Fit List of Latent Variable Models

Fit the same latent variable model, for a (potentially large) number of datasets.

Usage

lavaanList(model = NULL, dataList = NULL, dataFunction = NULL, 
    dataFunction.args = list(), ndat = length(dataList), cmd = "lavaan", 
    ..., store.slots = c("partable"), FUN = NULL, show.progress = FALSE,
    store.failed = FALSE, parallel = c("no", "multicore", "snow"), 
    ncpus = max(1L, parallel::detectCores() - 1L), cl = NULL, iseed = NULL)

semList(model = NULL, dataList = NULL, dataFunction = NULL,
    dataFunction.args = list(), ndat = length(dataList),
    ..., store.slots = c("partable"), FUN = NULL, show.progress = FALSE,
    store.failed = FALSE, parallel = c("no", "multicore", "snow"), 
    ncpus = max(1L, parallel::detectCores() - 1L), cl = NULL, iseed = NULL)

cfaList(model = NULL, dataList = NULL, dataFunction = NULL,
    dataFunction.args = list(), ndat = length(dataList),
    ..., store.slots = c("partable"), FUN = NULL, show.progress = FALSE,
    store.failed = FALSE, parallel = c("no", "multicore", "snow"), 
    ncpus = max(1L, parallel::detectCores() - 1L), cl = NULL, iseed = NULL)

Arguments

model: A description of the user-specified model. Typically, the model is described using the lavaan model syntax. See model.syntax for more information. Alternatively, a parameter table (eg. the output of the lavParTable() function) is also accepted.
dataList: List. Each element contains a full data frame containing the observed variables used in the model.
dataFunction: Function. A function that generated a full data frame containing the observed variables used in the model. It can also be a matrix, if the columns are named.
dataFunction.args: List. Optional list of arguments that are passed to the dataFunction function.
ndat: Integer. The number of datasets that should be generated using the dataFunction function.
cmd: Character. Which command is used to run the sem models. The possible choices are "sem", "cfa" or "lavaan", determining how we deal with default options.
...: Other named arguments for lavaan function.
store.slots: Character vector. Which slots (from a lavaan object) should be stored for each dataset? The possible choices are "timing", "partable", "data", "samplestats", "vcov", "test", "optim", "h1", "loglik", or "implied". Finally, "all" selects all slots.
FUN: Function. A function which when applied to the lavaan object returns the information of interest.
store.failed: Logical. If TRUE, write (to tempdir()) the dataset and (if available) the fitted object when the estimation for a particular dataset somehow failed. This will allow posthoc inspection of the problem.
parallel: The type of parallel operation to be used (if any). If missing, the default is "no".
ncpus: Integer. The number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs.
cl: An optional parallel or snow cluster for use if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the lavaanList call.
iseed: An integer to set the seed. Or NULL if no reproducible seeds are needed. To make this work, make sure the first RNGkind() element is "L'Ecuyer-CMRG". You can check this by typing RNGkind() in the console. You can set it by typing RNGkind("L'Ecuyer-CMRG"), before the lavaanList functions are called.
show.progress: If TRUE, show information for each dataset.

Value

An object of class lavaanList, for which several methods are available, including a summary method.

Examples

# The Holzinger and Swineford (1939) example
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

# a data generating function
generateData <- function() lavSimulateData(HS.model, sample.nobs = 100)

set.seed(1234)
fit <- semList(HS.model, dataFunction = generateData, ndat = 5,
               store.slots = "partable")

# show parameter estimates, per dataset
coef(fit)
#>                        [,1]        [,2]       [,3]        [,4]        [,5]
#> visual=~x2       0.91791609  0.92199215 0.92779203  0.99754819  0.99931892
#> visual=~x3       0.72505352  0.91797204 1.03929219  0.87855241  1.23528238
#> textual=~x5      0.91799277  1.09583845 0.95492722  0.83559376  0.74619256
#> textual=~x6      0.93577780  1.52055851 2.18398470  0.62835639  0.91749152
#> speed=~x8        1.32561097  0.89057977 1.52617465  0.76516216  0.93819692
#> speed=~x9        0.88858749  0.93360004 0.90851427  0.73308589  0.81127187
#> x1~~x1           0.72211092  0.60247837 0.91198778  0.85708214  1.22479435
#> x2~~x2           0.75299324  1.28994192 1.16727712  1.14479769  1.26660493
#> x3~~x3           1.15434338  1.03954685 0.94474628  0.71863907  0.80241954
#> x4~~x4           0.82758068  1.48125061 1.22981959  0.31992506  0.62025576
#> x5~~x5           0.74164972  1.05062993 1.05305411  0.98847761  1.13842967
#> x6~~x6           0.96232185  0.98753272 0.21967451  1.42791143  1.08105890
#> x7~~x7           1.34412447  0.82389693 1.38099916  0.77236139  0.99883918
#> x8~~x8           0.75586122  0.94070899 0.66176461  1.08818301  0.54392141
#> x9~~x9           1.25410648  1.10666300 0.98451747  0.66116027  0.92452652
#> visual~~visual   1.33868030  0.86134536 0.53604774  1.16373925  0.74400241
#> textual~~textual 1.03360092  0.52264499 0.37673118  1.58292303  1.27528568
#> speed~~speed     0.89982900  1.04999825 0.63539384  1.88852048  1.08798753
#> visual~~textual  0.13953853 -0.08091131 0.01454512 -0.17113781  0.16805796
#> visual~~speed    0.01923061  0.11188598 0.07747246 -0.07639808 -0.05479958
#> textual~~speed   0.12411009 -0.06291862 0.10011408  0.23035747 -0.13796677

Usage

Arguments

Value

See also

Examples