fit_many {modelbpp} | R Documentation |
Fit a list of models to a dataset.
fit_many(
model_list,
sem_out,
original = NULL,
parallel = FALSE,
ncores = max(parallel::detectCores(logical = FALSE) - 1, 1),
make_cluster_args = list(),
progress = TRUE,
verbose = TRUE
)
model_list |
A list of parameter
tables to be used by
|
sem_out |
The output from an
structural equation modeling
function. It currently supports
lavaan::lavaan objects
only. Usually
the one used in |
original |
String. If provided,
it should be a name of a model
in |
parallel |
If |
ncores |
Numeric. The number of
CPU cores to be used if |
make_cluster_args |
A list of
named arguments to be passed to
|
progress |
Whether a progress
bar will be displayed, implemented
by the |
verbose |
Whether additional
messages will be displayed, such
as the expected processing time.
Default is |
It receives a list of
models, defined by lavaan
parameter
tables (usually generated by
model_set()
, get_add()
or
get_drop()
),
and fit them to a dataset stored
in a lavaan
-class object.
This function is called by
model_set()
and usually users do
not need to call it. It is exported
for advanced users.
An object of the class
sem_outs
, a list with the
following major elements:
fit
: A named list of
lavaan::lavaan()
output objects or
update()
for fitting a model with
the added parameters.
change
: A numeric vector, of the
same length as fit
. The change
in model df for each fit compared
to the original model. A
positive number denotes one less
free parameter. A negative number
denotes one more free parameter or
one less constraint.
converged
: A named vector of
boolean values, of the same length
as fit
. Indicates whether each
fit converged or not.
post_check
: A named vector of
boolean values, of the same length
as fit
. Indicates whether the
solution of each fit is
admissible or not. Checked by
lavaan::lavInspect()
with
the what
argument set to
"post.check"
.
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
library(lavaan)
dat <- dat_path_model
mod <-
"
x3 ~ a*x1 + b*x2
x4 ~ a*x1
ab := a*b
"
fit <- sem(mod, dat_path_model, fixed.x = TRUE)
mod_to_add <- get_add(fit)
fit_many(mod_to_add, fit)