Package 'AlignLV' reference manual

Title:	Multiple Group Item Response Theory Alignment Helpers for 'lavaan' and 'mirt'
Description:	Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments.
Authors:	Maxwell Mansolf [aut, cre]
Maintainer:	Maxwell Mansolf <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0.0
Built:	2024-11-04 04:49:36 UTC
Source:	https://github.com/mmansolf/alignlv

Runs alignment optimizer

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

align.optim(
  stacked,
  n,
  estimator,
  nstarts = 50,
  ncores = 3,
  hyper.first,
  center.means,
  eps.alignment,
  clf.ignore.quantile,
  verbose
)
align.optim(
  stacked,
  n,
  estimator,
  nstarts = 50,
  ncores = 3,
  hyper.first,
  center.means,
  eps.alignment,
  clf.ignore.quantile,
  verbose
)

Arguments

`stacked`	Stacked parameter estimates from `stackEstimates`
`n`	Sample size in each group
`estimator`	See `Alignment` documentation.
`nstarts`	Number of starting values for alignment; default is 10
`ncores`	See `Alignment` documentation.
`hyper.first`	See `Alignment` documentation.
`center.means`	See `Alignment` documentation.
`eps.alignment`	See `Alignment` documentation.
`clf.ignore.quantile`	See `Alignment` documentation.
`verbose`	See `Alignment` documentation.

Details

See example for Alignment for examples

Value

A list of results from multiple runs of the alignment optimizer:

mv Means and variances from each alignment run.
parout A table of outputs from link[stats]{optim} containing the function values, convergence information, and resulting estimates of means and variances from each run.
nFailedRuns The number of runs that failed to complete. An error is returned if no runs fail.

Multiple-Group Factor Analysis Alignment from `mirt` or `lavaan`

Description

Performs alignment (https://www.statmodel.com/Alignment.shtml) using single-group models estimated in mirt or lavaan.

Usage

Alignment(
  fitList,
  estimator,
  SE = FALSE,
  eps.alignment = 0.01,
  clf.ignore.quantile = 0.1,
  bifactor.marginal = FALSE,
  hyper.first = "variances",
  center.means = TRUE,
  nstarts = 10,
  ncores = 1,
  verbose = TRUE
)
Alignment(
  fitList,
  estimator,
  SE = FALSE,
  eps.alignment = 0.01,
  clf.ignore.quantile = 0.1,
  bifactor.marginal = FALSE,
  hyper.first = "variances",
  center.means = TRUE,
  nstarts = 10,
  ncores = 1,
  verbose = TRUE
)

Arguments

`fitList`	A `list` of fitted model objects. Currently only works for single-group, unidimensional or bifactor models with no covariates estimated in `mirt` or `lavaan`.
`estimator`	The model type used, either `'mirt.grm'` for the graded response model estimated in `mirt` or `'lavaan.ordered'` for the categorical factor analysis model applied by `lavaan` when the `ordered` input includes all variables in the model.
`SE`	Whether to also return standard errors from parameter estimates after alignment. SE's are transformed using the delta method from those provided in the original model objects, which must (for `mirt`), have been fitted with standard errors estimated (`SE=TRUE`).
`eps.alignment`	A numeric scalar for the alignment simplicity function, given by (Asparouhov & Muthén, 2014, Structural Equation Modeling): $\sqrt{\sqrt{x^2+\epsilon}}$ where $x$ is the difference between corresponding estimates in each pair of aligned models. Lower values may cause numerical instability; default 0.01
`clf.ignore.quantile`	Another protection from numerical instability; CLF values less than the `clf.ignore.quantile` of the full set of CLF values are ignored when calculating the complexity function at each step. Default 0.1 for removing the lowest 10% of CLF values.
`bifactor.marginal`	A logical scalar indicating whether, for bifactor models, alignment should take place on the marginal, rather than conditional, metric for slopes (Ip, 2010, Applied Psychological Measurement).
`hyper.first`	A string scalar denoting which hyperparameter to align first. Asparouhov & Muthén (2014) align all parameters simultaneously (`'no'`); `'variances'` (default) performs a two-step process, first aligning variances, then aligning means conditional on variance estimates from the first step. `'means'` does the reverse.
`center.means`	A logical scalar. Alignment fixes the first group's mean to zero to estimate the others. If `center.means` is `TRUE` (default), aligned means and models are returned after subtracting the weighted mean `weighted.mean` from all mean estimates, yielding a (weighted) grand mean of zero. Variances are automatically rescaled such that their weighted product (i.e., log of weighted mean of `e^(variance)`) is 1.
`nstarts`	Number of starting values for alignment; default is 10
`ncores`	Number of processor cores to distribute alignment starts across; on systems that support multicore processing, using additional cores can speed up the alignment step by roughly a factor of the number of cores. Defaults to 1 for no parallel processing. Requires the `doRNG` package and defaults to sequential processing if not installed.
`verbose`	Whether stuff gets printed to the console. May help with debugging.

Details

Currently, no automated process provides statistical tests for DIF. Instead, I recommend interpreting the DIF impact directly by comparing scores obtained from a single-group model combining all groups, and the multiple models produced by Alignment. If standard errors are requested from getEstimates.mirt, or getEstimates.lavaan, and then the corresponding transformEstimates.mirt.grm or transformEstimates.lavaan.ordered is applied, SE's after alignment can be obtained and used for multiple comparison testing, but this is not yet automated. Alternatively, consider re-fitting models with means and variances fixed to those obtained from alignment to obtain these standard errors. In the latter case, especially when priors are used as in mirt, your estimates may not match those from Alignment exactly.

For lavaan, the metric for alignment must be the "theta" parameterization, which is not the default, in order to properly search for latent means and variances, because only then do the transformations apply. My current thinking: under the delta parameterization, the transformed estimates (calculate delta, incorporate it into parameters, then transform parameters, BUT don't reverse the delta transformation) do NOT yield an equivalent model, but DO yield a model that can be compared across groups. In order to get an equivalent model, you also need to reverse the delta transformation at the end. To account for this, if the the extra argument toCompare should be turned on TRUE if transformed parameters are to be compared for equivalence across groups. Turning it off results in NOT applying the reverse of the delta transformation at the end. This currently is fixed to TRUE and cannot be modified, but you can access transformEstimates.lavaan.ordered directly if you want to play around.

If parallel==TRUE, a parallel backend with the doParallel package leverages multi-core processing if the number of cores specified in ncores is greater than one. Uses %dorng% to pass the R session's seed to the alignment optimizer, such that you can replicate random starts with set.seed (see example).

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

A list with the following elements:

fit A list of fitted objects of type mirt or lavaan, depending on the estimator, where models were re-estimated with means and variances set to those obtained from alignment.
est.og A nested list of parameter estimates and standard errors provided to the alignment optimizer from the provided models. Each element corresponds to a provided model, and each element thereof corresponds to a parameter name (e.g., a and d parameters from mirt.grm) and contains a matrix of the corresponding estimates.
est The estimates from est.og, transformed after alignment using the obtained mean and variance estimates therefrom.
hypers A list of two-element numeric vectors, where mean gives the estimated mean from alignment in the corresponding group, and var the estimated variance.
parout Optimizer output for the alignment step, used to examine convergence. Contains the following columns:
- f The final complexity function value from alignment.
- convergence The convergence output from optim
- M.2 to M.(number of groups minus 2) The estimated means from alignment
- V.2 to M.(number of groups minus 2) The estimated variances from alignment

Examples

#load data
library(mirt)
library(lavaan)
library(purrr)
library(tibble)
library(magrittr)
dat=expand.table(Bock1997)
#fit configural models
fit.mirt=mirt(dat,1,SE=TRUE)
fit.lavaan=cfa(model='G =~ Item.1+Item.2+Item.3',data=dat,
               ordered=c('Item.1','Item.2','Item.3'),
               std.lv=TRUE,parameterization='delta')
(fit.lavaan@ParTable)%>%tibble::as_tibble()%>%print(n=Inf)
#test stuff
tab=fit.lavaan@ParTable
tab$start[23]=3
tab$est[23]=3
fit.lavaan2=lavaan(tab,data=fit.lavaan@Data)

#get estimates
est.mirt=getEstimates.mirt(fit.mirt,SE=TRUE,bifactor.marginal=FALSE)
est.lavaan=getEstimates.lavaan(fit.lavaan,SE=TRUE)

#test transformations
newMean=10
newVar=2
test.mirt=transformEstimates.mirt.grm(newMean,newVar,est.mirt)
test.lavaan=transformEstimates.lavaan.ordered(
              newMean,newVar,est.lavaan,toCompare=TRUE)
#load and test equivalence
tfit.mirt=loadEstimates.mirt.grm(fit.mirt,newMean,newVar,newpars=test.mirt,
                                 verbose=TRUE)
test.mirt=mirt::coef(fit.mirt)
test.mirt
tfit.lavaan=loadEstimates.lavaan.ordered(
              fit.lavaan,newMean,newVar,newpars=test.lavaan,
              verbose=TRUE)
tfit.lavaan@ParTable%>%tibble::as_tibble()%>%print(n=Inf)
test.lavaan

#now on stacked estimates
estList=list(est.mirt%>%purrr::imap(function(x,n){
  rownames(x)[2]=paste0(rownames(x)[2],'_ho')
  if(!n%in%c('a','se.a'))colnames(x)[2]=paste0(colnames(x)[2],'_ho')
  x
}),est.mirt%>%purrr::imap(function(x,n){
  rownames(x)[1]=paste0(rownames(x)[1],'_hi')
  if(!n%in%c('a','se.a'))colnames(x)[1]=paste0(colnames(x)[1],'_hi')
  x
}))
stack=stackEstimates(estList)
test.stack=transformEstimates.mirt.grm(c(0,0),c(1,1),stack)
sf.stack=SF.mplus3D(c(0,1),stack,combn(1:2,2),c(100,200),'mirt.grm',
                                       eps.alignment=0.01,
                                       clf.ignore.quantile=0.1)
test.stack2=transformEstimates.mirt.grm(c(0,1),c(1,1/2),stack)

#try align?
#lavaan
set.seed(0)
sim.base=list(simdata(a=as.numeric(est.mirt$a),d=est.mirt$d,N=5000,
                      itemtype='graded',sigma=matrix(1),mu=0),
              simdata(a=as.numeric(est.mirt$a),d=est.mirt$d,N=5000,
                      itemtype='graded',sigma=matrix(2),mu=1))
fit.base=sim.base%>%purrr::map(~cfa(model="G =~ Item_1 + Item_2 + Item_3",
                             data=as.data.frame(.),
                             ordered=paste0('Item_',1:3),std.lv=TRUE,
                             parameterization='delta'))
fit.base%>%purrr::map(lavInspect,'est')%>%purrr::transpose()
est.base=purrr::map(fit.base,getEstimates.lavaan,SE=TRUE)
#not run: using parallel processes with ncores=3
set.seed(1)
# align.stack=align.optim(stackEstimates(est.base),c(100,200),nstarts=3,
#                         hyper.first='variances',ncores=3,
#                         eps.alignment=0.01,clf.ignore.quantile=0.1,
#                        estimator='lavaan.ordered',center.means=FALSE,
#                        verbose=TRUE)
# #same seed
# set.seed(1)
# align.stack=align.optim(stackEstimates(est.base),c(100,200),nstarts=3,
#                         hyper.first='variances',ncores=3,
#                         eps.alignment=0.01,clf.ignore.quantile=0.1,
#                         estimator='lavaan.ordered',center.means=FALSE,
#                         verbose=TRUE)
#sequential
align.stack=align.optim(stackEstimates(est.base),c(100,200),nstarts=3,
                        hyper.first='variances',ncores=1,
                        eps.alignment=0.01,clf.ignore.quantile=0.1,
                        estimator='lavaan.ordered',center.means=FALSE,
                        verbose=TRUE)
align.stack
fit.align=Alignment(fit.base,'lavaan.ordered',center.means=FALSE,SE=TRUE,
            verbose=TRUE)

#mirt
fit.base2=list()
for(i in 1:length(sim.base)){
  fit.base2[[i]]=mirt(sim.base[[i]],1,'graded',SE=TRUE)
}
est.base2=purrr::map(fit.base2,getEstimates.mirt,SE=TRUE,
bifactor.marginal=FALSE)
#not run: using parallel processes with ncores=3
# align.stack2=align.optim(stackEstimates(est.base2),c(100,200),nstarts=3,
#                          hyper.first='variances',ncores=3,
#                          eps.alignment=0.01,clf.ignore.quantile=0.1,
#                          estimator='mirt.grm',center.means=FALSE)
align.stack2=align.optim(stackEstimates(est.base2),c(100,200),nstarts=3,
                         hyper.first='variances',ncores=1,
                         eps.alignment=0.01,clf.ignore.quantile=0.1,
                         estimator='mirt.grm',center.means=FALSE,
                         verbose=TRUE)
align.stack2
fit.align2=Alignment(fit.base2,'mirt.grm',center.means=FALSE,SE=TRUE)

#did it work?
fit.align$hypers
fit.align2$hypers
fit.align$est%>%purrr::transpose()%>%purrr::map(~mean(.[[1]]-.[[2]]))
fit.align2$est%>%purrr::transpose()%>%purrr::map(~mean(.[[1]]-.[[2]]))
fit.align$fit
fit.align2$fit
(fit.align$fit%>%purrr::map(~.@ParTable%>%
tibble::as_tibble()%>%dplyr::filter(free!=0))%>%
  purrr::transpose())[c('start','est')]%>%purrr::map(~mean(.[[1]]-.[[2]]))
(fit.align2$fit%>%purrr::map(coef)%>%
    purrr::transpose())[paste0('Item_',1:3)]%>%
    purrr::map(~mean(.[[1]]-.[[2]]))
#appears so!

#load data
library(mirt)
library(lavaan)
library(purrr)
library(tibble)
library(magrittr)
dat=expand.table(Bock1997)
#fit configural models
fit.mirt=mirt(dat,1,SE=TRUE)
fit.lavaan=cfa(model='G =~ Item.1+Item.2+Item.3',data=dat,
               ordered=c('Item.1','Item.2','Item.3'),
               std.lv=TRUE,parameterization='delta')
(fit.lavaan@ParTable)%>%tibble::as_tibble()%>%print(n=Inf)
#test stuff
tab=fit.lavaan@ParTable
tab$start[23]=3
tab$est[23]=3
fit.lavaan2=lavaan(tab,data=fit.lavaan@Data)

#get estimates
est.mirt=getEstimates.mirt(fit.mirt,SE=TRUE,bifactor.marginal=FALSE)
est.lavaan=getEstimates.lavaan(fit.lavaan,SE=TRUE)

#test transformations
newMean=10
newVar=2
test.mirt=transformEstimates.mirt.grm(newMean,newVar,est.mirt)
test.lavaan=transformEstimates.lavaan.ordered(
              newMean,newVar,est.lavaan,toCompare=TRUE)
#load and test equivalence
tfit.mirt=loadEstimates.mirt.grm(fit.mirt,newMean,newVar,newpars=test.mirt,
                                 verbose=TRUE)
test.mirt=mirt::coef(fit.mirt)
test.mirt
tfit.lavaan=loadEstimates.lavaan.ordered(
              fit.lavaan,newMean,newVar,newpars=test.lavaan,
              verbose=TRUE)
tfit.lavaan@ParTable%>%tibble::as_tibble()%>%print(n=Inf)
test.lavaan

#now on stacked estimates
estList=list(est.mirt%>%purrr::imap(function(x,n){
  rownames(x)[2]=paste0(rownames(x)[2],'_ho')
  if(!n%in%c('a','se.a'))colnames(x)[2]=paste0(colnames(x)[2],'_ho')
  x
}),est.mirt%>%purrr::imap(function(x,n){
  rownames(x)[1]=paste0(rownames(x)[1],'_hi')
  if(!n%in%c('a','se.a'))colnames(x)[1]=paste0(colnames(x)[1],'_hi')
  x
}))
stack=stackEstimates(estList)
test.stack=transformEstimates.mirt.grm(c(0,0),c(1,1),stack)
sf.stack=SF.mplus3D(c(0,1),stack,combn(1:2,2),c(100,200),'mirt.grm',
                                       eps.alignment=0.01,
                                       clf.ignore.quantile=0.1)
test.stack2=transformEstimates.mirt.grm(c(0,1),c(1,1/2),stack)

#try align?
#lavaan
set.seed(0)
sim.base=list(simdata(a=as.numeric(est.mirt$a),d=est.mirt$d,N=5000,
                      itemtype='graded',sigma=matrix(1),mu=0),
              simdata(a=as.numeric(est.mirt$a),d=est.mirt$d,N=5000,
                      itemtype='graded',sigma=matrix(2),mu=1))
fit.base=sim.base%>%purrr::map(~cfa(model="G =~ Item_1 + Item_2 + Item_3",
                             data=as.data.frame(.),
                             ordered=paste0('Item_',1:3),std.lv=TRUE,
                             parameterization='delta'))
fit.base%>%purrr::map(lavInspect,'est')%>%purrr::transpose()
est.base=purrr::map(fit.base,getEstimates.lavaan,SE=TRUE)
#not run: using parallel processes with ncores=3
set.seed(1)
# align.stack=align.optim(stackEstimates(est.base),c(100,200),nstarts=3,
#                         hyper.first='variances',ncores=3,
#                         eps.alignment=0.01,clf.ignore.quantile=0.1,
#                        estimator='lavaan.ordered',center.means=FALSE,
#                        verbose=TRUE)
# #same seed
# set.seed(1)
# align.stack=align.optim(stackEstimates(est.base),c(100,200),nstarts=3,
#                         hyper.first='variances',ncores=3,
#                         eps.alignment=0.01,clf.ignore.quantile=0.1,
#                         estimator='lavaan.ordered',center.means=FALSE,
#                         verbose=TRUE)
#sequential
align.stack=align.optim(stackEstimates(est.base),c(100,200),nstarts=3,
                        hyper.first='variances',ncores=1,
                        eps.alignment=0.01,clf.ignore.quantile=0.1,
                        estimator='lavaan.ordered',center.means=FALSE,
                        verbose=TRUE)
align.stack
fit.align=Alignment(fit.base,'lavaan.ordered',center.means=FALSE,SE=TRUE,
            verbose=TRUE)

#mirt
fit.base2=list()
for(i in 1:length(sim.base)){
  fit.base2[[i]]=mirt(sim.base[[i]],1,'graded',SE=TRUE)
}
est.base2=purrr::map(fit.base2,getEstimates.mirt,SE=TRUE,
bifactor.marginal=FALSE)
#not run: using parallel processes with ncores=3
# align.stack2=align.optim(stackEstimates(est.base2),c(100,200),nstarts=3,
#                          hyper.first='variances',ncores=3,
#                          eps.alignment=0.01,clf.ignore.quantile=0.1,
#                          estimator='mirt.grm',center.means=FALSE)
align.stack2=align.optim(stackEstimates(est.base2),c(100,200),nstarts=3,
                         hyper.first='variances',ncores=1,
                         eps.alignment=0.01,clf.ignore.quantile=0.1,
                         estimator='mirt.grm',center.means=FALSE,
                         verbose=TRUE)
align.stack2
fit.align2=Alignment(fit.base2,'mirt.grm',center.means=FALSE,SE=TRUE)

#did it work?
fit.align$hypers
fit.align2$hypers
fit.align$est%>%purrr::transpose()%>%purrr::map(~mean(.[[1]]-.[[2]]))
fit.align2$est%>%purrr::transpose()%>%purrr::map(~mean(.[[1]]-.[[2]]))
fit.align$fit
fit.align2$fit
(fit.align$fit%>%purrr::map(~.@ParTable%>%
tibble::as_tibble()%>%dplyr::filter(free!=0))%>%
  purrr::transpose())[c('start','est')]%>%purrr::map(~mean(.[[1]]-.[[2]]))
(fit.align2$fit%>%purrr::map(coef)%>%
    purrr::transpose())[paste0('Item_',1:3)]%>%
    purrr::map(~mean(.[[1]]-.[[2]]))
#appears so!

Prepare `lavaan` estimates for alignment

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

getEstimates.lavaan(fit, SE = TRUE)
getEstimates.lavaan(fit, SE = TRUE)

Arguments

`fit`	A `lavaan` object compatible with `Alignment`
`SE`	logical; whether to also obtain standard errors.

Details

See example for Alignment for examples

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

A list of estimates in a format amenable to subsequent alignment

Prepare `mirt` estimates for alignment

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

getEstimates.mirt(fit, SE = FALSE, bifactor.marginal = FALSE)
getEstimates.mirt(fit, SE = FALSE, bifactor.marginal = FALSE)

Arguments

`fit`	A `mirt` object compatible with `Alignment`
`SE`	logical; whether to also obtain standard errors.
`bifactor.marginal`	See `Alignment` documentation.

Details

See example for Alignment for examples

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

A list of estimates in a format amenable to subsequent alignment

Estimate `lavaan` models using aligned parameter estimates

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

loadEstimates.lavaan.ordered(
  fit,
  align.mean,
  align.variance,
  newpars,
  do.fit = TRUE,
  verbose = TRUE
)
loadEstimates.lavaan.ordered(
  fit,
  align.mean,
  align.variance,
  newpars,
  do.fit = TRUE,
  verbose = TRUE
)

Arguments

`fit`	A `mirt` object compatible with `Alignment`
`align.mean`	Mean to transform model to.
`align.variance`	Variance to transform model to.
`newpars`	New (transformed) estimates to load into model object.
`do.fit`	Whether to re-fit the model after loading and fixing estimates.
`verbose`	See `Alignment` documentation.

Details

See example for Alignment for examples

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

A lavaan object, based on fit but with modified parameters.

Estimate `mirt` models using aligned parameter estimates

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

loadEstimates.mirt.grm(
  fit,
  align.mean,
  align.variance,
  newpars,
  do.fit = TRUE,
  verbose = TRUE
)
loadEstimates.mirt.grm(
  fit,
  align.mean,
  align.variance,
  newpars,
  do.fit = TRUE,
  verbose = TRUE
)

Arguments

`fit`	A `mirt` object compatible with `Alignment`
`align.mean`	Mean to transform model to.
`align.variance`	Variance to transform model to.
`newpars`	New (transformed) estimates to load into model object.
`do.fit`	Whether to re-fit the model after loading and fixing estimates.
`verbose`	See `Alignment` documentation.

Details

See example for Alignment for examples

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

A mirt, object based on fit but with modified parameters.

Simplicity function for alignment

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

SF.mplus3D(
  pars,
  est,
  comb,
  nobs,
  estimator,
  eps.alignment,
  clf.ignore.quantile,
  hyper = "all",
  otherHyper = NULL
)
SF.mplus3D(
  pars,
  est,
  comb,
  nobs,
  estimator,
  eps.alignment,
  clf.ignore.quantile,
  hyper = "all",
  otherHyper = NULL
)

Arguments

`pars`	Hyperparameters to feed into optimizer
`est`	Estimates to transform, from `getEstimates.mirt` or `getEstimates.lavaan`
`comb`	All combinations of groups from `combn`
`nobs`	Sample size in each group
`estimator`	See `Alignment` documentation.
`eps.alignment`	See `Alignment` documentation.
`clf.ignore.quantile`	See `Alignment` documentation.
`hyper`	Hyperparameter to calculate simplicity function for; see `Alignment` documentation.
`otherHyper`	Non-included hyperparameter

Details

See example for Alignment for examples

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

A value of the simplicity function from Asparouhuv & Muthen, 2014.

Stack estimates for optimization

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

stackEstimates(estList)
stackEstimates(estList)

Arguments

estList

List of estimates from getEstimates.lavaan or getEstimates.mirt to stack to feed into SF.mplus3D

Details

See example for Alignment for examples

Value

A set of estimates prepared for efficient use with SF.mplus3D

Transform `lavaan` estimates using aligned estimates of latent mean and variance

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

transformEstimates.lavaan.ordered(
  align.mean,
  align.variance,
  est,
  toCompare = FALSE
)
transformEstimates.lavaan.ordered(
  align.mean,
  align.variance,
  est,
  toCompare = FALSE
)

Arguments

`align.mean`	Mean to transform model to.
`align.variance`	Variance to transform model to.
`est`	Estimates to transform, from `getEstimates.lavaan`
`toCompare`	Accounts for discrepancies between delta and theta parameterizations; see `Alignment` documentation.

Details

See example for Alignment for examples

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

Estimates in the same structure as from getEstimates.lavaan, but transformed from (assumed) mean 0 and variance 1 to the metric specified by align.mean and align.variance.

Transform `mirt` estimates using aligned estimates of latent mean and variance

Description

Not generally intended to be used on its own, but exported anyway for didactic purposes.

Usage

transformEstimates.mirt.grm(align.mean, align.variance, est)
transformEstimates.mirt.grm(align.mean, align.variance, est)

Arguments

`align.mean`	Mean to transform model to.
`align.variance`	Variance to transform model to.
`est`	Estimates to transform, from `getEstimates.mirt`

Details

See example for Alignment for examples

This program was designed based on the published work of Asparouhov & Muthen, and was not intended to match Mplus exactly, and may not.

Value

Estimates in the same structure as from getEstimates.mirt, but transformed from (assumed) mean 0 and variance 1 to the metric specified by align.mean and align.variance.

Package 'AlignLV'

Help Index

Runs alignment optimizer

Description

Usage

Arguments

Details

Value

Multiple-Group Factor Analysis Alignment from mirt or lavaan

Description

Usage

Arguments

Details

Value

Examples

Prepare lavaan estimates for alignment

Description

Usage

Arguments

Details

Value

Prepare mirt estimates for alignment

Description

Usage

Arguments

Details

Value

Estimate lavaan models using aligned parameter estimates

Description

Usage

Arguments

Details

Value

Estimate mirt models using aligned parameter estimates

Description

Usage

Arguments

Details

Value

Simplicity function for alignment

Description

Usage

Arguments

Details

Value

Stack estimates for optimization

Description

Usage

Arguments

Details

Value

Transform lavaan estimates using aligned estimates of latent mean and variance

Description

Usage

Arguments

Details

Value

Transform mirt estimates using aligned estimates of latent mean and variance

Description

Usage

Arguments

Details

Value

Multiple-Group Factor Analysis Alignment from `mirt` or `lavaan`

Prepare `lavaan` estimates for alignment

Prepare `mirt` estimates for alignment

Estimate `lavaan` models using aligned parameter estimates

Estimate `mirt` models using aligned parameter estimates

Transform `lavaan` estimates using aligned estimates of latent mean and variance

Transform `mirt` estimates using aligned estimates of latent mean and variance