| Title: | Generate Standardized Data |
| Version: | 0.6.3 |
| Date: | 2021-05-07 |
| Description: | Creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) <doi:10.1177/0734282913478046>. |
| Depends: | R (≥ 3.4.0) |
| License: | CC0 |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.1.1 |
| Imports: | lavaan, mvtnorm, tibble, stats, magrittr, rlang, purrr |
| Suggests: | knitr, rmarkdown, ggplot2, dplyr, tidyr, forcats, stringr, testthat, covr, badger |
| VignetteBuilder: | knitr |
| URL: | https://github.com/wjschne/simstandard |
| BugReports: | https://github.com/wjschne/simstandard/issues |
| Maintainer: | W. Joel Schneider <w.joel.schneider@gmail.com> |
| NeedsCompilation: | no |
| Packaged: | 2021-05-07 18:20:06 UTC; renee |
| Author: | W. Joel Schneider 
[aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2021-05-07 18:40:02 UTC |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
For each latent variable in a structural model, add a composite score to observed data.
Description
For each latent variable in a structural model, add a composite score to observed data.
Usage
add_composite_scores(
d,
m,
mu = 0,
sigma = 1,
names_suffix = "_Composite",
keep_observed_scores = TRUE,
...
)
Arguments
d |
A data.frame with observed data in standardized form (i.e, z-scores) |
m |
A character string with lavaan model |
mu |
Score means. Composite scores will also have this mean. Defaults to 0. |
sigma |
Score standard deviations. Composite scores will also have this standard deviation. Defaults to 1. |
names_suffix |
A character string added to each composite score name |
keep_observed_scores |
The observed scores are returned along with the composite scores. |
... |
parameters passed to simstandardized_matrices |
Value
data.frame with observed data and estimated factor scores
Examples
library(simstandard)
# lavaan model
m = "
X =~ 0.9 * X1 + 0.8 * X2 + 0.7 * X3
"
# Make data.frame for two cases
d <- data.frame(
X1 = c(1.2, -1.2),
X2 = c(1.5, -1.8),
X3 = c(1.8, -1.1))
# Compute composite scores for two cases
add_composite_scores(d, m)
For each latent variable in a structural model, add an estimated factor score to observed data.
Description
For each latent variable in a structural model, add an estimated factor score to observed data.
Usage
add_factor_scores(
d,
m,
mu = 0,
sigma = 1,
CI = FALSE,
p = 0.95,
names_suffix = "_FS",
keep_observed_scores = TRUE,
...
)
Arguments
d |
A data.frame with observed data in standardized form (i.e, z-scores) |
m |
A character string with lavaan model |
mu |
Population mean of the observed scores. Factor scores will also have this mean. Defaults to 0. |
sigma |
Population standard deviation of the observed scores. Factor scores will also have this standard deviation. Defaults to 1. |
CI |
Add confidence intervals? Defaults to 'FALSE'. If 'TRUE', For each factor score, a lower and upper bound of the confidence interval is created. For example, the lower bound of factor score 'X' is 'X_LB', and the upper bound is 'X_UB'. |
p |
confidence interval proportion. Defaults to 0.95 |
names_suffix |
A character string added to each factor score name |
keep_observed_scores |
The observed scores are returned along with the factor scores. |
... |
parameters passed to simstandardized_matrices |
Value
data.frame with observed data and estimated factor scores
Examples
library(simstandard)
# lavaan model
m = "
X =~ 0.9 * X1 + 0.8 * X2 + 0.7 * X3
"
# Make data.frame for two cases
d <- data.frame(
X1 = c(1.2, -1.2),
X2 = c(1.5, -1.8),
X3 = c(1.8, -1.1))
# Compute factor scores for two cases
add_factor_scores(d, m)
Checks matrices for matrix2lavaan function
Description
Checks matrices for matrix2lavaan function
Arguments
m |
matrix, data.frame or tibble |
mname |
Name of m |
Remove fixed parameters from a lavaan model
Description
Remove fixed parameters from a lavaan model
Usage
fixed2free(m)
Arguments
m |
Structural model represented by lavaan syntax |
Value
character string representing lavaan model
Examples
library(simstandard)
# lavaan model with fixed parameters
m = "
Latent_1 =~ 0.9 * Ob_11 + 0.8 * Ob_12 + 0.7 * Ob_13
Latent_2 =~ 0.9 * Ob_21 + 0.6 * Ob_22 + 0.4 * Ob_23
"
# Same model, but with fixed parameters removed.
m_free <- fixed2free(m)
cat(m_free)
Return factor score coefficients
Description
Return factor score coefficients
Usage
get_factor_score_coefficients(m, latent = TRUE, errors = FALSE, ...)
Arguments
m |
Structural model represented by lavaan syntax or output of sim_standardized_matrices function. |
latent |
Include latent variables. |
errors |
Include observed error and latent disturbances variables. |
... |
parameters passed to the 'sim_standardized_matrices' function |
Value
A matrix of factor score coefficients
Examples
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
get_factor_score_coefficients(m)
Return factor score validity coefficients
Description
Return factor score validity coefficients
Usage
get_factor_score_validity(m, latent = TRUE, errors = FALSE, ...)
Arguments
m |
Structural model represented by lavaan syntax or output of sim_standardized_matrices function. |
latent |
Include latent variables. |
errors |
Include observed error and latent disturbances variables. |
... |
parameters passed to the 'sim_standardized_matrices' function |
Value
A matrix of validity coefficients
Examples
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
get_factor_score_validity(m)
Return factor score validity coefficient standard errors
Description
Return factor score validity coefficient standard errors
Usage
get_factor_score_validity_se(m, latent = TRUE, errors = FALSE, ...)
Arguments
m |
Structural model represented by lavaan syntax or output of sim_standardized_matrices function. |
latent |
Include latent variables. |
errors |
Include observed error and latent disturbances variables. |
... |
parameters passed to the 'sim_standardized_matrices' function |
Value
A matrix of factor score standard errors
Examples
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
get_factor_score_validity_se(m)
Return model-implied correlation matrix
Description
Function that takes a lavaan model with standardized parameters and returns a model-implied correlation matrix
Usage
get_model_implied_correlations(
m,
observed = TRUE,
latent = FALSE,
errors = FALSE,
factor_scores = FALSE,
composites = FALSE,
...
)
Arguments
m |
Structural model represented by lavaan syntax or output of sim_standardized_matrices function. |
observed |
Include observed variables |
latent |
Include latent variables |
errors |
Include observed error and latent disturbances variables |
factor_scores |
Include factor score variables |
composites |
Include composite variables |
... |
parameters passed to the 'sim_standardized_matrices' function |
Value
A correlation matrix
Examples
library(simstandard)
# lavaan model
m = "Latent_1 =~ 0.8 * Ob_1 + 0.7 * Ob_2 + 0.4 * Ob_3"
get_model_implied_correlations(m)
Return model names
Description
Return model names
Usage
get_model_names(m, ...)
Arguments
m |
Structural model represented by lavaan syntax or output of sim_standardized_matrices function. |
... |
parameters passed to the 'sim_standardized_matrices' function |
Value
A list of variable names
Examples
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
get_model_names(m)
Extract standardized RAM matrices from a lavaan object
Description
Extract standardized RAM matrices from a lavaan object
Usage
lav2ram(fit)
Arguments
fit |
An object of class lavaan |
Value
list of RAM matrices A (asymmetric paths), S (symmetric paths), and F (filter matrix)
Create lavaan model syntax from matrix coefficients
Description
Create lavaan model syntax from matrix coefficients
Usage
matrix2lavaan(
measurement_model = NULL,
structural_model = NULL,
covariances = NULL
)
Arguments
measurement_model |
A matrix or data.frame with measurement model loadings. Column names are latent variables. Row names or the first column of a data.frame are indicator variables. |
structural_model |
A matrix or data.frame with structural model coefficients (i.e., regressions). Column names are "causal" variables. Row names or the first column of a data.frame are "effect" variables. |
covariances |
A matrix or data.frame with model covariances. Column names must match the row names. If a data.frame, row variable names can be specified in the first column. |
Value
a character string with lavaan syntax
Examples
library(simstandard)
# Specifying the measurement model:
# For a data.frame, the column names are latent variables,
# and the indicators can be specified as rownames.
m <- data.frame(X = c(0.7,0.8,0,0),
Y = c(0,0,0.8,0.9))
rownames(m) <- c("A", "B", "C", "D")
# Indicator variables can also be specified
# as the first column variable
# with subsequent column names as latent variables
m <- data.frame(Indicators = c("A", "B", "C", "D"),
X = c(0.7,0.8,0,0),
Y = c(0,0,0.8,0.9))
# Alternately, a matrix can be used:
m <- matrix(c(0.7,0.8,0,0,
0,0,0.8,0.9),
ncol = 2,
dimnames = list(c("A", "B", "C", "D"),
c("X", "Y")))
# Specifying the structural coefficients:
# The regression coefficients of the structural model can be
# specified as either a data.frame or a matrix. Column names
# are the predictors and row names are the criterion variables.
# With a data.frame, criterion variables can alternataly be
# specified with as the first column.
s <- matrix(0.5, nrow = 1, ncol = 1, dimnames = list("Y", "X"))
# The covariance matrix must be symmetric. Can also be specified
# as a data. frame.
Sigma <- matrix(c(1, 0.3,
0.3, 1),
nrow = 2,
ncol = 2,
dimnames = list(c("B","C"),
c("B","C")) )
model <- matrix2lavaan(measurement_model = m,
structural_model = s,
covariances = Sigma)
cat(model)
Function that takes a lavaan model with standardized paths and loadings and returns a complete lavaan model syntax with standardized variances
Description
Function that takes a lavaan model with standardized paths and loadings and returns a complete lavaan model syntax with standardized variances
Usage
model_complete(m)
Arguments
m |
Structural model represented by lavaan syntax |
Value
character string representing lavaan model
Examples
library(simstandard)
# lavaan model
m = "
Latent_1 =~ 0.9 * Ob_11 + 0.8 * Ob_12 + 0.7 * Ob_13
Latent_2 =~ 0.9 * Ob_21 + 0.6 * Ob_22 + 0.4 * Ob_23
Latent_2 ~ 0.6 * Latent_1
"
# Same lavaan syntax, but with standardized variances
m_complete <- model_complete(m)
cat(m_complete)
Generates simulated data with standardized parameters.
Description
This function takes a lavaan model with standardized parameters and simulates latent scores, errors, disturbances, and observed scores.
Usage
sim_standardized(
m,
n = 1000,
observed = TRUE,
latent = TRUE,
errors = TRUE,
factor_scores = FALSE,
composites = FALSE,
matrices = FALSE,
...
)
Arguments
m |
Structural model represented by lavaan syntax |
n |
Number of simulated cases |
observed |
Include observed variables |
latent |
Include latent variables |
errors |
Include observed error and latent disturbances variables |
factor_scores |
Include factor score variables |
composites |
Include composite variables |
matrices |
Include matrices as attribute of tibble |
... |
Arguments passed to 'simstandardized_matrices' |
Details
This function supports the '~' operator for regressions, the '~~' for covariances (but not variances), and the '=~' latent variable loadings. It does not support intercepts (e.g,. 'y ~ 1'), thresholds, scaling factors, formative factors, or equality constraints.
Value
tibble with standardized data
Examples
library(simstandard)
# Lavaan model
m = "Latent_1 =~ 0.8 * Ob_1 + 0.7 * Ob_2 + 0.4 * Ob_3"
# simulate 10 cases
sim_standardized(m, n = 10)
Return model characteristics
Description
Function that takes a lavaan model with standardized parameters and returns a list with model characteristics
Usage
sim_standardized_matrices(m, max_iterations = 100, composite_threshold = NULL)
Arguments
m |
Structural model represented by lavaan syntax |
max_iterations |
Maximum number of iterations before the algorithm fails |
composite_threshold |
Loadings with absolute values less than this threshold will not be counted as composite indicators |
Details
This function supports the '~' operator for regressions, the '~~' for covariances (but not variances), and the '=~' latent variable loadings. It does not support intercepts (e.g,. 'y ~ 1'), thresholds, scaling factors, formative factors, or equality constraints.
Value
list of path and covariance coefficients
Examples
library(simstandard)
# lavaan model
m = "Latent_1 =~ 0.8 * Ob_1 + 0.7 * Ob_2 + 0.4 * Ob_3"
sim_standardized_matrices(m)