Nothing
R/lm.sdf.R
In EdSurvey: Analysis of NCES Education Survey and Assessment Data
Defines functions
calc.lm.sdf
lm.sdf
Documented in
lm.sdf
#' @title EdSurvey Linear Models
#' @description Fits a linear model that uses weights and variance estimates appropriate for the data.
#'
#' @param formula a \ifelse{latex}{\code{formula}}{\code{\link[stats]{formula}}} for the
#' linear model. See \ifelse{latex}{\code{lm}}{\code{\link[stats]{lm}}}.
#' If \emph{y} is left blank, the default subject scale or subscale variable
#' will be used. (You can find the default using
#' \code{\link{showPlausibleValues}}.)
#' If \emph{y} is a variable for a subject scale or subscale (one of the
#' names shown by \code{\link{showPlausibleValues}}),
#' then that subject scale or subscale is used.
#' @param data an \code{edsurvey.data.frame}, a \code{light.edsurvey.data.frame},
#' or an \code{edsurvey.data.frame.list}
#' @param weightVar a character indicating the weight variable to use (see Details).
#' The \code{weightVar} must be one of the weights for the
#' \code{edsurvey.data.frame}. If \code{NULL}, it uses the default
#' for the \code{edsurvey.data.frame}.
#' @param varMethod a character set to \dQuote{jackknife} or \dQuote{Taylor} that indicates the variance
#' estimation method to be used. 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 (see
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}})
#' 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 relevels a list. Used to change the contrasts from the
#' default treatment contrasts to the treatment contrasts with a chosen omitted
#' group (the reference group). The name of each element should be the variable name, and the value
#' should be the group to be omitted (the reference group).
#' @param dropOmittedLevels a logical value. When set to the default value of \code{TRUE}, drops
#' those levels of all factor variables that are specified
#' in an \code{edsurvey.data.frame}. 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 an \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{list(}\code{var1} \code{=} \code{list(}\code{from=} \code{c("a",} \code{"b",} \code{"c"),} \code{to=} \code{"d"))}. See Examples.
#' @param returnVarEstInputs a logical value set to \code{TRUE} to return the
#' inputs to the jackknife and imputation variance
#' estimates, which allow for the computation
#' of covariances between estimates.
#' @param returnNumberOfPSU a logical value set to \code{TRUE} to return the number of
#' primary sampling units (PSUs)
#' @param standardizeWithSamplingVar a logical value indicating if the standardized coefficients
#' should have the variance of the regressors and outcome measured
#' with sampling variance. Defaults to \code{FALSE}.
#' @param verbose logical; indicates whether a detailed printout should display during execution
#'
#' @param omittedLevels this argument is deprecated. Use \code{dropOmittedLevels}
#'
#' @details
#'
#' This function implements an estimator that correctly handles left-hand
#' side variables that are either numeric or plausible values and allows for survey
#' sampling weights and estimates variances using the jackknife replication method.
#' The vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}
#' describes estimation of the reported statistics.
#'
#' Regardless of the variance estimation, the coefficients are estimated
#' using the sample weights according to the sections
#' \dQuote{Estimation of Weighted Means When Plausible Values Are Not Present}
#' or
#' \dQuote{Estimation of Weighted Means When Plausible Values Are Present,}
#' depending on if there are assessment variables or variables with plausible values
#' in them.
#'
#' How the standard errors of the coefficients are estimated depends on the
#' value of \code{varMethod} and the presence of plausible values (assessment variables),
#' But once it is obtained, the \emph{t} statistic
#' is given by \deqn{t=\frac{\hat{\beta}}{\sqrt{\mathrm{var}(\hat{\beta})}}} where
#' \eqn{ \hat{\beta} } is the estimated coefficient and \eqn{\mathrm{var}(\hat{\beta})} is
#' the variance of that estimate.
#'
#' The \bold{coefficient of determination (\emph{R}-squared value)} is similarly estimated by finding
#' the average \emph{R}-squared using the average across the plausible values.
#'
#' \subsection{Standardized regression coefficients}{
#' Standardized regression coefficients can be returned in a call to \code{summary},
#' by setting the argument \code{src} to \code{TRUE}. See Examples.
#'
#' By default, the standardized coefficients are calculated using standard
#' deviations of the variables themselves, including averaging the standard
#' deviation across any plausible values. When \code{standardizeWithSamplingVar}
#' is set to \code{TRUE}, the variance of the standardized coefficient is
#' calculated similar to a regression coefficient and therefore includes the
#' sampling variance in the variance estimate of the outcome variable.
#' }
#'
#' \subsection{Variance estimation of coefficients}{
#' All variance estimation methods are shown in the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}.
#' When \code{varMethod} is set to the \code{jackknife} and the predicted
#' value does not have plausible values, the variance of the coefficients
#' is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When
#' Plausible Values Are Not Present, Using the Jackknife Method.}
#'
#' When plausible values are present and \code{varMethod} is \code{jackknife}, the
#' variance of the coefficients is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When
#' Plausible Values Are Present, Using the Jackknife Method.}
#'
#' When plausible values are not present and \code{varMethod} is \code{Taylor}, the
#' variance of the coefficients is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When Plausible
#' Values Are Not Present, Using the Taylor Series Method.}
#'
#' When plausible values are present and \code{varMethod} is \code{Taylor}, the
#' variance of the coefficients is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When Plausible
#' Values Are Present, Using the Taylor Series Method.}
#' }
#'
#' @section Testing:
#' Of the common hypothesis tests for joint parameter testing, only the Wald
#' test is widely used with plausible values and sample weights. As such, it
#' replaces, if imperfectly, the Akaike Information Criteria (AIC), the
#' likelihood ratio test, chi-squared, and analysis of variance (ANOVA, including \emph{F}-tests). See \code{\link{waldTest}} or
#' the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-WaldTest.pdf}{\emph{Methods and Overview of Using EdSurvey for Running Wald Tests}}.
#'
#' @references
#' Binder, D. A. (1983). On the variances of asymptotically normal estimators from complex surveys. \emph{International Statistical Review}, \emph{51}(3), 279--292.
#'
#' Rubin, D. B. (1987). \emph{Multiple imputation for nonresponse in surveys}. New York, NY: Wiley.
#'
#' van Buuren, S. (2012). \emph{Flexible imputation of missing data}. New York, NY: CRC Press.
#'
#' Weisberg, S. (1985). \emph{Applied linear regression} (2nd ed.). New York, NY: Wiley.
#'
#' @return
#' An \code{edsurvey.lm} with the following elements:
#' \item{call}{the function call}
#' \item{formula}{the formula used to fit the model}
#' \item{coef}{the estimates of the coefficients}
#' \item{se}{the standard error estimates of the coefficients}
#' \item{Vimp}{the estimated variance from uncertainty in the scores (plausible value variables)}
#' \item{Vjrr}{the estimated variance from sampling}
#' \item{M}{the number of plausible values}
#' \item{varm}{the variance estimates under the various plausible values}
#' \item{coefm}{the values of the coefficients under the various plausible values}
#' \item{coefmat}{the coefficient matrix (typically produced by the summary of a model)}
#' \item{r.squared}{the coefficient of determination}
#' \item{weight}{the name of the weight variable}
#' \item{npv}{the number of plausible values}
#' \item{jrrIMax}{the \code{jrrIMax} value used in computation}
#' \item{njk}{the number of the jackknife replicates used; set to \code{NA}
#' when Taylor series variance estimates are used}
#' \item{varMethod}{one of \code{Taylor series} or the \code{jackknife}}
#' \item{residuals}{residuals from the average regression coefficients}
#' \item{PV.residuals}{residuals from the by plausible value coefficients}
#' \item{PV.fitted.values}{fitted values from the by plausible value coefficients}
#' \item{B}{imputation variance covariance matrix, before multiplication by (M+1)/M}
#' \item{U}{sampling variance covariance matrix}
#' \item{rbar}{average relative increase in variance; see van Buuren (2012, eq. 2.29)}
#' \item{nPSU}{number of PSUs used in calculation}
#' \item{n0}{number of rows on an \code{edsurvey.data.frame} before any conditions were applied}
#' \item{nUsed}{number of observations with valid data and weights larger than zero}
#' \item{data}{data used for the computation}
#' \item{Xstdev}{standard deviations of regressors, used for computing standardized
#' regression coefficients when \code{standardizeWithSamplingVar} is set to
#' \code{FALSE} (the default)}
#' \item{varSummary}{the result of running \code{summary2} (unweighted) on each variable in the
#' regression}
#' \item{varEstInputs}{when \code{returnVarEstInputs} is \code{TRUE},
#' this element is returned. These are
#' used for calculating covariances with
#' \code{\link{varEstToCov}}.}
#' \item{standardizeWithSamplingVar}{when \code{standardizeWithSamplingVar}
#' is set to \code{TRUE}, this element is
#' returned. Calculates the standard deviation
#' of the standardized
#' regression coefficients like any other
#' variable.}
#'
#' @seealso \ifelse{latex}{\code{lm}}{\code{\link[stats]{lm}}}
#' @author Paul Bailey
#'
#' @example \man\examples\lm.sdf.R
#' @importFrom Matrix sparse.model.matrix rankMatrix
#' @importFrom stats lm aggregate pt relevel model.matrix lm.wfit as.formula complete.cases
#' @importFrom Formula Formula
#' @importFrom MASS ginv
#' @export
#' @usage lm.sdf(formula, data, weightVar = NULL, relevels = list(),
#' varMethod = c("jackknife", "Taylor"), jrrIMax = 1,
#' dropOmittedLevels = TRUE, defaultConditions = TRUE, recode = NULL,
#' returnVarEstInputs = FALSE, returnNumberOfPSU = FALSE,
#' standardizeWithSamplingVar = FALSE, verbose=TRUE,
#' omittedLevels = deprecated())
lm.sdf <- function(formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor"),
jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
returnVarEstInputs = FALSE,
returnNumberOfPSU = FALSE,
standardizeWithSamplingVar = FALSE,
verbose = TRUE,
omittedLevels = deprecated()) {
call <- match.call()
checkDataClass(data, c("edsurvey.data.frame", "light.edsurvey.data.frame", "edsurvey.data.frame.list"))
weightVar <- iparse(substitute(weightVar), x = data)
if (lifecycle::is_present(omittedLevels)) {
lifecycle::deprecate_soft("4.0.0", "lm.sdf(omittedLevels)", "lm.sdf(dropOmittedLevels)")
dropOmittedLevels <- omittedLevels
}
if (!is.logical(dropOmittedLevels)) stop("The ", sQuote("dropOmittedLevels"), " argument must be logical.")
# if data is an edsurvey.data.frame.list, simply return a list with results
# for each edsurvey.data.frame
if (inherits(data, c("edsurvey.data.frame.list"))) {
res <- itterateESDFL(match.call(), data)
class(res) <- "edsurveyLmList"
return(res)
} else {
return(calc.lm.sdf(
formula = formula,
data = data,
weightVar = weightVar,
relevels = relevels,
varMethod = varMethod,
jrrIMax = jrrIMax,
dropOmittedLevels = dropOmittedLevels,
defaultConditions = defaultConditions,
missingDefaultConditions = missing(defaultConditions),
recode = recode,
returnVarEstInputs = returnVarEstInputs,
returnLm0 = FALSE,
call = call,
returnNumberOfPSU = returnNumberOfPSU,
standardizeWithSamplingVar = standardizeWithSamplingVar,
verbose = verbose
))
}
}
calc.lm.sdf <- function(formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor", "j", "t"),
jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
missingDefaultConditions = TRUE,
recode = NULL,
returnVarEstInputs = FALSE,
returnLm0 = FALSE,
call = NULL,
returnNumberOfPSU = FALSE,
standardizeWithSamplingVar = FALSE,
verbose = TRUE) {
if (is.null(call)) {
call <- match.call()
}
#######################
######## Outline ######
#######################
# 1) check, format inputs
# 2) get the data
# 3) deal with relevels.
# 4) deal with yvar having plausible values
# 5) run the main regression
# 6) run the regressions, form the inputs for variance estimation
# 7) form output, including final variance estimation
# 1) check, format inputs
checkDataClass(data, c("edsurvey.data.frame", "light.edsurvey.data.frame", "edsurvey.data.frame.list"))
sdf <- data # short for survey data.frame
# get varMethod, turn into first character as lower case
varMethod <- substr(tolower(varMethod[[1]]), 0, 1)
if (!varMethod %in% c("j", "t")) {
stop(paste0("The argument ", sQuote("varMethod"), " must be one of ", dQuote("Taylor"), " or ", dQuote("jackknife"), "."))
}
stopifnot(is.logical(verbose))
if (standardizeWithSamplingVar & varMethod == "t") {
warning(paste0(sQuote("standardizeWithSamplingVar"), " reset to ", dQuote("FALSE"), " because ", sQuote("varMethod"), " is ", dQuote("Taylor"), "."))
standardizeWithSamplingVar <- FALSE
}
# if the weight var is not set, use the default
wgt <- checkWeightVar(data, weightVar)
# check if there is an outcome variable and set it to the default if it is missing
zeroLengthLHS <- attr(terms(formula), "response") == 0
if (zeroLengthLHS) {
yvar <- attributes(getAttributes(sdf, "pvvars"))$default
formula <- update(formula, new = substitute(yvar ~ ., list(yvar = as.name(yvar))))
} else {
yvar <- all.vars(formula[[2]])
} # End of If/Else: if (zeroLengthLHS)
# grab the variables needed for the Taylor series method, if that is the variance estimation method being used
taylorVars <- c()
psuVar <- getPSUVar(data, weightVar = wgt)
stratumVar <- getStratumVar(data, weightVar = wgt)
# give an error if these do not exist.
tv <- checkTaylorVars(psuVar, stratumVar, wgt, varMethod, returnNumberOfPSU)
psuVar <- tv$psuVar
stratumVar <- tv$stratumVar
varMethod <- tv$varMethod
returnNumberOfPSU <- tv$returnNumberOfPSU
# in PIAAC there is sometimes an SRS, that is not appropriate for Taylor
if ("JK1" %in% stratumVar & varMethod == "t") {
varMethod <- "j"
warning("Cannot use Taylor series estimation on a one-stage simple random sample.")
}
if (varMethod == "t") {
taylorVars <- c(psuVar, stratumVar)
jrrIMax <- NA
} else {
if (all(c(psuVar, stratumVar) %in% colnames(data))) {
taylorVars <- c(psuVar, stratumVar)
}
}
formulaVars <- all.vars(formula)
getDataVarNames <- c(all.vars(formula), wgt, taylorVars)
tryCatch(getDataVarNames <- unique(c(getDataVarNames, 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
}
)
# 2) get the data
# This is most of the arguments
getDataArgs <- list(
data = sdf,
varnames = getDataVarNames,
returnJKreplicates = (varMethod == "j"),
drop = FALSE,
dropOmittedLevels = dropOmittedLevels,
recode = recode,
includeNaLabel = TRUE,
dropUnusedLevels = TRUE,
addAttributes = TRUE
)
# Default conditions should be included only if the user set it. This adds the argument only if needed
if (!missingDefaultConditions) {
getDataArgs <- c(getDataArgs, list(defaultConditions = defaultConditions))
}
# edf is the actual data
edf <- do.call(getData, getDataArgs)
# check for incomplete cases on the formula variables and weights (excluding Taylor vars)
relVars <- colnames(edf)
relVars <- relVars[!relVars %in% taylorVars]
incomplete <- !complete.cases(edf[ , relVars])
if (any(incomplete)) {
if (verbose) {
message("Removing ", sum(incomplete), " rows with NAs from analysis.")
}
edf <- edf[!incomplete, ]
}
# if doing Taylor series, check Taylor vars too
if (varMethod == "t") {
incomplete <- !complete.cases(edf[ , taylorVars])
if (any(incomplete)) {
if (any(incomplete[!edf[ , wgt] %in% c(NA, 0)])) {
if (verbose) {
message("Removing ", sum(incomplete[!edf[ , wgt] %in% c(NA, 0)]), " rows with NA PSU or stratum variables and positive weights from analysis.")
}
}
edf <- edf[!incomplete, ]
}
# check for NAs in stratumVar and psuVar
if (sum(is.na(edf[ , c(stratumVar, psuVar)])) != 0) {
stop("Taylor series variance estimation requested but stratum or PSU variables contian an NA value.")
}
}
# remove non-positive (full sample) weights
if (any(edf[ , wgt] <= 0)) {
posWeights <- edf[ , wgt] > 0
if (verbose) {
message("Removing ", sum(!posWeights), " rows with nonpositive weight from analysis.")
}
edf <- edf[posWeights, ]
}
# check that there is some data to work with
if (nrow(edf) <= 0) {
stop(paste0(
sQuote("data"), " must have more than 0 rows after a call to ",
sQuote("getData"), "."
))
}
# 3) deal with relevels.
# An argument that allows the user to change the omitted level for a factor variable
if (length(relevels) > 0) {
for (i in 1:length(relevels)) {
vari <- names(relevels)[i]
if (!vari %in% names(edf)) {
stop(paste0(
"In the ", sQuote("relevels"),
" argument, cannot find the variable named ", sQuote(vari), "."
))
} # End of if statment: ! vari %in% names(edf)
if (length(relevels[[i]]) != 1) {
stop(paste0(
"In the ", sQuote("relevels"),
" argument, each relevel must have exactly one level."
))
} # End of if statment: length(relevels[[i]]) != 1
# check that the level exists
lvls <- levels(edf[ , vari])
if (inherits(edf[ , vari], "lfactor")) {
# for a factor it can be either a level or a label
lvls <- c(lvls, labels(edf[ , vari]))
} # End of if statment: inherits(edf[ ,vari], "lfactor")
if (!relevels[[i]] %in% lvls) {
stop(paste0(
"In the ", sQuote("relevels"),
" argument, for the variable ", sQuote(vari), ", the level ",
sQuote(relevels[[i]]), " not found. Found levels are ",
pasteItems(dQuote(lvls)), "."
))
} # End of if statment !relevels[[i]] %in% lvls
edf[ , vari] <- relevel(edf[ , vari], ref = relevels[[i]])
} # end for(i in 1:length(relevels))
} # end if(length(relevels) > 0)
# droplevel on covariates to avoid problems with X is formed later.
# also, check that there are not factors with only one level and give more informative error.
for (i in 1:ncol(edf)) {
if (inherits(edf[ , i], "factor") & colnames(edf)[i] %in% formulaVars) {
edf[ , i] <- droplevels(edf[ , i])
if (length(levels(edf[ , i])) < 2) {
stop(paste0(
"The covariate ", dQuote(colnames(edf)[i]),
" has fewer than two levels after zero-weight cases are dropped. This covariate cannot be included in the regression."
))
}
}
}
sum2 <- function(x) {
res <- summary2(data = edf, variable = x, weightVar = NULL, dropOmittedLevels = FALSE)
res$call <- NULL
return(res)
}
varSummary <- lapply(formulaVars, sum2)
# 4) deal with yvar having plausible values
pvy <- hasPlausibleValue(yvar, sdf) # pvy is the plausible values of the y variable
yvars <- yvar
linkingError <- detectLinkingError(data, yvars)
if(any(pvy)) {
if(linkingError) {
if(standardizeWithSamplingVar) {
stop(paste0(sQuote("standardizeWithSamplingVar"), " not supported with linking error."))
}
pvs <- getPlausibleValue(yvars[max(pvy)], data)
yvars <- paste0(
"outcome",
1:length(pvs),
ifelse(grepl("_imp_", pvs), "_imp",
ifelse(grepl("_samp_", pvs), "_samp", "_est")
)
)
if (varMethod == "t") {
stop("Taylor series variance estimation not supported with NAEP linking error.")
}
} else { # end if linkingError
yvars <- paste0("outcome", 1:length(getPlausibleValue(yvars[max(pvy)], data)))
} # end if(any(pvy))
} else {
# no PVs, just make sure that this variable is numeric
edf[ , "yvar"] <- as.numeric(eval(formula[[2]], edf))
formula <- update(formula, new = substitute(yvar ~ ., list(yvar = as.name(yvar))))
yvars <- "yvar"
} # End of if statment: any(pvy)
yvar0 <- yvars[1]
# this allows that variable to not be dynamic variable, it is explicitly defined to be yvar0
if (any(pvy)) {
for (i in 1:length(yvars)) {
# PV, so we have not evaluated the I() yet (if any)
# first, by PV, rename the ith PVs to be e.g. composite
for (yvi in 1:length(pvy)) {
if (pvy[yvi]) {
edf[ , yvar[yvi]] <- edf[ , getPlausibleValue(yvar[yvi], data)[i]]
}
}
# then set yvars[i] (e.g. outcome1) to be the evaluation at this PV point
edf[ , yvars[i]] <- as.numeric(eval(formula[[2]], edf))
}
# finally, correctly set yvar0
edf$yvar0 <- edf[ , yvar0]
} # end if(any(pvy)), no else
# 5) run the main regression
# run a regression, starting with the first PV or maybe the only outcome variable
yvar0 <- yvars[1]
# this allows that variable to not be dynamic variable, it is explicitly defined to be yvar0
edf$yvar0 <- edf[ , yvar0]
frm <- update(formula, yvar0 ~ .)
edf$w <- edf[ , wgt]
lm0 <- lm(frm, data = edf, weights = w)
if (returnLm0) {
return(lm0)
}
# get standard devaitions for future standard mean difference (SMD)
# calculation
X <- model.matrix(frm, edf)
# fill this in regardless of standardizeWithSamplingVar to make a "shell"
std <- getStdev(edf[ , yvars], X, edf[ , wgt])
# this grabs the list of weight variable names
wgtl <- getAttributes(sdf, "weights")[[wgt]]
varEstInputs <- list()
# note we have not made the B matrix
madeB <- FALSE
# setup std as a matrix we can condense.
# there is now a very long set of conditionals
# used to estimate a regression at a particular y and weight. X is fixed.
stat_reg <- function(pv, w) {
lmi <- lm.wfit(x = X, y = pv, w = w)
coi <- coef(lmi)
yhat <- lmi$fitted.values
ybar <- sum(w * pv) / sum(w)
SSreg <- sum(w * (yhat - ybar)^2)
SStot <- sum(w * (pv - ybar)^2)
r2 <- SSreg / SStot
res <- c(coi, "r.squared" = r2)
return(res)
}
# 6) run the regressions, form the inputs for variance estimation
if (any(pvy)) { # the y variable is a plausible value
# this if condition takes care of the Taylor series method as well as the jackknife method
# for equations, see the statistics vignette
if (varMethod == "t") {
jrrIMax <- length(yvars)
} else {
jrrIMax <- min(jrrIMax, length(yvars))
}
# initialize: varm is the variance matrix by coefficient and PV (for V_jrr)
varM <- list()
varm <- matrix(NA, nrow = jrrIMax, ncol = length(coef(lm0)))
if (varMethod == "j") {
# y is a variable with plausible values and we are using the jackknife apporach
if (linkingError) {
if (jrrIMax != 1) {
warning("The linking error variance estimator only supports ", dQuote("jrrIMax=1"), ". Resetting to 1.")
jrrIMax <- 1
}
# NAEP linking error case, use linking error var estimator
repWeights <- paste0(wgtl$jkbase, wgtl$jksuffixes)
# get the mean estimate
est <- getLinkingEst(
data = edf,
pvEst = yvars[grep("_est", yvars)],
stat = stat_reg,
wgt = wgt
)
# coefficients matrix, r-squared vector
coefm <- est$coef[ , -ncol(est$coef), drop = FALSE]
r2s <- est$coef[ , ncol(est$coef), drop = FALSE]
# get imputation variance
impVar <- getLinkingImpVar(
data = edf,
pvImp = yvars[grep("_imp", yvars)],
ramCols = ncol(getRAM()),
stat = stat_reg,
wgt = wgt,
T0 = est$est,
T0Centered = FALSE
)
# get sampling variance
sampVar <- getLinkingSampVar(edf,
pvSamp = yvars[grep("_samp", yvars)],
stat = stat_reg,
rwgt = repWeights,
T0 = est$est,
T0Centered = FALSE
)
varM[[1]] <- sampVar$Bi
varEstInputs[["JK"]] <- sampVar$veiJK
varEstInputs[["PV"]] <- impVar$veiImp
# drop r.squared term
varm[1, ] <- sampVar$V[-length(sampVar$V)]
M <- length(yvars[grep("_est", yvars)])
Vimp <- impVar$V
Vimp <- Vimp[!names(Vimp) %in% "r.squared"]
} else { # END IF: NAEP linking error case, use linking error var estimator
# regression with each yvar
est <- getEst(
data = edf,
pvEst = yvars,
stat = stat_reg,
wgt = wgt
)
# coef matrix (coefm)
coefm <- est$coef[ , -ncol(est$coef), drop = FALSE]
# r-squared vector
r2s <- est$coef[ , ncol(est$coef), drop = FALSE]
# for standardized coefficients (matrix)
if (standardizeWithSamplingVar) {
coefmS <- t(apply(coefm, 1, function(co0row) standardizeCoef(co0row, std)))
varmS <- varm
}
varEstInputs[["JK"]] <- data.frame()
jkSumMult <- getAttributes(data, "jkSumMultiplier")
for (pvi in 1:jrrIMax) { # for each PV (up to jrrIMax)
res <- getVarEstJK(
stat = stat_reg,
yvar = edf[ , yvars[pvi]],
wgtM = edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)],
co0 = coefm[pvi, ],
jkSumMult = jkSumMult,
pvName = pvi
)
varM[[pvi]] <- res$Bi
varEstInputs[["JK"]] <- rbind(varEstInputs[["JK"]], res$veiJK)
varm[pvi, ] <- res$VsampInp
if (standardizeWithSamplingVar) {
stdev <- apply(
edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)], 2,
function(w) getStdev(edf[ , yvars[pvi]], X, w)
)
varmS[pvi, ] <- getVarEstJKStandard(
stat = stat_reg,
yvar = edf[ , yvars[pvi]],
wgtM = edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)],
co0 = coefm[pvi, ],
jkSumMult = jkSumMult,
pvName = pvi,
stdev = stdev,
coefmSi = coefmS[pvi, ]
)
}
} # end pvi for jrrIMax
# number of PVs
M <- length(yvars)
} # end not NAEP-linking-error
} else { # End of if statment: varMethod == "j"
# Taylor series, PVs
# y is a variable with plausible values and we are using the Taylor series approach
#
# here the regression uses the normal equations for weighted regression
# the QR decomposition is used to improve stability but
# the D matrix involves an inverse that cannot be avoided
X <- sparse.model.matrix(frm, edf)
XW <- X * sqrt(edf[ , wgt, drop = TRUE])
qrXW <- qr(XW)
W <- Diagonal(n = nrow(edf), edf[ , wgt, drop = TRUE])
D <- solve(t(X) %*% W %*% X)
# build per yvar
r2s <- c()
coefm <- matrix(0, nrow = length(yvars), ncol = nrow(D))
dofNum <- matrix(0, nrow = nrow(D), ncol = length(yvars))
dofDenom <- matrix(0, nrow = nrow(D), ncol = length(yvars))
warnDrop <- 0 # warn if a stratum is too thin to estimate variance on it
# variance and warning per yvar
for (mm in 1:length(yvars)) {
# for each PV, run the regression, form the coefficients and variance components
Y <- as(edf[ , yvars[mm], drop = FALSE], "Matrix")
YW <- Y * sqrt(edf[ , wgt, drop = TRUE])
# next line gets b (the coefficients) in the traditional
# but less stable way than the line after it, which is based on QR
# b <- D %*% t(X) %*% W %*% Y
b <- qr.coef(qrXW, YW)
# get residual
fitted <- X %*% b
e <- as.vector((Y - fitted))
# notation (uhij) from AM documentation
# this is the partial of the likelihood at the unit level
uhij <- e * as.matrix(X)
# vals by stratum (singleton PSUs removed in function)
res <- getVarTaylor(uhij, edf, D, wgt, psuVar, stratumVar)
# get R-squared
res2 <- getR2Taylor(res$vv, D, W, Y, e)
# collect
dofNum[ , mm] <- res$nums
dofDenom[ , mm] <- res$nums2
warnDrop <- warnDrop + res$warnDrop
coefm[mm, ] <- as.numeric(b)
varM[[mm]] <- as.matrix(res2$vc)
varm[mm, ] <- as.numeric(diag(res2$vc))
r2s <- c(r2s, res2$r2)
}
if (warnDrop > 0) {
warning(paste0(warnDrop / length(yvars), " strata had only one populated PSU. Units in these strata assumed to be certainties. You can condense the strata/PSU structure to avoid this."))
}
# number of PVs
M <- length(yvars)
} # End of if/else statment: varMethod == "j"
# imputaiton variance / variance due to uncertaintly about PVs
coef <- apply(coefm, 2, mean)
coefm0 <- t(t(coefm) - coef)
# calculate van Buuren B
B <- (1 / (M - 1)) * Reduce(
"+", # add up the matrix results of the sapply
sapply(1:nrow(coefm), function(q) {
# within each PV set, calculate the outer product
# (2.19 of Van Buuren)
outer(coefm0[q, ], coefm0[q, ])
}, simplify = FALSE)
)
madeB <- TRUE
# these formulas do not work for linking error
if (!linkingError) {
coefmPVByRow <- lapply(1:ncol(coefm0), function(coli) {
data.frame(
stringsAsFactors = FALSE,
PV = 1:nrow(coefm0),
variable = rep(names(coef(lm0))[coli], nrow(coefm0)),
value = coefm0[ , coli]
)
})
coefmPV <- do.call(rbind, coefmPVByRow)
varEstInputs[["PV"]] <- coefmPV
# imputation variance
Vimp <- (M + 1) / M * apply(coefm, 2, var)
}
# \bar{U} from 2.18 in var Buuren
Ubar <- (1 / length(varM)) * Reduce("+", varM)
# sampling variance
Vjrr <- apply(varm[1:jrrIMax, , drop = FALSE], 2, mean)
names(Vjrr) <- names(coef(lm0))
# r-squared
r2 <- mean(r2s)
if (standardizeWithSamplingVar) {
VSjrr <- apply(varmS[1:jrrIMax, , drop = FALSE], 2, mean)
VSimp <- (M + 1) / M * apply(coefmS, 2, var)
VS <- VSimp + VSjrr
}
} else { # end if(pvy)
# the y variable is not a plausible value
# this section handles jackknife and Taylor series estimation
if (varMethod == "j") {
# jackknife variance estimation
r2 <- summary(lm0)$r.squared
jkSumMult <- getAttributes(data, "jkSumMultiplier")
coef <- coef(lm0) # coefficients come from full sample weights run
res <- getVarEstJK(
stat = stat_reg,
yvar = edf[ , yvars[1]],
wgtM = edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)],
co0 = coef,
jkSumMult = jkSumMult,
pvName = 1
)
Ubar <- res$Bi
Vjrr <- res$VsampInp
varEstInputs[["JK"]] <- res$veiJK
} else { # end if(varMethod == "j")
# Taylor series, no PV
Y <- as(edf[ , yvars[1], drop = FALSE], "Matrix")
X <- sparse.model.matrix(frm, edf)
W <- Diagonal(n = nrow(edf), edf[ , wgt, drop = TRUE])
D <- vcov(lm0)
b <- coef(lm0)
# get residual
fitted <- X %*% b
e <- (as.vector((Y - fitted))) / summary(lm0)$sigma^2
# this is the partial of the likelihood at the unit level
uhij <- e * as.matrix(X)
# vals by stratum (singleton PSUs removed in function)
res <- getVarTaylor(uhij, edf, D, wgt, psuVar, stratumVar)
# get R-squared
e <- as.vector((Y - fitted))
res2 <- getR2Taylor(res$vv, D, W, Y, e)
if (res$warnDrop) {
warning("Some strata had only one populated PSU. Units in these strata assumed to be certainties. You can condense the strata/PSU structure to avoid this.")
}
dofNum <- res$nums
dofDenom <- res$nums2
r2 <- res2$r2
coef <- as.numeric(b)
Vjrr <- as.numeric(diag(res2$vc))
# this is the VC matrix, store it for vcov to recover
Ubar <- as.matrix(res2$vc)
} # end else for if(varMethod == "j")
# not pvy common
M <- 1 # only on replicate when there are no PVs
Vimp <- rep(0, length(Vjrr)) # no imputation variance when there are no PVs
varm <- NULL # no variance by PV
coefm <- NULL # no coefficients matrix by PV
} # end else for if(pvy)
# 7) form output, including final variance estimation
# for estimates with no sampling variance, make sure they return NA for SE
Vjrr[Vjrr == 0] <- NA
V <- Vjrr + Vimp
se <- sqrt(V)
# fix all the names0
names(se) <- names(V) <- names(Vjrr) <- names(Vimp) <- names(coef) <- names(coef(lm0))
# get fitted and resid based on overall coef
fitted1 <- as.vector(X %*% coef)
Y <- do.call(cbind, lapply(1:length(yvars), function(yi) {
as.vector(edf[ , yvars[yi]])
}))
resid1 <- Y - fitted1
colnames(resid1) <- colnames(Y) <- yvars
# residual df calculation
nobs <- nrow(edf)
n.ok <- nobs - sum(edf$origwt == 0)
nvars <- ncol(X)
rank <- rankMatrix(X, method = "qr")[1]
resdf <- n.ok - rank
df.r <- resdf
# get residual based on coef by PV
if (!is.null(coefm)) {
if (!is.matrix(coefm)) {
coefm <- t(as.matrix(coefm))
}
fitted2 <- as.matrix(X %*% t(coefm))
resid2 <- Y[ , 1:ncol(fitted2)] - fitted2
}
coefmat <- data.frame(
coef = coef,
se = se,
t = coef / se
)
# assign var that are almost 0 to 0 to make DOF correction run correctly
if (!is.null(varEstInputs$JK)) {
varEstInputs$JK$value[which(abs(varEstInputs$JK$value) < (sqrt(.Machine$double.eps) * sqrt(nrow(varEstInputs$JK))))] <- 0
}
if (varMethod == "t") {
m <- length(wgtl$jksuffixes)
# Johnson and Rust dfo correction
if (inherits(dofNum, "matrix")) {
coefmat$dof <- (3.16 - 2.77 / sqrt(m)) * apply(dofNum^2 / dofDenom, 1, mean)
} else {
coefmat$dof <- (3.16 - 2.77 / sqrt(m)) * dofNum^2 / dofDenom
}
} else {
coefmat$dof <- sapply(names(coef), function(cn) {
DoFCorrection(varEstA = varEstInputs, varA = cn, method = "JR")
})
}
pti <- pt(coefmat$t, df = coefmat$dof)
coefmat[ , "Pr(>|t|)"] <- 2 * pmin(pti, 1 - pti)
njk <- length(wgtl$jksuffixes)
if (varMethod == "t") {
njk <- NA
}
varmeth <- ifelse(varMethod == "t", "Taylor series", "jackknife")
res <- list(
call = call, formula = formula, coef = coef, se = se, Vimp = Vimp,
Vjrr = Vjrr, M = M, varm = varm, coefm = coefm,
coefmat = coefmat, r.squared = r2, weight = wgt,
npv = length(yvars), jrrIMax = min(jrrIMax, length(yvars)),
njk = njk, varMethod = varmeth,
residuals = unname(resid1), fitted.values = unname(fitted1), residual.df = resdf
)
if (standardizeWithSamplingVar) {
coefmatStd <- coefmat
coefmatStd$coef <- standardizeCoef(coef, std)
coefmatStd$se <- sqrt(VS)
res <- c(res, list(coefmatStd = coefmatStd))
}
if (!is.null(coefm)) {
res <- c(res, list(PV.residuals = unname(resid2), PV.fitted.values = unname(fitted2)))
}
if (returnVarEstInputs) {
res <- c(res, list(varEstInputs = varEstInputs))
if (varMethod == "t") {
warning(paste0(
"Taylor series method not supported with the ",
sQuote("varEstInputs"), " argument set to ", dQuote("TRUE"), "."
))
}
}
if (madeB) {
# equation 2.29 in var Buuren, pp 42
tryCatch(
rbar <- (1 + 1 / M) * (1 / nrow(Ubar)) * sum(diag(B %*% solve(Ubar))),
error = function(e) {
rbar <<- (1 + 1 / M) * (1 / nrow(Ubar)) * sum(diag(B %*% MASS::ginv(Ubar)))
frm <- Formula(frm)
if (terms(frm, lhs = 0, rhs = NULL) == ~1) {
warning("A variance estimate was replaced with NA because there was no variance across strata.", call. = FALSE)
}
}
)
Ttilde <- (1 + rbar) * Ubar
res <- c(res, list(B = B, U = Ubar, rbar = rbar, Ttilde = Ttilde))
} else {
# used for TS vcov
res <- c(res, list(U = Ubar))
if (!any(pvy)) {
res <- c(res, list(B = 0 * Ubar))
}
}
if (returnNumberOfPSU) {
if ("JK1" %in% stratumVar) {
res <- c(res, list(nPSU = nrow(edf)))
} else if (sum(is.na(edf[ , c(stratumVar, psuVar)])) == 0) {
res <- c(res, list(nPSU = nrow(unique(edf[ , c(stratumVar, psuVar)]))))
} else {
warning("Cannot return number of PSUs because the stratum or PSU variables contain NA values.")
}
}
if (all(c(stratumVar, psuVar) %in% colnames(edf))) {
if (sum(is.na(edf[ , c(stratumVar, psuVar)])) == 0) {
res <- c(res, list(waldDenomBaseDof = waldDof(edf, stratumVar, psuVar)))
}
}
res <- c(res, list(n0 = nrow2.edsurvey.data.frame(data), nUsed = nrow(edf)))
if (inherits(data, "edsurvey.data.frame")) {
res <- c(res, list(data = data))
} else {
res <- c(res, list(lm0 = lm0))
}
res <- c(res, list(Xstdev = std, varSummary = varSummary))
class(res) <- "edsurveyLm"
# fix factor/character 3/4 issue
if (version$major %in% "3") {
if ("varSummary" %in% names(res)) {
for (i in 1:length(res$varSummary)) {
if ("Variable" %in% colnames(res$varSummary[[i]]$summary)) {
res$varSummary[[i]]$summary$Variable <- as.character(res$varSummary[[i]]$summary$Variable)
}
}
}
}
return(res)
}
#' @method print edsurveyLm
#' @export
print.edsurveyLm <- function(x, ...) {
print(coef(x), ...)
}
#' @method print edsurveyLmList
#' @export
print.edsurveyLmList <- function(x, ...) {
for (i in 1:length(x)) {
cat("lm", i, "\n")
print(coef(x[[i]]), ...)
}
}
# @param src a logical indicating if the the standardized regression coefficients
# should be included in the coefficients table
#' @method summary edsurveyLm
#' @export
summary.edsurveyLm <- function(object, src = FALSE, ...) {
class(object) <- "summary.edsurveyLm"
if (src) {
if ("coefmatStd" %in% names(object)) {
cm <- object$coefmat
cm$stdCoef <- object$coefmatStd$coef
cm$stdSE <- object$coefmatStd$se
} else {
cm <- object$coefmat
std <- object$Xstdev
cm$stdCoef <- NA
cm$stdSE <- NA
for (i in 1:nrow(cm)) {
if (rownames(cm)[i] %in% names(std) && std[rownames(cm)[i]] > 0) {
# standardize regression coef = sdX_i/sdY * coef_i
cm$stdCoef[i] <- (std[[rownames(cm)[i]]] / std[["outcome.std"]]) * cm$coef[i]
# same formula for standareized SE
cm$stdSE[i] <- (std[[rownames(cm)[i]]] / std[["outcome.std"]]) * cm$se[i]
}
}
} # end else for if("coefmatStd" %in% names(object))
object$coefmat <- cm
} # end if(src)
return(object)
}
#' @method summary edsurveyLmList
#' @export
summary.edsurveyLmList <- function(object, smd = FALSE, ...) {
class(object) <- "summary.edsurveyLmList"
for (i in 1:length(object)) {
object[[i]] <- summary.edsurveyLm(object[[i]], smd)
}
object
}
#' @method print summary.edsurveyLm
#' @importFrom stats printCoefmat
#' @export
print.summary.edsurveyLm <- function(x, ...) {
cat(paste0("\nFormula: ", paste(deparse(x$formula), collapse = ""), "\n\n"))
cat(paste0("Weight variable: ", sQuote(x$weight), "\n"))
cat(paste0("Variance method: ", x$varMethod, "\n"))
if (!is.na(x$njk)) {
cat(paste0("JK replicates: ", x$njk, "\n"))
}
if (x$npv != 1) {
cat(paste0("Plausible values: ", x$npv, "\n"))
cat(paste0("jrrIMax: ", x$jrrIMax, "\n"))
}
cat(paste0("full data n: ", x$n0, "\n"))
if (!is.null(x$nPSU)) {
cat(paste0("n PSU: ", x$nPSU, "\n"))
}
cat(paste0("n used: ", x$nUsed, "\n\n"))
cat(paste0("Coefficients:\n"))
csind <- which(colnames(x$coefmat) %in% c("coef", "se", "stdCoef", "stdSE"))
# with standardized coefficients it produces a bogus warning for the intercept
suppressWarnings(printCoefmat(x$coefmat, P.values = TRUE, has.Pvalue = TRUE, cs.ind = csind))
cat("\n")
cat(paste0("Multiple R-squared: ", round(x$r.squared, 4), "\n\n"))
}
#' @method print summary.edsurveyLmList
#' @export
print.summary.edsurveyLmList <- function(x, ...) {
for (i in 1:length(x)) {
cat("lm", i, "\n")
print(x[[i]], ...)
}
}
#' @method coef edsurveyLm
#' @export
coef.edsurveyLm <- function(object, ...) {
object$coef
}
#' @method coef edsurveyLmList
#' @export
coef.edsurveyLmList <- function(object, ...) {
sapply(object, function(li) {
li$coef
})
}
#' @method vcov edsurveyLm
#' @export
vcov.edsurveyLm <- function(object, ...) {
if (all(c("U", "B") %in% names(object))) {
if (object$M > 1) {
# there are PVs, V_samp + V_imp
return(object$U + (object$M + 1) / object$M * object$B)
} else {
# no PVs, V_samp only
return(object$U)
}
}
if (is.null(object$varEstInputs)) {
stop("This model must be fit with returnVarEstInputs=TRUE or with Taylor series to find the covariance matrix.")
}
varnames <- expand.grid(names(coef(object)), names(coef(object)))
vc <- mapply(varEstToCov, varA = varnames$Var1, varB = varnames$Var2, MoreArgs = list(varEstA = object$varEstInputs, jkSumMultiplier = object$data$jkSumMultiplier))
matrix(vc, nrow = length(coef(object)), ncol = length(coef(object)))
}
# @export
setMethod(
"coef",
c(object = "edsurveyLm"),
coef.edsurveyLm
)
# @export
setMethod(
"coef",
c(object = "edsurveyLmList"),
coef.edsurveyLmList
)
#' @method plot edsurveyLm
#' @export
plot.edsurveyLm <- function(x, ...) {
if ("lm0" %in% names(x)) {
cr <- x$lm0
} else {
# reevaluate to get lm0
cl <- x$call
cl$returnLm0 <- TRUE
cl[[1]] <- quote(calc.lm.sdf)
cr <- eval(cl)
}
plot(cr)
}
# helper to get standard deviations for a weighted variable
getStdev <- function(outcomeData, XData, weightData) {
std <- c(outcome = fast.sd(outcomeData, weightData)["std"])
for (i in 1:ncol(XData)) {
if (!is.na(sd(XData[ , i])) & sd(XData[ , i]) > 0) {
std <- c(std, fast.sd(XData[ , i], weightData)["std"])
} else {
std <- c(std, 0)
}
names(std) <- c(names(std)[-length(std)], colnames(XData)[i])
}
return(std)
}
# @param coef is the (named) coefficients)
# @param std is the (named) standard deviations
standardizeCoef <- function(coef, std) {
if (length(names(coef)) == 0) {
stop("coef must have names.")
}
for (si in 1:length(coef)) {
coefName <- names(coef)[si]
if (std[[coefName]] > 0) {
# standardize regression coef = sdX_i/sdY * coef_i
coef[si] <- (std[[coefName]] / std[["outcome.std"]]) * coef[si]
} else {
coef[si] <- NA
}
}
return(coef)
}
# get imputation variance for DBA/PBA bridge data
# @param data a data frame with columns described below on it
# @param pvImp columns on \code{data} that are the imputation variance plausible values (PVs)
# @param ramCols number of columns in the RAM table
# @param stat a function that returns the statistic given a vector of plausible values and a vector of weights from data
# @param wgt the full sample weight variable name on data
# @param T0 The estimated value, used for names
# @param T0Centered if T0 should be the center of the variance estimator or the within RAM col mean
# @param jkSumMult the jackknife sum multiplier
# @return
# V the imputation variance estimate
# veiImp the varEstInputs matrix
getLinkingImpVar <- function(data, pvImp, ramCols, stat, wgt, T0, T0Centered, jkSumMult = 1) {
V_n <- matrix(0, ncol = length(T0), nrow = ramCols)
if (is.null(data)) {
w <- wgt
} else {
w <- data[ , wgt]
}
ramRows <- round(length(pvImp) / ramCols)
if (ramRows - length(pvImp) / ramCols > sqrt(.Machine$double.eps)) {
stop(paste0("RAM table not rectangular with ", ramCols, " columns and ", length(pvImp), " total entries."))
}
# these index sequentially acorss ram rows and columns
pvi <- 1
for (n in 1:ramCols) {
T_jn <- matrix(0, ncol = length(T0), nrow = ramRows)
for (j in 1:ramRows) {
if (is.null(data)) {
pv_nj <- pvImp[pvi]
} else {
pv_nj <- data[ , pvImp[pvi]]
}
T_jn[j, ] <- stat(pv = pv_nj, w = w)
pvi <- pvi + 1
}
T_n <- apply(T_jn, 2, mean)
# default: use jackknife bias reduced centering (jackknife variance estimation)
center <- T_n
if (T0Centered) {
# use reported statistic centering (jackknife MSE estimation)
center <- T0
}
for (i in 1:length(center)) {
V_n[n, i] <- (1 + 1 / 20) * sum((T_jn[ , i] - center[i])^2) / (20 - 1)
}
if (n == 1) {
veiPV <- (T_jn - center[i])
} else {
veiPV <- rbind(veiPV, (T_jn - center[i]))
}
}
V <- apply(V_n, 2, mean)
names(V) <- names(T0)
colnames(V_n) <- names(T0)
veiPV <- veiPV[ , !names(T0) %in% "r.squared", drop = FALSE]
npv <- nrow(veiPV)
nT0 <- length(T0)
if ("r.squared" %in% names(T0)) {
nT0 <- nT0 - 1
}
veiList <- lapply(1:nT0, function(i) {
data.frame(
PV = 1:npv, # actually RAM element
variable = rep(names(T0)[i], npv),
value = veiPV[ , i] - mean(veiPV[ , i])
)
})
vei <- do.call(rbind, veiList)
return(list(V = V, veiImp = vei))
}
# get sampling variance for DBA/PBA bridge data
# @param data a data frame with columns described below on it
# @param pvSamp columns on \code{data} that are the sampling variance plausible values (PVs)
# @param stat a function that returns the statistic given a vector of plausible values and a vector of weights from data
# @param wgt the replicate sample weight variable name on data
# @param T0 The estimated value, used for names
# @param T0Centered if T0 should be the center of the variance estimator or the within RAM col mean
# @param jkSumMult the jackknife sum multiplier
# @return
# Bi matrix that is varMVal for this set of PVs (really jrrIMax is always 1, this is varMVal for the first PV, kind of)
# V the sampling variance vector
# veiJK the JK component of varEstInputs
# VsampInp this is V again, but without the r.squared entry
getLinkingSampVar <- function(data, pvSamp, stat, rwgt, T0, T0Centered, jkSumMult = 1) {
I <- length(rwgt)
T_i <- matrix(0, ncol = length(T0), nrow = I)
for (i in 1:I) {
if (is.null(data)) {
pv <- pvSamp[i]
w <- rwgt[i]
} else {
pv <- data[ , pvSamp[i]]
w <- data[ , rwgt[i]]
}
T_i[i, ] <- stat(pv = pv, w = w)
}
Ta <- apply(T_i, 2, mean)
resi <- t(t(T_i) - Ta)
if (T0Centered) {
resi <- t(t(T_i) - T0)
}
V_samp <- apply(resi, 2, function(x) {
sum(x^2)
})
# for each coefficient, build varEstInputs (vei) for that coefficient
# skip r-squared with length(T0)-1
veiList <- lapply(1:length(T0), function(i) {
if (!"r.squared" %in% names(T0)[i]) {
data.frame(
PV = rep(1, nrow(resi)),
variable = rep(names(T0)[i], nrow(resi)),
value = resi[ , i],
JKreplicate = 1:nrow(resi)
)
}
})
vei <- do.call(rbind, veiList)
colnames(resi) <- names(T0)
resi <- resi[ , names(T0) != ("r.squared"), drop = FALSE]
Bi <- jkSumMult *
Reduce(
"+",
lapply(1:nrow(resi), function(jki) {
cc <- resi[jki, ]
# drop r-squared
cc[is.na(cc)] <- 0
outer(cc, cc)
})
)
names(V_samp) <- names(T0)
return(list(Bi = Bi, V = V_samp, veiJK = vei))
}
# return estimated value based on a set of plausible values and full sample weights
# @param data a data frame with columns described below on it
# @param pvEst columns on \code{data} that are the estimation plausible values (PVs)
# @param stat a function that returns the statistic given a vector of plausible values and a vector of weights from data
# @param wgt the full sample weight variable name on data
# @param longReturn, if the coefficients per PV should be included.
# @return if \code{longReturn} is \code{FALSE}, the estimate vector
# if \code{longReturn} is \code{FALSE}, a list with an element \code{est} that is the estimate vetor
# and another element \code{coef} that is the coefficient by pvEst variable, useful for further
# variance calculations
getEst <- function(data, pvEst, stat, wgt, longReturn = TRUE) {
w <- data[ , wgt]
pv_1 <- data[ , pvEst[1]]
T_0 <- stat(pv = pv_1, w = w)
T_n <- matrix(0, ncol = length(T_0), nrow = length(pvEst))
T_n[1, ] <- T_0
if (length(pvEst) > 1) {
for (n in 2:length(pvEst)) {
pv_n <- data[ , pvEst[n]]
T_n[n, ] <- stat(pv = pv_n, w = w)
}
}
Ta <- apply(T_n, 2, mean)
colnames(T_n) <- names(T_0)
names(Ta) <- names(T_0)
if (!longReturn) {
return(Ta)
}
res <- list(est = Ta, coef = T_n)
return(res)
}
# helper that correctly calls getEst for linking error
# @param see getEst
# @return see getEst
getLinkingEst <- function(data, pvEst, stat, wgt) {
return(getEst(data, pvEst, stat, wgt, longReturn = TRUE))
}
# @description calculate for each jrrIMax number
# @param stat statistic to calculate sampling variance for (within one PV)
# yvar pvi-th yvar
# wgtM weight matrix
# co0 vector of pvi-th coefficients from coefm (matrix of coefficients)
# jkSumMult the jkSumMultiplier
# pvName the name to give this PV in the varEstInputs, usually the index of the PV
# @ return a list with the following elements
# Bi vector to make Ubar (varMVal)
# veiJK dataframe of varEstInputs
# VsampInp matrix to make Vjrr (varmval)
getVarEstJK <- function(stat, yvar, wgtM, co0, jkSumMult, pvName) {
if (inherits(wgtM, "data.frame")) {
# wgtM is a matrix/data.frame
nwgt <- ncol(wgtM)
coefa <- lapply(1:nwgt, function(wgti) {
stat(pv = yvar, w = wgtM[ , wgti])
})
coefa <- do.call(cbind, coefa)
colnames(coefa) <- colnames(wgtM)
} else {
# wgtM is a vector of names, used in AL
nwgt <- length(wgtM)
coefa <- lapply(1:nwgt, function(wgti) {
stat(pv = yvar, w = wgtM[wgti])
})
coefa <- do.call(cbind, coefa)
colnames(coefa) <- wgtM
rownames(coefa) <- names(co0)
}
if (nrow(coefa) > 1 & !is.null(rownames(coefa))) {
coefa <- coefa[!rownames(coefa) %in% "r.squared", ]
}
coefa <- t(coefa - co0)
# find computational zeros and set them to actual zero
coefa[2 * abs(co0 * .Machine$double.eps) > abs(coefa)] <- 0
# store results for building varEstInputs$JK (veiJK)
if (nrow(coefa) == 1) {
coefa <- t(coefa)
colnames(coefa) <- "(Intercept)"
}
# build this data frame by covariate in the regression
veiByCol <- lapply(1:ncol(coefa), function(coli) {
data.frame(
stringsAsFactors = FALSE,
PV = rep(pvName, nwgt),
JKreplicate = 1:nwgt,
variable = rep(colnames(coefa)[coli], nwgt),
value = coefa[ , coli]
)
})
# veiJK is the JK part of varEstInputs
veiJK <- do.call(rbind, veiByCol)
rownames(veiJK) <- NULL
Bi <- jkSumMult *
Reduce(
"+",
lapply(1:nrow(coefa), function(jki) {
cc <- coefa[jki, ]
cc[is.na(cc)] <- 0
outer(cc, cc)
})
)
coefa <- coefa^2
varmVal <- jkSumMult * apply(coefa, 2, sum)
res <- list(
Bi = Bi,
veiJK = veiJK,
VsampInp = varmVal
)
}
# @description calculate for each each jrrIMax number - standardize
# @param stat statistic to calculate sampling variance for (within one PV)
# yvar pvi-th yvar
# wgtM weight matrix
# co0 vector of pvi-th coefficients from coefm (matrix of coefficients)
# jkSumMult the jkSumMultiplier
# pvName the name to give this PV in the varEstInputs, usually the index of the PV
# stdev list of standardized values for each weight
# coefmSi standardized coefficient for pvi-th yvar
# @ return VsampInpS standardized varmVal
getVarEstJKStandard <- function(stat, yvar, wgtM, co0, jkSumMult, pvName, stdev, coefmSi) {
coefa <- lapply(1:ncol(wgtM), function(wgti) {
stat(pv = yvar, w = wgtM[ , wgti])
})
coefa <- do.call(cbind, coefa)
colnames(coefa) <- colnames(wgtM)
coefa <- coefa[rownames(coefa) != "r.squared", ]
coefS <- sapply(
1:ncol(wgtM),
function(i) standardizeCoef(coefa[ , i], stdev[[i]])
)
coefS <- t(coefS - coefmSi)
coefS[2 * abs(coefmSi * .Machine$double.eps) > abs(coefS)] <- 0
# coefa <- t(coefa - co0)
# coefS[2*abs(co0 * .Machine$double.eps) > abs(coefa)] <- 0
coefS <- coefS^2
VsampInpS <- jkSumMult * apply(coefS, 2, sum)
return(VsampInpS)
}
# @description includes calculations for each stratum and psu, to be applied to each yvar
# @param uhij matrix of partial of the likelihood at the unit level
# edf dataframe including X and one y var and weight
# D ncoef by ncoef matrix
# wgt weight variable
# psuVar psu variable
# stratumVar stratum variable
# @ return a list with the following elements
# warnDrop number of strata with only 1 psu (for this yvar)
# vv ncoef by ncoef matrix
# nums vector for this yvar's dofNum
# nums2 vector for this yvar's dofDenum
getVarTaylor <- function(uhij, edf, D, wgt, psuVar, stratumVar) {
coefNames <- colnames(uhij)
uhil <- lapply(1:length(coefNames), function(j) {
# get the stratum/PSU based sum
edf$bb <- uhij[ , j] * edf[ , wgt]
res_sp <- aggregate(formula(paste0("bb ~ ", psuVar, " + ", stratumVar)), edf, sum)
# and the average of the previous line results across strata (notice data=res_sp)
res_s <- aggregate(formula(paste0("bb ~ ", stratumVar)), res_sp, mean)
coln <- coefNames[j]
names(res_sp)[3] <- coln
names(res_s)[2] <- paste0("uh_", coln)
uhi <- merge(res_sp, res_s, by = c(stratumVar), all = TRUE)
uhi[ , paste0("dx", coln)] <- uhi[ , coln] - uhi[ , paste0("uh_", coln)]
uhi
})
# cols: for each coef: sum by strat and psu, mean by strat, diff of two
uhi <- do.call(cbind, uhil)
# variance-covariance matrix
warnDrop <- 0
vv <- matrix(0, nrow = length(coefNames), ncol = length(coefNames))
nums <- vector(length = length(coefNames))
nums2 <- vector(length = length(coefNames))
for (ii in unique(uhi[ , stratumVar])) {
unkj <- unique(uhi[uhi[ , stratumVar] == ii, psuVar, drop = TRUE])
if (length(unkj) > 1) {
v <- do.call(cbind, lapply(1:length(unkj), function(jj) as.numeric(t(uhi[uhi[ , stratumVar] == ii & uhi[ , psuVar] == unkj[jj], paste0("dx", coefNames), drop = FALSE]))))
vvj <- v %*% t(v)
vvj <- vvj * ((length(unkj)) / (length(unkj) - 1))
num <- diag(D %*% vvj %*% D)
# add up
vv <- vv + vvj
nums <- nums + num
nums2 <- nums2 + num^2
} else {
warnDrop <- warnDrop + 1
}
}
res <- list(
warnDrop = warnDrop,
vv = vv,
nums = nums,
nums2 = nums2
)
return(res)
}
# @description calculate r2 for taylor method, to be applied to each yvar
# @param M vv matrix
# D ncoef by ncoef matrix
# W diagonal weight matrix
# Y yvar vector
# e residual vector
# @ return a list with the following elements
# r2 r2 value for this yvar
# vc ncoef by ncoef matrix
getR2Taylor <- function(M, D, W, Y, e) {
vc <- D %*% M %*% D
meanY <- sum(W %*% Y) / sum(diag(W))
dY <- Y - meanY
syy <- t(dY) %*% W %*% dY # Weisberg, and adding in weights
rss <- t(e) %*% W %*% e # Weisberg, page 81 eq 4.2
r2 <- as.numeric(1 - (rss / syy)) # Weisberg, page 49 eq 2.31
res <- list(
r2 = r2,
vc = vc
)
return(res)
}
getVarEstJKPercentile <- function(data, pvEst, stat, wgt, jkw, rvs, jmaxN) {
w <- data[ , wgt]
pv_1 <- data[ , pvEst[1]]
jkws <- data[ , jkw]
T_n <- stat(pv = pv_1, w = w, jkw = jkws, rv = rvs[1, ], jmax = T)
if (length(pvEst) > 1) {
for (n in 2:length(pvEst)) {
pv_n <- data[ , pvEst[n]]
if (n <= jmaxN) {
T_p <- stat(pv = pv_n, w = w, jkw = jkws, rv = rvs[n, ], jmax = T)
T_n$rprs <- rbind(T_n$rprs, T_p$rprs)
T_n$nsmall <- rbind(T_n$nsmall, T_p$nsmall)
} else {
T_p <- stat(pv = pv_n, w = w, jkw = jkws, rv = rvs[n, ], jmax = F)
T_n$nsmall <- rbind(T_n$nsmall, T_p$nsmall)
}
}
}
return (T_n)
}
detectLinkingError <- function(data, yvars) {
if(is.null(yvars)) {
return(FALSE)
}
if(length(yvars) == 0) {
return(FALSE)
}
if(is.null(data)) {
return(FALSE)
}
any(c("HSTS", "NAEP") %in% getAttributes(data, "survey")) & any(grepl("_linking", yvars, fixed=TRUE))
}
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Defines functions calc.lm.sdf lm.sdf
Documented in lm.sdf
#' @title EdSurvey Linear Models
#' @description Fits a linear model that uses weights and variance estimates appropriate for the data.
#'
#' @param formula a \ifelse{latex}{\code{formula}}{\code{\link[stats]{formula}}} for the
#' linear model. See \ifelse{latex}{\code{lm}}{\code{\link[stats]{lm}}}.
#' If \emph{y} is left blank, the default subject scale or subscale variable
#' will be used. (You can find the default using
#' \code{\link{showPlausibleValues}}.)
#' If \emph{y} is a variable for a subject scale or subscale (one of the
#' names shown by \code{\link{showPlausibleValues}}),
#' then that subject scale or subscale is used.
#' @param data an \code{edsurvey.data.frame}, a \code{light.edsurvey.data.frame},
#' or an \code{edsurvey.data.frame.list}
#' @param weightVar a character indicating the weight variable to use (see Details).
#' The \code{weightVar} must be one of the weights for the
#' \code{edsurvey.data.frame}. If \code{NULL}, it uses the default
#' for the \code{edsurvey.data.frame}.
#' @param varMethod a character set to \dQuote{jackknife} or \dQuote{Taylor} that indicates the variance
#' estimation method to be used. 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 (see
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}})
#' 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 relevels a list. Used to change the contrasts from the
#' default treatment contrasts to the treatment contrasts with a chosen omitted
#' group (the reference group). The name of each element should be the variable name, and the value
#' should be the group to be omitted (the reference group).
#' @param dropOmittedLevels a logical value. When set to the default value of \code{TRUE}, drops
#' those levels of all factor variables that are specified
#' in an \code{edsurvey.data.frame}. 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 an \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{list(}\code{var1} \code{=} \code{list(}\code{from=} \code{c("a",} \code{"b",} \code{"c"),} \code{to=} \code{"d"))}. See Examples.
#' @param returnVarEstInputs a logical value set to \code{TRUE} to return the
#' inputs to the jackknife and imputation variance
#' estimates, which allow for the computation
#' of covariances between estimates.
#' @param returnNumberOfPSU a logical value set to \code{TRUE} to return the number of
#' primary sampling units (PSUs)
#' @param standardizeWithSamplingVar a logical value indicating if the standardized coefficients
#' should have the variance of the regressors and outcome measured
#' with sampling variance. Defaults to \code{FALSE}.
#' @param verbose logical; indicates whether a detailed printout should display during execution
#'
#' @param omittedLevels this argument is deprecated. Use \code{dropOmittedLevels}
#'
#' @details
#'
#' This function implements an estimator that correctly handles left-hand
#' side variables that are either numeric or plausible values and allows for survey
#' sampling weights and estimates variances using the jackknife replication method.
#' The vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}
#' describes estimation of the reported statistics.
#'
#' Regardless of the variance estimation, the coefficients are estimated
#' using the sample weights according to the sections
#' \dQuote{Estimation of Weighted Means When Plausible Values Are Not Present}
#' or
#' \dQuote{Estimation of Weighted Means When Plausible Values Are Present,}
#' depending on if there are assessment variables or variables with plausible values
#' in them.
#'
#' How the standard errors of the coefficients are estimated depends on the
#' value of \code{varMethod} and the presence of plausible values (assessment variables),
#' But once it is obtained, the \emph{t} statistic
#' is given by \deqn{t=\frac{\hat{\beta}}{\sqrt{\mathrm{var}(\hat{\beta})}}} where
#' \eqn{ \hat{\beta} } is the estimated coefficient and \eqn{\mathrm{var}(\hat{\beta})} is
#' the variance of that estimate.
#'
#' The \bold{coefficient of determination (\emph{R}-squared value)} is similarly estimated by finding
#' the average \emph{R}-squared using the average across the plausible values.
#'
#' \subsection{Standardized regression coefficients}{
#' Standardized regression coefficients can be returned in a call to \code{summary},
#' by setting the argument \code{src} to \code{TRUE}. See Examples.
#'
#' By default, the standardized coefficients are calculated using standard
#' deviations of the variables themselves, including averaging the standard
#' deviation across any plausible values. When \code{standardizeWithSamplingVar}
#' is set to \code{TRUE}, the variance of the standardized coefficient is
#' calculated similar to a regression coefficient and therefore includes the
#' sampling variance in the variance estimate of the outcome variable.
#' }
#'
#' \subsection{Variance estimation of coefficients}{
#' All variance estimation methods are shown in the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-Statistics.pdf}{\emph{Statistical Methods Used in EdSurvey}}.
#' When \code{varMethod} is set to the \code{jackknife} and the predicted
#' value does not have plausible values, the variance of the coefficients
#' is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When
#' Plausible Values Are Not Present, Using the Jackknife Method.}
#'
#' When plausible values are present and \code{varMethod} is \code{jackknife}, the
#' variance of the coefficients is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When
#' Plausible Values Are Present, Using the Jackknife Method.}
#'
#' When plausible values are not present and \code{varMethod} is \code{Taylor}, the
#' variance of the coefficients is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When Plausible
#' Values Are Not Present, Using the Taylor Series Method.}
#'
#' When plausible values are present and \code{varMethod} is \code{Taylor}, the
#' variance of the coefficients is estimated according to the section
#' \dQuote{Estimation of Standard Errors of Weighted Means When Plausible
#' Values Are Present, Using the Taylor Series Method.}
#' }
#'
#' @section Testing:
#' Of the common hypothesis tests for joint parameter testing, only the Wald
#' test is widely used with plausible values and sample weights. As such, it
#' replaces, if imperfectly, the Akaike Information Criteria (AIC), the
#' likelihood ratio test, chi-squared, and analysis of variance (ANOVA, including \emph{F}-tests). See \code{\link{waldTest}} or
#' the vignette titled
#' \href{https://www.air.org/sites/default/files/EdSurvey-WaldTest.pdf}{\emph{Methods and Overview of Using EdSurvey for Running Wald Tests}}.
#'
#' @references
#' Binder, D. A. (1983). On the variances of asymptotically normal estimators from complex surveys. \emph{International Statistical Review}, \emph{51}(3), 279--292.
#'
#' Rubin, D. B. (1987). \emph{Multiple imputation for nonresponse in surveys}. New York, NY: Wiley.
#'
#' van Buuren, S. (2012). \emph{Flexible imputation of missing data}. New York, NY: CRC Press.
#'
#' Weisberg, S. (1985). \emph{Applied linear regression} (2nd ed.). New York, NY: Wiley.
#'
#' @return
#' An \code{edsurvey.lm} with the following elements:
#' \item{call}{the function call}
#' \item{formula}{the formula used to fit the model}
#' \item{coef}{the estimates of the coefficients}
#' \item{se}{the standard error estimates of the coefficients}
#' \item{Vimp}{the estimated variance from uncertainty in the scores (plausible value variables)}
#' \item{Vjrr}{the estimated variance from sampling}
#' \item{M}{the number of plausible values}
#' \item{varm}{the variance estimates under the various plausible values}
#' \item{coefm}{the values of the coefficients under the various plausible values}
#' \item{coefmat}{the coefficient matrix (typically produced by the summary of a model)}
#' \item{r.squared}{the coefficient of determination}
#' \item{weight}{the name of the weight variable}
#' \item{npv}{the number of plausible values}
#' \item{jrrIMax}{the \code{jrrIMax} value used in computation}
#' \item{njk}{the number of the jackknife replicates used; set to \code{NA}
#' when Taylor series variance estimates are used}
#' \item{varMethod}{one of \code{Taylor series} or the \code{jackknife}}
#' \item{residuals}{residuals from the average regression coefficients}
#' \item{PV.residuals}{residuals from the by plausible value coefficients}
#' \item{PV.fitted.values}{fitted values from the by plausible value coefficients}
#' \item{B}{imputation variance covariance matrix, before multiplication by (M+1)/M}
#' \item{U}{sampling variance covariance matrix}
#' \item{rbar}{average relative increase in variance; see van Buuren (2012, eq. 2.29)}
#' \item{nPSU}{number of PSUs used in calculation}
#' \item{n0}{number of rows on an \code{edsurvey.data.frame} before any conditions were applied}
#' \item{nUsed}{number of observations with valid data and weights larger than zero}
#' \item{data}{data used for the computation}
#' \item{Xstdev}{standard deviations of regressors, used for computing standardized
#' regression coefficients when \code{standardizeWithSamplingVar} is set to
#' \code{FALSE} (the default)}
#' \item{varSummary}{the result of running \code{summary2} (unweighted) on each variable in the
#' regression}
#' \item{varEstInputs}{when \code{returnVarEstInputs} is \code{TRUE},
#' this element is returned. These are
#' used for calculating covariances with
#' \code{\link{varEstToCov}}.}
#' \item{standardizeWithSamplingVar}{when \code{standardizeWithSamplingVar}
#' is set to \code{TRUE}, this element is
#' returned. Calculates the standard deviation
#' of the standardized
#' regression coefficients like any other
#' variable.}
#'
#' @seealso \ifelse{latex}{\code{lm}}{\code{\link[stats]{lm}}}
#' @author Paul Bailey
#'
#' @example \man\examples\lm.sdf.R
#' @importFrom Matrix sparse.model.matrix rankMatrix
#' @importFrom stats lm aggregate pt relevel model.matrix lm.wfit as.formula complete.cases
#' @importFrom Formula Formula
#' @importFrom MASS ginv
#' @export
#' @usage lm.sdf(formula, data, weightVar = NULL, relevels = list(),
#' varMethod = c("jackknife", "Taylor"), jrrIMax = 1,
#' dropOmittedLevels = TRUE, defaultConditions = TRUE, recode = NULL,
#' returnVarEstInputs = FALSE, returnNumberOfPSU = FALSE,
#' standardizeWithSamplingVar = FALSE, verbose=TRUE,
#' omittedLevels = deprecated())
lm.sdf <- function(formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor"),
jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
returnVarEstInputs = FALSE,
returnNumberOfPSU = FALSE,
standardizeWithSamplingVar = FALSE,
verbose = TRUE,
omittedLevels = deprecated()) {
call <- match.call()
checkDataClass(data, c("edsurvey.data.frame", "light.edsurvey.data.frame", "edsurvey.data.frame.list"))
weightVar <- iparse(substitute(weightVar), x = data)
if (lifecycle::is_present(omittedLevels)) {
lifecycle::deprecate_soft("4.0.0", "lm.sdf(omittedLevels)", "lm.sdf(dropOmittedLevels)")
dropOmittedLevels <- omittedLevels
}
if (!is.logical(dropOmittedLevels)) stop("The ", sQuote("dropOmittedLevels"), " argument must be logical.")
# if data is an edsurvey.data.frame.list, simply return a list with results
# for each edsurvey.data.frame
if (inherits(data, c("edsurvey.data.frame.list"))) {
res <- itterateESDFL(match.call(), data)
class(res) <- "edsurveyLmList"
return(res)
} else {
return(calc.lm.sdf(
formula = formula,
data = data,
weightVar = weightVar,
relevels = relevels,
varMethod = varMethod,
jrrIMax = jrrIMax,
dropOmittedLevels = dropOmittedLevels,
defaultConditions = defaultConditions,
missingDefaultConditions = missing(defaultConditions),
recode = recode,
returnVarEstInputs = returnVarEstInputs,
returnLm0 = FALSE,
call = call,
returnNumberOfPSU = returnNumberOfPSU,
standardizeWithSamplingVar = standardizeWithSamplingVar,
verbose = verbose
))
}
}
calc.lm.sdf <- function(formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor", "j", "t"),
jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
missingDefaultConditions = TRUE,
recode = NULL,
returnVarEstInputs = FALSE,
returnLm0 = FALSE,
call = NULL,
returnNumberOfPSU = FALSE,
standardizeWithSamplingVar = FALSE,
verbose = TRUE) {
if (is.null(call)) {
call <- match.call()
}
#######################
######## Outline ######
#######################
# 1) check, format inputs
# 2) get the data
# 3) deal with relevels.
# 4) deal with yvar having plausible values
# 5) run the main regression
# 6) run the regressions, form the inputs for variance estimation
# 7) form output, including final variance estimation
# 1) check, format inputs
checkDataClass(data, c("edsurvey.data.frame", "light.edsurvey.data.frame", "edsurvey.data.frame.list"))
sdf <- data # short for survey data.frame
# get varMethod, turn into first character as lower case
varMethod <- substr(tolower(varMethod[[1]]), 0, 1)
if (!varMethod %in% c("j", "t")) {
stop(paste0("The argument ", sQuote("varMethod"), " must be one of ", dQuote("Taylor"), " or ", dQuote("jackknife"), "."))
}
stopifnot(is.logical(verbose))
if (standardizeWithSamplingVar & varMethod == "t") {
warning(paste0(sQuote("standardizeWithSamplingVar"), " reset to ", dQuote("FALSE"), " because ", sQuote("varMethod"), " is ", dQuote("Taylor"), "."))
standardizeWithSamplingVar <- FALSE
}
# if the weight var is not set, use the default
wgt <- checkWeightVar(data, weightVar)
# check if there is an outcome variable and set it to the default if it is missing
zeroLengthLHS <- attr(terms(formula), "response") == 0
if (zeroLengthLHS) {
yvar <- attributes(getAttributes(sdf, "pvvars"))$default
formula <- update(formula, new = substitute(yvar ~ ., list(yvar = as.name(yvar))))
} else {
yvar <- all.vars(formula[[2]])
} # End of If/Else: if (zeroLengthLHS)
# grab the variables needed for the Taylor series method, if that is the variance estimation method being used
taylorVars <- c()
psuVar <- getPSUVar(data, weightVar = wgt)
stratumVar <- getStratumVar(data, weightVar = wgt)
# give an error if these do not exist.
tv <- checkTaylorVars(psuVar, stratumVar, wgt, varMethod, returnNumberOfPSU)
psuVar <- tv$psuVar
stratumVar <- tv$stratumVar
varMethod <- tv$varMethod
returnNumberOfPSU <- tv$returnNumberOfPSU
# in PIAAC there is sometimes an SRS, that is not appropriate for Taylor
if ("JK1" %in% stratumVar & varMethod == "t") {
varMethod <- "j"
warning("Cannot use Taylor series estimation on a one-stage simple random sample.")
}
if (varMethod == "t") {
taylorVars <- c(psuVar, stratumVar)
jrrIMax <- NA
} else {
if (all(c(psuVar, stratumVar) %in% colnames(data))) {
taylorVars <- c(psuVar, stratumVar)
}
}
formulaVars <- all.vars(formula)
getDataVarNames <- c(all.vars(formula), wgt, taylorVars)
tryCatch(getDataVarNames <- unique(c(getDataVarNames, 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
}
)
# 2) get the data
# This is most of the arguments
getDataArgs <- list(
data = sdf,
varnames = getDataVarNames,
returnJKreplicates = (varMethod == "j"),
drop = FALSE,
dropOmittedLevels = dropOmittedLevels,
recode = recode,
includeNaLabel = TRUE,
dropUnusedLevels = TRUE,
addAttributes = TRUE
)
# Default conditions should be included only if the user set it. This adds the argument only if needed
if (!missingDefaultConditions) {
getDataArgs <- c(getDataArgs, list(defaultConditions = defaultConditions))
}
# edf is the actual data
edf <- do.call(getData, getDataArgs)
# check for incomplete cases on the formula variables and weights (excluding Taylor vars)
relVars <- colnames(edf)
relVars <- relVars[!relVars %in% taylorVars]
incomplete <- !complete.cases(edf[ , relVars])
if (any(incomplete)) {
if (verbose) {
message("Removing ", sum(incomplete), " rows with NAs from analysis.")
}
edf <- edf[!incomplete, ]
}
# if doing Taylor series, check Taylor vars too
if (varMethod == "t") {
incomplete <- !complete.cases(edf[ , taylorVars])
if (any(incomplete)) {
if (any(incomplete[!edf[ , wgt] %in% c(NA, 0)])) {
if (verbose) {
message("Removing ", sum(incomplete[!edf[ , wgt] %in% c(NA, 0)]), " rows with NA PSU or stratum variables and positive weights from analysis.")
}
}
edf <- edf[!incomplete, ]
}
# check for NAs in stratumVar and psuVar
if (sum(is.na(edf[ , c(stratumVar, psuVar)])) != 0) {
stop("Taylor series variance estimation requested but stratum or PSU variables contian an NA value.")
}
}
# remove non-positive (full sample) weights
if (any(edf[ , wgt] <= 0)) {
posWeights <- edf[ , wgt] > 0
if (verbose) {
message("Removing ", sum(!posWeights), " rows with nonpositive weight from analysis.")
}
edf <- edf[posWeights, ]
}
# check that there is some data to work with
if (nrow(edf) <= 0) {
stop(paste0(
sQuote("data"), " must have more than 0 rows after a call to ",
sQuote("getData"), "."
))
}
# 3) deal with relevels.
# An argument that allows the user to change the omitted level for a factor variable
if (length(relevels) > 0) {
for (i in 1:length(relevels)) {
vari <- names(relevels)[i]
if (!vari %in% names(edf)) {
stop(paste0(
"In the ", sQuote("relevels"),
" argument, cannot find the variable named ", sQuote(vari), "."
))
} # End of if statment: ! vari %in% names(edf)
if (length(relevels[[i]]) != 1) {
stop(paste0(
"In the ", sQuote("relevels"),
" argument, each relevel must have exactly one level."
))
} # End of if statment: length(relevels[[i]]) != 1
# check that the level exists
lvls <- levels(edf[ , vari])
if (inherits(edf[ , vari], "lfactor")) {
# for a factor it can be either a level or a label
lvls <- c(lvls, labels(edf[ , vari]))
} # End of if statment: inherits(edf[ ,vari], "lfactor")
if (!relevels[[i]] %in% lvls) {
stop(paste0(
"In the ", sQuote("relevels"),
" argument, for the variable ", sQuote(vari), ", the level ",
sQuote(relevels[[i]]), " not found. Found levels are ",
pasteItems(dQuote(lvls)), "."
))
} # End of if statment !relevels[[i]] %in% lvls
edf[ , vari] <- relevel(edf[ , vari], ref = relevels[[i]])
} # end for(i in 1:length(relevels))
} # end if(length(relevels) > 0)
# droplevel on covariates to avoid problems with X is formed later.
# also, check that there are not factors with only one level and give more informative error.
for (i in 1:ncol(edf)) {
if (inherits(edf[ , i], "factor") & colnames(edf)[i] %in% formulaVars) {
edf[ , i] <- droplevels(edf[ , i])
if (length(levels(edf[ , i])) < 2) {
stop(paste0(
"The covariate ", dQuote(colnames(edf)[i]),
" has fewer than two levels after zero-weight cases are dropped. This covariate cannot be included in the regression."
))
}
}
}
sum2 <- function(x) {
res <- summary2(data = edf, variable = x, weightVar = NULL, dropOmittedLevels = FALSE)
res$call <- NULL
return(res)
}
varSummary <- lapply(formulaVars, sum2)
# 4) deal with yvar having plausible values
pvy <- hasPlausibleValue(yvar, sdf) # pvy is the plausible values of the y variable
yvars <- yvar
linkingError <- detectLinkingError(data, yvars)
if(any(pvy)) {
if(linkingError) {
if(standardizeWithSamplingVar) {
stop(paste0(sQuote("standardizeWithSamplingVar"), " not supported with linking error."))
}
pvs <- getPlausibleValue(yvars[max(pvy)], data)
yvars <- paste0(
"outcome",
1:length(pvs),
ifelse(grepl("_imp_", pvs), "_imp",
ifelse(grepl("_samp_", pvs), "_samp", "_est")
)
)
if (varMethod == "t") {
stop("Taylor series variance estimation not supported with NAEP linking error.")
}
} else { # end if linkingError
yvars <- paste0("outcome", 1:length(getPlausibleValue(yvars[max(pvy)], data)))
} # end if(any(pvy))
} else {
# no PVs, just make sure that this variable is numeric
edf[ , "yvar"] <- as.numeric(eval(formula[[2]], edf))
formula <- update(formula, new = substitute(yvar ~ ., list(yvar = as.name(yvar))))
yvars <- "yvar"
} # End of if statment: any(pvy)
yvar0 <- yvars[1]
# this allows that variable to not be dynamic variable, it is explicitly defined to be yvar0
if (any(pvy)) {
for (i in 1:length(yvars)) {
# PV, so we have not evaluated the I() yet (if any)
# first, by PV, rename the ith PVs to be e.g. composite
for (yvi in 1:length(pvy)) {
if (pvy[yvi]) {
edf[ , yvar[yvi]] <- edf[ , getPlausibleValue(yvar[yvi], data)[i]]
}
}
# then set yvars[i] (e.g. outcome1) to be the evaluation at this PV point
edf[ , yvars[i]] <- as.numeric(eval(formula[[2]], edf))
}
# finally, correctly set yvar0
edf$yvar0 <- edf[ , yvar0]
} # end if(any(pvy)), no else
# 5) run the main regression
# run a regression, starting with the first PV or maybe the only outcome variable
yvar0 <- yvars[1]
# this allows that variable to not be dynamic variable, it is explicitly defined to be yvar0
edf$yvar0 <- edf[ , yvar0]
frm <- update(formula, yvar0 ~ .)
edf$w <- edf[ , wgt]
lm0 <- lm(frm, data = edf, weights = w)
if (returnLm0) {
return(lm0)
}
# get standard devaitions for future standard mean difference (SMD)
# calculation
X <- model.matrix(frm, edf)
# fill this in regardless of standardizeWithSamplingVar to make a "shell"
std <- getStdev(edf[ , yvars], X, edf[ , wgt])
# this grabs the list of weight variable names
wgtl <- getAttributes(sdf, "weights")[[wgt]]
varEstInputs <- list()
# note we have not made the B matrix
madeB <- FALSE
# setup std as a matrix we can condense.
# there is now a very long set of conditionals
# used to estimate a regression at a particular y and weight. X is fixed.
stat_reg <- function(pv, w) {
lmi <- lm.wfit(x = X, y = pv, w = w)
coi <- coef(lmi)
yhat <- lmi$fitted.values
ybar <- sum(w * pv) / sum(w)
SSreg <- sum(w * (yhat - ybar)^2)
SStot <- sum(w * (pv - ybar)^2)
r2 <- SSreg / SStot
res <- c(coi, "r.squared" = r2)
return(res)
}
# 6) run the regressions, form the inputs for variance estimation
if (any(pvy)) { # the y variable is a plausible value
# this if condition takes care of the Taylor series method as well as the jackknife method
# for equations, see the statistics vignette
if (varMethod == "t") {
jrrIMax <- length(yvars)
} else {
jrrIMax <- min(jrrIMax, length(yvars))
}
# initialize: varm is the variance matrix by coefficient and PV (for V_jrr)
varM <- list()
varm <- matrix(NA, nrow = jrrIMax, ncol = length(coef(lm0)))
if (varMethod == "j") {
# y is a variable with plausible values and we are using the jackknife apporach
if (linkingError) {
if (jrrIMax != 1) {
warning("The linking error variance estimator only supports ", dQuote("jrrIMax=1"), ". Resetting to 1.")
jrrIMax <- 1
}
# NAEP linking error case, use linking error var estimator
repWeights <- paste0(wgtl$jkbase, wgtl$jksuffixes)
# get the mean estimate
est <- getLinkingEst(
data = edf,
pvEst = yvars[grep("_est", yvars)],
stat = stat_reg,
wgt = wgt
)
# coefficients matrix, r-squared vector
coefm <- est$coef[ , -ncol(est$coef), drop = FALSE]
r2s <- est$coef[ , ncol(est$coef), drop = FALSE]
# get imputation variance
impVar <- getLinkingImpVar(
data = edf,
pvImp = yvars[grep("_imp", yvars)],
ramCols = ncol(getRAM()),
stat = stat_reg,
wgt = wgt,
T0 = est$est,
T0Centered = FALSE
)
# get sampling variance
sampVar <- getLinkingSampVar(edf,
pvSamp = yvars[grep("_samp", yvars)],
stat = stat_reg,
rwgt = repWeights,
T0 = est$est,
T0Centered = FALSE
)
varM[[1]] <- sampVar$Bi
varEstInputs[["JK"]] <- sampVar$veiJK
varEstInputs[["PV"]] <- impVar$veiImp
# drop r.squared term
varm[1, ] <- sampVar$V[-length(sampVar$V)]
M <- length(yvars[grep("_est", yvars)])
Vimp <- impVar$V
Vimp <- Vimp[!names(Vimp) %in% "r.squared"]
} else { # END IF: NAEP linking error case, use linking error var estimator
# regression with each yvar
est <- getEst(
data = edf,
pvEst = yvars,
stat = stat_reg,
wgt = wgt
)
# coef matrix (coefm)
coefm <- est$coef[ , -ncol(est$coef), drop = FALSE]
# r-squared vector
r2s <- est$coef[ , ncol(est$coef), drop = FALSE]
# for standardized coefficients (matrix)
if (standardizeWithSamplingVar) {
coefmS <- t(apply(coefm, 1, function(co0row) standardizeCoef(co0row, std)))
varmS <- varm
}
varEstInputs[["JK"]] <- data.frame()
jkSumMult <- getAttributes(data, "jkSumMultiplier")
for (pvi in 1:jrrIMax) { # for each PV (up to jrrIMax)
res <- getVarEstJK(
stat = stat_reg,
yvar = edf[ , yvars[pvi]],
wgtM = edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)],
co0 = coefm[pvi, ],
jkSumMult = jkSumMult,
pvName = pvi
)
varM[[pvi]] <- res$Bi
varEstInputs[["JK"]] <- rbind(varEstInputs[["JK"]], res$veiJK)
varm[pvi, ] <- res$VsampInp
if (standardizeWithSamplingVar) {
stdev <- apply(
edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)], 2,
function(w) getStdev(edf[ , yvars[pvi]], X, w)
)
varmS[pvi, ] <- getVarEstJKStandard(
stat = stat_reg,
yvar = edf[ , yvars[pvi]],
wgtM = edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)],
co0 = coefm[pvi, ],
jkSumMult = jkSumMult,
pvName = pvi,
stdev = stdev,
coefmSi = coefmS[pvi, ]
)
}
} # end pvi for jrrIMax
# number of PVs
M <- length(yvars)
} # end not NAEP-linking-error
} else { # End of if statment: varMethod == "j"
# Taylor series, PVs
# y is a variable with plausible values and we are using the Taylor series approach
#
# here the regression uses the normal equations for weighted regression
# the QR decomposition is used to improve stability but
# the D matrix involves an inverse that cannot be avoided
X <- sparse.model.matrix(frm, edf)
XW <- X * sqrt(edf[ , wgt, drop = TRUE])
qrXW <- qr(XW)
W <- Diagonal(n = nrow(edf), edf[ , wgt, drop = TRUE])
D <- solve(t(X) %*% W %*% X)
# build per yvar
r2s <- c()
coefm <- matrix(0, nrow = length(yvars), ncol = nrow(D))
dofNum <- matrix(0, nrow = nrow(D), ncol = length(yvars))
dofDenom <- matrix(0, nrow = nrow(D), ncol = length(yvars))
warnDrop <- 0 # warn if a stratum is too thin to estimate variance on it
# variance and warning per yvar
for (mm in 1:length(yvars)) {
# for each PV, run the regression, form the coefficients and variance components
Y <- as(edf[ , yvars[mm], drop = FALSE], "Matrix")
YW <- Y * sqrt(edf[ , wgt, drop = TRUE])
# next line gets b (the coefficients) in the traditional
# but less stable way than the line after it, which is based on QR
# b <- D %*% t(X) %*% W %*% Y
b <- qr.coef(qrXW, YW)
# get residual
fitted <- X %*% b
e <- as.vector((Y - fitted))
# notation (uhij) from AM documentation
# this is the partial of the likelihood at the unit level
uhij <- e * as.matrix(X)
# vals by stratum (singleton PSUs removed in function)
res <- getVarTaylor(uhij, edf, D, wgt, psuVar, stratumVar)
# get R-squared
res2 <- getR2Taylor(res$vv, D, W, Y, e)
# collect
dofNum[ , mm] <- res$nums
dofDenom[ , mm] <- res$nums2
warnDrop <- warnDrop + res$warnDrop
coefm[mm, ] <- as.numeric(b)
varM[[mm]] <- as.matrix(res2$vc)
varm[mm, ] <- as.numeric(diag(res2$vc))
r2s <- c(r2s, res2$r2)
}
if (warnDrop > 0) {
warning(paste0(warnDrop / length(yvars), " strata had only one populated PSU. Units in these strata assumed to be certainties. You can condense the strata/PSU structure to avoid this."))
}
# number of PVs
M <- length(yvars)
} # End of if/else statment: varMethod == "j"
# imputaiton variance / variance due to uncertaintly about PVs
coef <- apply(coefm, 2, mean)
coefm0 <- t(t(coefm) - coef)
# calculate van Buuren B
B <- (1 / (M - 1)) * Reduce(
"+", # add up the matrix results of the sapply
sapply(1:nrow(coefm), function(q) {
# within each PV set, calculate the outer product
# (2.19 of Van Buuren)
outer(coefm0[q, ], coefm0[q, ])
}, simplify = FALSE)
)
madeB <- TRUE
# these formulas do not work for linking error
if (!linkingError) {
coefmPVByRow <- lapply(1:ncol(coefm0), function(coli) {
data.frame(
stringsAsFactors = FALSE,
PV = 1:nrow(coefm0),
variable = rep(names(coef(lm0))[coli], nrow(coefm0)),
value = coefm0[ , coli]
)
})
coefmPV <- do.call(rbind, coefmPVByRow)
varEstInputs[["PV"]] <- coefmPV
# imputation variance
Vimp <- (M + 1) / M * apply(coefm, 2, var)
}
# \bar{U} from 2.18 in var Buuren
Ubar <- (1 / length(varM)) * Reduce("+", varM)
# sampling variance
Vjrr <- apply(varm[1:jrrIMax, , drop = FALSE], 2, mean)
names(Vjrr) <- names(coef(lm0))
# r-squared
r2 <- mean(r2s)
if (standardizeWithSamplingVar) {
VSjrr <- apply(varmS[1:jrrIMax, , drop = FALSE], 2, mean)
VSimp <- (M + 1) / M * apply(coefmS, 2, var)
VS <- VSimp + VSjrr
}
} else { # end if(pvy)
# the y variable is not a plausible value
# this section handles jackknife and Taylor series estimation
if (varMethod == "j") {
# jackknife variance estimation
r2 <- summary(lm0)$r.squared
jkSumMult <- getAttributes(data, "jkSumMultiplier")
coef <- coef(lm0) # coefficients come from full sample weights run
res <- getVarEstJK(
stat = stat_reg,
yvar = edf[ , yvars[1]],
wgtM = edf[ , paste0(wgtl$jkbase, wgtl$jksuffixes)],
co0 = coef,
jkSumMult = jkSumMult,
pvName = 1
)
Ubar <- res$Bi
Vjrr <- res$VsampInp
varEstInputs[["JK"]] <- res$veiJK
} else { # end if(varMethod == "j")
# Taylor series, no PV
Y <- as(edf[ , yvars[1], drop = FALSE], "Matrix")
X <- sparse.model.matrix(frm, edf)
W <- Diagonal(n = nrow(edf), edf[ , wgt, drop = TRUE])
D <- vcov(lm0)
b <- coef(lm0)
# get residual
fitted <- X %*% b
e <- (as.vector((Y - fitted))) / summary(lm0)$sigma^2
# this is the partial of the likelihood at the unit level
uhij <- e * as.matrix(X)
# vals by stratum (singleton PSUs removed in function)
res <- getVarTaylor(uhij, edf, D, wgt, psuVar, stratumVar)
# get R-squared
e <- as.vector((Y - fitted))
res2 <- getR2Taylor(res$vv, D, W, Y, e)
if (res$warnDrop) {
warning("Some strata had only one populated PSU. Units in these strata assumed to be certainties. You can condense the strata/PSU structure to avoid this.")
}
dofNum <- res$nums
dofDenom <- res$nums2
r2 <- res2$r2
coef <- as.numeric(b)
Vjrr <- as.numeric(diag(res2$vc))
# this is the VC matrix, store it for vcov to recover
Ubar <- as.matrix(res2$vc)
} # end else for if(varMethod == "j")
# not pvy common
M <- 1 # only on replicate when there are no PVs
Vimp <- rep(0, length(Vjrr)) # no imputation variance when there are no PVs
varm <- NULL # no variance by PV
coefm <- NULL # no coefficients matrix by PV
} # end else for if(pvy)
# 7) form output, including final variance estimation
# for estimates with no sampling variance, make sure they return NA for SE
Vjrr[Vjrr == 0] <- NA
V <- Vjrr + Vimp
se <- sqrt(V)
# fix all the names0
names(se) <- names(V) <- names(Vjrr) <- names(Vimp) <- names(coef) <- names(coef(lm0))
# get fitted and resid based on overall coef
fitted1 <- as.vector(X %*% coef)
Y <- do.call(cbind, lapply(1:length(yvars), function(yi) {
as.vector(edf[ , yvars[yi]])
}))
resid1 <- Y - fitted1
colnames(resid1) <- colnames(Y) <- yvars
# residual df calculation
nobs <- nrow(edf)
n.ok <- nobs - sum(edf$origwt == 0)
nvars <- ncol(X)
rank <- rankMatrix(X, method = "qr")[1]
resdf <- n.ok - rank
df.r <- resdf
# get residual based on coef by PV
if (!is.null(coefm)) {
if (!is.matrix(coefm)) {
coefm <- t(as.matrix(coefm))
}
fitted2 <- as.matrix(X %*% t(coefm))
resid2 <- Y[ , 1:ncol(fitted2)] - fitted2
}
coefmat <- data.frame(
coef = coef,
se = se,
t = coef / se
)
# assign var that are almost 0 to 0 to make DOF correction run correctly
if (!is.null(varEstInputs$JK)) {
varEstInputs$JK$value[which(abs(varEstInputs$JK$value) < (sqrt(.Machine$double.eps) * sqrt(nrow(varEstInputs$JK))))] <- 0
}
if (varMethod == "t") {
m <- length(wgtl$jksuffixes)
# Johnson and Rust dfo correction
if (inherits(dofNum, "matrix")) {
coefmat$dof <- (3.16 - 2.77 / sqrt(m)) * apply(dofNum^2 / dofDenom, 1, mean)
} else {
coefmat$dof <- (3.16 - 2.77 / sqrt(m)) * dofNum^2 / dofDenom
}
} else {
coefmat$dof <- sapply(names(coef), function(cn) {
DoFCorrection(varEstA = varEstInputs, varA = cn, method = "JR")
})
}
pti <- pt(coefmat$t, df = coefmat$dof)
coefmat[ , "Pr(>|t|)"] <- 2 * pmin(pti, 1 - pti)
njk <- length(wgtl$jksuffixes)
if (varMethod == "t") {
njk <- NA
}
varmeth <- ifelse(varMethod == "t", "Taylor series", "jackknife")
res <- list(
call = call, formula = formula, coef = coef, se = se, Vimp = Vimp,
Vjrr = Vjrr, M = M, varm = varm, coefm = coefm,
coefmat = coefmat, r.squared = r2, weight = wgt,
npv = length(yvars), jrrIMax = min(jrrIMax, length(yvars)),
njk = njk, varMethod = varmeth,
residuals = unname(resid1), fitted.values = unname(fitted1), residual.df = resdf
)
if (standardizeWithSamplingVar) {
coefmatStd <- coefmat
coefmatStd$coef <- standardizeCoef(coef, std)
coefmatStd$se <- sqrt(VS)
res <- c(res, list(coefmatStd = coefmatStd))
}
if (!is.null(coefm)) {
res <- c(res, list(PV.residuals = unname(resid2), PV.fitted.values = unname(fitted2)))
}
if (returnVarEstInputs) {
res <- c(res, list(varEstInputs = varEstInputs))
if (varMethod == "t") {
warning(paste0(
"Taylor series method not supported with the ",
sQuote("varEstInputs"), " argument set to ", dQuote("TRUE"), "."
))
}
}
if (madeB) {
# equation 2.29 in var Buuren, pp 42
tryCatch(
rbar <- (1 + 1 / M) * (1 / nrow(Ubar)) * sum(diag(B %*% solve(Ubar))),
error = function(e) {
rbar <<- (1 + 1 / M) * (1 / nrow(Ubar)) * sum(diag(B %*% MASS::ginv(Ubar)))
frm <- Formula(frm)
if (terms(frm, lhs = 0, rhs = NULL) == ~1) {
warning("A variance estimate was replaced with NA because there was no variance across strata.", call. = FALSE)
}
}
)
Ttilde <- (1 + rbar) * Ubar
res <- c(res, list(B = B, U = Ubar, rbar = rbar, Ttilde = Ttilde))
} else {
# used for TS vcov
res <- c(res, list(U = Ubar))
if (!any(pvy)) {
res <- c(res, list(B = 0 * Ubar))
}
}
if (returnNumberOfPSU) {
if ("JK1" %in% stratumVar) {
res <- c(res, list(nPSU = nrow(edf)))
} else if (sum(is.na(edf[ , c(stratumVar, psuVar)])) == 0) {
res <- c(res, list(nPSU = nrow(unique(edf[ , c(stratumVar, psuVar)]))))
} else {
warning("Cannot return number of PSUs because the stratum or PSU variables contain NA values.")
}
}
if (all(c(stratumVar, psuVar) %in% colnames(edf))) {
if (sum(is.na(edf[ , c(stratumVar, psuVar)])) == 0) {
res <- c(res, list(waldDenomBaseDof = waldDof(edf, stratumVar, psuVar)))
}
}
res <- c(res, list(n0 = nrow2.edsurvey.data.frame(data), nUsed = nrow(edf)))
if (inherits(data, "edsurvey.data.frame")) {
res <- c(res, list(data = data))
} else {
res <- c(res, list(lm0 = lm0))
}
res <- c(res, list(Xstdev = std, varSummary = varSummary))
class(res) <- "edsurveyLm"
# fix factor/character 3/4 issue
if (version$major %in% "3") {
if ("varSummary" %in% names(res)) {
for (i in 1:length(res$varSummary)) {
if ("Variable" %in% colnames(res$varSummary[[i]]$summary)) {
res$varSummary[[i]]$summary$Variable <- as.character(res$varSummary[[i]]$summary$Variable)
}
}
}
}
return(res)
}
#' @method print edsurveyLm
#' @export
print.edsurveyLm <- function(x, ...) {
print(coef(x), ...)
}
#' @method print edsurveyLmList
#' @export
print.edsurveyLmList <- function(x, ...) {
for (i in 1:length(x)) {
cat("lm", i, "\n")
print(coef(x[[i]]), ...)
}
}
# @param src a logical indicating if the the standardized regression coefficients
# should be included in the coefficients table
#' @method summary edsurveyLm
#' @export
summary.edsurveyLm <- function(object, src = FALSE, ...) {
class(object) <- "summary.edsurveyLm"
if (src) {
if ("coefmatStd" %in% names(object)) {
cm <- object$coefmat
cm$stdCoef <- object$coefmatStd$coef
cm$stdSE <- object$coefmatStd$se
} else {
cm <- object$coefmat
std <- object$Xstdev
cm$stdCoef <- NA
cm$stdSE <- NA
for (i in 1:nrow(cm)) {
if (rownames(cm)[i] %in% names(std) && std[rownames(cm)[i]] > 0) {
# standardize regression coef = sdX_i/sdY * coef_i
cm$stdCoef[i] <- (std[[rownames(cm)[i]]] / std[["outcome.std"]]) * cm$coef[i]
# same formula for standareized SE
cm$stdSE[i] <- (std[[rownames(cm)[i]]] / std[["outcome.std"]]) * cm$se[i]
}
}
} # end else for if("coefmatStd" %in% names(object))
object$coefmat <- cm
} # end if(src)
return(object)
}
#' @method summary edsurveyLmList
#' @export
summary.edsurveyLmList <- function(object, smd = FALSE, ...) {
class(object) <- "summary.edsurveyLmList"
for (i in 1:length(object)) {
object[[i]] <- summary.edsurveyLm(object[[i]], smd)
}
object
}
#' @method print summary.edsurveyLm
#' @importFrom stats printCoefmat
#' @export
print.summary.edsurveyLm <- function(x, ...) {
cat(paste0("\nFormula: ", paste(deparse(x$formula), collapse = ""), "\n\n"))
cat(paste0("Weight variable: ", sQuote(x$weight), "\n"))
cat(paste0("Variance method: ", x$varMethod, "\n"))
if (!is.na(x$njk)) {
cat(paste0("JK replicates: ", x$njk, "\n"))
}
if (x$npv != 1) {
cat(paste0("Plausible values: ", x$npv, "\n"))
cat(paste0("jrrIMax: ", x$jrrIMax, "\n"))
}
cat(paste0("full data n: ", x$n0, "\n"))
if (!is.null(x$nPSU)) {
cat(paste0("n PSU: ", x$nPSU, "\n"))
}
cat(paste0("n used: ", x$nUsed, "\n\n"))
cat(paste0("Coefficients:\n"))
csind <- which(colnames(x$coefmat) %in% c("coef", "se", "stdCoef", "stdSE"))
# with standardized coefficients it produces a bogus warning for the intercept
suppressWarnings(printCoefmat(x$coefmat, P.values = TRUE, has.Pvalue = TRUE, cs.ind = csind))
cat("\n")
cat(paste0("Multiple R-squared: ", round(x$r.squared, 4), "\n\n"))
}
#' @method print summary.edsurveyLmList
#' @export
print.summary.edsurveyLmList <- function(x, ...) {
for (i in 1:length(x)) {
cat("lm", i, "\n")
print(x[[i]], ...)
}
}
#' @method coef edsurveyLm
#' @export
coef.edsurveyLm <- function(object, ...) {
object$coef
}
#' @method coef edsurveyLmList
#' @export
coef.edsurveyLmList <- function(object, ...) {
sapply(object, function(li) {
li$coef
})
}
#' @method vcov edsurveyLm
#' @export
vcov.edsurveyLm <- function(object, ...) {
if (all(c("U", "B") %in% names(object))) {
if (object$M > 1) {
# there are PVs, V_samp + V_imp
return(object$U + (object$M + 1) / object$M * object$B)
} else {
# no PVs, V_samp only
return(object$U)
}
}
if (is.null(object$varEstInputs)) {
stop("This model must be fit with returnVarEstInputs=TRUE or with Taylor series to find the covariance matrix.")
}
varnames <- expand.grid(names(coef(object)), names(coef(object)))
vc <- mapply(varEstToCov, varA = varnames$Var1, varB = varnames$Var2, MoreArgs = list(varEstA = object$varEstInputs, jkSumMultiplier = object$data$jkSumMultiplier))
matrix(vc, nrow = length(coef(object)), ncol = length(coef(object)))
}
# @export
setMethod(
"coef",
c(object = "edsurveyLm"),
coef.edsurveyLm
)
# @export
setMethod(
"coef",
c(object = "edsurveyLmList"),
coef.edsurveyLmList
)
#' @method plot edsurveyLm
#' @export
plot.edsurveyLm <- function(x, ...) {
if ("lm0" %in% names(x)) {
cr <- x$lm0
} else {
# reevaluate to get lm0
cl <- x$call
cl$returnLm0 <- TRUE
cl[[1]] <- quote(calc.lm.sdf)
cr <- eval(cl)
}
plot(cr)
}
# helper to get standard deviations for a weighted variable
getStdev <- function(outcomeData, XData, weightData) {
std <- c(outcome = fast.sd(outcomeData, weightData)["std"])
for (i in 1:ncol(XData)) {
if (!is.na(sd(XData[ , i])) & sd(XData[ , i]) > 0) {
std <- c(std, fast.sd(XData[ , i], weightData)["std"])
} else {
std <- c(std, 0)
}
names(std) <- c(names(std)[-length(std)], colnames(XData)[i])
}
return(std)
}
# @param coef is the (named) coefficients)
# @param std is the (named) standard deviations
standardizeCoef <- function(coef, std) {
if (length(names(coef)) == 0) {
stop("coef must have names.")
}
for (si in 1:length(coef)) {
coefName <- names(coef)[si]
if (std[[coefName]] > 0) {
# standardize regression coef = sdX_i/sdY * coef_i
coef[si] <- (std[[coefName]] / std[["outcome.std"]]) * coef[si]
} else {
coef[si] <- NA
}
}
return(coef)
}
# get imputation variance for DBA/PBA bridge data
# @param data a data frame with columns described below on it
# @param pvImp columns on \code{data} that are the imputation variance plausible values (PVs)
# @param ramCols number of columns in the RAM table
# @param stat a function that returns the statistic given a vector of plausible values and a vector of weights from data
# @param wgt the full sample weight variable name on data
# @param T0 The estimated value, used for names
# @param T0Centered if T0 should be the center of the variance estimator or the within RAM col mean
# @param jkSumMult the jackknife sum multiplier
# @return
# V the imputation variance estimate
# veiImp the varEstInputs matrix
getLinkingImpVar <- function(data, pvImp, ramCols, stat, wgt, T0, T0Centered, jkSumMult = 1) {
V_n <- matrix(0, ncol = length(T0), nrow = ramCols)
if (is.null(data)) {
w <- wgt
} else {
w <- data[ , wgt]
}
ramRows <- round(length(pvImp) / ramCols)
if (ramRows - length(pvImp) / ramCols > sqrt(.Machine$double.eps)) {
stop(paste0("RAM table not rectangular with ", ramCols, " columns and ", length(pvImp), " total entries."))
}
# these index sequentially acorss ram rows and columns
pvi <- 1
for (n in 1:ramCols) {
T_jn <- matrix(0, ncol = length(T0), nrow = ramRows)
for (j in 1:ramRows) {
if (is.null(data)) {
pv_nj <- pvImp[pvi]
} else {
pv_nj <- data[ , pvImp[pvi]]
}
T_jn[j, ] <- stat(pv = pv_nj, w = w)
pvi <- pvi + 1
}
T_n <- apply(T_jn, 2, mean)
# default: use jackknife bias reduced centering (jackknife variance estimation)
center <- T_n
if (T0Centered) {
# use reported statistic centering (jackknife MSE estimation)
center <- T0
}
for (i in 1:length(center)) {
V_n[n, i] <- (1 + 1 / 20) * sum((T_jn[ , i] - center[i])^2) / (20 - 1)
}
if (n == 1) {
veiPV <- (T_jn - center[i])
} else {
veiPV <- rbind(veiPV, (T_jn - center[i]))
}
}
V <- apply(V_n, 2, mean)
names(V) <- names(T0)
colnames(V_n) <- names(T0)
veiPV <- veiPV[ , !names(T0) %in% "r.squared", drop = FALSE]
npv <- nrow(veiPV)
nT0 <- length(T0)
if ("r.squared" %in% names(T0)) {
nT0 <- nT0 - 1
}
veiList <- lapply(1:nT0, function(i) {
data.frame(
PV = 1:npv, # actually RAM element
variable = rep(names(T0)[i], npv),
value = veiPV[ , i] - mean(veiPV[ , i])
)
})
vei <- do.call(rbind, veiList)
return(list(V = V, veiImp = vei))
}
# get sampling variance for DBA/PBA bridge data
# @param data a data frame with columns described below on it
# @param pvSamp columns on \code{data} that are the sampling variance plausible values (PVs)
# @param stat a function that returns the statistic given a vector of plausible values and a vector of weights from data
# @param wgt the replicate sample weight variable name on data
# @param T0 The estimated value, used for names
# @param T0Centered if T0 should be the center of the variance estimator or the within RAM col mean
# @param jkSumMult the jackknife sum multiplier
# @return
# Bi matrix that is varMVal for this set of PVs (really jrrIMax is always 1, this is varMVal for the first PV, kind of)
# V the sampling variance vector
# veiJK the JK component of varEstInputs
# VsampInp this is V again, but without the r.squared entry
getLinkingSampVar <- function(data, pvSamp, stat, rwgt, T0, T0Centered, jkSumMult = 1) {
I <- length(rwgt)
T_i <- matrix(0, ncol = length(T0), nrow = I)
for (i in 1:I) {
if (is.null(data)) {
pv <- pvSamp[i]
w <- rwgt[i]
} else {
pv <- data[ , pvSamp[i]]
w <- data[ , rwgt[i]]
}
T_i[i, ] <- stat(pv = pv, w = w)
}
Ta <- apply(T_i, 2, mean)
resi <- t(t(T_i) - Ta)
if (T0Centered) {
resi <- t(t(T_i) - T0)
}
V_samp <- apply(resi, 2, function(x) {
sum(x^2)
})
# for each coefficient, build varEstInputs (vei) for that coefficient
# skip r-squared with length(T0)-1
veiList <- lapply(1:length(T0), function(i) {
if (!"r.squared" %in% names(T0)[i]) {
data.frame(
PV = rep(1, nrow(resi)),
variable = rep(names(T0)[i], nrow(resi)),
value = resi[ , i],
JKreplicate = 1:nrow(resi)
)
}
})
vei <- do.call(rbind, veiList)
colnames(resi) <- names(T0)
resi <- resi[ , names(T0) != ("r.squared"), drop = FALSE]
Bi <- jkSumMult *
Reduce(
"+",
lapply(1:nrow(resi), function(jki) {
cc <- resi[jki, ]
# drop r-squared
cc[is.na(cc)] <- 0
outer(cc, cc)
})
)
names(V_samp) <- names(T0)
return(list(Bi = Bi, V = V_samp, veiJK = vei))
}
# return estimated value based on a set of plausible values and full sample weights
# @param data a data frame with columns described below on it
# @param pvEst columns on \code{data} that are the estimation plausible values (PVs)
# @param stat a function that returns the statistic given a vector of plausible values and a vector of weights from data
# @param wgt the full sample weight variable name on data
# @param longReturn, if the coefficients per PV should be included.
# @return if \code{longReturn} is \code{FALSE}, the estimate vector
# if \code{longReturn} is \code{FALSE}, a list with an element \code{est} that is the estimate vetor
# and another element \code{coef} that is the coefficient by pvEst variable, useful for further
# variance calculations
getEst <- function(data, pvEst, stat, wgt, longReturn = TRUE) {
w <- data[ , wgt]
pv_1 <- data[ , pvEst[1]]
T_0 <- stat(pv = pv_1, w = w)
T_n <- matrix(0, ncol = length(T_0), nrow = length(pvEst))
T_n[1, ] <- T_0
if (length(pvEst) > 1) {
for (n in 2:length(pvEst)) {
pv_n <- data[ , pvEst[n]]
T_n[n, ] <- stat(pv = pv_n, w = w)
}
}
Ta <- apply(T_n, 2, mean)
colnames(T_n) <- names(T_0)
names(Ta) <- names(T_0)
if (!longReturn) {
return(Ta)
}
res <- list(est = Ta, coef = T_n)
return(res)
}
# helper that correctly calls getEst for linking error
# @param see getEst
# @return see getEst
getLinkingEst <- function(data, pvEst, stat, wgt) {
return(getEst(data, pvEst, stat, wgt, longReturn = TRUE))
}
# @description calculate for each jrrIMax number
# @param stat statistic to calculate sampling variance for (within one PV)
# yvar pvi-th yvar
# wgtM weight matrix
# co0 vector of pvi-th coefficients from coefm (matrix of coefficients)
# jkSumMult the jkSumMultiplier
# pvName the name to give this PV in the varEstInputs, usually the index of the PV
# @ return a list with the following elements
# Bi vector to make Ubar (varMVal)
# veiJK dataframe of varEstInputs
# VsampInp matrix to make Vjrr (varmval)
getVarEstJK <- function(stat, yvar, wgtM, co0, jkSumMult, pvName) {
if (inherits(wgtM, "data.frame")) {
# wgtM is a matrix/data.frame
nwgt <- ncol(wgtM)
coefa <- lapply(1:nwgt, function(wgti) {
stat(pv = yvar, w = wgtM[ , wgti])
})
coefa <- do.call(cbind, coefa)
colnames(coefa) <- colnames(wgtM)
} else {
# wgtM is a vector of names, used in AL
nwgt <- length(wgtM)
coefa <- lapply(1:nwgt, function(wgti) {
stat(pv = yvar, w = wgtM[wgti])
})
coefa <- do.call(cbind, coefa)
colnames(coefa) <- wgtM
rownames(coefa) <- names(co0)
}
if (nrow(coefa) > 1 & !is.null(rownames(coefa))) {
coefa <- coefa[!rownames(coefa) %in% "r.squared", ]
}
coefa <- t(coefa - co0)
# find computational zeros and set them to actual zero
coefa[2 * abs(co0 * .Machine$double.eps) > abs(coefa)] <- 0
# store results for building varEstInputs$JK (veiJK)
if (nrow(coefa) == 1) {
coefa <- t(coefa)
colnames(coefa) <- "(Intercept)"
}
# build this data frame by covariate in the regression
veiByCol <- lapply(1:ncol(coefa), function(coli) {
data.frame(
stringsAsFactors = FALSE,
PV = rep(pvName, nwgt),
JKreplicate = 1:nwgt,
variable = rep(colnames(coefa)[coli], nwgt),
value = coefa[ , coli]
)
})
# veiJK is the JK part of varEstInputs
veiJK <- do.call(rbind, veiByCol)
rownames(veiJK) <- NULL
Bi <- jkSumMult *
Reduce(
"+",
lapply(1:nrow(coefa), function(jki) {
cc <- coefa[jki, ]
cc[is.na(cc)] <- 0
outer(cc, cc)
})
)
coefa <- coefa^2
varmVal <- jkSumMult * apply(coefa, 2, sum)
res <- list(
Bi = Bi,
veiJK = veiJK,
VsampInp = varmVal
)
}
# @description calculate for each each jrrIMax number - standardize
# @param stat statistic to calculate sampling variance for (within one PV)
# yvar pvi-th yvar
# wgtM weight matrix
# co0 vector of pvi-th coefficients from coefm (matrix of coefficients)
# jkSumMult the jkSumMultiplier
# pvName the name to give this PV in the varEstInputs, usually the index of the PV
# stdev list of standardized values for each weight
# coefmSi standardized coefficient for pvi-th yvar
# @ return VsampInpS standardized varmVal
getVarEstJKStandard <- function(stat, yvar, wgtM, co0, jkSumMult, pvName, stdev, coefmSi) {
coefa <- lapply(1:ncol(wgtM), function(wgti) {
stat(pv = yvar, w = wgtM[ , wgti])
})
coefa <- do.call(cbind, coefa)
colnames(coefa) <- colnames(wgtM)
coefa <- coefa[rownames(coefa) != "r.squared", ]
coefS <- sapply(
1:ncol(wgtM),
function(i) standardizeCoef(coefa[ , i], stdev[[i]])
)
coefS <- t(coefS - coefmSi)
coefS[2 * abs(coefmSi * .Machine$double.eps) > abs(coefS)] <- 0
# coefa <- t(coefa - co0)
# coefS[2*abs(co0 * .Machine$double.eps) > abs(coefa)] <- 0
coefS <- coefS^2
VsampInpS <- jkSumMult * apply(coefS, 2, sum)
return(VsampInpS)
}
# @description includes calculations for each stratum and psu, to be applied to each yvar
# @param uhij matrix of partial of the likelihood at the unit level
# edf dataframe including X and one y var and weight
# D ncoef by ncoef matrix
# wgt weight variable
# psuVar psu variable
# stratumVar stratum variable
# @ return a list with the following elements
# warnDrop number of strata with only 1 psu (for this yvar)
# vv ncoef by ncoef matrix
# nums vector for this yvar's dofNum
# nums2 vector for this yvar's dofDenum
getVarTaylor <- function(uhij, edf, D, wgt, psuVar, stratumVar) {
coefNames <- colnames(uhij)
uhil <- lapply(1:length(coefNames), function(j) {
# get the stratum/PSU based sum
edf$bb <- uhij[ , j] * edf[ , wgt]
res_sp <- aggregate(formula(paste0("bb ~ ", psuVar, " + ", stratumVar)), edf, sum)
# and the average of the previous line results across strata (notice data=res_sp)
res_s <- aggregate(formula(paste0("bb ~ ", stratumVar)), res_sp, mean)
coln <- coefNames[j]
names(res_sp)[3] <- coln
names(res_s)[2] <- paste0("uh_", coln)
uhi <- merge(res_sp, res_s, by = c(stratumVar), all = TRUE)
uhi[ , paste0("dx", coln)] <- uhi[ , coln] - uhi[ , paste0("uh_", coln)]
uhi
})
# cols: for each coef: sum by strat and psu, mean by strat, diff of two
uhi <- do.call(cbind, uhil)
# variance-covariance matrix
warnDrop <- 0
vv <- matrix(0, nrow = length(coefNames), ncol = length(coefNames))
nums <- vector(length = length(coefNames))
nums2 <- vector(length = length(coefNames))
for (ii in unique(uhi[ , stratumVar])) {
unkj <- unique(uhi[uhi[ , stratumVar] == ii, psuVar, drop = TRUE])
if (length(unkj) > 1) {
v <- do.call(cbind, lapply(1:length(unkj), function(jj) as.numeric(t(uhi[uhi[ , stratumVar] == ii & uhi[ , psuVar] == unkj[jj], paste0("dx", coefNames), drop = FALSE]))))
vvj <- v %*% t(v)
vvj <- vvj * ((length(unkj)) / (length(unkj) - 1))
num <- diag(D %*% vvj %*% D)
# add up
vv <- vv + vvj
nums <- nums + num
nums2 <- nums2 + num^2
} else {
warnDrop <- warnDrop + 1
}
}
res <- list(
warnDrop = warnDrop,
vv = vv,
nums = nums,
nums2 = nums2
)
return(res)
}
# @description calculate r2 for taylor method, to be applied to each yvar
# @param M vv matrix
# D ncoef by ncoef matrix
# W diagonal weight matrix
# Y yvar vector
# e residual vector
# @ return a list with the following elements
# r2 r2 value for this yvar
# vc ncoef by ncoef matrix
getR2Taylor <- function(M, D, W, Y, e) {
vc <- D %*% M %*% D
meanY <- sum(W %*% Y) / sum(diag(W))
dY <- Y - meanY
syy <- t(dY) %*% W %*% dY # Weisberg, and adding in weights
rss <- t(e) %*% W %*% e # Weisberg, page 81 eq 4.2
r2 <- as.numeric(1 - (rss / syy)) # Weisberg, page 49 eq 2.31
res <- list(
r2 = r2,
vc = vc
)
return(res)
}
getVarEstJKPercentile <- function(data, pvEst, stat, wgt, jkw, rvs, jmaxN) {
w <- data[ , wgt]
pv_1 <- data[ , pvEst[1]]
jkws <- data[ , jkw]
T_n <- stat(pv = pv_1, w = w, jkw = jkws, rv = rvs[1, ], jmax = T)
if (length(pvEst) > 1) {
for (n in 2:length(pvEst)) {
pv_n <- data[ , pvEst[n]]
if (n <= jmaxN) {
T_p <- stat(pv = pv_n, w = w, jkw = jkws, rv = rvs[n, ], jmax = T)
T_n$rprs <- rbind(T_n$rprs, T_p$rprs)
T_n$nsmall <- rbind(T_n$nsmall, T_p$nsmall)
} else {
T_p <- stat(pv = pv_n, w = w, jkw = jkws, rv = rvs[n, ], jmax = F)
T_n$nsmall <- rbind(T_n$nsmall, T_p$nsmall)
}
}
}
return (T_n)
}
detectLinkingError <- function(data, yvars) {
if(is.null(yvars)) {
return(FALSE)
}
if(length(yvars) == 0) {
return(FALSE)
}
if(is.null(data)) {
return(FALSE)
}
any(c("HSTS", "NAEP") %in% getAttributes(data, "survey")) & any(grepl("_linking", yvars, fixed=TRUE))
}
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