lgc: Prints the reliabilities of the indicators of latent...
In langenberg/semnova: Analyzing Interindividual Differences in Experiments
Lgc R Documentation
Prints the reliabilities of the indicators of latent variables.
Description
Some description
Usage
lgc(...)
lgc_dummy(...)
Details
Lgc Class
lgc() is a wrapper for the R6 class constructor
Lgc$new(). See Lgc$new() below for a list of arguments.
Active bindings
get_par_tableRead only. Retrieves the parameter table as tibble.
get_group_labelsRead only. Retrieves the group_labels.
exposeRead only. Returns the private structure of the object. For debugging purposes only.
get_hypothesesRead only. Returns the list of hypotheses. For debugging purposes only.
get_par_labelsRead only. Returns an array of parameter labels used in the lavaan syntax.
get_sem_objectRead only. Returns the estimated lavaan SEM object.
Methods
Public methods
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Method new()
Prints coefficients for the Lgc object.
Lgc class constructor.
Usage
Lgc$new()
Method get_facet_grid()
Usage
Lgc$get_facet_grid()
Method has_latent_dv()
Usage
Lgc$has_latent_dv()
Method has_latent_covariate()
Usage
Lgc$has_latent_covariate()
Method has_covariate()
Usage
Lgc$has_covariate()
Method has_within()
Usage
Lgc$has_within()
Method has_resid_cov()
Usage
Lgc$has_resid_cov()
Method get_labels()
Usage
Lgc$get_labels(
labels,
parse_action = c("underscore", "empty", "remove"),
format = c("plotmath", "text", "syntax"),
sep = ","
)
Method is_indicator()
Usage
Lgc$is_indicator(variables)
Method belongs_to()
Usage
Lgc$belongs_to(variables, whom)
Method get_etas_mmodel()
Usage
Lgc$get_etas_mmodel()
Method get_covariates_mmodel()
Usage
Lgc$get_covariates_mmodel()
Method is_latent()
Usage
Lgc$is_latent(variables)
Method specify()
Specifies an Lgc object.
Usage
Lgc$specify(
data = NULL,
etas,
c_matrix_within,
resid_cov = list(),
equal_resid_cov = list(),
invariance_within = "strong",
compound_symmetry = FALSE,
sphericity = list(),
c_matrix_between = TRUE,
invariance_between = "strong",
covariates = NULL,
fixed_covariates = TRUE,
lv_scaling = "effect",
group = NULL,
group_labels = NULL,
group_weights = "fixed",
variance_homogeneity = FALSE,
hypotheses = NULL,
append = NULL,
dummy = FALSE,
verbose = FALSE,
...
)
Arguments
datatibble. Will be converted to tibble if not already a tibble.
etasNamed list of character vectors of length q. Each list
element (character vector) represents an eta variable. The elements
of the character vector are manifest variables (i.e., indicators)
from the data set that measure the corresponding eta variable. The
list names correspond to the names of the eta variables.
E.g.:
etas = list(
eta1 = c("Y11", "Y12"),
eta2 = c("Y21", "Y22"),
eta3 = c("Y31", "Y32")
)
c_matrix_withinNumeric q x q matrix. Contrast matrix for the
within-subjects design. Must be a square matrix.
E.g.:
c_matrix_within = matrix(c(
1/3, -sqrt(1/2), sqrt(1/6),
1/3, 0, -sqrt(2/3),
1/3, sqrt(1/2), sqrt(1/6)
), nrow = 3)
resid_covList of character vectors. Each character vector
contains manifest variables from the data set among which residual
covariances should be implemented. Residual covariances can vary.
equal_resid_covList of character vectors. Each character vector
contains manifest variables from the data set among which residual
covariances should be implemented. Residual covariances are
constrained to be equal.
invariance_withinLogical. Default is TRUE. Indicates whether
strong measurement invariance across latent dependent variables eta
should be imposed.
compound_symmetryLogical. Indicates whether compound symmetry
should be imposed.
sphericityList of character vectors. Each character vector
contains indices of pi variables among which sphericity should be
imposed.
c_matrix_betweenNumeric p x p matrix. Contrast matrix for the
between-subjects design. Must be a square matrix.
E.g.:
c_matrix_between = matrix(c(
1/3, -sqrt(1/2), sqrt(1/6),
1/3, 0, -sqrt(2/3),
1/3, sqrt(1/2), sqrt(1/6)
), nrow = 3)
invariance_betweenLogical. Default is TRUE.
Indicates whether measurement invariance across groups should be
imposed.
covariatesNamed list of character vectors. Same format as the
argument etas.
fixed_covariatesLogical. Indicates whether manifest
covariates should be treated as fixed.
lv_scalingCharacter. Possible values:
c("effect", "referent"). Default is "effect". Scheme to
identify the latent variable scale. For "effect": effect coding
scheme freely estimates all loadings and intercepts of the indicators
and constraints the average of the loadings to 1 and the average of
the intercepts to 0. For "referent": referent indicator scheme
sets the first loading to 1 and the first intercept to 0.
groupCharacter. Contains the grouping variable from the data
set.
group_labelsCharacter vector of length p. Contains unique group
names of the group variable from the data set. Optional, default is
NULL. If not supplied, labels will be pulled from the data set using
the provided group variable.
E.g.: groups = c("group1", "group2", "group3")
group_weightsEither numeric vector of length p (same length
as group_labels) or one of the characters
c("fixed", "stochastic"). Default is "fixed".
variance_homogeneityLogical. Default is TRUE.
Indicates whether homogeneity of variance should be imposed across
groups.
hypothesesList of objects of class Hypothesis.
appendCharacter. lavaan syntax that is to be appended to the
model string.
dummyLogical. Indicates whether the model should be
estimated.
verboseLogical.
...Additional arguments passed down to lavaan.
Method specify_dummy()
Usage
Lgc$specify_dummy(
n_etas = 1,
n_indicators_eta = 1,
n_groups = 1,
c_matrix_within = NULL,
n_covariates = NULL,
hypotheses = NULL,
...
)
Method estimate()
Estimates the model.
Usage
Lgc$estimate(...)
Arguments
...Additional arguments.
Method get_lav_syntax()
Retrieves the lavaan syntax.
Usage
Lgc$get_lav_syntax(...)
Arguments
...Additional arguments.
Method get_estimates()
Returns the point estimates of the model.
Usage
Lgc$get_estimates(
what = c("variance", "covariance", "intercept", "measurement", "resid_var",
"resid_cov", "regression"),
only_unique_labels = FALSE,
...
)
Arguments
whatCharacter vector.
only_unique_labelsLogical. Default is FALSE.
...Additional arguments.
Method test()
Tests the provided hypothesis on the lgc object.
Usage
Lgc$test(hypothesis)
Arguments
hypothesisObject of class Hypothesis.
Method tests_table()
Tests multiple hypotheses on the lgc object and returns
the results as a table.
Usage
Lgc$tests_table(hypotheses)
Arguments
hypothesisList of objects of class Hypothesis.
Method print()
Prints the lavaan object.
Usage
Lgc$print(...)
Method summary()
Prints a summary for the Lgc object.
Usage
Lgc$summary(fit_measures = T, anova = T, coefficients = T, ...)
Arguments
fit_measuresLogical. Indicates whether fit measures should be
printed.
detailedLogical. Indicates whether a detailed summary
should be printed.
Method fit_measures()
Prints fit measures for the Lgc object.
Usage
Lgc$fit_measures(print = F, ...)
Method anova()
Prints hypothesis tests for the Lgc object.
Usage
Lgc$anova(print = F, detailed = F, ...)
Method compare()
Compares the lavaan objects contained in Lgc objects.
Usage
Lgc$compare(...)
Arguments
...Additional arguments passed down to
anova(lavaan_object, ...).
Method get_data()
Returns the data tibble.
Usage
Lgc$get_data()
Method set_data()
Sets the data tibble.
Usage
Lgc$set_data(data)
Arguments
datatibble.
Method power_analysis()
Plots a directed acyclic graph for the Lgc object.
Usage
Lgc$power_analysis(data_syntax, sample_size = 100L, replications = 500L, ...)
Arguments
data_syntaxList of characters. Each character represents
syntax to simulate data for a separate group.
sample_sizeVector of integers. Must be of length or the
same length as data_syntax.
replicationsInteger. Number of replications.
...Additional arguments.
Method reliabilities()
Usage
Lgc$reliabilities()
Method clone()
The objects of this class are cloneable with this method.
Usage
Lgc$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
langenberg/semnova documentation built on Feb. 12, 2025, 7:06 a.m.
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| Lgc | R Documentation |
Prints the reliabilities of the indicators of latent variables.
Description
Some description
Usage
lgc(...)
lgc_dummy(...)
Details
Lgc Class
lgc() is a wrapper for the R6 class constructor
Lgc$new(). See Lgc$new() below for a list of arguments.
Active bindings
get_par_tableRead only. Retrieves the parameter table as tibble.
get_group_labelsRead only. Retrieves the group_labels.
exposeRead only. Returns the private structure of the object. For debugging purposes only.
get_hypothesesRead only. Returns the list of hypotheses. For debugging purposes only.
get_par_labelsRead only. Returns an array of parameter labels used in the lavaan syntax.
get_sem_objectRead only. Returns the estimated lavaan SEM object.
Methods
Public methods
Method new()
Prints coefficients for the Lgc object.
Lgc class constructor.
Usage
Lgc$new()
Method get_facet_grid()
Usage
Lgc$get_facet_grid()
Method has_latent_dv()
Usage
Lgc$has_latent_dv()
Method has_latent_covariate()
Usage
Lgc$has_latent_covariate()
Method has_covariate()
Usage
Lgc$has_covariate()
Method has_within()
Usage
Lgc$has_within()
Method has_resid_cov()
Usage
Lgc$has_resid_cov()
Method get_labels()
Usage
Lgc$get_labels(
labels,
parse_action = c("underscore", "empty", "remove"),
format = c("plotmath", "text", "syntax"),
sep = ","
)Method is_indicator()
Usage
Lgc$is_indicator(variables)
Method belongs_to()
Usage
Lgc$belongs_to(variables, whom)
Method get_etas_mmodel()
Usage
Lgc$get_etas_mmodel()
Method get_covariates_mmodel()
Usage
Lgc$get_covariates_mmodel()
Method is_latent()
Usage
Lgc$is_latent(variables)
Method specify()
Specifies an Lgc object.
Usage
Lgc$specify( data = NULL, etas, c_matrix_within, resid_cov = list(), equal_resid_cov = list(), invariance_within = "strong", compound_symmetry = FALSE, sphericity = list(), c_matrix_between = TRUE, invariance_between = "strong", covariates = NULL, fixed_covariates = TRUE, lv_scaling = "effect", group = NULL, group_labels = NULL, group_weights = "fixed", variance_homogeneity = FALSE, hypotheses = NULL, append = NULL, dummy = FALSE, verbose = FALSE, ... )
Arguments
datatibble. Will be converted to tibble if not already a tibble.
etasNamed list of character vectors of length q. Each list element (character vector) represents an eta variable. The elements of the character vector are manifest variables (i.e., indicators) from the data set that measure the corresponding eta variable. The list names correspond to the names of the eta variables. E.g.:
etas = list( eta1 = c("Y11", "Y12"), eta2 = c("Y21", "Y22"), eta3 = c("Y31", "Y32") )c_matrix_withinNumeric q x q matrix. Contrast matrix for the within-subjects design. Must be a square matrix. E.g.:
c_matrix_within = matrix(c( 1/3, -sqrt(1/2), sqrt(1/6), 1/3, 0, -sqrt(2/3), 1/3, sqrt(1/2), sqrt(1/6) ), nrow = 3)resid_covList of character vectors. Each character vector contains manifest variables from the data set among which residual covariances should be implemented. Residual covariances can vary.
equal_resid_covList of character vectors. Each character vector contains manifest variables from the data set among which residual covariances should be implemented. Residual covariances are constrained to be equal.
invariance_withinLogical. Default is TRUE. Indicates whether strong measurement invariance across latent dependent variables eta should be imposed.
compound_symmetryLogical. Indicates whether compound symmetry should be imposed.
sphericityList of character vectors. Each character vector contains indices of pi variables among which sphericity should be imposed.
c_matrix_betweenNumeric p x p matrix. Contrast matrix for the between-subjects design. Must be a square matrix. E.g.:
c_matrix_between = matrix(c( 1/3, -sqrt(1/2), sqrt(1/6), 1/3, 0, -sqrt(2/3), 1/3, sqrt(1/2), sqrt(1/6) ), nrow = 3)invariance_betweenLogical. Default is
TRUE. Indicates whether measurement invariance across groups should be imposed.covariatesNamed list of character vectors. Same format as the argument etas.
fixed_covariatesLogical. Indicates whether manifest covariates should be treated as fixed.
lv_scalingCharacter. Possible values:
c("effect", "referent"). Default is "effect". Scheme to identify the latent variable scale. For"effect": effect coding scheme freely estimates all loadings and intercepts of the indicators and constraints the average of the loadings to 1 and the average of the intercepts to 0. For"referent": referent indicator scheme sets the first loading to 1 and the first intercept to 0.groupCharacter. Contains the grouping variable from the data set.
group_labelsCharacter vector of length p. Contains unique group names of the group variable from the data set. Optional, default is NULL. If not supplied, labels will be pulled from the data set using the provided group variable. E.g.:
groups = c("group1", "group2", "group3")group_weightsEither numeric vector of length p (same length as
group_labels) or one of the charactersc("fixed", "stochastic"). Default is"fixed".variance_homogeneityLogical. Default is
TRUE. Indicates whether homogeneity of variance should be imposed across groups.hypothesesList of objects of class Hypothesis.
appendCharacter. lavaan syntax that is to be appended to the model string.
dummyLogical. Indicates whether the model should be estimated.
verboseLogical.
...Additional arguments passed down to lavaan.
Method specify_dummy()
Usage
Lgc$specify_dummy( n_etas = 1, n_indicators_eta = 1, n_groups = 1, c_matrix_within = NULL, n_covariates = NULL, hypotheses = NULL, ... )
Method estimate()
Estimates the model.
Usage
Lgc$estimate(...)
Arguments
...Additional arguments.
Method get_lav_syntax()
Retrieves the lavaan syntax.
Usage
Lgc$get_lav_syntax(...)
Arguments
...Additional arguments.
Method get_estimates()
Returns the point estimates of the model.
Usage
Lgc$get_estimates(
what = c("variance", "covariance", "intercept", "measurement", "resid_var",
"resid_cov", "regression"),
only_unique_labels = FALSE,
...
)Arguments
whatCharacter vector.
only_unique_labelsLogical. Default is FALSE.
...Additional arguments.
Method test()
Tests the provided hypothesis on the lgc object.
Usage
Lgc$test(hypothesis)
Arguments
hypothesisObject of class Hypothesis.
Method tests_table()
Tests multiple hypotheses on the lgc object and returns the results as a table.
Usage
Lgc$tests_table(hypotheses)
Arguments
hypothesisList of objects of class Hypothesis.
Method print()
Prints the lavaan object.
Usage
Lgc$print(...)
Method summary()
Prints a summary for the Lgc object.
Usage
Lgc$summary(fit_measures = T, anova = T, coefficients = T, ...)
Arguments
fit_measuresLogical. Indicates whether fit measures should be printed.
detailedLogical. Indicates whether a detailed summary should be printed.
Method fit_measures()
Prints fit measures for the Lgc object.
Usage
Lgc$fit_measures(print = F, ...)
Method anova()
Prints hypothesis tests for the Lgc object.
Usage
Lgc$anova(print = F, detailed = F, ...)
Method compare()
Compares the lavaan objects contained in Lgc objects.
Usage
Lgc$compare(...)
Arguments
...Additional arguments passed down to anova(lavaan_object, ...).
Method get_data()
Returns the data tibble.
Usage
Lgc$get_data()
Method set_data()
Sets the data tibble.
Usage
Lgc$set_data(data)
Arguments
datatibble.
Method power_analysis()
Plots a directed acyclic graph for the Lgc object.
Usage
Lgc$power_analysis(data_syntax, sample_size = 100L, replications = 500L, ...)
Arguments
data_syntaxList of characters. Each character represents syntax to simulate data for a separate group.
sample_sizeVector of integers. Must be of length or the same length as data_syntax.
replicationsInteger. Number of replications.
...Additional arguments.
Method reliabilities()
Usage
Lgc$reliabilities()
Method clone()
The objects of this class are cloneable with this method.
Usage
Lgc$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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