R/mplus_dmacs.R
In ddueber/dmacs: Measurement Nonequivalence Effect Size Calculator
Defines functions
mplus_dmacs
Documented in
mplus_dmacs
#' Summary of measurement nonequivalence effects
#'
#' \code{mplus_dmacs} returns a summary of measurement non-equivalence
#' effects given an Mplus .out file.
#'
#' @param fit is an Mplus .out file of a multigroup CFA analysis. The default
#' is to launch a window for choosing the file.
#' @param RefGroup can be the name of the reference group (as a string),
#' or the index of the reference group (as a number). RefGroup defaults to
#' the first group if no value is provided. It is strongly recommended to
#' provide the reference group as a string, since group names in data are
#' often ordered by their appearance in the data, not alphabetically.
#' @param dtype described the pooling of standard deviations for use in the
#' denominator of the dmacs effect size. Possibilities are "pooled" for
#' pooled standard deviations, or "glass" for always using the standard
#' deviation of the reference group.
#'
#' @return A list, indexed by group, of lists of measurement nonequivalence
#' effects from Nye and Drasgow (2011), including dmacs, expected bias in
#' the mean score by item,
#' expected bias in the mean total score, and expected bias in the variance
#' of the total score. Expected bias in the variance of the total score is
#' only supplied for unidimensional models in the current version of this
#' package
#'
#'
#' @section References:
#' Nye, C. & Drasgow, F. (2011). Effect size indices for analyses of
#' measurement equivalence: Understanding the practical importance of
#' differences between groups. \emph{Journal of Applied Psychology, 96}(5),
#' 966-980.
#' @export
mplus_dmacs <- function(fit = file.choose(), RefGroup = 1, dtype = "pooled", MEtype = "Group") {
## We will need the raw text file later if continuous variables with pooled dtype
fit0 <- fit
## Read in the model with MplusAutomation
if (!any(class(fit) == "mplus.model")) {
fit <- MplusAutomation::readModels(fit)
}
## Things proceed completely differently for longitudinal vs group invariance
if (grepl("ong", MEtype, fixed = TRUE)) {
## Because it will save a lot of space, and just make life easier, let's give fit$parameters$unstandardized a name
Params <- fit$parameters$unstandardized
## Group names are now factor names
Groups <- unique(Params[grepl(".BY", Params$paramHeader), "paramHeader"])
Groups <- gsub('.{3}$', '', Groups)
names(Groups) <- Groups
## If RefGroup is a string, turn it into an index, else issue a warning that maybe it should be a string
if (is.character(RefGroup)) {
RefGroup <- match(RefGroup, Groups)
} else {
warning(paste("It is recommended that you provide the name of the factor for the reference time to RefGroup = as a string; see ?mplus_dmacs. The reference group being used is", Groups[RefGroup]))
}
## Now, let's grab the loading vectors for each timepoint
## Warning! No item names are attached to these loadings
LambdaList <- lapply (Groups, function (G) {
as.matrix(Params[(Params$paramHeader == paste0(G, ".BY")), "est"])
})
## Get an array of item names for the reference time. We will need this later to attach names to items
ItemNames <- unique(Params[grepl(paste0(Groups[RefGroup],".BY"), Params$paramHeader), "param"])
names(ItemNames) <- ItemNames
## Make a list of factor means
MeanList <- lapply(Groups, function (G) {
Params[(Params$paramHeader == "Means") & (Params$param == G), "est"]
})
## Make a list of factor variances
VarList <- lapply(Groups, function (G) {
Params[(Params$paramHeader == "Variances") & (Params$param == G), "est"]
})
## Make a list of item intercepts (and thresholds and residual variances)
## THIS IS A HACK - IT ONLY CONSIDERS THE CONTINUOUS CASE. PLEASE FIX LATER TO ALSO CONSIDER THE CATEGORICAL CASE!!
categorical <- FALSE
## Make a list of items names by timepoint
GroupItems <- lapply(Groups, function (G) {
ItemNames <- unique(Params[grepl(paste0(G,".BY"), Params$paramHeader), "param"])
})
## now fetch NuList
NuList <- lapply(Groups, function (G) {
sapply(GroupItems[[G]], function (I) {
## Intercept value for Item I, Group G
Params[(Params$paramHeader == "Intercepts") & (Params$param == I), "est"]
})
})
## ThreshList and ThetaList do not exist here
ThreshList <- NULL
ThetaList <- NULL
## Now fetch item SDs
## THIS IS A HACK - IT ONLY CONSIDERS THE CONTINUOUS CASE. PLEASE FIX LATER TO ALSO CONSIDER THE CATEGORICAL CASE!!
## Should probably throw an error if sampstat is not included
AllSDs <- sqrt(diag(fit$sampstat$covariances))
RawSDList <- lapply(Groups, function (G) {
AllSDs[GroupItems[[G]]]
})
## Do "pooled" and "glass" separately
if (dtype == "pooled") {
# Since sample sizes are the same, pooled just means "average"
SDList <- lapply(Groups, function (G) {
(RawSDList[[G]] + RawSDList[[RefGroup]]) / 2
})
} else if (dtype == "glass") {
ItemSDs <- RawSDList[[RefGroup]]
SDList <- lapply(Groups, function (x) {ItemSDs})
} else {
stop("Only \"pooled\" and \"glass\" SD types are supported")
}
} else { ## Group Invariance testing
## Get an array of group names.
Groups <- unique(fit$parameters$unstandardized$Group)
names(Groups) <- Groups
## If RefGroup is a string, turn it into an index, else issue a warning that maybe it should be a string
if (is.character(RefGroup)) {
RefGroup <- match(RefGroup, Groups)
} else {
warning(paste("It is recommended that you provide the name of the reference group as a string; see ?mplus_dmacs. The reference group being used is", Groups[RefGroup]))
}
## Because it will save a lot of space, and just make life easier, let's give fit$parameters$unstandardized a name
Params <- fit$parameters$unstandardized
## Get an array of factor names
FactorNames = unique(Params[which(Params$paramHeader == "Means"),"param"])
names(FactorNames) <- FactorNames
## Get an array of item names
ItemNames <- unique(Params[grepl(".BY", Params$paramHeader), "param"])
names(ItemNames) <- ItemNames
## Make a list of loading matrices
LambdaList <- lapply(Groups, function (G) {
sapply(FactorNames, function (Fac) {
sapply(ItemNames, function (I) {
## loading value for factor F, item I, Group G
lambda <- Params[(Params$paramHeader == paste0(Fac, ".BY")) & (Params$param == I) & (Params$Group == G), "est"]
## If lambda is empty, return zero
if (length(lambda) == 1) {
lambda
} else {
0
}
})
})
})
## Make a list of factor means
MeanList <- lapply(Groups, function (G) {
sapply(FactorNames, function (Fac) {
## mean value for factor Fac, group G
Params[(Params$paramHeader == "Means") & (Params$param == Fac) & (Params$Group == G), "est"]
})
})
## Make a list of factor variances
VarList <- lapply(Groups, function (G) {
sapply(FactorNames, function (Fac) {
## variance value for factor Fac, group G
Params[(Params$paramHeader == "Variances") & (Params$param == Fac) & (Params$Group == G), "est"]
})
})
## Make a list of Intercepts/Thresholds
## Check to see if we are in a continuous or categorical context, because ThreshList works very differently in those two contexts
if (length(fit$input$variable$categorical) == 0) {
categorical <- FALSE
## now fetch NuList
NuList <- lapply(Groups, function (G) {
sapply(ItemNames, function (I) {
## Intercept value for Item I, Group G
Params[(Params$paramHeader == "Intercepts") & (Params$param == I) & (Params$Group == G), "est"]
})
})
## ThreshList and ThetaList do not exist here
ThreshList <- NULL
ThetaList <- NULL
} else {
categorical <- TRUE
## If "Scales" is in paramHeader, throw an error and demand THETA parameterization
if ("Scales" %in% Params$paramHeader) stop(cat("Only THETA parameterization is supported for models fit by Mplus. Please insert \n Analysis: parameterization = THETA;"))
## Create NuList which is all zeros.
NuList <- lapply(Groups, function (G) {
sapply(ItemNames, function (I) {
## Intercept value for Item I, Group G
0
})
})
## now fetch ThreshList
ThreshList <- lapply(Groups, function (G) {
lapply(ItemNames, function(I) {
## All threshold values (in a vector... they are in order in "Params") of item I, group G
Params[(Params$paramHeader == "Thresholds") & (grepl(paste0("^", I, "\\$"), Params$param)) & (Params$Group == G), "est"]
})
})
## Now fetch ThetaList
ThetaList <- lapply(Groups, function (G) {
sapply(ItemNames, function (I) {
# All residual variances in a vector
Params[(Params$paramHeader == "Residual.Variances") & (Params$param == I) & (Params$Group == G), "est"]
})
})
}
## All that's left is to compute the SDs... which doesn't sound fun at all
if (categorical) {
if (dtype == "pooled") {
## Make a list of (item SDs and item sample sizes) indexed by the groups
## We need the sample sizes so that we can pool
## This is faster, but harder to follow than making separate SD and SS lists
RawSDList <- lapply (Groups, function (G) {
## MplusAutomation does a terrible job at organizing fit$sampstat - the groups are given the wrong names!!
## So we are going to take the Group name and turn it into an index
GIndex <- which(Groups == G)
## This variable is just for readability
GroupCounts <- fit$sampstat[[GIndex]]$proportions.counts
## Make a vector of item SDs
sapply(ItemNames, function (I) {
## Get the counts for each category, make a vector with the right number in each category, then get the SD
ItemCounts <- GroupCounts[grepl(I, GroupCounts$variable),]
ItemData <- NULL
for (i in 1:length(ItemCounts)) {
ItemData <- c(ItemData, rep(ItemCounts[i, "category"], times = ItemCounts[i, "count"]))
}
c(sd(ItemData), length(ItemData))
})
})
SDList <- lapply (Groups, function (G) {
## everything is vectorized here, so we can just have at it, I hope!!
(RawSDList[[G]][1,]*(RawSDList[[G]][2,] - 1) + RawSDList[[RefGroup]][1,]*(RawSDList[[RefGroup]][2,] - 1)) /
(RawSDList[[G]][2,] - 1 + RawSDList[[RefGroup]][2,] - 1)
})
} else if (dtype == "glass") {
## MplusAutomation organizes fit$sampstat strangely, so we need the index here
GroupCounts <- fit$sampstat[[RefGroup]]$proportions.counts
## Make a vector of item SDs
ItemSDs <- sapply(ItemNames, function (I) {
## Get the counts for each category, make a vector with the right number in each category, then get the SD
ItemCounts <- GroupCounts[grepl(I, GroupCounts$variable),]
ItemData <- NULL
for (i in 1:length(ItemCounts)) {
ItemData <- c(ItemData, rep(ItemCounts[i, "category"], times = ItemCounts[i, "count"]))
}
sd(ItemData)
})
## Now, lapply that vector for each group
SDList <- lapply(Groups, function (x) {ItemSDs})
} else {
stop("Only \"pooled\" and \"glass\" SD types are supported")
}
} else { ## We are in continuous case, and should extract SDs from sampstat if we can... estimate from model (with warning) if no sampstat
if (dtype == "pooled") {
## Should probably throw an error if sampstat is not included
RawSDList <- lapply(Groups, function (G) {
## MplusAutomation does a terrible job at organizing fit$sampstat - the groups are given the wrong names!!
## So we are going to take the Group name and turn it into an index
GIndex <- which(Groups == G)
sqrt(diag(fit$sampstat[[GIndex]]$covariances))
})
## Now we need to fetch the group sample sizes... which MplusAutomation does not do for us!!!
FitLines <- readLines(fit0)
GroupCounts <- lapply (Groups, function (G) {
## The first time something like "Group GROUP1" shows us is the sample size line for that group
GroupCountLine <- FitLines[grepl(paste("Group", G), FitLines)][1]
JustGroupCount <- gsub(paste("Group", G), "", GroupCountLine)
## The only thing left in the string is spaces and the group sample size
as.numeric(JustGroupCount)
})
## Now, let's pool the results
SDList <- lapply(Groups, function (G) {
(RawSDList[[G]]*(GroupCounts[[G]]-1) + RawSDList[[RefGroup]]*(GroupCounts[[RefGroup]]-1)) /
((GroupCounts[[G]]-1) + (GroupCounts[[RefGroup]]-1))
})
} else if (dtype == "glass") {
ItemSDs <- sqrt(diag(fit$sampstat[[RefGroup]]$covariances))
SDList <- lapply(Groups, function (x) {ItemSDs})
} else {
stop("Only \"pooled\" and \"glass\" SD types are supported")
}
}
}
Results <- dmacs_summary(LambdaList, NuList,
MeanList, VarList, SDList,
Groups, RefGroup,
ThreshList, ThetaList,
categorical)
## Note to self - we may need to insert some names here!!
Results
}
ddueber/dmacs documentation built on Feb. 8, 2023, 11:31 p.m.
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Defines functions mplus_dmacs
Documented in mplus_dmacs
#' Summary of measurement nonequivalence effects
#'
#' \code{mplus_dmacs} returns a summary of measurement non-equivalence
#' effects given an Mplus .out file.
#'
#' @param fit is an Mplus .out file of a multigroup CFA analysis. The default
#' is to launch a window for choosing the file.
#' @param RefGroup can be the name of the reference group (as a string),
#' or the index of the reference group (as a number). RefGroup defaults to
#' the first group if no value is provided. It is strongly recommended to
#' provide the reference group as a string, since group names in data are
#' often ordered by their appearance in the data, not alphabetically.
#' @param dtype described the pooling of standard deviations for use in the
#' denominator of the dmacs effect size. Possibilities are "pooled" for
#' pooled standard deviations, or "glass" for always using the standard
#' deviation of the reference group.
#'
#' @return A list, indexed by group, of lists of measurement nonequivalence
#' effects from Nye and Drasgow (2011), including dmacs, expected bias in
#' the mean score by item,
#' expected bias in the mean total score, and expected bias in the variance
#' of the total score. Expected bias in the variance of the total score is
#' only supplied for unidimensional models in the current version of this
#' package
#'
#'
#' @section References:
#' Nye, C. & Drasgow, F. (2011). Effect size indices for analyses of
#' measurement equivalence: Understanding the practical importance of
#' differences between groups. \emph{Journal of Applied Psychology, 96}(5),
#' 966-980.
#' @export
mplus_dmacs <- function(fit = file.choose(), RefGroup = 1, dtype = "pooled", MEtype = "Group") {
## We will need the raw text file later if continuous variables with pooled dtype
fit0 <- fit
## Read in the model with MplusAutomation
if (!any(class(fit) == "mplus.model")) {
fit <- MplusAutomation::readModels(fit)
}
## Things proceed completely differently for longitudinal vs group invariance
if (grepl("ong", MEtype, fixed = TRUE)) {
## Because it will save a lot of space, and just make life easier, let's give fit$parameters$unstandardized a name
Params <- fit$parameters$unstandardized
## Group names are now factor names
Groups <- unique(Params[grepl(".BY", Params$paramHeader), "paramHeader"])
Groups <- gsub('.{3}$', '', Groups)
names(Groups) <- Groups
## If RefGroup is a string, turn it into an index, else issue a warning that maybe it should be a string
if (is.character(RefGroup)) {
RefGroup <- match(RefGroup, Groups)
} else {
warning(paste("It is recommended that you provide the name of the factor for the reference time to RefGroup = as a string; see ?mplus_dmacs. The reference group being used is", Groups[RefGroup]))
}
## Now, let's grab the loading vectors for each timepoint
## Warning! No item names are attached to these loadings
LambdaList <- lapply (Groups, function (G) {
as.matrix(Params[(Params$paramHeader == paste0(G, ".BY")), "est"])
})
## Get an array of item names for the reference time. We will need this later to attach names to items
ItemNames <- unique(Params[grepl(paste0(Groups[RefGroup],".BY"), Params$paramHeader), "param"])
names(ItemNames) <- ItemNames
## Make a list of factor means
MeanList <- lapply(Groups, function (G) {
Params[(Params$paramHeader == "Means") & (Params$param == G), "est"]
})
## Make a list of factor variances
VarList <- lapply(Groups, function (G) {
Params[(Params$paramHeader == "Variances") & (Params$param == G), "est"]
})
## Make a list of item intercepts (and thresholds and residual variances)
## THIS IS A HACK - IT ONLY CONSIDERS THE CONTINUOUS CASE. PLEASE FIX LATER TO ALSO CONSIDER THE CATEGORICAL CASE!!
categorical <- FALSE
## Make a list of items names by timepoint
GroupItems <- lapply(Groups, function (G) {
ItemNames <- unique(Params[grepl(paste0(G,".BY"), Params$paramHeader), "param"])
})
## now fetch NuList
NuList <- lapply(Groups, function (G) {
sapply(GroupItems[[G]], function (I) {
## Intercept value for Item I, Group G
Params[(Params$paramHeader == "Intercepts") & (Params$param == I), "est"]
})
})
## ThreshList and ThetaList do not exist here
ThreshList <- NULL
ThetaList <- NULL
## Now fetch item SDs
## THIS IS A HACK - IT ONLY CONSIDERS THE CONTINUOUS CASE. PLEASE FIX LATER TO ALSO CONSIDER THE CATEGORICAL CASE!!
## Should probably throw an error if sampstat is not included
AllSDs <- sqrt(diag(fit$sampstat$covariances))
RawSDList <- lapply(Groups, function (G) {
AllSDs[GroupItems[[G]]]
})
## Do "pooled" and "glass" separately
if (dtype == "pooled") {
# Since sample sizes are the same, pooled just means "average"
SDList <- lapply(Groups, function (G) {
(RawSDList[[G]] + RawSDList[[RefGroup]]) / 2
})
} else if (dtype == "glass") {
ItemSDs <- RawSDList[[RefGroup]]
SDList <- lapply(Groups, function (x) {ItemSDs})
} else {
stop("Only \"pooled\" and \"glass\" SD types are supported")
}
} else { ## Group Invariance testing
## Get an array of group names.
Groups <- unique(fit$parameters$unstandardized$Group)
names(Groups) <- Groups
## If RefGroup is a string, turn it into an index, else issue a warning that maybe it should be a string
if (is.character(RefGroup)) {
RefGroup <- match(RefGroup, Groups)
} else {
warning(paste("It is recommended that you provide the name of the reference group as a string; see ?mplus_dmacs. The reference group being used is", Groups[RefGroup]))
}
## Because it will save a lot of space, and just make life easier, let's give fit$parameters$unstandardized a name
Params <- fit$parameters$unstandardized
## Get an array of factor names
FactorNames = unique(Params[which(Params$paramHeader == "Means"),"param"])
names(FactorNames) <- FactorNames
## Get an array of item names
ItemNames <- unique(Params[grepl(".BY", Params$paramHeader), "param"])
names(ItemNames) <- ItemNames
## Make a list of loading matrices
LambdaList <- lapply(Groups, function (G) {
sapply(FactorNames, function (Fac) {
sapply(ItemNames, function (I) {
## loading value for factor F, item I, Group G
lambda <- Params[(Params$paramHeader == paste0(Fac, ".BY")) & (Params$param == I) & (Params$Group == G), "est"]
## If lambda is empty, return zero
if (length(lambda) == 1) {
lambda
} else {
0
}
})
})
})
## Make a list of factor means
MeanList <- lapply(Groups, function (G) {
sapply(FactorNames, function (Fac) {
## mean value for factor Fac, group G
Params[(Params$paramHeader == "Means") & (Params$param == Fac) & (Params$Group == G), "est"]
})
})
## Make a list of factor variances
VarList <- lapply(Groups, function (G) {
sapply(FactorNames, function (Fac) {
## variance value for factor Fac, group G
Params[(Params$paramHeader == "Variances") & (Params$param == Fac) & (Params$Group == G), "est"]
})
})
## Make a list of Intercepts/Thresholds
## Check to see if we are in a continuous or categorical context, because ThreshList works very differently in those two contexts
if (length(fit$input$variable$categorical) == 0) {
categorical <- FALSE
## now fetch NuList
NuList <- lapply(Groups, function (G) {
sapply(ItemNames, function (I) {
## Intercept value for Item I, Group G
Params[(Params$paramHeader == "Intercepts") & (Params$param == I) & (Params$Group == G), "est"]
})
})
## ThreshList and ThetaList do not exist here
ThreshList <- NULL
ThetaList <- NULL
} else {
categorical <- TRUE
## If "Scales" is in paramHeader, throw an error and demand THETA parameterization
if ("Scales" %in% Params$paramHeader) stop(cat("Only THETA parameterization is supported for models fit by Mplus. Please insert \n Analysis: parameterization = THETA;"))
## Create NuList which is all zeros.
NuList <- lapply(Groups, function (G) {
sapply(ItemNames, function (I) {
## Intercept value for Item I, Group G
0
})
})
## now fetch ThreshList
ThreshList <- lapply(Groups, function (G) {
lapply(ItemNames, function(I) {
## All threshold values (in a vector... they are in order in "Params") of item I, group G
Params[(Params$paramHeader == "Thresholds") & (grepl(paste0("^", I, "\\$"), Params$param)) & (Params$Group == G), "est"]
})
})
## Now fetch ThetaList
ThetaList <- lapply(Groups, function (G) {
sapply(ItemNames, function (I) {
# All residual variances in a vector
Params[(Params$paramHeader == "Residual.Variances") & (Params$param == I) & (Params$Group == G), "est"]
})
})
}
## All that's left is to compute the SDs... which doesn't sound fun at all
if (categorical) {
if (dtype == "pooled") {
## Make a list of (item SDs and item sample sizes) indexed by the groups
## We need the sample sizes so that we can pool
## This is faster, but harder to follow than making separate SD and SS lists
RawSDList <- lapply (Groups, function (G) {
## MplusAutomation does a terrible job at organizing fit$sampstat - the groups are given the wrong names!!
## So we are going to take the Group name and turn it into an index
GIndex <- which(Groups == G)
## This variable is just for readability
GroupCounts <- fit$sampstat[[GIndex]]$proportions.counts
## Make a vector of item SDs
sapply(ItemNames, function (I) {
## Get the counts for each category, make a vector with the right number in each category, then get the SD
ItemCounts <- GroupCounts[grepl(I, GroupCounts$variable),]
ItemData <- NULL
for (i in 1:length(ItemCounts)) {
ItemData <- c(ItemData, rep(ItemCounts[i, "category"], times = ItemCounts[i, "count"]))
}
c(sd(ItemData), length(ItemData))
})
})
SDList <- lapply (Groups, function (G) {
## everything is vectorized here, so we can just have at it, I hope!!
(RawSDList[[G]][1,]*(RawSDList[[G]][2,] - 1) + RawSDList[[RefGroup]][1,]*(RawSDList[[RefGroup]][2,] - 1)) /
(RawSDList[[G]][2,] - 1 + RawSDList[[RefGroup]][2,] - 1)
})
} else if (dtype == "glass") {
## MplusAutomation organizes fit$sampstat strangely, so we need the index here
GroupCounts <- fit$sampstat[[RefGroup]]$proportions.counts
## Make a vector of item SDs
ItemSDs <- sapply(ItemNames, function (I) {
## Get the counts for each category, make a vector with the right number in each category, then get the SD
ItemCounts <- GroupCounts[grepl(I, GroupCounts$variable),]
ItemData <- NULL
for (i in 1:length(ItemCounts)) {
ItemData <- c(ItemData, rep(ItemCounts[i, "category"], times = ItemCounts[i, "count"]))
}
sd(ItemData)
})
## Now, lapply that vector for each group
SDList <- lapply(Groups, function (x) {ItemSDs})
} else {
stop("Only \"pooled\" and \"glass\" SD types are supported")
}
} else { ## We are in continuous case, and should extract SDs from sampstat if we can... estimate from model (with warning) if no sampstat
if (dtype == "pooled") {
## Should probably throw an error if sampstat is not included
RawSDList <- lapply(Groups, function (G) {
## MplusAutomation does a terrible job at organizing fit$sampstat - the groups are given the wrong names!!
## So we are going to take the Group name and turn it into an index
GIndex <- which(Groups == G)
sqrt(diag(fit$sampstat[[GIndex]]$covariances))
})
## Now we need to fetch the group sample sizes... which MplusAutomation does not do for us!!!
FitLines <- readLines(fit0)
GroupCounts <- lapply (Groups, function (G) {
## The first time something like "Group GROUP1" shows us is the sample size line for that group
GroupCountLine <- FitLines[grepl(paste("Group", G), FitLines)][1]
JustGroupCount <- gsub(paste("Group", G), "", GroupCountLine)
## The only thing left in the string is spaces and the group sample size
as.numeric(JustGroupCount)
})
## Now, let's pool the results
SDList <- lapply(Groups, function (G) {
(RawSDList[[G]]*(GroupCounts[[G]]-1) + RawSDList[[RefGroup]]*(GroupCounts[[RefGroup]]-1)) /
((GroupCounts[[G]]-1) + (GroupCounts[[RefGroup]]-1))
})
} else if (dtype == "glass") {
ItemSDs <- sqrt(diag(fit$sampstat[[RefGroup]]$covariances))
SDList <- lapply(Groups, function (x) {ItemSDs})
} else {
stop("Only \"pooled\" and \"glass\" SD types are supported")
}
}
}
Results <- dmacs_summary(LambdaList, NuList,
MeanList, VarList, SDList,
Groups, RefGroup,
ThreshList, ThetaList,
categorical)
## Note to self - we may need to insert some names here!!
Results
}
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