be used for calculating
#' covariances with \code{\link{varEstToCov}}.
#'
#' \strong{the data argument is an edsurvey.data.frame.list}
#' When the \code{data} argument is an \code{edsurvey.data.frame.list},
#' \code{gap} returns an S3 object of class \code{gapList}.
#'
#' The \code{results} object in the \code{edsurveyResultList} is
#' a \code{data.frame}. Each row regards a particular dataset from the
#' \code{edsurvey.data.frame}, and a reference dataset is dictated by
#' the \code{referenceDataIndex} argument.
#'
#' The \code{percentage} object is a \code{data.frame} with the following elements:
#' \item{covs}{a data frame with a column for each column in the \code{covs}. See previous
#' section for more details.}
#' \item{...}{all elements in the \code{percentage} object in the
#' previous section}
#' \item{diffAA}{the difference in \code{pctA} between the reference data
#' and this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covAA}{the covariance of \code{pctA} in the reference data and
#' \code{pctA} on this row. Used in
#' calculating \code{diffAAse}.}
#' \item{diffAAse}{the standard error for \code{diffAA}}
#' \item{diffAApValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAA}
#' is zero}
#' \item{diffBB}{the difference in \code{pctB} between the reference data
#' and this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covBB}{the covariance of \code{pctB} in the reference data and
#' \code{pctB} on this row. Used in
#' calculating \code{diffAAse}.}
#' \item{diffBBse}{the standard error for \code{diffBB}}
#' \item{diffBBpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffBB}
#' is zero}
#' \item{diffABAB}{the value of \code{diffAB} in the reference dataset
#' minus the value of \code{diffAB} in this dataset. Set
#' to \code{NA} for the reference dataset.}
#' \item{covABAB}{the covariance of \code{diffAB} in the reference data and
#' \code{diffAB} on this row. Used in
#' calculating \code{diffABABse}.}
#' \item{diffABABse}{the standard error for \code{diffABAB}}
#' \item{diffABABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffABAB}
#' is zero}
#'
#' The \code{results} object is a \code{data.frame} with the following elements:
#' \item{...}{all elements in the \code{results} object in the
#' previous section}
#' \item{diffAA}{the value of \code{groupA} in the reference dataset minus
#' the value in this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covAA}{the covariance of \code{meanA} in the reference data and
#' \code{meanA} on this row. Used in
#' calculating \code{diffAAse}.}
#' \item{diffAAse}{the standard error for \code{diffAA}}
#' \item{diffAApValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAA}
#' is zero}
#' \item{diffBB}{the value of \code{groupB} in the reference dataset minus
#' the value in this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covBB}{the covariance of \code{meanB} in the reference data and
#' \code{meanB} on this row. Used in
#' calculating \code{diffBBse}.}
#' \item{diffBBse}{the standard error for \code{diffBB}}
#' \item{diffBBpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffBB}
#' is zero}
#' \item{diffABAB}{the value of \code{diffAB} in the reference dataset
#' minus the value of \code{diffAB}
#' in this dataset. Set
#' to \code{NA} for the reference dataset.}
#' \item{covABAB}{the covariance of \code{diffAB} in the reference data and
#' \code{diffAB} on this row. Used in
#' calculating \code{diffABABse}.}
#' \item{diffABABse}{the standard error for \code{diffABAB}}
#' \item{diffABABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffABAB}
#' is zero}
#' \item{sameSurvey}{a logical value indicating if this line uses the same
#' survey as the reference line. Set to \code{NA} for the
#' reference line.}
#'
#' @author Paul Bailey, Trang Nguyen, and Huade Huo
#' @importFrom stats formula
#' @example man/examples/gap.R
#' @export
gap <- function(variable, data, groupA = "default", groupB = "default",
percentiles = NULL,
achievementLevel = NULL,
achievementDiscrete = FALSE,
stDev = FALSE,
targetLevel = NULL,
weightVar = NULL, jrrIMax = 1,
varMethod = c("jackknife"),
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
referenceDataIndex = 1,
returnVarEstInputs = FALSE,
returnSimpleDoF = FALSE,
returnSimpleN = FALSE,
returnNumberOfPSU = FALSE,
noCov = FALSE,
pctMethod = c("unbiased", "symmetric", "simple"),
includeLinkingError = FALSE,
omittedLevels = deprecated()) {
if (is.character(substitute(groupA))) {
groupA <- parse(text = groupA)[[1]]
}
if (all(as.character(substitute(groupA)) == "default")) {
groupB <- NULL
}
if (!missing(groupB)) {
if (is.character(substitute(groupB))) {
groupB <- parse(text = groupB)[[1]]
}
}
if (!missing(varMethod)) {
warning(paste0("Argument ", dQuote("varMethod"), " deprecated. The ", dQuote("gap"), " function always uses jackknife variance estimation."))
}
if (lifecycle::is_present(omittedLevels)) {
lifecycle::deprecate_soft("4.0.0", "gap(omittedLevels)", "gap(dropOmittedLevels)")
dropOmittedLevels <- omittedLevels
}
if (!is.logical(dropOmittedLevels)) stop("The ", sQuote("dropOmittedLevels"), " argument must be logical.")
# check incoming variables
call <- match.call()
# get valid pctMethod
pctMethod <- match.arg(pctMethod)
checkDataClass(data, c("edsurvey.data.frame", "light.edsurvey.data.frame", "edsurvey.data.frame.list"))
# if recode list is provided, it will be added to userConditions at first (before subset in groupA and groupB)
if (!is.null(recode)) {
data <- recode.sdf(data, recode)
}
if (inherits(data, "edsurvey.data.frame") | inherits(data, "light.edsurvey.data.frame")) {
survey <- getAttributes(data, "survey")
} else {
# edsurvey.data.frame.list
robustSurvey <- function(dat) {
res <- tryCatch(getAttributes(dat, "survey"),
error = function(e) {
NULL
}
)
return(res)
}
survey <- unique(unlist(lapply(data$data, robustSurvey)))
}
# check if linking error is supported for this survey
if (!missing(includeLinkingError) && includeLinkingError) {
checkDataClass(data, c("edsurvey.data.frame.list"))
if (!any(c("NAEP", "PISA") %in% survey)) {
stop("the argument ", dQuote("includeLinkingError"), " can only be set to ", dQuote("TRUE"), " when EdSurvey suports linking error for the survey. Currently EdSurvey supports linking error for NAEP and PISA.")
}
}
# Set dbaLabels as NULL for linking error with non ESDFL object or
# includeLinkingError set as False
dbaLabels <- NULL
# gapStat required for PISA regardless of linking error
if ("PISA" %in% survey) {
if (!is.null(percentiles)) {
gapStat <- "percentile"
} else if (!is.null(achievementLevel)) {
gapStat <- "AL"
} else if (stDev) {
gapStat <- "SD"
} else {
gapStat <- "Mean"
}
}
# Get parameters for linking error
if (!missing(includeLinkingError) && includeLinkingError) {
# the following is only for NAEP which has linking error within survey
if ("NAEP" %in% survey) {
if (inherits(data, "edsurvey.data.frame.list")) {
uniqueSubject <- unique(unlist(lapply(data$data, getAttributes, "subject")))
if (length(uniqueSubject) != 1) {
stop(paste0("Linking error only support ", dQuote("edsurvey.data.frame.list"), " with a single subject. Found subjects: ", pasteItems(uniqueSubject), "."))
}
uniqueGrade <- unique(unlist(lapply(data$data, getAttributes, "gradeLevel")))
if (length(uniqueGrade) != 1) {
stop(paste0("Linking error only support ", dQuote("edsurvey.data.frame.list"), " with a single grade level. Found grades: ", pasteItems(uniqueGrade), "."))
}
}
# For achievementDiscrete=TRUE, return "At basic" (not "below basic") if "achievementLevel = "basic"
alLinkingErrorhelper <- function(alstrings, discrete) {
matchedAL <- NULL
for (alstring in alstrings) {
if (discrete) {
if (grepl("^basic", alstring, ignore.case = TRUE)) {
matchedAL <- c(matchedAL, "At basic")
} else {
matchedAL <- c(
matchedAL,
c(
"Below basic", "At basic", "At proficient",
"At advanced"
)[grepl(alstring, c(
"Below basic", "At basic",
"At proficient", "At advanced"
),
ignore.case = TRUE
)]
)
}
} else {
matchedAL <- c(
matchedAL,
c(
"At or above basic",
"At or above proficient",
"At advanced"
)[
grepl(alstring, c("At or above basic", "At or above proficient", "At advanced"),
ignore.case = TRUE
)
]
)
}
}
if (length(matchedAL) != length(alstrings)) {
stop("Linking error for provided achievement level ", pasteItems(alstrings[!alstrings %in% matchedAL]), "is not available.")
}
return(matchedAL)
}
# Get DBA labels from DBA years
# 2017 is the first NAEP DBA year
if (inherits(data, "edsurvey.data.frame.list")) {
allYears <- unlist(itterateESDFL(call("getAttributes", attribute = "year"), data))
dbaLabels <- data[[2]]$labels[allYears >= 2017]
if (!all(dbaLabels %in% data[[2]]$labels)) {
stop(paste0(
pasteItems(dbaLabels[!dbaLabels %in% data[[2]]$labels]),
" is not in the ", dQuote("edsurvey.data.frame.list"), " labels."
))
}
} else {
allYears <- getAttributes(data, attribute = "year")
dbaLabels <- NULL
}
if (!is.null(percentiles) && !all(percentiles %in% c(10, 25, 50, 75, 90))) {
stop(
"NAEP linking error only supported for 10th, 25th, 50th, 75th, 90th percentiles.",
"Set ", dQuote("includeLinkingError"), " to ", dQuote("FALSE"),
"to calculate any percentile without linking error."
)
}
if (!is.null(percentiles)) {
gapStat <- percentiles
} else if (!is.null(achievementLevel)) {
gapStat <- alLinkingErrorhelper(achievementLevel, achievementDiscrete)
} else if (stDev) {
gapStat <- "SD"
} else {
gapStat <- "Mean"
}
}
}
# deal with the possibility that there is more than one line of results for various possible reasons
if (inherits(data, "edsurvey.data.frame.list") | length(percentiles) > 1 | length(achievementLevel) > 1) {
# a flag that is TRUE when there is no information in data$covs. Default to false
nocovs <- FALSE
if (!inherits(data, "edsurvey.data.frame.list")) {
# this is a situation where data is not an esdfl. The following code
# required it to be one, so make it one. But note that covs
# does not have real information in it.
nocovs <- TRUE
data <- edsurvey.data.frame.list(list(data), labels = "data")
}
ll <- length(data$datalist)
lpct <- ifelse(is.null(percentiles), 1, length(percentiles))
lal <- ifelse(is.null(achievementLevel), 1, length(achievementLevel))
# check variable specific to edsurvey.data.frame.list
suppressWarnings(refi <- as.integer(referenceDataIndex))
if (!refi %in% 1:ll) {
stop(paste0("The argument ", sQuote("referenceDataIndex"), " must be an integer between 1 and ", ll, "."))
}
res <- list(
results = list(),
labels = list(A = substitute(groupA), B = substitute(groupB))
)
pctdf0 <- data$covs
if (!nocovs) {
pctdf0$covAA <- 0
pctdf0$dofAA <- Inf
pctdf0$covBB <- 0
pctdf0$dofBB <- Inf
pctdf0$covABAB <- 0
pctdf0$dofABAB <- Inf
}
# make sure refi gets calculated first
llvec <- unique(c(refi, 1:ll))
resilist <- list()
# call gap for each edsurvey.data.frame
for (i in llvec) {
# for each element of the edsurvey.data.frame.list
calli <- call
# adjust the call per the needs of this call
calli$data <- data$datalist[[i]]
# we need the these inputs to calculate across essurvey.data.frame
# covariances (but, only if they are the same dataset) an issue
# that will be resolved later.
calli$returnVarEstInputs <- TRUE
# this returns the degrees of freedom for meanA and meanB
# used for diffAA and diffBB, respectively
calli$returnSimpleDoF <- TRUE
# if these are NULL then setting them changes nothing
calli$percentiles <- percentiles
calli$achievementLevel <- achievementLevel
calli <- as.list(calli)
# use the same list of arguments but change the function to gapHelper
calli[[1]] <- as.name("gapHelper")
resilist[[i]] <- tryCatch(resi <- eval(as.call(calli)),
error = function(cond) {
message(paste0(
"An error occurred while working on a dataset ",
pasteItems(dQuote(data$covs[i, ])),
". The results from that dataset will be excluded. Error message: ",
cond
))
return(0)
}
)
# if there was not an error
if (!inherits(resilist[[i]], "gap")) {
# there was an error
if (i %in% referenceDataIndex) {
stop("An error prevented processing of the reference dataset. This must be fixed before comparisons to the reference dataset can be made.")
}
} else {
resi$call <- NULL
# the following code is to clean up the results (adding some columns to make the code to extract results from gapHelper more consistent)
# after this code, we should expect that each gap result will have:
# 1. resi$results will have 'statisticsLevel' column: indicate percentile or achievementLevel, or 'data' if none is specified
# 2. resi$varEstInputs: PV and JK data frame will have 'Level' column similar to 'statisticsLevel' column in resi$results
if (is.null(percentiles) && is.null(achievementLevel)) {
resi$results$statisticsLevel <- "data"
}
# if no or only 1 percentile/achievementLevel is specified
if (length(percentiles) <= 1 && length(achievementLevel) <= 1) {
resi$varEstInputs$JK$Level <- "data"
if (!is.null(resi$varEstInputs$PV)) { # PV is null for type = "eq"
resi$varEstInputs$PV$Level <- "data"
}
}
# in gapHelper, type = 'pct' will return 'percentiles' column and type = 'al' will return 'achievementLevel' column
# this replacement is to use 1 consistent name for the result extraction code below
# it will be renamed back to original names after the extraction
colnames(resi$results) <- gsub("achievementLevel|percentiles", "statisticsLevel", colnames(resi$results))
resilist[[i]] <- resi
}
} # end for(i in llvec)
# Extract results and compute AA or BB statistics
lstats <- max(lpct, lal)
for (j in 1:lstats) {
resdf <- data$covs
if (!nocovs) {
# only generate these columns if there are multiple datasets
resdf$covAA <- 0
resdf$dofAA <- Inf
resdf$covBB <- 0
resdf$dofBB <- Inf
resdf$covABAB <- 0
resdf$dofABAB <- Inf
}
# to check whether the two samples are from the same survey or not
# we need to use DOFCorrection and compute covariance if the two samples are from the same survey
resdf$sameSurvey <- ifelse(1:nrow(resdf) == refi,
rep(NA, nrow(resdf)),
rep(FALSE, nrow(resdf))
)
if (lpct > 1) {
resdf$percentiles <- percentiles[[j]]
}
for (i in llvec) {
if (!inherits(resilist[[i]], "gap")) {
# there was an error
if (i %in% referenceDataIndex) {
stop("An error prevented processing of the reference dataset. This must be fixed before comparisons to the reference dataset can be made.")
}
# this is to make sure that all achievementLevels or percentiles are created as to the reference edsurvey.data.frame
# Missing values will be NA for statistics
if (!is.null(achievementLevel)) {
resdf$achievementLevel[i] <- resilist[[refi]]$results$statisticsLevel[j]
} else if (!is.null(percentiles)) {
resdf$percentiles[i] <- resilist[[refi]]$results$statisticsLevel[j]
}
} else { # there was not an error
# dataframes do not like vectorwise assignment by column name,
# so assign one at a time
resi <- resilist[[i]]
resi$results <- resi$results[j, ]
resi$varEstInputs$JK <- resi$varEstInputs$JK[tolower(resi$varEstInputs$JK$Level) %in% tolower(c(resi$results$statisticsLevel, "data")), ]
if (!is.null(resi$varEstInputs$PV)) {
resi$varEstInputs$PV <- resi$varEstInputs$PV[tolower(resi$varEstInputs$PV$Level) %in% tolower(c(resi$results$statisticsLevel, "data")), ]
}
if (!is.null(achievementLevel)) {
colnames(resi$results) <- gsub("statisticsLevel", "achievementLevel", colnames(resi$results))
} else if (!is.null(percentiles)) {
colnames(resi$results) <- gsub("statisticsLevel", "percentiles", colnames(resi$results))
} else {
resi$results$statisticsLevel <- NULL
}
for (ii in 1:length(resi$results)) {
resdf[i, names(resi$results)[ii]] <- resi$results[[ii]]
}
skipB <- is.null(resi$labels$B) # a boolean to indicate whether groupB estimates are available in the results of gapHelper
if (i == refi) {
refVarEstInputs <- resi$varEstInputs
refPctVarEstInputs <- resi$pctVarEstInputs
}
# refi is the index of reference dataset
# AA and BB statistics of reference dataset will be set to NA
if (i != refi) {
resdf$dofAA[i] <- dofCompute((resi$results)$estimateAse, resdf[refi, "estimateAse"], (resi$results)[["dofA"]], resdf[refi, "dofA"])
if (!skipB) {
resdf$dofBB[i] <- dofCompute((resi$results)$estimateBse, resdf[refi, "estimateBse"], (resi$results)[["dofB"]], resdf[refi, "dofB"])
resdf$dofABAB[i] <- dofCompute((resi$results)$diffABse, resdf[refi, "diffABse"], (resi$results)[["dofAB"]], resdf[refi, "dofAB"])
}
# if the datasets and from the same sample, add covariances and do DoFCorrection
if (sameSurvey(data$datalist[[i]], data$datalist[[refi]])) {
if (nrow(resi$varEstInputs$JK) != 0) { # some Level (percentile or achievementLevel) do not have varEstInput data so the subset
# in line 476 will return an empty data.frame
resdf$dofAA[i] <- DoFCorrection(resi$varEstInputs, refVarEstInputs, "A", method = "JR")
}
resdf$covAA[i] <- varEstToCov(resi$varEstInputs, refVarEstInputs, "A", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
if (!skipB & nrow(resi$varEstInputs$JK) != 0) {
resdf$dofBB[i] <- DoFCorrection(resi$varEstInputs, refVarEstInputs, "B", method = "JR")
resdf$dofABAB[i] <- DoFCorrection(resi$varEstInputs, refVarEstInputs, "A-B", method = "JR")
resdf$covBB[i] <- varEstToCov(resi$varEstInputs, refVarEstInputs, "B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
resdf$covABAB[i] <- varEstToCov(resi$varEstInputs, refVarEstInputs, "A-B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
}
resdf$sameSurvey[i] <- TRUE
} else {
if ("PISA" %in% survey) {
linke <- 0
if (includeLinkingError & gapStat %in% c("Mean", "percentile", "SD")) {
if (variable %in% c("read", "math", "scie")) {
yearref <- getAttributes(data$datalist[[refi]], "year")
yeari <- getAttributes(data$datalist[[i]], "year")
if (yeari != yearref) {
linke <- calLinkingErrorPISA(subject = variable, years = c(yearref, yeari))
}
} else {
warning("No published linking error for variable ", dQuote(variable), "\n")
}
}
covvars <- c("covAA", "covBB", "covABAB")
for (cvi in 1:length(covvars)) {
if (covvars[cvi] %in% colnames(resdf)) {
resdf[i, covvars[cvi]] <- -1 / 2 * linke^2
}
}
}
}
} else { # end if(i != refi)
# if this is refi, set these to NULL
resdf$covAA[i] <- resdf$covBB[i] <- resdf$covABAB[i] <- NA
resdf$dofAA[i] <- resdf$dofBB[i] <- resdf$dofABAB[i] <- NA
}
# for the first j value only, grab the percent
if (j == 1) {
for (ii in 1:ncol(resi$percentage)) {
pctdf0[i, colnames(resi$percentage)[ii]] <- resi$percentage[ , ii]
if (i != refi) {
pctdf0$dofAA[i] <- dofCompute((resi$percentage)$pctAse, pctdf0[refi, "pctAse"], (resi$percentage)[["dofA"]], pctdf0[refi, "dofA"])
if (!skipB) {
pctdf0$dofBB[i] <- dofCompute((resi$percentage)$pctBse, pctdf0[refi, "pctBse"], (resi$percentage)[["dofB"]], pctdf0[refi, "dofB"])
pctdf0$dofABAB[i] <- dofCompute((resi$percentage)$diffABse, pctdf0[refi, "diffABse"], (resi$percentage)[["dofAB"]], pctdf0[refi, "dofAB"])
}
if (sameSurvey(data$datalist[[i]], data$datalist[[refi]])) {
pctdf0$dofAA[i] <- DoFCorrection(resi$pctVarEstInputs, refPctVarEstInputs, "A", method = "JR")
pctdf0$covAA[i] <- varEstToCov(resi$pctVarEstInputs, refPctVarEstInputs, "A", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
if (!skipB) {
pctdf0$dofBB[i] <- DoFCorrection(resi$pctVarEstInputs, refPctVarEstInputs, "B", method = "JR")
pctdf0$dofABAB[i] <- DoFCorrection(resi$pctVarEstInputs, refPctVarEstInputs, "A-B", method = "JR")
pctdf0$covBB[i] <- varEstToCov(resi$pctVarEstInputs, refPctVarEstInputs, "B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
pctdf0$covABAB[i] <- varEstToCov(resi$pctVarEstInputs, refPctVarEstInputs, "A-B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
}
}
} else { # end if(i != refi)
# if this is refi, set these to NULL
pctdf0$covAA[i] <- pctdf0$covBB[i] <- pctdf0$covABAB[i] <- NA
pctdf0$dofAA[i] <- pctdf0$dofBB[i] <- pctdf0$dofABAB[i] <- NA
} # end else for (i != refi)
} # end for(ii in 1:ncol(resi$percentage))
if (returnSimpleN) {
# return the n counts
pctdf0$nA[i] <- resi$labels$nUsedA
pctdf0$nB[i] <- resi$labels$nUsedB
}
if (returnNumberOfPSU) {
pctdf0$nPSUA[i] <- ifelse(is.null(resi$labels$nPSUA), NA, resi$labels$nPSUA)
pctdf0$nPSUB[i] <- ifelse(is.null(resi$labels$nPSUB), NA, resi$labels$nPSUB)
}
} # end if(j == 1)
} # end if(inherits(temp, "gap"))
} # End of for(i in 1:i) loop
# Compute diff statistics (across different edsurvey.data.frame)
resdf$diffAA <- ifelse(1:nrow(resdf) == refi, NA, resdf$estimateA[refi] - resdf$estimateA)
if (!is.null(dbaLabels)) {
# get linking error
leAA <- calLinkingError(
X = resdf$estimateA[refi],
subjectI = uniqueSubject,
gradeI = uniqueGrade,
dependentVarI = variable,
statElementI = gapStat,
gapI = FALSE
)[j]
resdf$diffAAse <- ifelse(1:nrow(resdf) == refi, NA,
ifelse(resdf$labels %in% dbaLabels,
sqrt(resdf$estimateAse[refi]^2 + resdf$estimateAse^2 - 2 * resdf$covAA),
sqrt(resdf$estimateAse[refi]^2 + resdf$estimateAse^2 - 2 * resdf$covAA + leAA)
)
)
} else {
resdf$diffAAse <- ifelse(1:nrow(resdf) == refi, NA, sqrt(resdf$estimateAse[refi]^2 + resdf$estimateAse^2 - 2 * resdf$covAA))
}
resdf$diffAApValue <- ifelse(1:nrow(resdf) == refi, NA, 2 * (1 - pt2(abs(resdf$diffAA / resdf$diffAAse), df = resdf$dofAA)))
if (!skipB) {
resdf$diffBB <- ifelse(1:nrow(resdf) == refi, NA, resdf$estimateB[refi] - resdf$estimateB)
if (!is.null(dbaLabels)) {
leBB <- calLinkingError(
X = resdf$estimateB[refi],
subjectI = uniqueSubject,
gradeI = uniqueGrade,
dependentVarI = variable,
statElementI = gapStat,
gapI = FALSE
)[j]
resdf$diffBBse <- ifelse(1:nrow(resdf) == refi, NA,
ifelse(resdf$labels %in% dbaLabels,
sqrt(resdf$estimateBse[refi]^2 + resdf$estimateBse^2 - 2 * resdf$covBB),
sqrt(resdf$estimateBse[refi]^2 + resdf$estimateBse^2 - 2 * resdf$covBB + leBB)
)
)
} else {
resdf$diffBBse <- ifelse(1:nrow(resdf) == refi, NA, sqrt(resdf$estimateBse[refi]^2 + resdf$estimateBse^2 - 2 * resdf$covBB))
}
resdf$diffBBpValue <- ifelse(1:nrow(resdf) == refi, NA, 2 * (1 - pt2(abs(resdf$diffBB / resdf$diffBBse), df = resdf$dofBB)))
resdf$diffABAB <- ifelse(1:nrow(resdf) == refi, NA, resdf$diffAB[refi] - resdf$diffAB)
if (!is.null(dbaLabels)) {
leABAB <- calLinkingError(
X = resdf$diffAB[refi],
subjectI = uniqueSubject,
gradeI = uniqueGrade,
dependentVarI = variable,
statElementI = gapStat,
gapI = TRUE
)[j]
resdf$diffABABse <- ifelse(1:nrow(resdf) == refi, NA,
ifelse(resdf$labels %in% dbaLabels,
sqrt(resdf$diffABse[refi]^2 + resdf$diffABse^2 - 2 * resdf$covABAB),
sqrt(resdf$diffABse[refi]^2 + resdf$diffABse^2 - 2 * resdf$covABAB + leABAB)
)
)
} else {
resdf$diffABABse <- ifelse(1:nrow(resdf) == refi, NA, sqrt(resdf$diffABse[refi]^2 + resdf$diffABse^2 - 2 * resdf$covABAB))
}
resdf$diffABABpValue <- ifelse(1:nrow(resdf) == refi, NA, 2 * (1 - pt2(abs(resdf$diffABAB / resdf$diffABABse), df = resdf$dofABAB)))
}
# only generate these
if (!nocovs) {
pctdf0$diffAA <- ifelse(1:nrow(pctdf0) == refi, NA, pctdf0$pctA[refi] - pctdf0$pctA)
pctdf0$diffAAse <- ifelse(1:nrow(pctdf0) == refi, NA, sqrt(pctdf0$pctAse[refi]^2 + pctdf0$pctAse^2 - 2 * pctdf0$covAA))
pctdf0$diffAApValue <- ifelse(1:nrow(pctdf0) == refi, NA, 2 * (1 - pt2(abs(pctdf0$diffAA / pctdf0$diffAAse), df = pctdf0$dofAA)))
if (!skipB) {
pctdf0$diffBB <- ifelse(1:nrow(pctdf0) == refi, NA, pctdf0$pctB[refi] - pctdf0$pctB)
pctdf0$diffBBse <- ifelse(1:nrow(pctdf0) == refi, NA, sqrt(pctdf0$pctBse[refi]^2 + pctdf0$pctBse^2 - 2 * pctdf0$covBB))
pctdf0$diffBBpValue <- ifelse(1:nrow(pctdf0) == refi, NA, 2 * (1 - pt2(abs(pctdf0$diffBB / pctdf0$diffBBse), df = pctdf0$dofBB)))
pctdf0$diffABAB <- ifelse(1:nrow(pctdf0) == refi, NA, pctdf0$diffAB[refi] - pctdf0$diffAB)
pctdf0$diffABABse <- ifelse(1:nrow(pctdf0) == refi, NA, sqrt(pctdf0$diffABse[refi]^2 + pctdf0$diffABse^2 - 2 * pctdf0$covABAB))
pctdf0$diffABABpValue <- ifelse(1:nrow(pctdf0) == refi, NA, 2 * (1 - pt2(abs(pctdf0$diffABAB / pctdf0$diffABABse), df = pctdf0$dofABAB)))
}
}
if (j == 1) {
resdf0 <- resdf
} else {
resdf0 <- rbind(resdf0, resdf)
}
} # end for(j in 1:lstats) loop
if (nocovs) {
# remove the labels columns which are just populated
# with the unhelpful text "data"
pctdf0$labels <- NULL
resdf0$labels <- NULL
# reorder columns when there are no covariates
if (returnSimpleDoF) {
if (!skipB) {
resvar0 <- c(
"estimateA", "estimateAse", "dofA",
"estimateB", "estimateBse", "dofB"
)
} else {
resvar0 <- c("estimateA", "estimateAse", "dofA")
}
} else {
if (!skipB) {
resvar0 <- c(
"estimateA", "estimateAse",
"estimateB", "estimateBse"
)
} else {
resvar0 <- c("estimateA", "estimateAse")
}
}
if (!skipB) {
resvar0 <- c(
resvar0,
"diffAB", "covAB",
"diffABse", "diffABpValue", "dofAB"
)
}
addons <- c("achievementLevel", "percentiles")
resvar <- c(addons[addons %in% names(resdf0)], resvar0)
if ("sameSurvey" %in% names(resdf0) && sum(!is.na(resdf0$sameSurvey)) > 0) {
resvar <- c(resvar, "sameSurvey")
}
resdf0 <- resdf0[ , resvar]
# remove "estimate" with "pct" where "estimate" must start the variable name
resvar <- gsub("^estimate", "pct", resvar0)
if (returnSimpleN) {
if (!skipB) {
resvar <- c(resvar, "nA", "nB")
} else {
resvar <- c(resvar, "nA")
}
}
if (returnNumberOfPSU) {
if (!skipB) {
resvar <- c(resvar, "nPSUA", "nPSUB")
} else {
resvar <- c(resvar, "nPSUA")
}
}
pctdf0 <- pctdf0[ , resvar]
} else { # end if(nocovs)
# reorder columns when there are covs
if (returnSimpleDoF) {
if (!skipB) {
resvar0 <- c(
names(data$covs), "estimateA", "estimateAse", "dofA",
"estimateB", "estimateBse", "dofB"
)
} else {
resvar0 <- c(names(data$covs), "estimateA", "estimateAse", "dofA")
}
} else {
if (!skipB) {
resvar0 <- c(
names(data$covs), "estimateA", "estimateAse",
"estimateB", "estimateBse"
)
} else {
resvar0 <- c(names(data$covs), "estimateA", "estimateAse")
}
}
if (!skipB) {
resvar0 <- c(
resvar0,
"diffAB", "covAB", "diffABse",
"diffABpValue", "dofAB",
"diffAA", "covAA", "diffAAse",
"diffAApValue", "dofAA",
"diffBB", "covBB", "diffBBse",
"diffBBpValue", "dofBB",
"diffABAB", "covABAB", "diffABABse",
"diffABABpValue", "dofABAB"
)
} else {
resvar0 <- c(
resvar0,
"diffAA", "covAA", "diffAAse",
"diffAApValue", "dofAA"
)
}
addons <- c("achievementLevel", "percentiles")
resvar <- c(addons[addons %in% names(resdf0)], resvar0)
if ("sameSurvey" %in% names(resdf0) && sum(!is.na(resdf0$sameSurvey)) > 0) {
resvar <- c(resvar, "sameSurvey")
}
resdf0 <- resdf0[ , resvar]
resvar <- gsub("^estimate", "pct", resvar0)
missing <- resvar[!resvar %in% names(pctdf0)]
if (returnSimpleN) {
if (!skipB) {
resvar <- c(resvar, "nA", "nB")
} else {
resvar <- c(resvar, "nA")
}
}
pctdf0 <- pctdf0[ , resvar]
} # end else if(nocovs)
res[["results"]] <- resdf0
res[["percentage"]] <- pctdf0
res[["call"]] <- call
class(res) <- "gapList"
return(res)
} else { # end if(inherits(data, "edsurvey.data.frame.list"))
calli <- as.list(call)
calli[[1]] <- as.name("gapHelper")
calli$data <- data
resi <- eval(as.call(calli))
resi$call <- call
return(resi)
} # end else for if(inherits(data, "edsurvey.data.frame.list")) {
}
# this is called when data is edsurvey.data.frame or light.edsurvey.data.frame
gapHelper <- function(variable, data, groupA = "default", groupB = "default",
percentiles = NULL,
achievementLevel = NULL,
achievementDiscrete = FALSE,
stDev = FALSE,
targetLevel = NULL,
weightVar = NULL, jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
referenceDataIndex = 1,
returnVarEstInputs = FALSE,
returnSimpleDoF = FALSE,
returnSimpleN = FALSE,
returnNumberOfPSU = FALSE,
noCov = FALSE,
pctMethod = c("unbiased", "symmetric", "simple"),
varMethod = NULL,
includeLinkingError = NULL) {
if (is.character(substitute(groupA))) {
groupA <- parse(text = groupA)[[1]]
}
if (all(as.character(substitute(groupA)) == "default")) {
groupB <- NULL
}
if (!missing(groupB)) {
if (is.character(substitute(groupB))) {
groupB <- parse(text = groupB)[[1]]
}
}
pctMethod <- match.arg(pctMethod)
# get the weight var
# if the weight var is not set, use the default
wgt <- checkWeightVar(data, weightVar)
varEstInputs <- NULL
type <- "mu" # mean is the default
if (!is.null(percentiles)) {
type <- "pct"
}
if (!is.null(achievementLevel)) {
if (type != "mu") {
stop(paste0(
"Only one of ", sQuote("percentiles"), ", ", sQuote("achievementLevel"),
", ", sQuote("SD"), ", and ", sQuote("targetLevel"), " can be defined."
))
}
type <- "AL"
achievementLevel <- gmatchAttr(achievementLevel, getALNames(data, variable))
}
if (!is.null(targetLevel)) {
if (type != "mu") {
stop(paste0(
"Only one of ", sQuote("percentiles"), ", ", sQuote("achievementLevel"),
", ", sQuote("SD"), ", and ", sQuote("targetLevel"), " can be defined."
))
}
type <- "eq"
}
if (stDev) {
if (type != "mu") {
stop(paste0(
"Only one of ", sQuote("percentiles"), ", ", sQuote("achievementLevel"),
", ", sQuote("SD"), ", and ", sQuote("targetLevel"), " can be defined."
))
}
type <- "stdev"
}
# make the data with just groupA
if (all(as.character(substitute(groupA)) == "default")) {
dataA <- data
} else {
dataA <- subset(data, substitute(groupA), inside = TRUE)
}
# skipB is a boolean to indicate whether we need to compute statistics for group B
# skipB = TRUE happens when (1) groupB is set to NULL or (2) groupA = 'default' (full sample)
skipB <- FALSE
# if groupB is null, just use data as the reference.
# NOTE: the call to is.null requires "substitute" or groupB
# will be evaluated right here
if (is.null(substitute(groupB))) {
skipB <- TRUE
} else if (all(as.character(substitute(groupB)) == "default")) {
dataB <- data
dataAB <- dataA
} else {
# otherwise filter on groupB
dataB <- subset(data, substitute(groupB), inside = TRUE)
dataAB <- subset(dataA, substitute(groupB), inside = TRUE)
}
callv <- list(
weightVar = wgt,
jrrIMax = jrrIMax,
dropOmittedLevels = dropOmittedLevels,
recode = NULL
) # recode.sdf is already done in line 335
if (!missing(defaultConditions)) {
callv <- c(callv, list(defaultConditions = defaultConditions))
}
if (type == "mu") {
# gap in mean
calllm <- c(
list(formula(paste0(variable, " ~ 1"))),
callv,
list(returnVarEstInputs = TRUE)
) # necessary for covariance est
calllmA <- c(calllm, list(data = dataA))
meanA <- do.call(lm.sdf, calllmA)
coefA <- meanA$coefmat
resA <- coefA[1, 1]
if (!skipB) {
calllmB <- c(calllm, list(data = dataB))
meanB <- do.call(lm.sdf, calllmB)
diff <- unname(meanA$coef[1] - meanB$coef[1]) # unname removes the name (Intercept) that otherwise ends up in the retruned value
coefB <- meanB$coefmat
resB <- coefB[1, 1]
SEs <- list(estimateAse = coefA[1, 2], estimateBse = coefB[1, 2])
} else {
SEs <- list(estimateAse = coefA[1, 2])
}
# prepare varEstInputs
# make varEstInputs$JK
maJK <- subset(meanA$varEstInputs$JK, variable == "(Intercept)")
maJK$variable <- "A"
if (!skipB) {
mbJK <- subset(meanB$varEstInputs$JK, variable == "(Intercept)")
mbJK$variable <- "B"
# calculate the difference
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
if (!is.null(meanA$varEstInput$PV)) {
maPV <- subset(meanA$varEstInputs$PV, variable == "(Intercept)")
maPV$variable <- "A"
if (!skipB) {
mbPV <- subset(meanB$varEstInputs$PV, variable == "(Intercept)")
mbPV$variable <- "B"
# calculate the difference
mdPV <- merge(maPV, mbPV, by = c("PV"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
# only group A
varEstPV <- maPV
}
} else { # end if(!is.null(meanA$varEstInput$PV))
# no PVs, so no error from them
varEstPV <- NULL
} # end else for if(!is.null(meanA$varEstInput$PV))
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if(type == "mu")
if (type == "pct") {
# gap at a percentile
# here the length of percentiles is always one
callpct <- c(
list(
variable = variable,
percentiles = percentiles,
confInt = FALSE, # do not share CI, so do not calculate it
pctMethod = pctMethod
),
callv,
returnVarEstInputs = TRUE
)
callpctA <- c(callpct, list(data = dataA))
meanA <- do.call(percentile, callpctA)
resA <- meanA$estimate
statisticsLevelOut <- paste0("P", percentiles)
if (!skipB) {
callpctB <- c(callpct, list(data = dataB))
meanB <- do.call(percentile, callpctB)
resB <- meanB$estimate
SEs <- list(estimateAse = meanA$se, estimateBse = meanB$se)
} else {
SEs <- list(estimateAse = meanA$se)
}
maJK <- attributes(meanA)$varEstInputs$JK
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- attributes(meanB)$varEstInputs$JK
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
# calculate the difference
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate", "Level"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
maPV <- attributes(meanA)$varEstInputs$PV
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- attributes(meanB)$varEstInputs$PV
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
# calculate the difference
mdPV <- merge(maPV, mbPV, by = c("PV", "Level"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
varEstPV <- maPV
}
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if (type == 'pct')
if (type == "AL") {
# gap in percent at or above an achievement level
callal <- c(
list(
achievementVars = variable,
returnVarEstInputs = TRUE,
returnCumulative = !achievementDiscrete
),
callv
)
als <- getALNames(data, variable)
callalA <- c(callal, list(data = dataA))
meanA <- do.call(achievementLevels, callalA)
if (!is.null(meanA$discrete)) {
colnames(meanA$discrete)[grepl("Level$", colnames(meanA$discrete))] <- "Level"
}
if (!is.null(meanA$cumulative)) {
colnames(meanA$cumulative)[grepl("Level$", colnames(meanA$cumulative))] <- "Level"
}
if (!skipB) {
callalB <- c(callal, list(data = dataB))
meanB <- do.call(achievementLevels, callalB)
if (!is.null(meanB$discrete)) {
colnames(meanB$discrete)[grepl("Level$", colnames(meanB$discrete))] <- "Level"
}
if (!is.null(meanB$cumulative)) {
colnames(meanB$cumulative)[grepl("Level$", colnames(meanB$cumulative))] <- "Level"
}
}
# achievementDiscrete indicates whether we should extract cumulative or discrete achievement level results
if (achievementDiscrete) {
# get index of desired achievement levels from the results
if (typeof(als) == "character") {
lA <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = TRUE)
grep(lal, meanA$discrete$Level, ignore.case = TRUE)
})
} else {
lA <- rep(TRUE, length(als[[achievementLevel]]))
}
if (length(lA) == 0) {
stop("Achievement level cannot be found in the results of the call of the function ", sQuote("achievementLevel"), ".")
}
if (!skipB) {
if (typeof(als) == "character") {
lB <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = TRUE)
grep(lal, meanB$discrete$Level, ignore.case = TRUE)
})
} else {
lB <- rep(TRUE, length(als[[achievementLevel]]))
}
if (length(lB) == 0) {
stop("Achievement level cannot be found in the results of the call of the function ", sQuote("achievementLevel"), ".")
}
}
resA <- meanA$discrete$Percent[unlist(lA)]
if (!skipB) {
resB <- meanB$discrete$Percent[unlist(lB)]
SEs <- list(
estimateAse = meanA$discrete$StandardError[unlist(lA)],
estimateBse = meanB$discrete$StandardError[unlist(lB)]
)
} else {
SEs <- list(estimateAse = meanA$discrete$StandardError[unlist(lA)])
}
} else {
# get index of desired achievement levels from the results
if (typeof(als) == "character") {
lA <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = FALSE)
grep(lal, meanA$cumulative$Level, ignore.case = TRUE)
})
} else {
lA <- rep(TRUE, length(als[[achievementLevel]]))
}
if (length(lA) == 0) {
stop("Achievement level cannot be found in the results of the call of the function ", sQuote("achievementLevel"), ".")
}
if (!skipB) {
if (typeof(als) == "character") {
lB <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = FALSE)
grep(lal, meanB$cumulative$Level, ignore.case = TRUE)
})
} else {
lB <- rep(TRUE, length(als[[achievementLevel]]))
}
}
resA <- meanA$cumulative$Percent[unlist(lA)]
if (!skipB) {
resB <- meanB$cumulative$Percent[unlist(lB)]
SEs <- list(
estimateAse = meanA$cumulative$StandardError[unlist(lA)],
estimateBse = meanB$cumulative$StandardError[unlist(lB)]
)
} else {
SEs <- list(estimateAse = meanA$cumulative$StandardError[unlist(lA)])
}
}
# used inside next if, but also outside
statisticsLevelOut <- meanA$discrete$Level[unlist(lA)]
# get Cov
if (achievementDiscrete) {
maJK <- subset(meanA$discVarEstInputs$JK, variable %in% meanA$discrete$Level[unlist(lA)])
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- subset(meanB$discVarEstInputs$JK, variable %in% meanB$discrete$Level[unlist(lB)])
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
}
} # end if(achievementDiscrete)
else {
maJK <- subset(meanA$cumVarEstInputs$JK, variable %in% meanA$cumulative$Level[unlist(lA)])
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- subset(meanB$cumVarEstInputs$JK, variable %in% meanB$cumulative$Level[unlist(lB)])
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
}
} # end else if(achievementDiscrete)
if (!skipB) {
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate", "Level"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
if (achievementDiscrete) {
maPV <- subset(meanA$discVarEstInputs$PV, variable %in% meanA$discrete$Level[unlist(lA)])
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- subset(meanB$discVarEstInputs$PV, variable %in% meanB$discrete$Level[unlist(lB)])
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
}
} # end if(achievementDiscrete)
else {
statisticsLevelOut <- meanA$cumulative$Level[unlist(lA)]
maPV <- subset(meanA$cumVarEstInputs$PV, variable %in% meanA$cumulative$Level[unlist(lA)])
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- subset(meanB$cumVarEstInputs$PV, variable %in% meanB$cumulative$Level[unlist(lB)])
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
}
} # end else if(achievementDiscrete)
# calculate the difference
if (!skipB) {
mdPV <- merge(maPV, mbPV, by = c("PV", "Level"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
varEstPV <- maPV
}
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if (type == "AL")
if (type == "stdev") {
# gap at a standard deviation
# here the length of standard deviation is always one
callStDev <- c(list(variable = variable),
callv,
returnVarEstInputs = TRUE
)
if ("weightVar" %in% names(callStDev) && is.null(callStDev[["weightVar"]])) {
# default is used only if weightVar is missing.
callStDev[["weightVar"]] <- NULL
}
callStDevA <- c(callStDev, list(data = dataA))
meanA <- do.call(SD, callStDevA)
resA <- meanA$std
statisticsLevelOut <- "SD"
if (!skipB) {
callStDevB <- c(callStDev, list(data = dataB))
meanB <- do.call(SD, callStDevB)
resB <- meanB$std
SEs <- list(estimateAse = meanA$stdSE, estimateBse = meanB$stdSE)
} else {
SEs <- list(estimateAse = meanA$stdSE)
}
maJK <- meanA$varEstInputs$JK
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- meanB$varEstInputs$JK
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
# calculate the difference
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate", "Level"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
if (!is.null(meanA$varEstInput$PV)) {
maPV <- meanA$varEstInputs$PV
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- meanB$varEstInputs$PV
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
# calculate the difference
mdPV <- merge(maPV, mbPV, by = c("PV", "Level"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
varEstPV <- maPV
}
} else { # end if(!is.null(meanA$varEstInput$PV))
# no PVs, so no error from them
varEstPV <- NULL
} # end else for if(!is.null(meanA$varEstInput$PV))
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if (type == 'stDev')
groupA_0 <- FALSE
groupB_0 <- FALSE
if (type == "eq") {
# gap in percent in some covariate
calleq <- c(callv, list(
formula = formula(paste0(" ~ ", variable)),
returnMeans = FALSE,
pctAggregationLevel = NULL,
returnSepct = TRUE,
drop = FALSE,
returnVarEstInputs = TRUE
))
calleqA <- c(calleq, list(data = dataA))
meanA <- do.call(edsurveyTable, calleqA)
if (!targetLevel %in% meanA$data[[variable]]) {
groupA_0 <- TRUE
}
resA <- ifelse(groupA_0, 0, meanA$data[meanA$data[variable] == targetLevel, 4])
if (!skipB) {
calleqB <- c(calleq, list(data = dataB))
meanB <- do.call(edsurveyTable, calleqB)
if (!targetLevel %in% meanB$data[[variable]]) {
groupB_0 <- TRUE
}
resB <- ifelse(groupB_0, 0, meanB$data[meanB$data[variable] == targetLevel, 4])
SEs <- list(
estimateAse = ifelse(groupB_0, NA, meanA$data[meanA$data[variable] == targetLevel, 5]),
estimateBse = ifelse(groupB_0, NA, meanB$data[meanB$data[variable] == targetLevel, 5])
)
} else {
SEs <- list(estimateAse = ifelse(groupB_0, NA, meanA$data[meanA$data[variable] == targetLevel, 5]))
}
# make varEstInputs$JK
if (!groupA_0) {
maJK <- meanA$pctVarEstInputs$JK[meanA$pctVarEstInputs$JK$variable == paste0(variable, "=", targetLevel), ]
maJK$variable <- "A"
} else {
maJK <- NULL
}
if (!skipB) {
if (!groupB_0) {
mbJK <- meanB$pctVarEstInputs$JK[meanB$pctVarEstInputs$JK$variable == paste0(variable, "=", targetLevel), ]
mbJK$variable <- "B"
} else {
mbJK <- NULL
}
# calculate the difference
if (!groupA_0 & !groupB_0) {
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
} else {
mdJK <- NULL
}
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs
if (!is.null(varEstJK)) {
row.names(varEstJK) <- NULL
varEstInputs <- list(JK = varEstJK, PV = NULL)
} else {
varEstInputs <- NULL
}
} # end if (type == "eq")
# Compute difference statistics (diffAB, covAB, pooledse, pooleddf)
if (!skipB) {
diff <- unname(resA - resB) # unname removes the name (Intercept) that otherwise ends up in the retruned value
nSize <- c(nrow(dataA), nrow(dataB))
total <- which.max(nSize) # total is 1 if groupA is the total and 2 if groupB is the total
p <- nrow(dataAB) / nSize[total]
cov <- p * (SEs[[total]])^2
wgtl <- getAttributes(data, "weights")[[wgt]]
JRdfA <- JRdfB <- JRdf <- length(wgtl$jksuffixes)
if (!is.null(varEstInputs)) {
if (type %in% c("pct", "AL")) {
cov <- c()
JRdfA <- c()
JRdfB <- c()
JRdf <- c()
# we need to calculate cov by each Level
for (si in statisticsLevelOut) {
varEstInputsTemp <- list(
JK = subset(varEstInputs$JK, Level %in% si),
PV = subset(varEstInputs$PV, Level %in% si)
)
if (nrow(varEstInputsTemp$JK) == 0) {
varEstInputsTemp$JK <- NULL
}
if (nrow(varEstInputsTemp$PV) == 0) {
varEstInputsTemp$PV <- NULL
}
if (!is.null(varEstInputsTemp$JK)) {
cov <- c(cov, varEstToCov(varEstInputsTemp,
varA = "A", varB = "B", jkSumMultiplier = getAttributes(data, "jkSumMultiplier")
))
JRdfA <- c(JRdfA, DoFCorrection(varEstInputsTemp, varA = "A", method = c("JR")))
JRdfB <- c(JRdfB, DoFCorrection(varEstInputsTemp, varA = "B", method = c("JR")))
JRdf <- c(JRdf, DoFCorrection(varEstInputsTemp, varA = "A", varB = "B", method = c("JR")))
} # end if (!is.null(varEstInputsTemp$JK))
else {
cov <- c(cov, NA)
JRdfA <- c(JRdfA, NA)
JRdfB <- c(JRdfB, NA)
JRdf <- c(JRdf, NA)
}
}
} # end if (type %in% c("pct","AL"))
else {
cov <- ifelse(groupA_0 | groupB_0, cov, varEstToCov(varEstInputs, varA = "A", varB = "B", jkSumMultiplier = getAttributes(data, "jkSumMultiplier")))
JRdfA <- ifelse(groupA_0, NA, DoFCorrection(varEstInputs, varA = "A", method = c("JR")))
JRdfB <- ifelse(groupB_0, NA, DoFCorrection(varEstInputs, varA = "B", method = c("JR")))
JRdf <- ifelse(groupA_0 | groupB_0, NA, DoFCorrection(varEstInputs, varA = "A", varB = "B", method = c("JR")))
}
} # end if (!is.null(varEstInputs))
if (noCov) {
cov <- 0 * cov
JRdf <- ifelse(groupA_0 | groupB_0, NA, (SEs[[1]]^2 + SEs[[2]]^2)^2 / (SEs[[1]]^4 / JRdfA + SEs[[2]]^4 / JRdfB))
}
diffSE <- mapply(function(a, b, c) {
unname(sqrt(a^2 + b^2 - 2 * c))
}, SEs[[1]], SEs[[2]], cov)
diffSE <- unlist(diffSE)
# get percent in each group
# get variables used in conditions for groupA and groupB
vn <- c(parseVars(substitute(groupA), data), parseVars(substitute(groupB), data))
# add weights and variable itself
vn <- c(vn, wgt, variable)
tryCatch(vn <- unique(c(vn, PSUStratumNeeded(returnNumberOfPSU, data))),
error = function(e) {
warning(paste0("Stratum and PSU variables are required for this call and are not on the incoming data. Ignoring ", dQuote("returnNumberOfPSU=TRUE"), "."))
returnNumberOfPSU <<- FALSE
}
)
# setup non-data call variables
callv <- list(
varnames = vn,
drop = FALSE,
dropOmittedLevels = dropOmittedLevels,
recode = NULL
) # recode is already done in line 335
if (!missing(defaultConditions)) {
callv <- c(callv, list(defaultConditions = defaultConditions))
}
# add weight call variables and do calls
wgtcall0 <- c(callv, list(data = data))
d0 <- do.call(getData, wgtcall0)
wgtcallA <- c(callv, list(data = dataA))
dA <- do.call(getData, wgtcallA)
wgtcallB <- c(callv, list(data = dataB))
dB <- do.call(getData, wgtcallB)
pctA <- sum(dA[ , wgt]) / sum(d0[ , wgt])
pctB <- sum(dB[ , wgt]) / sum(d0[ , wgt])
# calculate covAB using JK method
wgtl <- getAttributes(data, "weights")[[wgt]]
pctJK <- t(sapply(wgtl$jksuffixes, function(suffix) {
w0 <- sum(d0[ , paste0(wgtl$jkbase, suffix)])
wA <- sum(dA[ , paste0(wgtl$jkbase, suffix)])
wB <- sum(dB[ , paste0(wgtl$jkbase, suffix)])
c(pctA = wA / w0, pctB = wB / w0)
}))
seA <- 100 * sqrt(getAttributes(data, "jkSumMultiplier") * sum((pctJK[ , 1] - pctA)^2))
seB <- 100 * sqrt(getAttributes(data, "jkSumMultiplier") * sum((pctJK[ , 2] - pctB)^2))
varJK <- data.frame(
stringsAsFactors = FALSE,
PV = rep(0, 3 * nrow(pctJK)),
JKreplicate = rep(1:nrow(pctJK), 3),
variable = rep(c("A", "B", "A-B"), each = nrow(pctJK)),
value = c(
pctJK[ , 1] - pctA,
pctJK[ , 2] - pctB,
(pctJK[ , 1] - pctA) - (pctJK[ , 2] - pctB)
)
)
# there is no PV here because weights are not imputed
pctVarEstInputs <- list(JK = varJK, PV = NULL)
if (noCov) {
covAB <- rep(0, length(seA))
pctDoFA <- DoFCorrection(pctVarEstInputs, varA = "A", method = "JR")
pctDoFB <- DoFCorrection(pctVarEstInputs, varA = "B", method = "JR")
# Satterthwaite approximation
pctJRdf <- (seA^2 + seB^2)^2 / (seA^4 / pctDoFA + seB^4 / pctDoFB)
} else {
covAB <- varEstToCov(pctVarEstInputs, varA = "A", varB = "B", jkSumMultiplier = getAttributes(data, "jkSumMultiplier"))
pctDoFA <- DoFCorrection(pctVarEstInputs, varA = "A", method = "JR")
pctDoFB <- DoFCorrection(pctVarEstInputs, varA = "B", method = "JR")
pctJRdf <- DoFCorrection(pctVarEstInputs, varA = "A-B", method = "JR")
}
# end covAB calculation
pctA <- 100 * pctA
pctB <- 100 * pctB
seAB <- unname(sqrt(seA^2 + seB^2 - 2 * 100 * 100 * covAB))
# start return vector, first name achievement level
vec <- switch(type,
pct = data.frame(stringsAsFactors = FALSE, percentiles = as.numeric(percentiles)),
AL = data.frame(stringsAsFactors = FALSE, achievementLevel = gsub("Below", "below", as.character(statisticsLevelOut))),
data.frame()
)
if (returnSimpleDoF) {
# add dofA and dofB if requested
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA,
estimateB = resB, estimateBse = SEs[[2]], dofB = JRdfB
)
)
} else { # this is equivalent to if (type %in% c("mu","eq"))
vec <- data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA,
estimateB = resB, estimateBse = SEs[[2]], dofB = JRdfB
)
}
} # end if(returnSimpleDoF)
else {
# otherwise, no dofA or dofB
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]],
estimateB = resB, estimateBse = SEs[[2]]
)
)
} else { # this is equivalent to if (type %in% c("mu","eq"))
vec <- data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]],
estimateB = resB, estimateBse = SEs[[2]]
)
}
}
diffp <- 2 * (1 - pt2(abs(diff / diffSE), df = JRdf))
vec <- cbind(
vec,
data.frame(
stringsAsFactors = FALSE,
diffAB = diff, covAB = unname(cov),
diffABse = diffSE,
diffABpValue = diffp,
dofAB = JRdf
)
)
row.names(vec) <- NULL
if (returnSimpleDoF) {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA,
dofA = pctDoFA,
pctB = pctB,
pctBse = seB,
dofB = pctDoFB
)
} else {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA,
pctB = pctB,
pctBse = seB
)
}
pdiffp <- 2 * (1 - pt2(abs(pctA - pctB) / seAB, df = pctJRdf))
percentage <- cbind(
percentage,
data.frame(
stringsAsFactors = FALSE,
diffAB = pctA - pctB,
covAB = 100 * 100 * covAB,
diffABse = seAB,
diffABpValue = pdiffp,
dofAB = pctJRdf
)
)
lst <- list(
results = vec,
labels = list(
A = substitute(groupA), B = substitute(groupB),
n0A = nrow2.edsurvey.data.frame(dataA),
n0B = nrow2.edsurvey.data.frame(dataB),
nUsedA = nrow(dA),
nUsedB = nrow(dB)
),
percentage = percentage
)
if (returnNumberOfPSU) {
lst$labels$nPSUA <- nrow(unique(dA[ , c(getAttributes(dataA, "stratumVar"), getAttributes(dataA, "psuVar"))]))
lst$labels$nPSUB <- nrow(unique(dB[ , c(getAttributes(dataB, "stratumVar"), getAttributes(dataB, "psuVar"))]))
}
} else { # end if(!skipB)
wgtl <- getAttributes(data, "weights")[[wgt]]
JRdfA <- ifelse(groupA_0, NA, length(wgtl$jksuffixes))
if (!is.null(varEstInputs)) {
if (type %in% c("pct", "AL")) {
JRdfA <- c()
for (si in statisticsLevelOut) {
varEstInputsTemp <- list(
JK = subset(varEstInputs$JK, Level %in% si),
PV = subset(varEstInputs$PV, Level %in% si)
)
if (nrow(varEstInputsTemp$JK) == 0) {
varEstInputsTemp$JK <- NULL
}
if (nrow(varEstInputsTemp$PV) == 0) {
varEstInputsTemp$PV <- NULL
}
if (!is.null(varEstInputsTemp$JK)) {
JRdfA <- c(JRdfA, DoFCorrection(varEstInputsTemp, varA = "A", method = c("JR")))
} else {
JRdfA <- c(JRdfA, NA)
}
}
} # end if (type %in% c("pct","AL"))
else {
JRdfA <- ifelse(groupA_0, NA, DoFCorrection(varEstInputs, varA = "A", method = c("JR")))
}
}
# get variables used in conditions for groupA and groupB
vn <- c(parseVars(substitute(groupA), data), parseVars(substitute(groupB), data))
# add weights and variable itself
vn <- c(vn, wgt, variable)
tryCatch(vn <- unique(c(vn, PSUStratumNeeded(returnNumberOfPSU, data))),
error = function(e) {
warning(paste0("Stratum and PSU variables are required for this call and are not on the incoming data. Ignoring ", dQuote("returnNumberOfPSU=TRUE"), "."))
returnNumberOfPSU <<- FALSE
}
)
# setup non-data call variables
callv <- list(
varnames = vn,
drop = FALSE,
dropOmittedLevels = dropOmittedLevels,
recode = NULL,
addAttributes = TRUE
) # recode is already done in line 335
if (!missing(defaultConditions)) {
callv <- c(callv, list(defaultConditions = defaultConditions))
}
# add weight call variables and do calls
wgtcall0 <- c(callv, list(data = data))
d0 <- do.call(getData, wgtcall0)
wgtcallA <- c(callv, list(data = dataA))
dA <- do.call(getData, wgtcallA)
pctA <- sum(dA[ , wgt]) / sum(d0[ , wgt])
pctJK <- t(sapply(wgtl$jksuffixes, function(suffix) {
w0 <- sum(d0[ , paste0(wgtl$jkbase, suffix)])
wA <- sum(dA[ , paste0(wgtl$jkbase, suffix)])
c(pctA = wA / w0, pctB = 0)
}))
seA <- 100 * sqrt(getAttributes(data, "jkSumMultiplier") * sum((pctJK[ , 1] - pctA)^2))
varJK <- data.frame(
stringsAsFactors = FALSE,
PV = rep(0, nrow(pctJK)),
JKreplicate = 1:nrow(pctJK),
variable = "A",
value = pctJK[ , 1] - pctA
)
# there is no PV here because weights are not imputed
pctVarEstInputs <- list(JK = varJK, PV = NULL)
pctDoFA <- DoFCorrection(pctVarEstInputs, varA = "A", method = "JR")
pctA <- 100 * pctA
# start return vector, first name achievement level
vec <- switch(type,
pct = data.frame(stringsAsFactors = FALSE, percentiles = as.numeric(percentiles)),
AL = data.frame(stringsAsFactors = FALSE, achievementLevel = gsub("Below", "below", as.character(statisticsLevelOut))),
data.frame()
)
if (returnSimpleDoF) {
# add dofA and dofB if requested
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA)
)
} else {
vec <- data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA)
}
} else {
# otherwise, no dofA or dofB
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]])
)
} else {
vec <- data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]])
}
}
row.names(vec) <- NULL
if (returnSimpleDoF) {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA,
dofA = pctDoFA
)
} else {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA
)
}
lst <- list(
results = vec,
labels = list(
A = substitute(groupA),
n0A = nrow2.edsurvey.data.frame(dataA),
n0B = NA,
nUsedA = nrow(dA),
nUsedB = NA
),
percentage = percentage
)
if (returnNumberOfPSU) {
if (getAttributes(dA, "stratumVar") %in% "JK1") {
lst$labels$nPSUA <- nrow(dA)
} else {
lst$labels$nPSUA <- nrow(dA[ , c(getAttributes(dA, "stratumVar"), getAttributes(dA, "psuVar"))])
}
lst$labels$nPSUB <- NA
}
}
if (returnVarEstInputs) {
if ("Level" %in% colnames(varEstInputs$JK)) { # equivalent to type = 'pct' or 'AL'
if (type == "pct") {
varEstInputs$JK$Level <- as.numeric(gsub("^P", "", varEstInputs$JK$Level))
varEstInputs$PV$Level <- as.numeric(gsub("^P", "", varEstInputs$PV$Level))
}
if (length(percentiles) <= 1 & length(achievementLevel) <= 1) {
varEstInputs$JK$Level <- NULL
varEstInputs$PV$Level <- NULL
}
}
# assign values close to 0 to 0
varEstInputs$JK$value[which(abs(varEstInputs$JK$value) < (sqrt(.Machine$double.eps) * sqrt(nrow(varEstInputs$JK))))] <- 0
pctVarEstInputs$JK$value[which(abs(pctVarEstInputs$JK$value) < (sqrt(.Machine$double.eps) * sqrt(nrow(pctVarEstInputs$JK))))] <- 0
lst <- c(lst, list(
varEstInputs = varEstInputs,
pctVarEstInputs = pctVarEstInputs
))
}
class(lst) <- "gap"
return(lst)
}
# @title Linking error for PISA 2015 and subsequent DBA v prior PBA
# @param years a vector of two years involved, including 2000, 2003, 2006, 2009, 2012, 2015 or 2018
# @param subject, one of "read", "math", or "scei"
# The linking error for PISA is entirely cross year and one value
# @author Paul Bailey
calLinkingErrorPISA <- function(subject = c("read", "math", "scie"),
years = c(2000, 2003, 2006, 2009, 2012, 2015, 2018)) {
years <- as.numeric(years)
linkingErrorsFinance <- data.frame(
subject = rep("fin", 3),
yearSmall = c(2012, 2015, 2012),
yearBig = c(2018, 2018, 2015),
error = c(5.55, 9.37, 5.3309),
stringsAsFactors = FALSE
)
linkingErrorsRead <- data.frame(
subject = rep("read", 11),
yearSmall = c(2000, 2003, 2006, 2009, 2012, 2000, 2003, 2006, 2009, 2012, 2015),
yearBig = c(2015, 2015, 2015, 2015, 2015, 2018, 2018, 2018, 2018, 2018, 2018),
error = c(6.8044, 5.3907, 6.6064, 3.4301, 5.2535, 4.04, 7.77, 5.24, 3.52, 3.74, 3.93),
stringsAsFactors = FALSE
)
linkingErrorsMath <- data.frame(
subject = rep("math", 9),
yearSmall = c(2003, 2006, 2009, 2012, 2003, 2006, 2009, 2012, 2015),
yearBig = c(2015, 2015, 2015, 2015, 2018, 2018, 2018, 2018, 2018),
error = c(5.6080, 3.511, 3.7853, 3.5462, 2.80, 3.18, 3.54, 3.34, 2.33),
stringsAsFactors = FALSE
)
linkingErrorsScei <- data.frame(
subject = rep("scei", 7),
yearSmall = c(2006, 2009, 2012, 2006, 2009, 2012, 2015),
yearBig = c(2015, 2015, 2015, 2018, 2018, 2018, 2018),
error = c(4.4821, 4.5016, 3.9228, 3.47, 3.59, 4.01, 1.51),
stringsAsFactors = FALSE
)
linkingErrorsPISA <- rbind(linkingErrorsFinance, linkingErrorsRead, linkingErrorsMath, linkingErrorsScei)
sbj <- match.arg(subject)
years <- years[years %in% c(2000, 2003, 2006, 2009, 2012, 2015, 2018)]
if (length(years) != 2) {
stop("Argument ", dQuote("years"), " must have 2 valid years so there are two years to contrast.")
}
lep <- linkingErrorsPISA[linkingErrorsPISA$subject == sbj, ]
res <- expand.grid(yearSmall = years, yearBig = years)
res <- res[res$yearSmall < res$yearBig, ]
res <- merge(lep, res, by = c("yearSmall", "yearBig"), all = FALSE)
if (nrow(res) == 0) {
return(0)
}
if (nrow(res) > 1) {
stop("Multiple returns.")
}
return(res$error)
}
# @title Linking error for NAEP DBA/PBA gap analysis.
# @param X a numeric value estimated from digitally based assessments
# @param subjectI a character value set to "Mathematics" or "Reading" for subject information
# @param gradeI a character value set to "Grade 4" or "Grade 8" as grade level
# @param gapI a logical indicating if the linking error is a gap
# @param dependentVarI a character value set to one of NAEP math or reading subscales
# @param statElementI a character vector selected from "Mean", "Row Percent", "10", "25", "50", "75", "90",
# "At or above basic", "At or above proficient", "Below basic", "At basic", "At proficient", or "At advanced" indicating the relevant statistic
# @author Huade Huo
calLinkingError <- function(X,
subjectI = NULL,
gradeI = NULL,
dependentVarI = NULL,
statElementI = NULL,
gapI = FALSE) {
linkingErrorsRDS <- readRDS(system.file("linkingErrors", "linkingErrors.rds", package = "EdSurvey"))
linkingErrorRDSi <- linkingErrorsRDS[linkingErrorsRDS$subject %in% subjectI &
linkingErrorsRDS$dependentVar %in% dependentVarI &
linkingErrorsRDS$grade %in% gradeI &
linkingErrorsRDS$statElement %in% statElementI &
linkingErrorsRDS$gap %in% gapI, ]
linkingErrorRDSi$statElement <- factor(linkingErrorRDSi$statElement, levels = statElementI)
linkingErrorRDSi <- linkingErrorRDSi[order(linkingErrorRDSi$statElement), ]
if (nrow(linkingErrorRDSi) == 0) {
stop("Linking error is not avaialble.")
}
linkingErrori <- linkingErrorRDSi$Aterm * X^2 + linkingErrorRDSi$Bterm * X + linkingErrorRDSi$Cterm
return(linkingErrori)
}
#' @rdname printGap
#' @title Gap Analysis Printing
#'
#' @description Prints labels and a results vector of a gap analysis.
#' @param x an \code{R} object representing a \code{gap} of class \code{gap} or \code{gapList}
#' @param printPercentage a logical value set to \code{TRUE} to request printing
#' of the percentage in the groups. Defaults to \code{TRUE}.
#' @param ... these arguments are not passed anywhere and are included only for compatibility
#' @method print gap
#' @author Paul Bailey
#' @aliases print.gapList
#' @export
print.gap <- function(x, ..., printPercentage = TRUE) {
cat("Call: ")
print(x$call)
cat("\nLabels:\n")
lab <- data.frame(
stringsAsFactors = FALSE,
group = c("A", "B"),
definition = c(deparse(x$labels$A), deparse(x$labels$B)),
nFullData = c(x$labels$n0A, x$labels$n0B),
nUsed = c(x$labels$nUsedA, x$labels$nUsedB)
)
if (!is.null(x$labels$nPSUA)) {
lab$nPSU <- c(x$labels$nPSUA, x$labels$nPSUB)
}
print(lab, row.names = FALSE)
if (printPercentage) {
cat("\nPercentage:\n")
print(x$percentage, row.names = FALSE)
}
cat("\nResults:\n")
print(x$results, row.names = FALSE, ...)
}
#' @rdname printGap
#' @method print gapList
#' @export
print.gapList <- function(x, ..., printPercentage = TRUE) {
cat("gapList\n")
cat(paste0("Call: "))
print(x$call)
cat("\nLabels:\n")
lab <- data.frame(
stringsAsFactors = FALSE,
group = c("A", "B"),
definition = c(deparse(x$labels$A), deparse(x$labels$B))
)
print(lab, row.names = FALSE)
if (printPercentage) {
cat("\nPercentage:\n")
pct <- data.frame(x$percentage, stringsAsFactors = FALSE)
print(pct, row.names = FALSE)
}
cat("\nResults:\n")
print(x$results, row.names = FALSE)
}
# helper that identifies variables in a call
# ccall is the call to parse
# x is an edsurvey.data.frame or light.edsurvey.data.frame
# @author Paul Bailey
parseVars <- function(ccall, x) {
vars <- c()
# for each element
if (typeof(ccall) == "symbol") {
return(vars)
}
for (i in 1:length(ccall)) {
# if it is a name
if (inherits(ccall[[i]], "name")) {
ccall_c <- as.character(ccall[[i]])
# if it is not in the data and is in the parent.frame, it might be a variable.
if (ccall_c %in% colnames(x)) { # orignally:: if(ccall_c %in% names(x$data) ) { TOM FINK::dataList update
if (ccall[[i]] == "%in%" || is.function(ccall[[i]])) {
# do nothing
} else {
vars <- c(vars, ccall_c)
}
} # End of if statment: if (ccall_c %in% names(x$data))
} # end if(class(ccall[[i]]) %in% c("name")) {
if (inherits(ccall[[i]], "name")) {
# if this is a call, recursively parse that
vars <- c(vars, parseVars(ccall[[i]], x))
} # end of if statment: if class(ccall[[i]]) %in% "call"
} # end of for loop: i in 1:length(ccall)
vars
} # End of fucntion: parseVars
# helpter function that calculates "pooled" dof, used for two distinct samples
# @author Trang Nguyen and Paul Bailey
dofCompute <- function(seA, seB, dofA, dofB) {
return(mapply(function(seA, seB, dofA, dofB) {
ifelse(seA + seB > 0, (seA^2 + seB^2)^2 / (seA^4 / dofA + seB^4 / dofB), 0)
}, seA, seB, dofA, dofB))
}
# returns NA when df is 0, but passes Info to pt to avoid a warning
# @author Paul Bailey
pt2 <- function(q, df) {
df2 <- ifelse(df == 0, Inf, df)
return(ifelse(df == 0, NA, pt(q, df2)))
}
# match strings to attributes in a case insensitive way
# throwing an error if the string matches more than one achievement level
# or matches none
gmatchAttr <- function(strings, achievementLevelNames) {
s0 <- strings
alN0 <- achievementLevelNames
# lower case everyting to make the function case insensitive
strings <- tolower(trimws(strings))
alN <- tolower(achievementLevelNames)
# find and store the prefix, then remove it from strings
prefix <- ifelse(grepl("^at or above", strings), substr(s0, 0, nchar("at or above") + 1), "")
prefix <- ifelse(nchar(prefix) == 0 & grepl("^at ", strings), substr(s0, 0, 3), prefix)
prefix <- ifelse(grepl("^below", strings), substr(s0, 0, nchar("below") + 1), prefix)
strings <- trimws(substr(strings, nchar(prefix) + 1, nchar(strings)))
out <- rep("", length(strings))
error <- rep("", length(strings))
for (i in 1:length(out)) {
for (j in 1:length(alN)) {
update <- grepl(strings[i], alN[j], fixed = TRUE)
if (update) {
if (nchar(out[i]) == 0) {
# base case, store the output
out[i] <- ifelse(update, alN0[j], out[i])
} else {
# append this level to previously found levels in an error
# we can then replay all the levels to the user at the end.
# first...
# if this is the second match, store the previous
# one as an error
if (nchar(error[i]) == 0) {
error[i] <- sQuote(out[i])
}
error[i] <- paste0(c(error[i], sQuote(alN0[j])), collapse = ", ")
out[i] <- "Error"
}
}
}
}
# finalize formatting for errors
error <- ifelse(nchar(error) > 0, paste0(" matched to ", error), "")
error <- ifelse(nchar(out) == 0, paste0(" is not one of the achievement Level for the current survey. Choose one from ", pasteItems(achievementLevelNames)), error)
# if there is an error, format it and throw it
if (any(nchar(error) > nchar(out))) {
evars <- nchar(error) > 0
ferror <- paste0(sQuote(s0[evars]), error[evars], collapse = "; ")
stop(paste0(ferror, "."))
}
out <- trimws(paste(trimws(prefix), trimws(out), sep = " "))
return(out)
}
getALNames <- function(data, variable) {
al <- getAttributes(data, "achievementLevels")
if (is.list(al)) {
if (variable %in% names(al)) {
al <- al[[variable]]
} else {
# assumes all achievement levels are the same
al <- al[[1]]
}
}
names(al)
}
addALPrefix <- function(al, als, discrete) {
if (discrete) {
if (grepl("below", al, ignore.case = TRUE)) {
lal <- al
} else {
if (!grepl("^at", al, ignore.case = TRUE)) {
lal <- paste0("At ", al)
} else {
lal <- al
}
}
} else {
if (grepl("^below", al, ignore.case = TRUE)) { # cumulative should not have Below achievementLevel
return(NULL)
} else {
# if it doesn't have the at prefix, add it
if (!grepl("^at", al, ignore.case = TRUE)) {
lal <- paste0(
ifelse(tolower(al) == tolower(als[length(als)]),
"At ",
"At or Above "
),
al
)
} else {
lal <- al
}
}
}
return(lal)
}
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Defines functions addALPrefix getALNames gmatchAttr pt2 dofCompute parseVars print.gapList print.gap calLinkingError calLinkingErrorPISA gapHelper gap
Documented in gap print.gap print.gapList
#' @title Gap Analysis
#'
#' @description Compares the average levels of a variable between two groups
#' that potentially share members.
#'
#' @param variable a character indicating the variable to be compared,
#' potentially with a subject scale or subscale
#' @param data an \code{edsurvey.data.frame}, a \code{light.edsurvey.data.frame}, or an \code{edsurvey.data.frame.list}
#' @param groupA an expression or character expression that defines a condition for the subset.
#' This subset will be compared to \code{groupB}. If not specified, it will define
#' a whole sample as in \code{data}.
#' @param groupB an expression or character expression that defines a condition for the subset.
#' This subset will be compared to \code{groupA}. If not specified, it will define
#' a whole sample as in \code{data}. If set to \code{NULL}, estimates for the second group
#' will be dropped.
#' @param percentiles a numeric vector. The \code{gap} function calculates the
#' mean when this
#' argument is omitted or set to \code{NULL}. Otherwise,
#' the gap at the percentile given is calculated.
#' @param achievementLevel the achievement level(s) at which percentages
#' should be calculated
#' @param achievementDiscrete a logical indicating if the achievement level
#' specified in the \code{achievementLevel}
#' argument should be interpreted as discrete
#' so that
#' just the percentage in that particular achievement
#' level
#' will be included. Defaults to \code{FALSE}
#' so that
#' the percentage at or above that achievement level
#' will be
#' included in the percentage.
#' @param stDev a logical, set to \code{TRUE} to calculate the gap in standard deviations.
#' @param targetLevel a character string. When specified, calculates the gap in
#' the percentage of students at
#' \code{targetLevel} in the \code{variable} argument. This is useful for
#' comparing the gap in the percentage of students at a
#' survey response level.
#' @param weightVar a character indicating the weight variable to use.
#' See Details.
#' @param jrrIMax a numeric value; when using the jackknife variance estimation method, the default estimation option, \code{jrrIMax=1}, uses the
#' sampling variance from the first plausible value as the component for sampling variance estimation. The \code{Vjrr}
#' term, or sampeling variance term, can be estimated with any number of plausible values, and values larger than the number of
#' plausible values on the survey (including \code{Inf}) will result in all plausible values being used.
#' Higher values of \code{jrrIMax} lead to longer computing times and more accurate variance estimates.
#' @param varMethod deprecated parameter, \code{gap} always uses the jackknife variance estimation
#' @param dropOmittedLevels a logical value. When set to the default value of
#' \code{TRUE}, drops those levels of
#' all factor variables.
#' Use \code{print} on an \code{edsurvey.data.frame}
#' to see the omitted levels.
#' @param defaultConditions a logical value. When set to the default value
#' of \code{TRUE}, uses the default
#' conditions stored in \code{edsurvey.data.frame}
#' to subset the data.
#' Use \code{print} on an \code{edsurvey.data.frame}
#' to see the default conditions.
#' @param recode a list of lists to recode variables. Defaults to \code{NULL}.
#' Can be set as
#' \code{recode} \code{=} \code{list(var1} \code{=}
#' \code{list(from} \code{=} \code{c("a",} \code{"b",}
#' \code{"c"),} \code{to} \code{=} \code{"d"))}.
#' @param referenceDataIndex a numeric used only when the \code{data} argument is an
#' \code{edsurvey.data.frame.list},
#' indicating which dataset is the reference
#' dataset that other datasets are compared with.
#' Defaults to 1.
#' @param returnVarEstInputs a logical value; set to \code{TRUE} to return the
#' inputs to the jackknife and imputation variance
#' estimates which allows for the
#' computation
#' of covariances between estimates.
#' @param returnSimpleDoF a logical value set to \code{TRUE} to return the degrees
#' of freedom for some statistics (see Value
#' section) that do not have a
#' \emph{t}-test; useful primarily for further computation
#' @param returnSimpleN a logical value set to \code{TRUE} to add the count
#' (\emph{n}-size) of observations included in groups A and B
#' in the percentage object
#' @param returnNumberOfPSU a logical value set to \code{TRUE} to return the number of
#' PSUs used in the calculation
#' @param noCov set the covariances to zero in result
#' @param pctMethod a character that is one of \code{unbiased} or \code{simple}.
#' See the help for \code{\link{percentile}} for more information.
#' @param includeLinkingError a logical value set to \code{TRUE} to include the
#' linking error in variance estimation.
#' Standard errors (e.g., \code{diffAAse}, \code{diffBBse},
#' and \code{diffABABse}) and \emph{p}-values (e.g., \code{diffAApValue},
#' \code{diffBBpValue}, and \code{diffABABpValue}) would be adjusted for
#' comparisons between digitally based assessments (DBA) and
#' paper-based assessments (PBA) data.
#' This option is supported only for NAEP data.
#'
#' @param omittedLevels this argument is deprecated. Use \code{dropOmittedLevels}.
#'
#' @details This function calculates the gap between \code{groupA} and \code{groupB} (which
#' may be omitted to indicate the full sample). The gap is
#' calculated for one of four statistics:
#' \describe{
#' \item{the gap in means}{The mean score gap (in the score
#' variable) identified in the \code{variable} argument.
#' This is the default. The means and their standard errors are
#' calculated using the methods
#' described in the \code{\link{lm.sdf}} function documentation.}
#' \item{the gap in percentiles}{The gap between respondents at
#' the percentiles specified in the \code{percentiles} argument.
#' This is returned when the \code{percentiles} argument is
#' defined. The mean and standard error are computed as described in the
#' \code{\link{percentile}} function documentation.}
#' \item{the gap in achievement levels}{The gap in the percentage of
#' students at (when \code{achievementDiscrete} is \code{TRUE}) or at
#' or above (when \code{achievementDiscrete} is \code{FALSE}) a
#' particular achievement level. This is used when the
#' \code{achievementLevel} argument is defined. The mean and standard error
#' are calculated as described in the \code{\link{achievementLevels}}
#' function documentation.}
#' \item{the gap in a survey response}{The gap in the percentage of
#' respondents responding at \code{targetLevel} to
#' \code{variable}. This is used when \code{targetLevel} is
#' defined. The mean and standard deviation are calculated as described in
#' the \code{\link{edsurveyTable}} function documentation.}
#' }
#'
#' @return
#' The return type depends on if the class of the \code{data} argument is an
#' \code{edsurvey.data.frame} or an \code{edsurvey.data.frame.list}. Both
#' include the call (called \code{call}), a list called \code{labels},
#' an object named \code{percentage}
#' that shows the percentage in \code{groupA} and \code{groupB}, and an object
#' that shows the gap called \code{results}.
#'
#' The labels include the following elements:
#' \item{definition}{the definitions of the groups}
#' \item{nFullData}{the \emph{n}-size for the full dataset (before applying the definition)}
#' \item{nUsed}{the \emph{n}-size for the data after the group is subsetted and other
#' restrictions (such as omitted values) are applied}
#' \item{nPSU}{the number of PSUs used in calculation--only returned when
#' \code{returnNumberOfPSU} \code{=} \code{TRUE}}
#'
#' The percentages are computed according to the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}
#' in the section
#' \dQuote{Estimation of Weighted Percentages When Plausible Values Are Not Present.}
#' The standard errors are calculated according to
#' \dQuote{Estimation of the Standard Error of Weighted Percentages When Plausible Values Are Not Present, Using the Jackknife Method.}
#' Standard errors of differences are calculated as the square root of the typical
#' variance formula
#' \deqn{Var(A-B) = Var(A) + Var(B) - 2 Cov(A,B)}
#' where the covariance term is calculated as described in the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}
#' in the section
#' \dQuote{Estimation of Covariances.} These degrees of freedom are available only
#' with the jackknife variance estimation. The degrees of freedom used for hypothesis testing
#' are always set to the number of jackknife replicates in the data.
#'
#' \strong{the data argument is an edsurvey.data.frame}
#' When the \code{data} argument is an \code{edsurvey.data.frame},
#' \code{gap} returns an S3 object of class \code{gap}.
#'
#' The \code{percentage} object is a numeric vector with the following elements:
#' \item{pctA}{the percentage of respondents in \code{groupA} compared with the whole sample in \code{data}}
#' \item{pctAse}{the standard error on the percentage of respondents in
#' \code{groupA}}
#' \item{dofA}{degrees of freedom appropriate for a \emph{t}-test involving \code{pctA}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{pctB}{the percentage of respondents in \code{groupB}.}
#' \item{pctBse}{the standard error on the percentage of respondents in
#' \code{groupB}}
#' \item{dofB}{degrees of freedom appropriate for a \emph{t}-test involving \code{pctA}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{diffAB}{the value of \code{pctA} minus \code{pctB}}
#' \item{covAB}{the covariance of \code{pctA} and \code{pctB}; used in
#' calculating \code{diffABse}.}
#' \item{diffABse}{the standard error of \code{pctA}
#' minus \code{pctB}}
#' \item{diffABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAB}
#' is zero}
#' \item{dofAB}{degrees of freedom used in calculating
#' \code{diffABpValue}}
#'
#' The \code{results} object is a numeric data frame with the following elements:
#' \item{estimateA}{the mean estimate of \code{groupA} (or the percentage estimate
#' if \code{achievementLevel} or \code{targetLevel} is specified)}
#' \item{estimateAse}{the standard error of \code{estimateA}}
#' \item{dofA}{degrees of freedom appropriate for a \emph{t}-test involving \code{meanA}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{estimateB}{the mean estimate of \code{groupB} (or the percentage estimate
#' if \code{achievementLevel} or \code{targetLevel} is specified)}
#' \item{estimateBse}{the standard error of \code{estimateB}}
#' \item{dofB}{degrees of freedom appropriate for a \emph{t}-test involving \code{meanB}.
#' This value is returned only if
#' \code{returnSimpleDoF}\code{=}\code{TRUE}.}
#' \item{diffAB}{the value of \code{estimateA} minus \code{estimateB}}
#' \item{covAB}{the covariance of \code{estimateA} and \code{estimateB}. Used in
#' calculating \code{diffABse}.}
#' \item{diffABse}{the standard error of \code{diffAB}}
#' \item{diffABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAB}
#' is zero.}
#' \item{dofAB}{degrees of freedom used for the \emph{t}-test on \code{diffAB}}
#'
#' If the gap was in achievement levels or percentiles and more
#' than one percentile or achievement level is requested,
#' then an additional column
#' labeled \code{percentiles} or \code{achievementLevel} is included
#' in the \code{results} object.
#'
#' When \code{results} has a single row and when \code{returnVarEstInputs}
#' is \code{TRUE}, the additional elements \code{varEstInputs} and
#' \code{pctVarEstInputs} also are returned. These can be used for calculating
#' covariances with \code{\link{varEstToCov}}.
#'
#' \strong{the data argument is an edsurvey.data.frame.list}
#' When the \code{data} argument is an \code{edsurvey.data.frame.list},
#' \code{gap} returns an S3 object of class \code{gapList}.
#'
#' The \code{results} object in the \code{edsurveyResultList} is
#' a \code{data.frame}. Each row regards a particular dataset from the
#' \code{edsurvey.data.frame}, and a reference dataset is dictated by
#' the \code{referenceDataIndex} argument.
#'
#' The \code{percentage} object is a \code{data.frame} with the following elements:
#' \item{covs}{a data frame with a column for each column in the \code{covs}. See previous
#' section for more details.}
#' \item{...}{all elements in the \code{percentage} object in the
#' previous section}
#' \item{diffAA}{the difference in \code{pctA} between the reference data
#' and this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covAA}{the covariance of \code{pctA} in the reference data and
#' \code{pctA} on this row. Used in
#' calculating \code{diffAAse}.}
#' \item{diffAAse}{the standard error for \code{diffAA}}
#' \item{diffAApValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAA}
#' is zero}
#' \item{diffBB}{the difference in \code{pctB} between the reference data
#' and this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covBB}{the covariance of \code{pctB} in the reference data and
#' \code{pctB} on this row. Used in
#' calculating \code{diffAAse}.}
#' \item{diffBBse}{the standard error for \code{diffBB}}
#' \item{diffBBpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffBB}
#' is zero}
#' \item{diffABAB}{the value of \code{diffAB} in the reference dataset
#' minus the value of \code{diffAB} in this dataset. Set
#' to \code{NA} for the reference dataset.}
#' \item{covABAB}{the covariance of \code{diffAB} in the reference data and
#' \code{diffAB} on this row. Used in
#' calculating \code{diffABABse}.}
#' \item{diffABABse}{the standard error for \code{diffABAB}}
#' \item{diffABABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffABAB}
#' is zero}
#'
#' The \code{results} object is a \code{data.frame} with the following elements:
#' \item{...}{all elements in the \code{results} object in the
#' previous section}
#' \item{diffAA}{the value of \code{groupA} in the reference dataset minus
#' the value in this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covAA}{the covariance of \code{meanA} in the reference data and
#' \code{meanA} on this row. Used in
#' calculating \code{diffAAse}.}
#' \item{diffAAse}{the standard error for \code{diffAA}}
#' \item{diffAApValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffAA}
#' is zero}
#' \item{diffBB}{the value of \code{groupB} in the reference dataset minus
#' the value in this dataset. Set to \code{NA} for the
#' reference dataset.}
#' \item{covBB}{the covariance of \code{meanB} in the reference data and
#' \code{meanB} on this row. Used in
#' calculating \code{diffBBse}.}
#' \item{diffBBse}{the standard error for \code{diffBB}}
#' \item{diffBBpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffBB}
#' is zero}
#' \item{diffABAB}{the value of \code{diffAB} in the reference dataset
#' minus the value of \code{diffAB}
#' in this dataset. Set
#' to \code{NA} for the reference dataset.}
#' \item{covABAB}{the covariance of \code{diffAB} in the reference data and
#' \code{diffAB} on this row. Used in
#' calculating \code{diffABABse}.}
#' \item{diffABABse}{the standard error for \code{diffABAB}}
#' \item{diffABABpValue}{the \emph{p}-value associated with the \emph{t}-test used
#' for the hypothesis test that \code{diffABAB}
#' is zero}
#' \item{sameSurvey}{a logical value indicating if this line uses the same
#' survey as the reference line. Set to \code{NA} for the
#' reference line.}
#'
#' @author Paul Bailey, Trang Nguyen, and Huade Huo
#' @importFrom stats formula
#' @example man/examples/gap.R
#' @export
gap <- function(variable, data, groupA = "default", groupB = "default",
percentiles = NULL,
achievementLevel = NULL,
achievementDiscrete = FALSE,
stDev = FALSE,
targetLevel = NULL,
weightVar = NULL, jrrIMax = 1,
varMethod = c("jackknife"),
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
referenceDataIndex = 1,
returnVarEstInputs = FALSE,
returnSimpleDoF = FALSE,
returnSimpleN = FALSE,
returnNumberOfPSU = FALSE,
noCov = FALSE,
pctMethod = c("unbiased", "symmetric", "simple"),
includeLinkingError = FALSE,
omittedLevels = deprecated()) {
if (is.character(substitute(groupA))) {
groupA <- parse(text = groupA)[[1]]
}
if (all(as.character(substitute(groupA)) == "default")) {
groupB <- NULL
}
if (!missing(groupB)) {
if (is.character(substitute(groupB))) {
groupB <- parse(text = groupB)[[1]]
}
}
if (!missing(varMethod)) {
warning(paste0("Argument ", dQuote("varMethod"), " deprecated. The ", dQuote("gap"), " function always uses jackknife variance estimation."))
}
if (lifecycle::is_present(omittedLevels)) {
lifecycle::deprecate_soft("4.0.0", "gap(omittedLevels)", "gap(dropOmittedLevels)")
dropOmittedLevels <- omittedLevels
}
if (!is.logical(dropOmittedLevels)) stop("The ", sQuote("dropOmittedLevels"), " argument must be logical.")
# check incoming variables
call <- match.call()
# get valid pctMethod
pctMethod <- match.arg(pctMethod)
checkDataClass(data, c("edsurvey.data.frame", "light.edsurvey.data.frame", "edsurvey.data.frame.list"))
# if recode list is provided, it will be added to userConditions at first (before subset in groupA and groupB)
if (!is.null(recode)) {
data <- recode.sdf(data, recode)
}
if (inherits(data, "edsurvey.data.frame") | inherits(data, "light.edsurvey.data.frame")) {
survey <- getAttributes(data, "survey")
} else {
# edsurvey.data.frame.list
robustSurvey <- function(dat) {
res <- tryCatch(getAttributes(dat, "survey"),
error = function(e) {
NULL
}
)
return(res)
}
survey <- unique(unlist(lapply(data$data, robustSurvey)))
}
# check if linking error is supported for this survey
if (!missing(includeLinkingError) && includeLinkingError) {
checkDataClass(data, c("edsurvey.data.frame.list"))
if (!any(c("NAEP", "PISA") %in% survey)) {
stop("the argument ", dQuote("includeLinkingError"), " can only be set to ", dQuote("TRUE"), " when EdSurvey suports linking error for the survey. Currently EdSurvey supports linking error for NAEP and PISA.")
}
}
# Set dbaLabels as NULL for linking error with non ESDFL object or
# includeLinkingError set as False
dbaLabels <- NULL
# gapStat required for PISA regardless of linking error
if ("PISA" %in% survey) {
if (!is.null(percentiles)) {
gapStat <- "percentile"
} else if (!is.null(achievementLevel)) {
gapStat <- "AL"
} else if (stDev) {
gapStat <- "SD"
} else {
gapStat <- "Mean"
}
}
# Get parameters for linking error
if (!missing(includeLinkingError) && includeLinkingError) {
# the following is only for NAEP which has linking error within survey
if ("NAEP" %in% survey) {
if (inherits(data, "edsurvey.data.frame.list")) {
uniqueSubject <- unique(unlist(lapply(data$data, getAttributes, "subject")))
if (length(uniqueSubject) != 1) {
stop(paste0("Linking error only support ", dQuote("edsurvey.data.frame.list"), " with a single subject. Found subjects: ", pasteItems(uniqueSubject), "."))
}
uniqueGrade <- unique(unlist(lapply(data$data, getAttributes, "gradeLevel")))
if (length(uniqueGrade) != 1) {
stop(paste0("Linking error only support ", dQuote("edsurvey.data.frame.list"), " with a single grade level. Found grades: ", pasteItems(uniqueGrade), "."))
}
}
# For achievementDiscrete=TRUE, return "At basic" (not "below basic") if "achievementLevel = "basic"
alLinkingErrorhelper <- function(alstrings, discrete) {
matchedAL <- NULL
for (alstring in alstrings) {
if (discrete) {
if (grepl("^basic", alstring, ignore.case = TRUE)) {
matchedAL <- c(matchedAL, "At basic")
} else {
matchedAL <- c(
matchedAL,
c(
"Below basic", "At basic", "At proficient",
"At advanced"
)[grepl(alstring, c(
"Below basic", "At basic",
"At proficient", "At advanced"
),
ignore.case = TRUE
)]
)
}
} else {
matchedAL <- c(
matchedAL,
c(
"At or above basic",
"At or above proficient",
"At advanced"
)[
grepl(alstring, c("At or above basic", "At or above proficient", "At advanced"),
ignore.case = TRUE
)
]
)
}
}
if (length(matchedAL) != length(alstrings)) {
stop("Linking error for provided achievement level ", pasteItems(alstrings[!alstrings %in% matchedAL]), "is not available.")
}
return(matchedAL)
}
# Get DBA labels from DBA years
# 2017 is the first NAEP DBA year
if (inherits(data, "edsurvey.data.frame.list")) {
allYears <- unlist(itterateESDFL(call("getAttributes", attribute = "year"), data))
dbaLabels <- data[[2]]$labels[allYears >= 2017]
if (!all(dbaLabels %in% data[[2]]$labels)) {
stop(paste0(
pasteItems(dbaLabels[!dbaLabels %in% data[[2]]$labels]),
" is not in the ", dQuote("edsurvey.data.frame.list"), " labels."
))
}
} else {
allYears <- getAttributes(data, attribute = "year")
dbaLabels <- NULL
}
if (!is.null(percentiles) && !all(percentiles %in% c(10, 25, 50, 75, 90))) {
stop(
"NAEP linking error only supported for 10th, 25th, 50th, 75th, 90th percentiles.",
"Set ", dQuote("includeLinkingError"), " to ", dQuote("FALSE"),
"to calculate any percentile without linking error."
)
}
if (!is.null(percentiles)) {
gapStat <- percentiles
} else if (!is.null(achievementLevel)) {
gapStat <- alLinkingErrorhelper(achievementLevel, achievementDiscrete)
} else if (stDev) {
gapStat <- "SD"
} else {
gapStat <- "Mean"
}
}
}
# deal with the possibility that there is more than one line of results for various possible reasons
if (inherits(data, "edsurvey.data.frame.list") | length(percentiles) > 1 | length(achievementLevel) > 1) {
# a flag that is TRUE when there is no information in data$covs. Default to false
nocovs <- FALSE
if (!inherits(data, "edsurvey.data.frame.list")) {
# this is a situation where data is not an esdfl. The following code
# required it to be one, so make it one. But note that covs
# does not have real information in it.
nocovs <- TRUE
data <- edsurvey.data.frame.list(list(data), labels = "data")
}
ll <- length(data$datalist)
lpct <- ifelse(is.null(percentiles), 1, length(percentiles))
lal <- ifelse(is.null(achievementLevel), 1, length(achievementLevel))
# check variable specific to edsurvey.data.frame.list
suppressWarnings(refi <- as.integer(referenceDataIndex))
if (!refi %in% 1:ll) {
stop(paste0("The argument ", sQuote("referenceDataIndex"), " must be an integer between 1 and ", ll, "."))
}
res <- list(
results = list(),
labels = list(A = substitute(groupA), B = substitute(groupB))
)
pctdf0 <- data$covs
if (!nocovs) {
pctdf0$covAA <- 0
pctdf0$dofAA <- Inf
pctdf0$covBB <- 0
pctdf0$dofBB <- Inf
pctdf0$covABAB <- 0
pctdf0$dofABAB <- Inf
}
# make sure refi gets calculated first
llvec <- unique(c(refi, 1:ll))
resilist <- list()
# call gap for each edsurvey.data.frame
for (i in llvec) {
# for each element of the edsurvey.data.frame.list
calli <- call
# adjust the call per the needs of this call
calli$data <- data$datalist[[i]]
# we need the these inputs to calculate across essurvey.data.frame
# covariances (but, only if they are the same dataset) an issue
# that will be resolved later.
calli$returnVarEstInputs <- TRUE
# this returns the degrees of freedom for meanA and meanB
# used for diffAA and diffBB, respectively
calli$returnSimpleDoF <- TRUE
# if these are NULL then setting them changes nothing
calli$percentiles <- percentiles
calli$achievementLevel <- achievementLevel
calli <- as.list(calli)
# use the same list of arguments but change the function to gapHelper
calli[[1]] <- as.name("gapHelper")
resilist[[i]] <- tryCatch(resi <- eval(as.call(calli)),
error = function(cond) {
message(paste0(
"An error occurred while working on a dataset ",
pasteItems(dQuote(data$covs[i, ])),
". The results from that dataset will be excluded. Error message: ",
cond
))
return(0)
}
)
# if there was not an error
if (!inherits(resilist[[i]], "gap")) {
# there was an error
if (i %in% referenceDataIndex) {
stop("An error prevented processing of the reference dataset. This must be fixed before comparisons to the reference dataset can be made.")
}
} else {
resi$call <- NULL
# the following code is to clean up the results (adding some columns to make the code to extract results from gapHelper more consistent)
# after this code, we should expect that each gap result will have:
# 1. resi$results will have 'statisticsLevel' column: indicate percentile or achievementLevel, or 'data' if none is specified
# 2. resi$varEstInputs: PV and JK data frame will have 'Level' column similar to 'statisticsLevel' column in resi$results
if (is.null(percentiles) && is.null(achievementLevel)) {
resi$results$statisticsLevel <- "data"
}
# if no or only 1 percentile/achievementLevel is specified
if (length(percentiles) <= 1 && length(achievementLevel) <= 1) {
resi$varEstInputs$JK$Level <- "data"
if (!is.null(resi$varEstInputs$PV)) { # PV is null for type = "eq"
resi$varEstInputs$PV$Level <- "data"
}
}
# in gapHelper, type = 'pct' will return 'percentiles' column and type = 'al' will return 'achievementLevel' column
# this replacement is to use 1 consistent name for the result extraction code below
# it will be renamed back to original names after the extraction
colnames(resi$results) <- gsub("achievementLevel|percentiles", "statisticsLevel", colnames(resi$results))
resilist[[i]] <- resi
}
} # end for(i in llvec)
# Extract results and compute AA or BB statistics
lstats <- max(lpct, lal)
for (j in 1:lstats) {
resdf <- data$covs
if (!nocovs) {
# only generate these columns if there are multiple datasets
resdf$covAA <- 0
resdf$dofAA <- Inf
resdf$covBB <- 0
resdf$dofBB <- Inf
resdf$covABAB <- 0
resdf$dofABAB <- Inf
}
# to check whether the two samples are from the same survey or not
# we need to use DOFCorrection and compute covariance if the two samples are from the same survey
resdf$sameSurvey <- ifelse(1:nrow(resdf) == refi,
rep(NA, nrow(resdf)),
rep(FALSE, nrow(resdf))
)
if (lpct > 1) {
resdf$percentiles <- percentiles[[j]]
}
for (i in llvec) {
if (!inherits(resilist[[i]], "gap")) {
# there was an error
if (i %in% referenceDataIndex) {
stop("An error prevented processing of the reference dataset. This must be fixed before comparisons to the reference dataset can be made.")
}
# this is to make sure that all achievementLevels or percentiles are created as to the reference edsurvey.data.frame
# Missing values will be NA for statistics
if (!is.null(achievementLevel)) {
resdf$achievementLevel[i] <- resilist[[refi]]$results$statisticsLevel[j]
} else if (!is.null(percentiles)) {
resdf$percentiles[i] <- resilist[[refi]]$results$statisticsLevel[j]
}
} else { # there was not an error
# dataframes do not like vectorwise assignment by column name,
# so assign one at a time
resi <- resilist[[i]]
resi$results <- resi$results[j, ]
resi$varEstInputs$JK <- resi$varEstInputs$JK[tolower(resi$varEstInputs$JK$Level) %in% tolower(c(resi$results$statisticsLevel, "data")), ]
if (!is.null(resi$varEstInputs$PV)) {
resi$varEstInputs$PV <- resi$varEstInputs$PV[tolower(resi$varEstInputs$PV$Level) %in% tolower(c(resi$results$statisticsLevel, "data")), ]
}
if (!is.null(achievementLevel)) {
colnames(resi$results) <- gsub("statisticsLevel", "achievementLevel", colnames(resi$results))
} else if (!is.null(percentiles)) {
colnames(resi$results) <- gsub("statisticsLevel", "percentiles", colnames(resi$results))
} else {
resi$results$statisticsLevel <- NULL
}
for (ii in 1:length(resi$results)) {
resdf[i, names(resi$results)[ii]] <- resi$results[[ii]]
}
skipB <- is.null(resi$labels$B) # a boolean to indicate whether groupB estimates are available in the results of gapHelper
if (i == refi) {
refVarEstInputs <- resi$varEstInputs
refPctVarEstInputs <- resi$pctVarEstInputs
}
# refi is the index of reference dataset
# AA and BB statistics of reference dataset will be set to NA
if (i != refi) {
resdf$dofAA[i] <- dofCompute((resi$results)$estimateAse, resdf[refi, "estimateAse"], (resi$results)[["dofA"]], resdf[refi, "dofA"])
if (!skipB) {
resdf$dofBB[i] <- dofCompute((resi$results)$estimateBse, resdf[refi, "estimateBse"], (resi$results)[["dofB"]], resdf[refi, "dofB"])
resdf$dofABAB[i] <- dofCompute((resi$results)$diffABse, resdf[refi, "diffABse"], (resi$results)[["dofAB"]], resdf[refi, "dofAB"])
}
# if the datasets and from the same sample, add covariances and do DoFCorrection
if (sameSurvey(data$datalist[[i]], data$datalist[[refi]])) {
if (nrow(resi$varEstInputs$JK) != 0) { # some Level (percentile or achievementLevel) do not have varEstInput data so the subset
# in line 476 will return an empty data.frame
resdf$dofAA[i] <- DoFCorrection(resi$varEstInputs, refVarEstInputs, "A", method = "JR")
}
resdf$covAA[i] <- varEstToCov(resi$varEstInputs, refVarEstInputs, "A", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
if (!skipB & nrow(resi$varEstInputs$JK) != 0) {
resdf$dofBB[i] <- DoFCorrection(resi$varEstInputs, refVarEstInputs, "B", method = "JR")
resdf$dofABAB[i] <- DoFCorrection(resi$varEstInputs, refVarEstInputs, "A-B", method = "JR")
resdf$covBB[i] <- varEstToCov(resi$varEstInputs, refVarEstInputs, "B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
resdf$covABAB[i] <- varEstToCov(resi$varEstInputs, refVarEstInputs, "A-B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
}
resdf$sameSurvey[i] <- TRUE
} else {
if ("PISA" %in% survey) {
linke <- 0
if (includeLinkingError & gapStat %in% c("Mean", "percentile", "SD")) {
if (variable %in% c("read", "math", "scie")) {
yearref <- getAttributes(data$datalist[[refi]], "year")
yeari <- getAttributes(data$datalist[[i]], "year")
if (yeari != yearref) {
linke <- calLinkingErrorPISA(subject = variable, years = c(yearref, yeari))
}
} else {
warning("No published linking error for variable ", dQuote(variable), "\n")
}
}
covvars <- c("covAA", "covBB", "covABAB")
for (cvi in 1:length(covvars)) {
if (covvars[cvi] %in% colnames(resdf)) {
resdf[i, covvars[cvi]] <- -1 / 2 * linke^2
}
}
}
}
} else { # end if(i != refi)
# if this is refi, set these to NULL
resdf$covAA[i] <- resdf$covBB[i] <- resdf$covABAB[i] <- NA
resdf$dofAA[i] <- resdf$dofBB[i] <- resdf$dofABAB[i] <- NA
}
# for the first j value only, grab the percent
if (j == 1) {
for (ii in 1:ncol(resi$percentage)) {
pctdf0[i, colnames(resi$percentage)[ii]] <- resi$percentage[ , ii]
if (i != refi) {
pctdf0$dofAA[i] <- dofCompute((resi$percentage)$pctAse, pctdf0[refi, "pctAse"], (resi$percentage)[["dofA"]], pctdf0[refi, "dofA"])
if (!skipB) {
pctdf0$dofBB[i] <- dofCompute((resi$percentage)$pctBse, pctdf0[refi, "pctBse"], (resi$percentage)[["dofB"]], pctdf0[refi, "dofB"])
pctdf0$dofABAB[i] <- dofCompute((resi$percentage)$diffABse, pctdf0[refi, "diffABse"], (resi$percentage)[["dofAB"]], pctdf0[refi, "dofAB"])
}
if (sameSurvey(data$datalist[[i]], data$datalist[[refi]])) {
pctdf0$dofAA[i] <- DoFCorrection(resi$pctVarEstInputs, refPctVarEstInputs, "A", method = "JR")
pctdf0$covAA[i] <- varEstToCov(resi$pctVarEstInputs, refPctVarEstInputs, "A", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
if (!skipB) {
pctdf0$dofBB[i] <- DoFCorrection(resi$pctVarEstInputs, refPctVarEstInputs, "B", method = "JR")
pctdf0$dofABAB[i] <- DoFCorrection(resi$pctVarEstInputs, refPctVarEstInputs, "A-B", method = "JR")
pctdf0$covBB[i] <- varEstToCov(resi$pctVarEstInputs, refPctVarEstInputs, "B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
pctdf0$covABAB[i] <- varEstToCov(resi$pctVarEstInputs, refPctVarEstInputs, "A-B", jkSumMultiplier = getAttributes(data$datalist[[i]], "jkSumMultiplier"))
}
}
} else { # end if(i != refi)
# if this is refi, set these to NULL
pctdf0$covAA[i] <- pctdf0$covBB[i] <- pctdf0$covABAB[i] <- NA
pctdf0$dofAA[i] <- pctdf0$dofBB[i] <- pctdf0$dofABAB[i] <- NA
} # end else for (i != refi)
} # end for(ii in 1:ncol(resi$percentage))
if (returnSimpleN) {
# return the n counts
pctdf0$nA[i] <- resi$labels$nUsedA
pctdf0$nB[i] <- resi$labels$nUsedB
}
if (returnNumberOfPSU) {
pctdf0$nPSUA[i] <- ifelse(is.null(resi$labels$nPSUA), NA, resi$labels$nPSUA)
pctdf0$nPSUB[i] <- ifelse(is.null(resi$labels$nPSUB), NA, resi$labels$nPSUB)
}
} # end if(j == 1)
} # end if(inherits(temp, "gap"))
} # End of for(i in 1:i) loop
# Compute diff statistics (across different edsurvey.data.frame)
resdf$diffAA <- ifelse(1:nrow(resdf) == refi, NA, resdf$estimateA[refi] - resdf$estimateA)
if (!is.null(dbaLabels)) {
# get linking error
leAA <- calLinkingError(
X = resdf$estimateA[refi],
subjectI = uniqueSubject,
gradeI = uniqueGrade,
dependentVarI = variable,
statElementI = gapStat,
gapI = FALSE
)[j]
resdf$diffAAse <- ifelse(1:nrow(resdf) == refi, NA,
ifelse(resdf$labels %in% dbaLabels,
sqrt(resdf$estimateAse[refi]^2 + resdf$estimateAse^2 - 2 * resdf$covAA),
sqrt(resdf$estimateAse[refi]^2 + resdf$estimateAse^2 - 2 * resdf$covAA + leAA)
)
)
} else {
resdf$diffAAse <- ifelse(1:nrow(resdf) == refi, NA, sqrt(resdf$estimateAse[refi]^2 + resdf$estimateAse^2 - 2 * resdf$covAA))
}
resdf$diffAApValue <- ifelse(1:nrow(resdf) == refi, NA, 2 * (1 - pt2(abs(resdf$diffAA / resdf$diffAAse), df = resdf$dofAA)))
if (!skipB) {
resdf$diffBB <- ifelse(1:nrow(resdf) == refi, NA, resdf$estimateB[refi] - resdf$estimateB)
if (!is.null(dbaLabels)) {
leBB <- calLinkingError(
X = resdf$estimateB[refi],
subjectI = uniqueSubject,
gradeI = uniqueGrade,
dependentVarI = variable,
statElementI = gapStat,
gapI = FALSE
)[j]
resdf$diffBBse <- ifelse(1:nrow(resdf) == refi, NA,
ifelse(resdf$labels %in% dbaLabels,
sqrt(resdf$estimateBse[refi]^2 + resdf$estimateBse^2 - 2 * resdf$covBB),
sqrt(resdf$estimateBse[refi]^2 + resdf$estimateBse^2 - 2 * resdf$covBB + leBB)
)
)
} else {
resdf$diffBBse <- ifelse(1:nrow(resdf) == refi, NA, sqrt(resdf$estimateBse[refi]^2 + resdf$estimateBse^2 - 2 * resdf$covBB))
}
resdf$diffBBpValue <- ifelse(1:nrow(resdf) == refi, NA, 2 * (1 - pt2(abs(resdf$diffBB / resdf$diffBBse), df = resdf$dofBB)))
resdf$diffABAB <- ifelse(1:nrow(resdf) == refi, NA, resdf$diffAB[refi] - resdf$diffAB)
if (!is.null(dbaLabels)) {
leABAB <- calLinkingError(
X = resdf$diffAB[refi],
subjectI = uniqueSubject,
gradeI = uniqueGrade,
dependentVarI = variable,
statElementI = gapStat,
gapI = TRUE
)[j]
resdf$diffABABse <- ifelse(1:nrow(resdf) == refi, NA,
ifelse(resdf$labels %in% dbaLabels,
sqrt(resdf$diffABse[refi]^2 + resdf$diffABse^2 - 2 * resdf$covABAB),
sqrt(resdf$diffABse[refi]^2 + resdf$diffABse^2 - 2 * resdf$covABAB + leABAB)
)
)
} else {
resdf$diffABABse <- ifelse(1:nrow(resdf) == refi, NA, sqrt(resdf$diffABse[refi]^2 + resdf$diffABse^2 - 2 * resdf$covABAB))
}
resdf$diffABABpValue <- ifelse(1:nrow(resdf) == refi, NA, 2 * (1 - pt2(abs(resdf$diffABAB / resdf$diffABABse), df = resdf$dofABAB)))
}
# only generate these
if (!nocovs) {
pctdf0$diffAA <- ifelse(1:nrow(pctdf0) == refi, NA, pctdf0$pctA[refi] - pctdf0$pctA)
pctdf0$diffAAse <- ifelse(1:nrow(pctdf0) == refi, NA, sqrt(pctdf0$pctAse[refi]^2 + pctdf0$pctAse^2 - 2 * pctdf0$covAA))
pctdf0$diffAApValue <- ifelse(1:nrow(pctdf0) == refi, NA, 2 * (1 - pt2(abs(pctdf0$diffAA / pctdf0$diffAAse), df = pctdf0$dofAA)))
if (!skipB) {
pctdf0$diffBB <- ifelse(1:nrow(pctdf0) == refi, NA, pctdf0$pctB[refi] - pctdf0$pctB)
pctdf0$diffBBse <- ifelse(1:nrow(pctdf0) == refi, NA, sqrt(pctdf0$pctBse[refi]^2 + pctdf0$pctBse^2 - 2 * pctdf0$covBB))
pctdf0$diffBBpValue <- ifelse(1:nrow(pctdf0) == refi, NA, 2 * (1 - pt2(abs(pctdf0$diffBB / pctdf0$diffBBse), df = pctdf0$dofBB)))
pctdf0$diffABAB <- ifelse(1:nrow(pctdf0) == refi, NA, pctdf0$diffAB[refi] - pctdf0$diffAB)
pctdf0$diffABABse <- ifelse(1:nrow(pctdf0) == refi, NA, sqrt(pctdf0$diffABse[refi]^2 + pctdf0$diffABse^2 - 2 * pctdf0$covABAB))
pctdf0$diffABABpValue <- ifelse(1:nrow(pctdf0) == refi, NA, 2 * (1 - pt2(abs(pctdf0$diffABAB / pctdf0$diffABABse), df = pctdf0$dofABAB)))
}
}
if (j == 1) {
resdf0 <- resdf
} else {
resdf0 <- rbind(resdf0, resdf)
}
} # end for(j in 1:lstats) loop
if (nocovs) {
# remove the labels columns which are just populated
# with the unhelpful text "data"
pctdf0$labels <- NULL
resdf0$labels <- NULL
# reorder columns when there are no covariates
if (returnSimpleDoF) {
if (!skipB) {
resvar0 <- c(
"estimateA", "estimateAse", "dofA",
"estimateB", "estimateBse", "dofB"
)
} else {
resvar0 <- c("estimateA", "estimateAse", "dofA")
}
} else {
if (!skipB) {
resvar0 <- c(
"estimateA", "estimateAse",
"estimateB", "estimateBse"
)
} else {
resvar0 <- c("estimateA", "estimateAse")
}
}
if (!skipB) {
resvar0 <- c(
resvar0,
"diffAB", "covAB",
"diffABse", "diffABpValue", "dofAB"
)
}
addons <- c("achievementLevel", "percentiles")
resvar <- c(addons[addons %in% names(resdf0)], resvar0)
if ("sameSurvey" %in% names(resdf0) && sum(!is.na(resdf0$sameSurvey)) > 0) {
resvar <- c(resvar, "sameSurvey")
}
resdf0 <- resdf0[ , resvar]
# remove "estimate" with "pct" where "estimate" must start the variable name
resvar <- gsub("^estimate", "pct", resvar0)
if (returnSimpleN) {
if (!skipB) {
resvar <- c(resvar, "nA", "nB")
} else {
resvar <- c(resvar, "nA")
}
}
if (returnNumberOfPSU) {
if (!skipB) {
resvar <- c(resvar, "nPSUA", "nPSUB")
} else {
resvar <- c(resvar, "nPSUA")
}
}
pctdf0 <- pctdf0[ , resvar]
} else { # end if(nocovs)
# reorder columns when there are covs
if (returnSimpleDoF) {
if (!skipB) {
resvar0 <- c(
names(data$covs), "estimateA", "estimateAse", "dofA",
"estimateB", "estimateBse", "dofB"
)
} else {
resvar0 <- c(names(data$covs), "estimateA", "estimateAse", "dofA")
}
} else {
if (!skipB) {
resvar0 <- c(
names(data$covs), "estimateA", "estimateAse",
"estimateB", "estimateBse"
)
} else {
resvar0 <- c(names(data$covs), "estimateA", "estimateAse")
}
}
if (!skipB) {
resvar0 <- c(
resvar0,
"diffAB", "covAB", "diffABse",
"diffABpValue", "dofAB",
"diffAA", "covAA", "diffAAse",
"diffAApValue", "dofAA",
"diffBB", "covBB", "diffBBse",
"diffBBpValue", "dofBB",
"diffABAB", "covABAB", "diffABABse",
"diffABABpValue", "dofABAB"
)
} else {
resvar0 <- c(
resvar0,
"diffAA", "covAA", "diffAAse",
"diffAApValue", "dofAA"
)
}
addons <- c("achievementLevel", "percentiles")
resvar <- c(addons[addons %in% names(resdf0)], resvar0)
if ("sameSurvey" %in% names(resdf0) && sum(!is.na(resdf0$sameSurvey)) > 0) {
resvar <- c(resvar, "sameSurvey")
}
resdf0 <- resdf0[ , resvar]
resvar <- gsub("^estimate", "pct", resvar0)
missing <- resvar[!resvar %in% names(pctdf0)]
if (returnSimpleN) {
if (!skipB) {
resvar <- c(resvar, "nA", "nB")
} else {
resvar <- c(resvar, "nA")
}
}
pctdf0 <- pctdf0[ , resvar]
} # end else if(nocovs)
res[["results"]] <- resdf0
res[["percentage"]] <- pctdf0
res[["call"]] <- call
class(res) <- "gapList"
return(res)
} else { # end if(inherits(data, "edsurvey.data.frame.list"))
calli <- as.list(call)
calli[[1]] <- as.name("gapHelper")
calli$data <- data
resi <- eval(as.call(calli))
resi$call <- call
return(resi)
} # end else for if(inherits(data, "edsurvey.data.frame.list")) {
}
# this is called when data is edsurvey.data.frame or light.edsurvey.data.frame
gapHelper <- function(variable, data, groupA = "default", groupB = "default",
percentiles = NULL,
achievementLevel = NULL,
achievementDiscrete = FALSE,
stDev = FALSE,
targetLevel = NULL,
weightVar = NULL, jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
referenceDataIndex = 1,
returnVarEstInputs = FALSE,
returnSimpleDoF = FALSE,
returnSimpleN = FALSE,
returnNumberOfPSU = FALSE,
noCov = FALSE,
pctMethod = c("unbiased", "symmetric", "simple"),
varMethod = NULL,
includeLinkingError = NULL) {
if (is.character(substitute(groupA))) {
groupA <- parse(text = groupA)[[1]]
}
if (all(as.character(substitute(groupA)) == "default")) {
groupB <- NULL
}
if (!missing(groupB)) {
if (is.character(substitute(groupB))) {
groupB <- parse(text = groupB)[[1]]
}
}
pctMethod <- match.arg(pctMethod)
# get the weight var
# if the weight var is not set, use the default
wgt <- checkWeightVar(data, weightVar)
varEstInputs <- NULL
type <- "mu" # mean is the default
if (!is.null(percentiles)) {
type <- "pct"
}
if (!is.null(achievementLevel)) {
if (type != "mu") {
stop(paste0(
"Only one of ", sQuote("percentiles"), ", ", sQuote("achievementLevel"),
", ", sQuote("SD"), ", and ", sQuote("targetLevel"), " can be defined."
))
}
type <- "AL"
achievementLevel <- gmatchAttr(achievementLevel, getALNames(data, variable))
}
if (!is.null(targetLevel)) {
if (type != "mu") {
stop(paste0(
"Only one of ", sQuote("percentiles"), ", ", sQuote("achievementLevel"),
", ", sQuote("SD"), ", and ", sQuote("targetLevel"), " can be defined."
))
}
type <- "eq"
}
if (stDev) {
if (type != "mu") {
stop(paste0(
"Only one of ", sQuote("percentiles"), ", ", sQuote("achievementLevel"),
", ", sQuote("SD"), ", and ", sQuote("targetLevel"), " can be defined."
))
}
type <- "stdev"
}
# make the data with just groupA
if (all(as.character(substitute(groupA)) == "default")) {
dataA <- data
} else {
dataA <- subset(data, substitute(groupA), inside = TRUE)
}
# skipB is a boolean to indicate whether we need to compute statistics for group B
# skipB = TRUE happens when (1) groupB is set to NULL or (2) groupA = 'default' (full sample)
skipB <- FALSE
# if groupB is null, just use data as the reference.
# NOTE: the call to is.null requires "substitute" or groupB
# will be evaluated right here
if (is.null(substitute(groupB))) {
skipB <- TRUE
} else if (all(as.character(substitute(groupB)) == "default")) {
dataB <- data
dataAB <- dataA
} else {
# otherwise filter on groupB
dataB <- subset(data, substitute(groupB), inside = TRUE)
dataAB <- subset(dataA, substitute(groupB), inside = TRUE)
}
callv <- list(
weightVar = wgt,
jrrIMax = jrrIMax,
dropOmittedLevels = dropOmittedLevels,
recode = NULL
) # recode.sdf is already done in line 335
if (!missing(defaultConditions)) {
callv <- c(callv, list(defaultConditions = defaultConditions))
}
if (type == "mu") {
# gap in mean
calllm <- c(
list(formula(paste0(variable, " ~ 1"))),
callv,
list(returnVarEstInputs = TRUE)
) # necessary for covariance est
calllmA <- c(calllm, list(data = dataA))
meanA <- do.call(lm.sdf, calllmA)
coefA <- meanA$coefmat
resA <- coefA[1, 1]
if (!skipB) {
calllmB <- c(calllm, list(data = dataB))
meanB <- do.call(lm.sdf, calllmB)
diff <- unname(meanA$coef[1] - meanB$coef[1]) # unname removes the name (Intercept) that otherwise ends up in the retruned value
coefB <- meanB$coefmat
resB <- coefB[1, 1]
SEs <- list(estimateAse = coefA[1, 2], estimateBse = coefB[1, 2])
} else {
SEs <- list(estimateAse = coefA[1, 2])
}
# prepare varEstInputs
# make varEstInputs$JK
maJK <- subset(meanA$varEstInputs$JK, variable == "(Intercept)")
maJK$variable <- "A"
if (!skipB) {
mbJK <- subset(meanB$varEstInputs$JK, variable == "(Intercept)")
mbJK$variable <- "B"
# calculate the difference
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
if (!is.null(meanA$varEstInput$PV)) {
maPV <- subset(meanA$varEstInputs$PV, variable == "(Intercept)")
maPV$variable <- "A"
if (!skipB) {
mbPV <- subset(meanB$varEstInputs$PV, variable == "(Intercept)")
mbPV$variable <- "B"
# calculate the difference
mdPV <- merge(maPV, mbPV, by = c("PV"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
# only group A
varEstPV <- maPV
}
} else { # end if(!is.null(meanA$varEstInput$PV))
# no PVs, so no error from them
varEstPV <- NULL
} # end else for if(!is.null(meanA$varEstInput$PV))
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if(type == "mu")
if (type == "pct") {
# gap at a percentile
# here the length of percentiles is always one
callpct <- c(
list(
variable = variable,
percentiles = percentiles,
confInt = FALSE, # do not share CI, so do not calculate it
pctMethod = pctMethod
),
callv,
returnVarEstInputs = TRUE
)
callpctA <- c(callpct, list(data = dataA))
meanA <- do.call(percentile, callpctA)
resA <- meanA$estimate
statisticsLevelOut <- paste0("P", percentiles)
if (!skipB) {
callpctB <- c(callpct, list(data = dataB))
meanB <- do.call(percentile, callpctB)
resB <- meanB$estimate
SEs <- list(estimateAse = meanA$se, estimateBse = meanB$se)
} else {
SEs <- list(estimateAse = meanA$se)
}
maJK <- attributes(meanA)$varEstInputs$JK
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- attributes(meanB)$varEstInputs$JK
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
# calculate the difference
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate", "Level"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
maPV <- attributes(meanA)$varEstInputs$PV
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- attributes(meanB)$varEstInputs$PV
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
# calculate the difference
mdPV <- merge(maPV, mbPV, by = c("PV", "Level"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
varEstPV <- maPV
}
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if (type == 'pct')
if (type == "AL") {
# gap in percent at or above an achievement level
callal <- c(
list(
achievementVars = variable,
returnVarEstInputs = TRUE,
returnCumulative = !achievementDiscrete
),
callv
)
als <- getALNames(data, variable)
callalA <- c(callal, list(data = dataA))
meanA <- do.call(achievementLevels, callalA)
if (!is.null(meanA$discrete)) {
colnames(meanA$discrete)[grepl("Level$", colnames(meanA$discrete))] <- "Level"
}
if (!is.null(meanA$cumulative)) {
colnames(meanA$cumulative)[grepl("Level$", colnames(meanA$cumulative))] <- "Level"
}
if (!skipB) {
callalB <- c(callal, list(data = dataB))
meanB <- do.call(achievementLevels, callalB)
if (!is.null(meanB$discrete)) {
colnames(meanB$discrete)[grepl("Level$", colnames(meanB$discrete))] <- "Level"
}
if (!is.null(meanB$cumulative)) {
colnames(meanB$cumulative)[grepl("Level$", colnames(meanB$cumulative))] <- "Level"
}
}
# achievementDiscrete indicates whether we should extract cumulative or discrete achievement level results
if (achievementDiscrete) {
# get index of desired achievement levels from the results
if (typeof(als) == "character") {
lA <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = TRUE)
grep(lal, meanA$discrete$Level, ignore.case = TRUE)
})
} else {
lA <- rep(TRUE, length(als[[achievementLevel]]))
}
if (length(lA) == 0) {
stop("Achievement level cannot be found in the results of the call of the function ", sQuote("achievementLevel"), ".")
}
if (!skipB) {
if (typeof(als) == "character") {
lB <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = TRUE)
grep(lal, meanB$discrete$Level, ignore.case = TRUE)
})
} else {
lB <- rep(TRUE, length(als[[achievementLevel]]))
}
if (length(lB) == 0) {
stop("Achievement level cannot be found in the results of the call of the function ", sQuote("achievementLevel"), ".")
}
}
resA <- meanA$discrete$Percent[unlist(lA)]
if (!skipB) {
resB <- meanB$discrete$Percent[unlist(lB)]
SEs <- list(
estimateAse = meanA$discrete$StandardError[unlist(lA)],
estimateBse = meanB$discrete$StandardError[unlist(lB)]
)
} else {
SEs <- list(estimateAse = meanA$discrete$StandardError[unlist(lA)])
}
} else {
# get index of desired achievement levels from the results
if (typeof(als) == "character") {
lA <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = FALSE)
grep(lal, meanA$cumulative$Level, ignore.case = TRUE)
})
} else {
lA <- rep(TRUE, length(als[[achievementLevel]]))
}
if (length(lA) == 0) {
stop("Achievement level cannot be found in the results of the call of the function ", sQuote("achievementLevel"), ".")
}
if (!skipB) {
if (typeof(als) == "character") {
lB <- sapply(achievementLevel, function(al) {
lal <- addALPrefix(al = al, als = als, discrete = FALSE)
grep(lal, meanB$cumulative$Level, ignore.case = TRUE)
})
} else {
lB <- rep(TRUE, length(als[[achievementLevel]]))
}
}
resA <- meanA$cumulative$Percent[unlist(lA)]
if (!skipB) {
resB <- meanB$cumulative$Percent[unlist(lB)]
SEs <- list(
estimateAse = meanA$cumulative$StandardError[unlist(lA)],
estimateBse = meanB$cumulative$StandardError[unlist(lB)]
)
} else {
SEs <- list(estimateAse = meanA$cumulative$StandardError[unlist(lA)])
}
}
# used inside next if, but also outside
statisticsLevelOut <- meanA$discrete$Level[unlist(lA)]
# get Cov
if (achievementDiscrete) {
maJK <- subset(meanA$discVarEstInputs$JK, variable %in% meanA$discrete$Level[unlist(lA)])
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- subset(meanB$discVarEstInputs$JK, variable %in% meanB$discrete$Level[unlist(lB)])
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
}
} # end if(achievementDiscrete)
else {
maJK <- subset(meanA$cumVarEstInputs$JK, variable %in% meanA$cumulative$Level[unlist(lA)])
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- subset(meanB$cumVarEstInputs$JK, variable %in% meanB$cumulative$Level[unlist(lB)])
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
}
} # end else if(achievementDiscrete)
if (!skipB) {
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate", "Level"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
if (achievementDiscrete) {
maPV <- subset(meanA$discVarEstInputs$PV, variable %in% meanA$discrete$Level[unlist(lA)])
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- subset(meanB$discVarEstInputs$PV, variable %in% meanB$discrete$Level[unlist(lB)])
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
}
} # end if(achievementDiscrete)
else {
statisticsLevelOut <- meanA$cumulative$Level[unlist(lA)]
maPV <- subset(meanA$cumVarEstInputs$PV, variable %in% meanA$cumulative$Level[unlist(lA)])
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- subset(meanB$cumVarEstInputs$PV, variable %in% meanB$cumulative$Level[unlist(lB)])
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
}
} # end else if(achievementDiscrete)
# calculate the difference
if (!skipB) {
mdPV <- merge(maPV, mbPV, by = c("PV", "Level"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
varEstPV <- maPV
}
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if (type == "AL")
if (type == "stdev") {
# gap at a standard deviation
# here the length of standard deviation is always one
callStDev <- c(list(variable = variable),
callv,
returnVarEstInputs = TRUE
)
if ("weightVar" %in% names(callStDev) && is.null(callStDev[["weightVar"]])) {
# default is used only if weightVar is missing.
callStDev[["weightVar"]] <- NULL
}
callStDevA <- c(callStDev, list(data = dataA))
meanA <- do.call(SD, callStDevA)
resA <- meanA$std
statisticsLevelOut <- "SD"
if (!skipB) {
callStDevB <- c(callStDev, list(data = dataB))
meanB <- do.call(SD, callStDevB)
resB <- meanB$std
SEs <- list(estimateAse = meanA$stdSE, estimateBse = meanB$stdSE)
} else {
SEs <- list(estimateAse = meanA$stdSE)
}
maJK <- meanA$varEstInputs$JK
maJK$Level <- maJK$variable
maJK$variable <- "A"
if (!skipB) {
mbJK <- meanB$varEstInputs$JK
mbJK$Level <- mbJK$variable
mbJK$variable <- "B"
# calculate the difference
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate", "Level"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs$PV
if (!is.null(meanA$varEstInput$PV)) {
maPV <- meanA$varEstInputs$PV
maPV$Level <- maPV$variable
maPV$variable <- "A"
if (!skipB) {
mbPV <- meanB$varEstInputs$PV
mbPV$Level <- mbPV$variable
mbPV$variable <- "B"
# calculate the difference
mdPV <- merge(maPV, mbPV, by = c("PV", "Level"), suffixes = c(".A", ".B"))
mdPV$variable <- "A-B"
mdPV$value <- mdPV$value.A - mdPV$value.B
mdPV$value.A <- mdPV$value.B <- NULL
mdPV$variable.A <- mdPV$variable.B <- NULL
varEstPV <- rbind(maPV, mbPV, mdPV)
} else {
varEstPV <- maPV
}
} else { # end if(!is.null(meanA$varEstInput$PV))
# no PVs, so no error from them
varEstPV <- NULL
} # end else for if(!is.null(meanA$varEstInput$PV))
# make varEstInputs
row.names(varEstJK) <- NULL
row.names(varEstPV) <- NULL
varEstInputs <- list(JK = varEstJK, PV = varEstPV)
} # end if (type == 'stDev')
groupA_0 <- FALSE
groupB_0 <- FALSE
if (type == "eq") {
# gap in percent in some covariate
calleq <- c(callv, list(
formula = formula(paste0(" ~ ", variable)),
returnMeans = FALSE,
pctAggregationLevel = NULL,
returnSepct = TRUE,
drop = FALSE,
returnVarEstInputs = TRUE
))
calleqA <- c(calleq, list(data = dataA))
meanA <- do.call(edsurveyTable, calleqA)
if (!targetLevel %in% meanA$data[[variable]]) {
groupA_0 <- TRUE
}
resA <- ifelse(groupA_0, 0, meanA$data[meanA$data[variable] == targetLevel, 4])
if (!skipB) {
calleqB <- c(calleq, list(data = dataB))
meanB <- do.call(edsurveyTable, calleqB)
if (!targetLevel %in% meanB$data[[variable]]) {
groupB_0 <- TRUE
}
resB <- ifelse(groupB_0, 0, meanB$data[meanB$data[variable] == targetLevel, 4])
SEs <- list(
estimateAse = ifelse(groupB_0, NA, meanA$data[meanA$data[variable] == targetLevel, 5]),
estimateBse = ifelse(groupB_0, NA, meanB$data[meanB$data[variable] == targetLevel, 5])
)
} else {
SEs <- list(estimateAse = ifelse(groupB_0, NA, meanA$data[meanA$data[variable] == targetLevel, 5]))
}
# make varEstInputs$JK
if (!groupA_0) {
maJK <- meanA$pctVarEstInputs$JK[meanA$pctVarEstInputs$JK$variable == paste0(variable, "=", targetLevel), ]
maJK$variable <- "A"
} else {
maJK <- NULL
}
if (!skipB) {
if (!groupB_0) {
mbJK <- meanB$pctVarEstInputs$JK[meanB$pctVarEstInputs$JK$variable == paste0(variable, "=", targetLevel), ]
mbJK$variable <- "B"
} else {
mbJK <- NULL
}
# calculate the difference
if (!groupA_0 & !groupB_0) {
mdJK <- merge(maJK, mbJK, by = c("PV", "JKreplicate"), suffixes = c(".A", ".B"))
mdJK$variable <- "A-B"
mdJK$value <- mdJK$value.A - mdJK$value.B
mdJK$value.A <- mdJK$value.B <- NULL
mdJK$variable.A <- mdJK$variable.B <- NULL
} else {
mdJK <- NULL
}
varEstJK <- rbind(maJK, mbJK, mdJK)
} else {
varEstJK <- maJK
}
# make varEstInputs
if (!is.null(varEstJK)) {
row.names(varEstJK) <- NULL
varEstInputs <- list(JK = varEstJK, PV = NULL)
} else {
varEstInputs <- NULL
}
} # end if (type == "eq")
# Compute difference statistics (diffAB, covAB, pooledse, pooleddf)
if (!skipB) {
diff <- unname(resA - resB) # unname removes the name (Intercept) that otherwise ends up in the retruned value
nSize <- c(nrow(dataA), nrow(dataB))
total <- which.max(nSize) # total is 1 if groupA is the total and 2 if groupB is the total
p <- nrow(dataAB) / nSize[total]
cov <- p * (SEs[[total]])^2
wgtl <- getAttributes(data, "weights")[[wgt]]
JRdfA <- JRdfB <- JRdf <- length(wgtl$jksuffixes)
if (!is.null(varEstInputs)) {
if (type %in% c("pct", "AL")) {
cov <- c()
JRdfA <- c()
JRdfB <- c()
JRdf <- c()
# we need to calculate cov by each Level
for (si in statisticsLevelOut) {
varEstInputsTemp <- list(
JK = subset(varEstInputs$JK, Level %in% si),
PV = subset(varEstInputs$PV, Level %in% si)
)
if (nrow(varEstInputsTemp$JK) == 0) {
varEstInputsTemp$JK <- NULL
}
if (nrow(varEstInputsTemp$PV) == 0) {
varEstInputsTemp$PV <- NULL
}
if (!is.null(varEstInputsTemp$JK)) {
cov <- c(cov, varEstToCov(varEstInputsTemp,
varA = "A", varB = "B", jkSumMultiplier = getAttributes(data, "jkSumMultiplier")
))
JRdfA <- c(JRdfA, DoFCorrection(varEstInputsTemp, varA = "A", method = c("JR")))
JRdfB <- c(JRdfB, DoFCorrection(varEstInputsTemp, varA = "B", method = c("JR")))
JRdf <- c(JRdf, DoFCorrection(varEstInputsTemp, varA = "A", varB = "B", method = c("JR")))
} # end if (!is.null(varEstInputsTemp$JK))
else {
cov <- c(cov, NA)
JRdfA <- c(JRdfA, NA)
JRdfB <- c(JRdfB, NA)
JRdf <- c(JRdf, NA)
}
}
} # end if (type %in% c("pct","AL"))
else {
cov <- ifelse(groupA_0 | groupB_0, cov, varEstToCov(varEstInputs, varA = "A", varB = "B", jkSumMultiplier = getAttributes(data, "jkSumMultiplier")))
JRdfA <- ifelse(groupA_0, NA, DoFCorrection(varEstInputs, varA = "A", method = c("JR")))
JRdfB <- ifelse(groupB_0, NA, DoFCorrection(varEstInputs, varA = "B", method = c("JR")))
JRdf <- ifelse(groupA_0 | groupB_0, NA, DoFCorrection(varEstInputs, varA = "A", varB = "B", method = c("JR")))
}
} # end if (!is.null(varEstInputs))
if (noCov) {
cov <- 0 * cov
JRdf <- ifelse(groupA_0 | groupB_0, NA, (SEs[[1]]^2 + SEs[[2]]^2)^2 / (SEs[[1]]^4 / JRdfA + SEs[[2]]^4 / JRdfB))
}
diffSE <- mapply(function(a, b, c) {
unname(sqrt(a^2 + b^2 - 2 * c))
}, SEs[[1]], SEs[[2]], cov)
diffSE <- unlist(diffSE)
# get percent in each group
# get variables used in conditions for groupA and groupB
vn <- c(parseVars(substitute(groupA), data), parseVars(substitute(groupB), data))
# add weights and variable itself
vn <- c(vn, wgt, variable)
tryCatch(vn <- unique(c(vn, PSUStratumNeeded(returnNumberOfPSU, data))),
error = function(e) {
warning(paste0("Stratum and PSU variables are required for this call and are not on the incoming data. Ignoring ", dQuote("returnNumberOfPSU=TRUE"), "."))
returnNumberOfPSU <<- FALSE
}
)
# setup non-data call variables
callv <- list(
varnames = vn,
drop = FALSE,
dropOmittedLevels = dropOmittedLevels,
recode = NULL
) # recode is already done in line 335
if (!missing(defaultConditions)) {
callv <- c(callv, list(defaultConditions = defaultConditions))
}
# add weight call variables and do calls
wgtcall0 <- c(callv, list(data = data))
d0 <- do.call(getData, wgtcall0)
wgtcallA <- c(callv, list(data = dataA))
dA <- do.call(getData, wgtcallA)
wgtcallB <- c(callv, list(data = dataB))
dB <- do.call(getData, wgtcallB)
pctA <- sum(dA[ , wgt]) / sum(d0[ , wgt])
pctB <- sum(dB[ , wgt]) / sum(d0[ , wgt])
# calculate covAB using JK method
wgtl <- getAttributes(data, "weights")[[wgt]]
pctJK <- t(sapply(wgtl$jksuffixes, function(suffix) {
w0 <- sum(d0[ , paste0(wgtl$jkbase, suffix)])
wA <- sum(dA[ , paste0(wgtl$jkbase, suffix)])
wB <- sum(dB[ , paste0(wgtl$jkbase, suffix)])
c(pctA = wA / w0, pctB = wB / w0)
}))
seA <- 100 * sqrt(getAttributes(data, "jkSumMultiplier") * sum((pctJK[ , 1] - pctA)^2))
seB <- 100 * sqrt(getAttributes(data, "jkSumMultiplier") * sum((pctJK[ , 2] - pctB)^2))
varJK <- data.frame(
stringsAsFactors = FALSE,
PV = rep(0, 3 * nrow(pctJK)),
JKreplicate = rep(1:nrow(pctJK), 3),
variable = rep(c("A", "B", "A-B"), each = nrow(pctJK)),
value = c(
pctJK[ , 1] - pctA,
pctJK[ , 2] - pctB,
(pctJK[ , 1] - pctA) - (pctJK[ , 2] - pctB)
)
)
# there is no PV here because weights are not imputed
pctVarEstInputs <- list(JK = varJK, PV = NULL)
if (noCov) {
covAB <- rep(0, length(seA))
pctDoFA <- DoFCorrection(pctVarEstInputs, varA = "A", method = "JR")
pctDoFB <- DoFCorrection(pctVarEstInputs, varA = "B", method = "JR")
# Satterthwaite approximation
pctJRdf <- (seA^2 + seB^2)^2 / (seA^4 / pctDoFA + seB^4 / pctDoFB)
} else {
covAB <- varEstToCov(pctVarEstInputs, varA = "A", varB = "B", jkSumMultiplier = getAttributes(data, "jkSumMultiplier"))
pctDoFA <- DoFCorrection(pctVarEstInputs, varA = "A", method = "JR")
pctDoFB <- DoFCorrection(pctVarEstInputs, varA = "B", method = "JR")
pctJRdf <- DoFCorrection(pctVarEstInputs, varA = "A-B", method = "JR")
}
# end covAB calculation
pctA <- 100 * pctA
pctB <- 100 * pctB
seAB <- unname(sqrt(seA^2 + seB^2 - 2 * 100 * 100 * covAB))
# start return vector, first name achievement level
vec <- switch(type,
pct = data.frame(stringsAsFactors = FALSE, percentiles = as.numeric(percentiles)),
AL = data.frame(stringsAsFactors = FALSE, achievementLevel = gsub("Below", "below", as.character(statisticsLevelOut))),
data.frame()
)
if (returnSimpleDoF) {
# add dofA and dofB if requested
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA,
estimateB = resB, estimateBse = SEs[[2]], dofB = JRdfB
)
)
} else { # this is equivalent to if (type %in% c("mu","eq"))
vec <- data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA,
estimateB = resB, estimateBse = SEs[[2]], dofB = JRdfB
)
}
} # end if(returnSimpleDoF)
else {
# otherwise, no dofA or dofB
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]],
estimateB = resB, estimateBse = SEs[[2]]
)
)
} else { # this is equivalent to if (type %in% c("mu","eq"))
vec <- data.frame(
stringsAsFactors = FALSE,
estimateA = resA, estimateAse = SEs[[1]],
estimateB = resB, estimateBse = SEs[[2]]
)
}
}
diffp <- 2 * (1 - pt2(abs(diff / diffSE), df = JRdf))
vec <- cbind(
vec,
data.frame(
stringsAsFactors = FALSE,
diffAB = diff, covAB = unname(cov),
diffABse = diffSE,
diffABpValue = diffp,
dofAB = JRdf
)
)
row.names(vec) <- NULL
if (returnSimpleDoF) {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA,
dofA = pctDoFA,
pctB = pctB,
pctBse = seB,
dofB = pctDoFB
)
} else {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA,
pctB = pctB,
pctBse = seB
)
}
pdiffp <- 2 * (1 - pt2(abs(pctA - pctB) / seAB, df = pctJRdf))
percentage <- cbind(
percentage,
data.frame(
stringsAsFactors = FALSE,
diffAB = pctA - pctB,
covAB = 100 * 100 * covAB,
diffABse = seAB,
diffABpValue = pdiffp,
dofAB = pctJRdf
)
)
lst <- list(
results = vec,
labels = list(
A = substitute(groupA), B = substitute(groupB),
n0A = nrow2.edsurvey.data.frame(dataA),
n0B = nrow2.edsurvey.data.frame(dataB),
nUsedA = nrow(dA),
nUsedB = nrow(dB)
),
percentage = percentage
)
if (returnNumberOfPSU) {
lst$labels$nPSUA <- nrow(unique(dA[ , c(getAttributes(dataA, "stratumVar"), getAttributes(dataA, "psuVar"))]))
lst$labels$nPSUB <- nrow(unique(dB[ , c(getAttributes(dataB, "stratumVar"), getAttributes(dataB, "psuVar"))]))
}
} else { # end if(!skipB)
wgtl <- getAttributes(data, "weights")[[wgt]]
JRdfA <- ifelse(groupA_0, NA, length(wgtl$jksuffixes))
if (!is.null(varEstInputs)) {
if (type %in% c("pct", "AL")) {
JRdfA <- c()
for (si in statisticsLevelOut) {
varEstInputsTemp <- list(
JK = subset(varEstInputs$JK, Level %in% si),
PV = subset(varEstInputs$PV, Level %in% si)
)
if (nrow(varEstInputsTemp$JK) == 0) {
varEstInputsTemp$JK <- NULL
}
if (nrow(varEstInputsTemp$PV) == 0) {
varEstInputsTemp$PV <- NULL
}
if (!is.null(varEstInputsTemp$JK)) {
JRdfA <- c(JRdfA, DoFCorrection(varEstInputsTemp, varA = "A", method = c("JR")))
} else {
JRdfA <- c(JRdfA, NA)
}
}
} # end if (type %in% c("pct","AL"))
else {
JRdfA <- ifelse(groupA_0, NA, DoFCorrection(varEstInputs, varA = "A", method = c("JR")))
}
}
# get variables used in conditions for groupA and groupB
vn <- c(parseVars(substitute(groupA), data), parseVars(substitute(groupB), data))
# add weights and variable itself
vn <- c(vn, wgt, variable)
tryCatch(vn <- unique(c(vn, PSUStratumNeeded(returnNumberOfPSU, data))),
error = function(e) {
warning(paste0("Stratum and PSU variables are required for this call and are not on the incoming data. Ignoring ", dQuote("returnNumberOfPSU=TRUE"), "."))
returnNumberOfPSU <<- FALSE
}
)
# setup non-data call variables
callv <- list(
varnames = vn,
drop = FALSE,
dropOmittedLevels = dropOmittedLevels,
recode = NULL,
addAttributes = TRUE
) # recode is already done in line 335
if (!missing(defaultConditions)) {
callv <- c(callv, list(defaultConditions = defaultConditions))
}
# add weight call variables and do calls
wgtcall0 <- c(callv, list(data = data))
d0 <- do.call(getData, wgtcall0)
wgtcallA <- c(callv, list(data = dataA))
dA <- do.call(getData, wgtcallA)
pctA <- sum(dA[ , wgt]) / sum(d0[ , wgt])
pctJK <- t(sapply(wgtl$jksuffixes, function(suffix) {
w0 <- sum(d0[ , paste0(wgtl$jkbase, suffix)])
wA <- sum(dA[ , paste0(wgtl$jkbase, suffix)])
c(pctA = wA / w0, pctB = 0)
}))
seA <- 100 * sqrt(getAttributes(data, "jkSumMultiplier") * sum((pctJK[ , 1] - pctA)^2))
varJK <- data.frame(
stringsAsFactors = FALSE,
PV = rep(0, nrow(pctJK)),
JKreplicate = 1:nrow(pctJK),
variable = "A",
value = pctJK[ , 1] - pctA
)
# there is no PV here because weights are not imputed
pctVarEstInputs <- list(JK = varJK, PV = NULL)
pctDoFA <- DoFCorrection(pctVarEstInputs, varA = "A", method = "JR")
pctA <- 100 * pctA
# start return vector, first name achievement level
vec <- switch(type,
pct = data.frame(stringsAsFactors = FALSE, percentiles = as.numeric(percentiles)),
AL = data.frame(stringsAsFactors = FALSE, achievementLevel = gsub("Below", "below", as.character(statisticsLevelOut))),
data.frame()
)
if (returnSimpleDoF) {
# add dofA and dofB if requested
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA)
)
} else {
vec <- data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]], dofA = JRdfA)
}
} else {
# otherwise, no dofA or dofB
if (nrow(vec) > 0) {
vec <- cbind(
vec,
data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]])
)
} else {
vec <- data.frame(stringsAsFactors = FALSE, estimateA = resA, estimateAse = SEs[[1]])
}
}
row.names(vec) <- NULL
if (returnSimpleDoF) {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA,
dofA = pctDoFA
)
} else {
percentage <- data.frame(
stringsAsFactors = FALSE,
pctA = pctA,
pctAse = seA
)
}
lst <- list(
results = vec,
labels = list(
A = substitute(groupA),
n0A = nrow2.edsurvey.data.frame(dataA),
n0B = NA,
nUsedA = nrow(dA),
nUsedB = NA
),
percentage = percentage
)
if (returnNumberOfPSU) {
if (getAttributes(dA, "stratumVar") %in% "JK1") {
lst$labels$nPSUA <- nrow(dA)
} else {
lst$labels$nPSUA <- nrow(dA[ , c(getAttributes(dA, "stratumVar"), getAttributes(dA, "psuVar"))])
}
lst$labels$nPSUB <- NA
}
}
if (returnVarEstInputs) {
if ("Level" %in% colnames(varEstInputs$JK)) { # equivalent to type = 'pct' or 'AL'
if (type == "pct") {
varEstInputs$JK$Level <- as.numeric(gsub("^P", "", varEstInputs$JK$Level))
varEstInputs$PV$Level <- as.numeric(gsub("^P", "", varEstInputs$PV$Level))
}
if (length(percentiles) <= 1 & length(achievementLevel) <= 1) {
varEstInputs$JK$Level <- NULL
varEstInputs$PV$Level <- NULL
}
}
# assign values close to 0 to 0
varEstInputs$JK$value[which(abs(varEstInputs$JK$value) < (sqrt(.Machine$double.eps) * sqrt(nrow(varEstInputs$JK))))] <- 0
pctVarEstInputs$JK$value[which(abs(pctVarEstInputs$JK$value) < (sqrt(.Machine$double.eps) * sqrt(nrow(pctVarEstInputs$JK))))] <- 0
lst <- c(lst, list(
varEstInputs = varEstInputs,
pctVarEstInputs = pctVarEstInputs
))
}
class(lst) <- "gap"
return(lst)
}
# @title Linking error for PISA 2015 and subsequent DBA v prior PBA
# @param years a vector of two years involved, including 2000, 2003, 2006, 2009, 2012, 2015 or 2018
# @param subject, one of "read", "math", or "scei"
# The linking error for PISA is entirely cross year and one value
# @author Paul Bailey
calLinkingErrorPISA <- function(subject = c("read", "math", "scie"),
years = c(2000, 2003, 2006, 2009, 2012, 2015, 2018)) {
years <- as.numeric(years)
linkingErrorsFinance <- data.frame(
subject = rep("fin", 3),
yearSmall = c(2012, 2015, 2012),
yearBig = c(2018, 2018, 2015),
error = c(5.55, 9.37, 5.3309),
stringsAsFactors = FALSE
)
linkingErrorsRead <- data.frame(
subject = rep("read", 11),
yearSmall = c(2000, 2003, 2006, 2009, 2012, 2000, 2003, 2006, 2009, 2012, 2015),
yearBig = c(2015, 2015, 2015, 2015, 2015, 2018, 2018, 2018, 2018, 2018, 2018),
error = c(6.8044, 5.3907, 6.6064, 3.4301, 5.2535, 4.04, 7.77, 5.24, 3.52, 3.74, 3.93),
stringsAsFactors = FALSE
)
linkingErrorsMath <- data.frame(
subject = rep("math", 9),
yearSmall = c(2003, 2006, 2009, 2012, 2003, 2006, 2009, 2012, 2015),
yearBig = c(2015, 2015, 2015, 2015, 2018, 2018, 2018, 2018, 2018),
error = c(5.6080, 3.511, 3.7853, 3.5462, 2.80, 3.18, 3.54, 3.34, 2.33),
stringsAsFactors = FALSE
)
linkingErrorsScei <- data.frame(
subject = rep("scei", 7),
yearSmall = c(2006, 2009, 2012, 2006, 2009, 2012, 2015),
yearBig = c(2015, 2015, 2015, 2018, 2018, 2018, 2018),
error = c(4.4821, 4.5016, 3.9228, 3.47, 3.59, 4.01, 1.51),
stringsAsFactors = FALSE
)
linkingErrorsPISA <- rbind(linkingErrorsFinance, linkingErrorsRead, linkingErrorsMath, linkingErrorsScei)
sbj <- match.arg(subject)
years <- years[years %in% c(2000, 2003, 2006, 2009, 2012, 2015, 2018)]
if (length(years) != 2) {
stop("Argument ", dQuote("years"), " must have 2 valid years so there are two years to contrast.")
}
lep <- linkingErrorsPISA[linkingErrorsPISA$subject == sbj, ]
res <- expand.grid(yearSmall = years, yearBig = years)
res <- res[res$yearSmall < res$yearBig, ]
res <- merge(lep, res, by = c("yearSmall", "yearBig"), all = FALSE)
if (nrow(res) == 0) {
return(0)
}
if (nrow(res) > 1) {
stop("Multiple returns.")
}
return(res$error)
}
# @title Linking error for NAEP DBA/PBA gap analysis.
# @param X a numeric value estimated from digitally based assessments
# @param subjectI a character value set to "Mathematics" or "Reading" for subject information
# @param gradeI a character value set to "Grade 4" or "Grade 8" as grade level
# @param gapI a logical indicating if the linking error is a gap
# @param dependentVarI a character value set to one of NAEP math or reading subscales
# @param statElementI a character vector selected from "Mean", "Row Percent", "10", "25", "50", "75", "90",
# "At or above basic", "At or above proficient", "Below basic", "At basic", "At proficient", or "At advanced" indicating the relevant statistic
# @author Huade Huo
calLinkingError <- function(X,
subjectI = NULL,
gradeI = NULL,
dependentVarI = NULL,
statElementI = NULL,
gapI = FALSE) {
linkingErrorsRDS <- readRDS(system.file("linkingErrors", "linkingErrors.rds", package = "EdSurvey"))
linkingErrorRDSi <- linkingErrorsRDS[linkingErrorsRDS$subject %in% subjectI &
linkingErrorsRDS$dependentVar %in% dependentVarI &
linkingErrorsRDS$grade %in% gradeI &
linkingErrorsRDS$statElement %in% statElementI &
linkingErrorsRDS$gap %in% gapI, ]
linkingErrorRDSi$statElement <- factor(linkingErrorRDSi$statElement, levels = statElementI)
linkingErrorRDSi <- linkingErrorRDSi[order(linkingErrorRDSi$statElement), ]
if (nrow(linkingErrorRDSi) == 0) {
stop("Linking error is not avaialble.")
}
linkingErrori <- linkingErrorRDSi$Aterm * X^2 + linkingErrorRDSi$Bterm * X + linkingErrorRDSi$Cterm
return(linkingErrori)
}
#' @rdname printGap
#' @title Gap Analysis Printing
#'
#' @description Prints labels and a results vector of a gap analysis.
#' @param x an \code{R} object representing a \code{gap} of class \code{gap} or \code{gapList}
#' @param printPercentage a logical value set to \code{TRUE} to request printing
#' of the percentage in the groups. Defaults to \code{TRUE}.
#' @param ... these arguments are not passed anywhere and are included only for compatibility
#' @method print gap
#' @author Paul Bailey
#' @aliases print.gapList
#' @export
print.gap <- function(x, ..., printPercentage = TRUE) {
cat("Call: ")
print(x$call)
cat("\nLabels:\n")
lab <- data.frame(
stringsAsFactors = FALSE,
group = c("A", "B"),
definition = c(deparse(x$labels$A), deparse(x$labels$B)),
nFullData = c(x$labels$n0A, x$labels$n0B),
nUsed = c(x$labels$nUsedA, x$labels$nUsedB)
)
if (!is.null(x$labels$nPSUA)) {
lab$nPSU <- c(x$labels$nPSUA, x$labels$nPSUB)
}
print(lab, row.names = FALSE)
if (printPercentage) {
cat("\nPercentage:\n")
print(x$percentage, row.names = FALSE)
}
cat("\nResults:\n")
print(x$results, row.names = FALSE, ...)
}
#' @rdname printGap
#' @method print gapList
#' @export
print.gapList <- function(x, ..., printPercentage = TRUE) {
cat("gapList\n")
cat(paste0("Call: "))
print(x$call)
cat("\nLabels:\n")
lab <- data.frame(
stringsAsFactors = FALSE,
group = c("A", "B"),
definition = c(deparse(x$labels$A), deparse(x$labels$B))
)
print(lab, row.names = FALSE)
if (printPercentage) {
cat("\nPercentage:\n")
pct <- data.frame(x$percentage, stringsAsFactors = FALSE)
print(pct, row.names = FALSE)
}
cat("\nResults:\n")
print(x$results, row.names = FALSE)
}
# helper that identifies variables in a call
# ccall is the call to parse
# x is an edsurvey.data.frame or light.edsurvey.data.frame
# @author Paul Bailey
parseVars <- function(ccall, x) {
vars <- c()
# for each element
if (typeof(ccall) == "symbol") {
return(vars)
}
for (i in 1:length(ccall)) {
# if it is a name
if (inherits(ccall[[i]], "name")) {
ccall_c <- as.character(ccall[[i]])
# if it is not in the data and is in the parent.frame, it might be a variable.
if (ccall_c %in% colnames(x)) { # orignally:: if(ccall_c %in% names(x$data) ) { TOM FINK::dataList update
if (ccall[[i]] == "%in%" || is.function(ccall[[i]])) {
# do nothing
} else {
vars <- c(vars, ccall_c)
}
} # End of if statment: if (ccall_c %in% names(x$data))
} # end if(class(ccall[[i]]) %in% c("name")) {
if (inherits(ccall[[i]], "name")) {
# if this is a call, recursively parse that
vars <- c(vars, parseVars(ccall[[i]], x))
} # end of if statment: if class(ccall[[i]]) %in% "call"
} # end of for loop: i in 1:length(ccall)
vars
} # End of fucntion: parseVars
# helpter function that calculates "pooled" dof, used for two distinct samples
# @author Trang Nguyen and Paul Bailey
dofCompute <- function(seA, seB, dofA, dofB) {
return(mapply(function(seA, seB, dofA, dofB) {
ifelse(seA + seB > 0, (seA^2 + seB^2)^2 / (seA^4 / dofA + seB^4 / dofB), 0)
}, seA, seB, dofA, dofB))
}
# returns NA when df is 0, but passes Info to pt to avoid a warning
# @author Paul Bailey
pt2 <- function(q, df) {
df2 <- ifelse(df == 0, Inf, df)
return(ifelse(df == 0, NA, pt(q, df2)))
}
# match strings to attributes in a case insensitive way
# throwing an error if the string matches more than one achievement level
# or matches none
gmatchAttr <- function(strings, achievementLevelNames) {
s0 <- strings
alN0 <- achievementLevelNames
# lower case everyting to make the function case insensitive
strings <- tolower(trimws(strings))
alN <- tolower(achievementLevelNames)
# find and store the prefix, then remove it from strings
prefix <- ifelse(grepl("^at or above", strings), substr(s0, 0, nchar("at or above") + 1), "")
prefix <- ifelse(nchar(prefix) == 0 & grepl("^at ", strings), substr(s0, 0, 3), prefix)
prefix <- ifelse(grepl("^below", strings), substr(s0, 0, nchar("below") + 1), prefix)
strings <- trimws(substr(strings, nchar(prefix) + 1, nchar(strings)))
out <- rep("", length(strings))
error <- rep("", length(strings))
for (i in 1:length(out)) {
for (j in 1:length(alN)) {
update <- grepl(strings[i], alN[j], fixed = TRUE)
if (update) {
if (nchar(out[i]) == 0) {
# base case, store the output
out[i] <- ifelse(update, alN0[j], out[i])
} else {
# append this level to previously found levels in an error
# we can then replay all the levels to the user at the end.
# first...
# if this is the second match, store the previous
# one as an error
if (nchar(error[i]) == 0) {
error[i] <- sQuote(out[i])
}
error[i] <- paste0(c(error[i], sQuote(alN0[j])), collapse = ", ")
out[i] <- "Error"
}
}
}
}
# finalize formatting for errors
error <- ifelse(nchar(error) > 0, paste0(" matched to ", error), "")
error <- ifelse(nchar(out) == 0, paste0(" is not one of the achievement Level for the current survey. Choose one from ", pasteItems(achievementLevelNames)), error)
# if there is an error, format it and throw it
if (any(nchar(error) > nchar(out))) {
evars <- nchar(error) > 0
ferror <- paste0(sQuote(s0[evars]), error[evars], collapse = "; ")
stop(paste0(ferror, "."))
}
out <- trimws(paste(trimws(prefix), trimws(out), sep = " "))
return(out)
}
getALNames <- function(data, variable) {
al <- getAttributes(data, "achievementLevels")
if (is.list(al)) {
if (variable %in% names(al)) {
al <- al[[variable]]
} else {
# assumes all achievement levels are the same
al <- al[[1]]
}
}
names(al)
}
addALPrefix <- function(al, als, discrete) {
if (discrete) {
if (grepl("below", al, ignore.case = TRUE)) {
lal <- al
} else {
if (!grepl("^at", al, ignore.case = TRUE)) {
lal <- paste0("At ", al)
} else {
lal <- al
}
}
} else {
if (grepl("^below", al, ignore.case = TRUE)) { # cumulative should not have Below achievementLevel
return(NULL)
} else {
# if it doesn't have the at prefix, add it
if (!grepl("^at", al, ignore.case = TRUE)) {
lal <- paste0(
ifelse(tolower(al) == tolower(als[length(als)]),
"At ",
"At or Above "
),
al
)
} else {
lal <- al
}
}
}
return(lal)
}
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