Title:	Model BIC Posterior Probability
Version:	0.1.5
Description:	Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities, using the method presented in Wu, Cheung, and Leung (2020) <doi:10.1080/00273171.2019.1574546>.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Suggests:	knitr, rmarkdown, tinytest
Depends:	R (≥ 4.0.0)
Imports:	lavaan, parallel, pbapply, igraph, manymome
VignetteBuilder:	knitr
URL:	https://sfcheung.github.io/modelbpp/
BugReports:	https://github.com/sfcheung/modelbpp/issues
LazyData:	true
NeedsCompilation:	no
Packaged:	2024-09-16 14:27:06 UTC; shufa
Author:	Shu Fai Cheung [aut, cre], Huiping Wu [aut], Shing On Leung [aut], Ivan Jacob Agaloos Pesigan [ctb]
Maintainer:	Shu Fai Cheung <shufai.cheung@gmail.com>
Repository:	CRAN
Date/Publication:	2024-09-16 15:20:02 UTC

modelbpp: Model BIC Posterior Probability

Description

Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities, using the method presented in Wu, Cheung, and Leung (2020) doi:10.1080/00273171.2019.1574546.

Author(s)

Maintainer: Shu Fai Cheung shufai.cheung@gmail.com (ORCID)

Authors:

• Huiping Wu

• Shing On Leung (ORCID)

Other contributors:

• Ivan Jacob Agaloos Pesigan r.jeksterslab@gmail.com (ORCID) [contributor]

See Also

Useful links:

• https://sfcheung.github.io/modelbpp/

• Report bugs at https://github.com/sfcheung/modelbpp/issues

Manipulate Parameter Tables

Description

Functions to manipulate a partables-class object

Usage

## S3 method for class 'partables'
c(...)

## S3 method for class 'model_set'
c(...)

partables_drop(x, model_names = NULL)

Arguments

Details

The partables-class objects have a c() method that can be used to combine parameter tables. Each object must be

a. a partables-class object, b. a model_set-class object, c. a lavaan-class object, or d. a parameter table of the class lavaan.data.frame(), usually generated by lavaan::parameterTable(). Other objects will be discarded.

Names will be used when combining objects. If two objects have the same names, then only the first one will be retained. No warning message will be issued. Users are encouraged to explicitly name all objects carefully.

Note that, to invoke this method, the first object must be a partables object.

The model_set class also has a c-method. It will replace the first object by the stored partables and then call the c-method of partables objects.

The function partables_drop() is for dropping models from a partables-class object.

Value

A partables-class objects with all the objects supplied combined together. If an object is a lavaan-class object, its parameter table will be retrieved by lavaan::parameterTable(). If an object is a model_set-class object, the stored partables-class object will be retrieved.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples

library(lavaan)

moda <-
"
x3 ~ a*x1 + b*x2
x4 ~ a*x1
ab := a*b
"

fita <- sem(moda, dat_path_model, fixed.x = TRUE)

outa <- model_set(fita,
                  progress = FALSE,
                  parallel = FALSE)

modb <-
"
x3 ~ a*x1 + b*x2
x4 ~ a*x2
ab := a*b
"

fitb <- sem(modb, dat_path_model, fixed.x = TRUE)

outb <- model_set(fitb,
                  progress = FALSE,
                  parallel = FALSE)

mod2 <-
"
x2 ~ 0*x3 + 0*x4
x1 ~ 0*x3
"
fit2 <- sem(mod2, dat_path_model)

mod3 <-
"
x2 ~ x3 + 0*x4
x1 ~ x3
"
fit3 <- sem(mod3, dat_path_model)

out <- c(outa$models, user2 = fit2, outb$models, user3 = fit3)
out

out2 <- c(outa, user2 = fit2, outb$models, user3 = fit3)
out2

out3 <- c(outa, user2 = fit2, outb, user3 = fit3)
out3

A Sample Dataset Based On a Confirmatory Factor Analysis Model (For Testing)

Description

Generated from a confirmatory factor analysis model (n = 200).

Usage

dat_cfa

Format

A data frame with six variables:

x1

Indicator

x2

Indicator

x3

Indicator

x4

Indicator

x5

Indicator

x6

Indicator

Details

The model used to generate this dataset:

f1 =~ x1 + x2 + x3 + x5
f2 =~ x3 + x4 + x5 + x6
f1 ~~ f2

A Sample Dataset Based on a Path Model (For Testing)

Description

Generated from the a path model (n = 100).

Usage

dat_path_model

Format

A data frame with four variables:

x1

Predictor

x2

Predictor

x3

Mediator

x4

Outcome

Details

The model used to generate this dataset:

x1 ~~ x2
x3 ~ x1 + x2
x4 ~ x3 + x1 + x2

A Sample Dataset Based On a Complex Path Model (For Testing)

Description

Generated from a complex path model (n = 200).

Usage

dat_path_model_p06

Format

A data frame with six variables:

x1

Predictor

x2

Predictor

x3

Predictor

y4

Mediator

y5

Mediator

y6

Outcome

Details

The model used to generate this dataset:

y4 ~  x1 + x2 + x3
y5 ~  y4 + x1 + x2
y6 ~  y4 + y5 + x1 + x2 + x3
x1 ~~ x2 + x3
x2 ~~ x3

A Sample Dataset Based On a Structural Model (For Testing)

Description

Generated from a structural model with latent variables (n = 250).

Usage

dat_sem

Format

An object of class data.frame with 250 rows and 16 columns.

Details

The model to be fitted:

f1 =~ x1 + x2 + x3 + x4
f2 =~ x5 + x6 + x7 + x8
f3 =~ x9 + x10 + x11 + x12
f4 =~ x13 + x14 + x15 + x16
f3 ~ f1 + f2
f4 ~ f3

A Sample Dataset Based On a Serial Mediation Model (For Testing)

Description

Generated from a serial mediation model (n = 100).

Usage

dat_serial_4

Format

A data frame with four variables:

x

Predictor

m1

Mediator

m2

Mediator

y

Outcome

Details

The model used to generate this dataset:

m1 ~ x
m2 ~ m1
y  ~ m2

A Sample Dataset Based On a Serial Mediation Model With Weak Paths (For Testing)

Description

Generated from a serial mediation model (n = 100).

Usage

dat_serial_4_weak

Format

A data frame with four variables:

x

Predictor

m1

Mediator

m2

Mediator

y

Outcome

Details

The model to be fitted:

m1 ~ x
m2 ~ m1 + x
y  ~ m2 + m1 + x

Fit a List of Models

Description

Fit a list of models to a dataset.

Usage

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
)

Arguments

Details

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.

Value

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".

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples

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)

Models That Are Less Restricted

Description

Generate a list of models with one or more fixed parameter freed.

Usage

get_add(
  sem_out,
  must_add = NULL,
  must_not_add = NULL,
  remove_constraints = TRUE,
  exclude_error_cov = TRUE,
  exclude_feedback = FALSE,
  exclude_xy_cov = FALSE,
  df_change = 1,
  model_id = NA,
  keep_correct_df_change = TRUE,
  remove_duplicated = TRUE,
  progress = FALSE
)

Arguments

Details

It generates a list of models with one or more fixed parameter freed (and the degrees of freedom, df, increases by one or more). If a model has equality constraints, models with one or more of the constraints between two free parameters released will also be included.

Graphically, paths or covariances are "added" to form the list of models.

The models to be included are identified by lavaan::modificationIndices().

The models will be checked by lavaan to make sure that the decrease in model degrees of freedom is of the expected value.

This function is called by model_set() and usually users do not need to call it. It is exported for advanced users.

Value

An object of the class partables, a named list of parameter tables, each of them to be used by lavaan::lavaan() or update() for fitting a model with the added parameters.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

See Also

print.partables()

Examples

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)
mod_to_add

Models That Are More Restricted

Description

Generate a list of models with one or more free parameter dropped (fixed to zero).

Usage

get_drop(
  sem_out,
  must_drop = NULL,
  must_not_drop = NULL,
  df_change = 1,
  model_id = NA,
  keep_correct_df_change = TRUE,
  remove_duplicated = TRUE,
  progress = FALSE
)

Arguments

Details

It generates a list of models with one or more free parameters dropped, that is, fixed to zero (with degrees of freedom, df, increases by one or more).

All free parameters are included in the pool of candidates, except for those explicitly requested to be kept.

The models will be checked by lavaan to make sure that the increase in model degrees of freedom is of the expected value.

This function is called by model_set() and usually users do not need to call it. It is exported for advanced users.

Value

An object of the class partables, a named list of parameter tables, each of them to be used by lavaan::lavaan() or update() for fitting a model with the added parameters.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

See Also

print.partables()

Examples

library(lavaan)

dat <- dat_path_model
mod <-
"
x3 ~ a*x1 + b*x2
x4 ~ a*x1 + x2
ab := a*b
"

fit <- sem(mod, dat_path_model, fixed.x = TRUE)
mod_to_drop <- get_drop(fit)
mod_to_drop

Measurement Invariance Models

Description

Generate metric and scalar invariance models and their partial invariance versions.

Usage

measurement_invariance_models(
  cfa_out,
  max_free = 1,
  metric = TRUE,
  scalar = TRUE,
  progress = TRUE
)

Arguments

Details

This a helper function to generate, based on a multigroup confirmatory factor analysis (CFA) model with no between-group equality constraints, the following models:

• A metric invariance model (loadings constrained to be equal across groups).

• A scalar invariance model (intercepts and loadings constrained to be equal across groups).

• Partial invariance versions of the previous two models, such as a model with the loadings of all items, except for one, constrained to be equal across groups.

The models generated can then be used in model_set() to compute BPPs.

Requirements

The model used as the input needs to be fitted with no between group constrains, that is, it is a configural invariance model. Although not a must, it is advised to use the default way to identify each factor (that is, fixing a loading to one).

Implementation

This function simply use the group.partial and group.equal argument of lavaan::cfa() to generate the models.

Value

A list of lavaan::cfa() output. The names are automatically generated to indicate whether a model is configural, metric, or scalar invariance, or the item(s) without between-group constraints on the loadings (for partial metric invariance) or intercepts (for partial scalar invariance).

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

See Also

model_set()

Examples

library(lavaan)
# For illustration, only one factor is used,
# with one item from another factor added
# just to make the model not saturated.
HSmod <-
"
spatial =~ x1 + x2 + x3 + x4
"
fit_config <- cfa(model = HSmod,
                  data = HolzingerSwineford1939,
                  group = "school")
fit_mi <- measurement_invariance_models(fit_config)
names(fit_mi)
# Need to add 'skip_check_sem_out = TRUE' to use multigroup models.
out <- model_set(sem_out = fit_mi,
                 skip_check_sem_out = TRUE,
                 progress = FALSE,
                 parallel = FALSE)
print(out)

Minimum Prior

Description

Find the minimum prior probability required to achieve the desired BIC posterior probability.

Usage

min_prior(bic, bpp_target, target_name = "original")

Arguments

Details

It assumes that all models other than the original model have the same prior probabilities.

This function is called by model_set() or print.model_set() and usually users do not need to call it. It is exported for advanced users.

Value

A scalar. The required prior probability.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

References

Wu, H., Cheung, S. F., & Leung, S. O. (2020). Simple use of BIC to assess model selection uncertainty: An illustration using mediation and moderation models. Multivariate Behavioral Research, 55(1), 1–16. doi:10.1080/00273171.2019.1574546

See Also

model_set() and print.model_set()

Examples

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)

out <- model_set(fit)
min_prior(out$bic, bpp_target = .8)

Generate a Graph of Models

Description

Generate an 'igraph' object from a 'model_set' object.

Usage

model_graph(
  object,
  node_size_by_x = TRUE,
  x = NULL,
  node_size = 5,
  min_size = 5,
  max_size = 35,
  color_original = "lightblue",
  color_add = "burlywood1",
  color_drop = "lightgreen",
  color_others = "lightgrey",
  color_label = "black",
  node_label_size = 1,
  original = "original",
  drop_redundant_direct_paths = TRUE,
  label_arrow_by_df = NULL,
  arrow_label_size = 1,
  weight_arrows_by_df = c("inverse", "normal", "none"),
  arrow_min_width = 0.5,
  arrow_max_width = 2,
  progress = TRUE,
  short_names = FALSE,
  min_bpp_labelled = NULL,
  ...
)

Arguments

Details

It extracts the model list stored in object, creates an adjacency matrix, and then creates an igraph object customized for visualizing model relations.

Construction of the Graph

This is the default way to construct the graph when the model set is automatically by model_set().

• Each model is connected by an arrow, pointing from one model to another model that

  a. can be formed by adding one or more free parameter, or

  b. can be formed by releasing one or more equality constraint between two parameters.

  c. has nested relation with this model as determined by the method proposed by Bentler and Satorra (2010), if the models are not generated internally.

That is, it points to a model with more degrees of freedom (more complicated), and is nested within that model in either parameter sense or covariance sense.

• By default, the size of the node for each model is scaled by its BIC posterior probability, if available. See The Size of a Node below.

• If a model is designated as the original (target) model, than he original model, the models with more degrees of freedom than the original model, and the models with fewer degrees of freedom than the original models, are colored differently.

• The default layout is the Sugiyama layout, with simpler models (models with fewer degrees of freedom) on the top. The lower a model is in the network, the more the degrees of freedom it has. This layout is suitable for showing the nested relations of the models. Models on the same level (layer) horizontally have the same model df.

The output is an igraph object. Users can customize it in any way they want using functions from the igraph package.

If a model has no nested relation with all other model, it will not be connected to other models.

If no model is named original (default is "original"), then no model is colored as the original model.

User-Provided Models

If object contained one or more user-provided models which are not generated automatically by model_set() or similar functions (e.g., gen_models()), then the method by Bentler and Satorra (2010) will be used to determine model relations. Models connected by an arrow has a nested relation based on the NET method by Bentler and Satorra (2010). An internal function inspired by the net function from the semTools package is used to implement the NET method.

The Size of a Node

When a model is scaled by x, which usually is the BIC posterior probability, its size is determined by:

max_size * (x - min(x))/(max(x) - min(x)) + min_size

Value

A model_graph-class object that can be used as as an igraph-object, with a plot method (plot.model_graph()) with settings suitable for plotting a network of models with BIC posterior probabilities computed.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448 The internal function for nesting inspired by the net function from the semTools package, which was developed by Terrence D. Jorgensen.

References

Bentler, P. M., & Satorra, A. (2010). Testing model nesting and equivalence. Psychological Methods, 15(2), 111–123. doi:10.1037/a0019625 Asparouhov, T., & Muthén, B. (2019). Nesting and Equivalence Testing for Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 26(2), 302–309. doi:10.1080/10705511.2018.1513795

Examples

library(lavaan)

mod <-
"
m1 ~ x
y ~ m1
"

fit <- sem(mod, dat_serial_4, fixed.x = TRUE)

out <- model_set(fit)
out

g <- model_graph(out)
plot(g)

BIC Posterior Probabilities of Neighboring Models

Description

Identify neighboring models, fit them, and return the BIC posterior probabilities.

Usage

model_set(
  sem_out,
  partables = NULL,
  model_set_out = NULL,
  prior_sem_out = NULL,
  must_add = NULL,
  must_not_add = NULL,
  must_drop = NULL,
  must_not_drop = NULL,
  remove_constraints = TRUE,
  exclude_error_cov = TRUE,
  exclude_feedback = TRUE,
  exclude_xy_cov = TRUE,
  df_change_add = 1,
  df_change_drop = 1,
  remove_duplicated = TRUE,
  fit_models = ifelse(!is.null(model_set_out$fit), FALSE, TRUE),
  compute_bpp = TRUE,
  original = "original",
  parallel = FALSE,
  ncores = max(parallel::detectCores(logical = FALSE) - 1, 1),
  make_cluster_args = list(),
  progress = TRUE,
  verbose = TRUE,
  skip_check_sem_out = FALSE,
  drop_equivalent_models = TRUE
)

gen_models(
  sem_out,
  must_add = NULL,
  must_not_add = NULL,
  must_drop = NULL,
  must_not_drop = NULL,
  remove_constraints = TRUE,
  exclude_error_cov = TRUE,
  df_change_add = 1,
  df_change_drop = 1,
  remove_duplicated = TRUE,
  progress = TRUE,
  output = c("partables", "model_set")
)

Arguments

Details

It computes the BIC posterior probabilities of a set of models by the method presented in Wu, Cheung, and Leung (2020).

First, a list of model is identified based on user-specified criteria. By default, models differ from a fitted model by one degree of freedom, the 1-df-away neighboring models, will be found using get_add() and get_drop.

Second, these models will be fitted to the sample dataset, and their BICs will be computed.

Third, their BIC posterior probabilities will be computed using their BICs. By default, equal prior probabilities for all the models being fitted will be assumed in the current version. This can be changed by prior_sem_out.

The results can then be printed, with the models sorted by descending order of BIC posterior probabilities. The results can also be visualized using model_graph().

Value

The function model_set() returns an object of the class model_set, a list with the following major elements:

• models: A named list of parameter tables. Each represent the models identified.

• bic: A numeric vector, of the same length as model. The BIC of each model.

• postprob: A numeric vector, of the same length as model. The BIC posterior probability of each model.

• fit: A named list of lavaan::lavaan() output objects or update() for fitting a model with the added parameters, of the same length as model.

• change: A numeric vector, of the same length as model. The change in model df for each fit. 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 model. Indicates whether each fit converged or not.

• post_check: A named vector of boolean values, of the same length as model. Indicates whether the solution of each fit is admissible or not. Checked by lavaan::lavInspect().

The object returned by gen_models() depends on the argument output. See the argument output for the details

Functions

• model_set(): Compute the BPPs of a list of models. Can generate the models and/or fit the models. Can also accept pregenerated models, or just update BPPs.

• gen_models(): Generate a list of models (parameter tables).

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

References

Wu, H., Cheung, S. F., & Leung, S. O. (2020). Simple use of BIC to assess model selection uncertainty: An illustration using mediation and moderation models. Multivariate Behavioral Research, 55(1), 1–16. doi:10.1080/00273171.2019.1574546

See Also

print.model_set()

Examples

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)

out <- model_set(fit)
out

Helper Functions For partables-Class Objects

Description

For tasks such as comparing two parameter tables inside a partables-class object.

Usage

identical_partables(object1, object2)

is_partable(x)

same_variables(x)

get_partables(x)

to_partables(...)

Arguments

Details

The function identical_partables() compare two lavaan parameter tables and see whether they are identical. (Adapted from a similar function in the package semhelpinghands).

The function is_partable() tries to check whether an object is "likely" to be a parameter table that can be used by lavaan::lavaan() and its wrappers, such as lavaan::sem().

Note that there is no guarantee the the parameter table makes sense or will not lead to error when fitted. It can only check whether it has the required columns.

The function same_variables() check whether all parameter labels in a partables-class object use the same observed variables.

The function get_partables() extract the partable object from a model_set-class object.

The function to_partables() combine objects to create a partables-class object. The objects to be combined can be a lavaan-class object (e.g., the output of lavaan::sem()) or a parameter table of lavaan.

Value

The function identical_partables() returns either TRUE or FALSE.

The function is_partable() returns either TRUE or FALSE.

The function same_variables() returns either TRUE or FALSE.

The function get_partables() returns a partables-class object.

The function to_partables() returns a partables-class object, created from the objects supplied.

Examples

library(lavaan)
mod1 <-
"
x3 ~ x1
x2 ~ x4
"
mod2 <-
"
x2 ~ x4
x3 ~ x1
"
fit1 <- sem(mod1, dat_path_model)
fit2 <- sem(mod2, dat_path_model)
pt1 <- parameterTable(fit1)
pt2 <- parameterTable(fit2)
identical_partables(pt1, pt2)


is_partable(pt1)


out <- model_set(fit1,
                 fit_models = FALSE)
same_variables(get_partables(out))



out <- model_set(fit1,
                 fit_models = FALSE)
get_partables(out)


fit1 <- sem(mod1, dat_path_model)
fit2 <- sem(mod2, dat_path_model)
pt1 <- parameterTable(fit1)
pt2 <- parameterTable(fit2)

to_partables(fit1, fit2)
to_partables(pt1, pt2)

Plot a Network of Models

Description

Plot a network of models generated by model_graph().

Usage

## S3 method for class 'model_graph'
plot(x, ...)

Arguments

Details

This function is the plot method of model_graph objects, the output of model_graph().

For now, it simply passes the object to plot-method of an igraph object. This function is created for possible customization of the plot in the future.

Value

NULL. Called for its side effect.

See Also

model_graph()

Examples

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)

out <- model_set(fit)
out

g <- model_graph(out)
plot(g)

Print a model_set-Class Object

Description

Print the content of a model_set-class object.

Usage

## S3 method for class 'model_set'
print(
  x,
  bic_digits = 3,
  bpp_digits = 3,
  sort_models = TRUE,
  max_models = 20,
  bpp_target = NULL,
  target_name = "original",
  more_fit_measures = c("cfi", "rmsea"),
  fit_measures_digits = 3,
  short_names = FALSE,
  cumulative_bpp = FALSE,
  ...
)

Arguments

Details

It is the print method of the output of model_set().

Value

x is returned invisibly. Called for its side effect.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

See Also

A model_set-class object is generated by model_set().

Examples

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)

out <- model_set(fit)
out

Print a partables-Class Object

Description

Print the content of a partables-class object.

Usage

## S3 method for class 'partables'
print(x, max_tables = 10, ...)

Arguments

Details

The print method for the output of gen_models(), get_add(), and get_drop().

Value

x is returned invisibly. Called for its side effect.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

See Also

gen_models(), get_add(), and get_drop().

Examples

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)
mod_to_add
print(mod_to_add, max_tables = 1)
mod_to_drop <- get_drop(fit)
mod_to_drop
print(mod_to_drop, max_tables = 1)

Print an sem_outs-Class Object

Description

Print the content of an sem_outs-class object.

Usage

## S3 method for class 'sem_outs'
print(x, max_models = 20, ...)

Arguments

Details

The print method for the output of fit_many().

Value

x is returned invisibly. Called for its side effect.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

See Also

An sem_outs-class object is generated by fit_many().

Examples

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)
out <- fit_many(mod_to_add, fit)
out
print(out, max_models = 1)