Package 'lavaanExtra' reference manual

Title:	Convenience Functions for Package 'lavaan'
Description:	Affords an alternative, vector-based syntax to 'lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.
Authors:	Rémi Thériault [aut, cre]
Maintainer:	Rémi Thériault <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.1
Built:	2024-10-29 05:22:06 UTC
Source:	https://github.com/rempsyc/lavaanExtra

Fit and plot CFA simultaneously

Description

Prints and saves CFA fit, as well as plots CFA factor loadings, simultaneously.

Usage

cfa_fit_plot(
  model,
  data,
  covs = FALSE,
  estimator = "MLR",
  remove.items = "",
  print = TRUE,
  save.as.pdf = FALSE,
  file.name,
  ...
)
cfa_fit_plot(
  model,
  data,
  covs = FALSE,
  estimator = "MLR",
  remove.items = "",
  print = TRUE,
  save.as.pdf = FALSE,
  file.name,
  ...
)

Arguments

`model`	CFA model to fit.
`data`	Data set on which to fit the CFA model.
`covs`	Logical, whether to include covariances on the lavaan plot.
`estimator`	What estimator to use for the CFA.
`remove.items`	Optional, if one wants to remove items from the CFA model without having to redefine it completely again.
`print`	Logical, whether to print model summary to console.
`save.as.pdf`	Logical, whether to save as PDF for a high-resolution, scalable vector graphic quality plot. Defaults to saving to the "/model" subfolder of the working directory. If it doesn't exist, it creates it. Then automatically open the created PDF in the default browser. Defaults to false.
`file.name`	Optional (when `save.as.pdf` is set to `TRUE`), if one wants something different than the default file name. It saves to pdf per default, so the .pdf extension should not be specified as it will add it automatically.
`...`	Arguments to be passed to function lavaan::cfa.

Value

The function returns a lavaan fit object. However, it also: prints a summary of the lavaan fit object to the console, and; prints a lavaanPlot of the lavaan fit object.

Illustrations

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

HS.model <- write_lavaan(latent = latent)
cat(HS.model)

library(lavaan)
fit <- cfa_fit_plot(HS.model, HolzingerSwineford1939)

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

HS.model <- write_lavaan(latent = latent)
cat(HS.model)

library(lavaan)
fit <- cfa_fit_plot(HS.model, HolzingerSwineford1939)

Extract relevant covariance/correlation indices from lavaan model

Description

Extract relevant covariance/correlation indices from lavaan lavaan::parameterEstimates and lavaan::standardizedsolution.

Usage

lavaan_cov(fit, nice_table = FALSE, ...)

lavaan_cor(fit, nice_table = FALSE, ...)
lavaan_cov(fit, nice_table = FALSE, ...)

lavaan_cor(fit, nice_table = FALSE, ...)

Arguments

`fit`	lavaan fit object to extract covariance indices from
`nice_table`	Logical, whether to print the table as a rempsyc::nice_table as well as print the reference values at the bottom of the table.
`...`	Arguments to be passed to rempsyc::nice_table

Value

A dataframe of covariances/correlation, including the covaried variables, the covariance/correlation, and corresponding p-value.

Functions

lavaan_cor(): Shortcut for lavaan_cov(fit, estimate = "r")

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

(covariance <- list(speed = "textual", ageyr = "grade"))

HS.model <- write_lavaan(
  regression = regression, covariance = covariance,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_cov(fit)

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

(covariance <- list(speed = "textual", ageyr = "grade"))

HS.model <- write_lavaan(
  regression = regression, covariance = covariance,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_cov(fit)

Extract relevant user-defined parameter (e.g., indirect or total effects) indices from lavaan model

Description

Extract relevant user-defined parameters (e.g., indirect or total effects) indices from lavaan model through lavaan::parameterEstimates and lavaan::standardizedsolution.

Usage

lavaan_defined(
  fit,
  underscores_to_symbol = "→",
  lhs_name = "User-Defined Parameter",
  rhs_name = "Paths",
  nice_table = FALSE,
  ...
)
lavaan_defined(
  fit,
  underscores_to_symbol = "→",
  lhs_name = "User-Defined Parameter",
  rhs_name = "Paths",
  nice_table = FALSE,
  ...
)

Arguments

`fit`	lavaan fit object to extract fit indices from
`underscores_to_symbol`	Character to convert underscores to arrows in the first column, like for indirect effects. Default to the right arrow symbol, but can be set to NULL or "_", or to any other desired symbol. It is also possible to provide a vector of replacements if they they are not all the same.
`lhs_name`	Name of first column, referring to the left-hand side expression (lhs).
`rhs_name`	Name of first column, referring to the right-hand side expression (rhs).
`nice_table`	Logical, whether to print the table as a rempsyc::nice_table as well as print the reference values at the bottom of the table.
`...`	Arguments to be passed to rempsyc::nice_table

Value

A dataframe, including the indirect effect ("lhs"), corresponding paths ("rhs"), standardized regression coefficient ("std.all"), corresponding p-value, as well as the unstandardized regression coefficient ("est") and its confidence interval ("ci.lower", "ci.upper").

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(mediation <- list(
  speed = "visual",
  textual = "visual",
  visual = c("ageyr", "grade")
))

(indirect <- list(
  IV = c("ageyr", "grade"),
  M = "visual",
  DV = c("speed", "textual")
))

HS.model <- write_lavaan(mediation,
  indirect = indirect,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_defined(fit, lhs_name = "Indirect Effect")

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(mediation <- list(
  speed = "visual",
  textual = "visual",
  visual = c("ageyr", "grade")
))

(indirect <- list(
  IV = c("ageyr", "grade"),
  M = "visual",
  DV = c("speed", "textual")
))

HS.model <- write_lavaan(mediation,
  indirect = indirect,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_defined(fit, lhs_name = "Indirect Effect")

Extract relevant indices from lavaan model based on specified operator

Description

Extract relevant indices from lavaan model through lavaan::parameterEstimates and lavaan::standardizedsolution.

Usage

lavaan_extract(
  fit,
  operator = NULL,
  lhs_name = "Left-Hand Side",
  rhs_name = "Right-Hand Side",
  underscores_to_symbol = "→",
  diag = NULL,
  nice_table = FALSE,
  ...
)
lavaan_extract(
  fit,
  operator = NULL,
  lhs_name = "Left-Hand Side",
  rhs_name = "Right-Hand Side",
  underscores_to_symbol = "→",
  diag = NULL,
  nice_table = FALSE,
  ...
)

Arguments

`fit`	lavaan fit object to extract fit indices from
`operator`	Which operator to subselect with.
`lhs_name`	Name of first column, referring to the left-hand side expression (lhs).
`rhs_name`	Name of first column, referring to the right-hand side expression (rhs).
`underscores_to_symbol`	Character to convert underscores to arrows in the first column, like for indirect effects. Default to the right arrow symbol, but can be set to NULL or "_", or to any other desired symbol. It is also possible to provide a vector of replacements if they they are not all the same.
`diag`	When extracting covariances (`⁠~~⁠`), whether to include or exclude diagonal values (one of "exclude" or "include").
`nice_table`	Logical, whether to print the table as a rempsyc::nice_table as well as print the reference values at the bottom of the table.
`...`	Arguments to be passed to rempsyc::nice_table

Value

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(mediation <- list(
  speed = "visual",
  textual = "visual",
  visual = c("ageyr", "grade")
))

(indirect <- list(
  IV = c("ageyr", "grade"),
  M = "visual",
  DV = c("speed", "textual")
))

HS.model <- write_lavaan(mediation,
  indirect = indirect,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_extract(fit, lhs_name = "Indirect Effect", operator = ":=")

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(mediation <- list(
  speed = "visual",
  textual = "visual",
  visual = c("ageyr", "grade")
))

(indirect <- list(
  IV = c("ageyr", "grade"),
  M = "visual",
  DV = c("speed", "textual")
))

HS.model <- write_lavaan(mediation,
  indirect = indirect,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_extract(fit, lhs_name = "Indirect Effect", operator = ":=")

Extract relevant regression indices from lavaan model

Description

Extract relevant regression indices from lavaan model through lavaan::parameterEstimates and lavaan::standardizedsolution.

Usage

lavaan_reg(fit, nice_table = FALSE, ...)
lavaan_reg(fit, nice_table = FALSE, ...)

Arguments

`fit`	lavaan fit object to extract fit indices from
`nice_table`	Logical, whether to print the table as a rempsyc::nice_table as well as print the reference values at the bottom of the table.
`...`	Arguments to be passed to rempsyc::nice_table

Value

A dataframe, including the outcome ("lhs"), predictor ("rhs"), standardized regression coefficient ("std.all"), corresponding p-value, as well as the unstandardized regression coefficient ("est") and its confidence interval ("ci.lower", "ci.upper").

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

HS.model <- write_lavaan(latent = latent, regression = regression)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_reg(fit)

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

HS.model <- write_lavaan(latent = latent, regression = regression)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_reg(fit)

Extract relevant variance indices from lavaan model

Description

Extract relevant variance indices from lavaan model through lavaan::parameterEstimates (when estimate = "sigma", est column)) or lavaan::standardizedsolution (when estimate = "r2", est.std column). R2 values are then calculated as 1 - est.std, and the new p values for the R2, with the following formula: stats::pnorm((1 - est) / se).

Usage

lavaan_var(fit, estimate = "r2", nice_table = FALSE, ...)
lavaan_var(fit, estimate = "r2", nice_table = FALSE, ...)

Arguments

`fit`	lavaan fit object to extract covariance indices from
`estimate`	What estimate to use, either the standardized estimate ("r2", default), or unstandardized estimate ("sigma2").
`nice_table`	Logical, whether to print the table as a rempsyc::nice_table as well as print the reference values at the bottom of the table.
`...`	Arguments to be passed to rempsyc::nice_table

Value

A dataframe of covariances/correlation, including the covaried variables, the covariance/correlation, and corresponding p-value.

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

(covariance <- list(speed = "textual", ageyr = "grade"))

HS.model <- write_lavaan(
  regression = regression, covariance = covariance,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_var(fit)

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

(covariance <- list(speed = "textual", ageyr = "grade"))

HS.model <- write_lavaan(
  regression = regression, covariance = covariance,
  latent = latent, label = TRUE
)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
lavaan_var(fit)

Extract relevant fit indices from lavaan model

Description

Compares fit from one or several lavaan models. Also optionally includes references values. The reference fit values are based on Schreiber (2017), Table 3.

Usage

nice_fit(
  model,
  model.labels,
  nice_table = FALSE,
  guidelines = TRUE,
  stars = FALSE,
  verbose = TRUE
)
nice_fit(
  model,
  model.labels,
  nice_table = FALSE,
  guidelines = TRUE,
  stars = FALSE,
  verbose = TRUE
)

Arguments

`model`	lavaan model object(s) to extract fit indices from
`model.labels`	Model labels to use. If a named list is provided for `model`, default to the names of the list. Otherwise, if the list is unnamed, defaults to generic numbering.
`nice_table`	Logical, whether to print the table as a rempsyc::nice_table.
`guidelines`	Logical, if `nice_table = TRUE`, whether to display include reference values based on Schreiber (2017), Table 3, at the bottom of the table.
`stars`	Logical, if `nice_table = TRUE`, whether to display significance stars (defaults to `FALSE`).
`verbose`	Logical, whether to display messages and warnings.

Details

Note that nice_fit reports the unbiased SRMR through lavaan::lavResiduals() because the standard SRMR is upwardly biased (doi:10.1007/s11336-016-9552-7) in a noticeable way for smaller samples (thanks to James Uanhoro for this change).

If using guidelines = TRUE, please carefully consider the following 2023 quote from Terrence D. Jorgensen:

I do not recommend including cutoffs in the table, as doing so would perpetuate their misuse. Fit indices are not test statistics, and their suggested cutoffs are not critical values associated with known Type I error rates. Numerous simulation studies have shown how poorly cutoffs perform in model selection (e.g., , Jorgensen et al. (2018). Instead of test statistics, fit indices were designed to be measures of effect size (practical significance), which complement the chi-squared test of statistical significance. The range of RMSEA interpretations above is more reminiscent of the range of small/medium/large effect sizes proposed by Cohen for use in power analyses, which are as arbitrary as alpha levels, but at least they better respect the idea that (mis)fit is a matter of magnitude, not nearly so simple as "perfect or imperfect."

Value

A dataframe, representing select fit indices (chi2, df, chi2/df, p-value of the chi2 test, CFI, TLI, RMSEA and its 90% CI, unbiased SRMR, AIC, and BIC).

References

Schreiber, J. B. (2017). Update to core reporting practices in structural equation modeling. Research in social and administrative pharmacy, 13(3), 634-643. doi:10.1016/j.sapharm.2016.06.006

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

HS.model <- write_lavaan(latent = latent, regression = regression)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
nice_fit(fit)

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

HS.model <- write_lavaan(latent = latent, regression = regression)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
nice_fit(fit)

Make a quick `lavaanPlot`

Description

Make a quick and decent-looking lavaanPlot.

Usage

nice_lavaanPlot(
  model,
  node_options = list(shape = "box", fontname = "Helvetica"),
  edge_options = c(color = "black"),
  coefs = TRUE,
  stand = TRUE,
  covs = FALSE,
  stars = c("regress", "latent", "covs"),
  sig = 0.05,
  graph_options = c(rankdir = "LR"),
  ...
)
nice_lavaanPlot(
  model,
  node_options = list(shape = "box", fontname = "Helvetica"),
  edge_options = c(color = "black"),
  coefs = TRUE,
  stand = TRUE,
  covs = FALSE,
  stars = c("regress", "latent", "covs"),
  sig = 0.05,
  graph_options = c(rankdir = "LR"),
  ...
)

Arguments

`model`	SEM or CFA model to plot.
`node_options`	Shape and font name.
`edge_options`	Colour of edges.
`coefs`	Logical, whether to plot coefficients. Defaults to TRUE.
`stand`	Logical, whether to use standardized coefficients. Defaults to TRUE.
`covs`	Logical, whether to plot covariances. Defaults to FALSE.
`stars`	Which links to plot significance stars for. One of `c("regress", "latent", "covs")`.
`sig`	Which significance threshold to use to plot coefficients (defaults to .05). To plot all coefficients, set `sig` to 1.
`graph_options`	Read from left to right, rather than from top to bottom.
`...`	Arguments to be passed to function lavaanPlot::lavaanPlot.

Value

A lavaanPlot, of classes c("grViz", "htmlwidget"), representing the specified lavaan model.

Illustrations

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

HS.model <- write_lavaan(latent = latent)
cat(HS.model)

library(lavaan)
fit <- cfa(HS.model, HolzingerSwineford1939)
nice_lavaanPlot(fit)

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

HS.model <- write_lavaan(latent = latent)
cat(HS.model)

library(lavaan)
fit <- cfa(HS.model, HolzingerSwineford1939)
nice_lavaanPlot(fit)

Extract relevant modification indices along item labels

Description

Extract relevant modification indices along item labels, with a similarity score provided to help guide decision-making for removing redundant items with high covariance.

Usage

nice_modindices(fit, labels = NULL, method = "lcs", sort = TRUE, ...)
nice_modindices(fit, labels = NULL, method = "lcs", sort = TRUE, ...)

Arguments

`fit`	lavaan fit object to extract modification indices from
`labels`	Dataframe of labels. If the original data frame is provided, and that it contains labelled variables, will automatically attempt to extract the correct labels from the dataframe.
`method`	Method for distance calculation from stringdist::stringsim. Defaults to `"lcs"`.
`sort`	Logical. If TRUE, sort the output using the values of the modification index values. Higher values appear first. Defaults to `TRUE`.
`...`	Arguments to be passed to lavaan::modindices

Value

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

HS.model <- write_lavaan(latent = latent, regression = regression)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
nice_modindices(fit, maximum.number = 5)
data_labels <- data.frame(
  x1 = "I have good visual perception",
  x2 = "I have good cube perception",
  x3 = "I have good at lozenge perception",
  x4 = "I have paragraph comprehension",
  x5 = "I am good at sentence completion",
  x6 = "I excel at finding the meaning of words",
  x7 = "I am quick at doing mental additions",
  x8 = "I am quick at counting dots",
  x9 = "I am quick at discriminating straight and curved capitals"
)
nice_modindices(fit, maximum.number = 10, labels = data_labels, op = "~~")

x <- HolzingerSwineford1939
x <- sjlabelled::set_label(x, label = c(rep("", 6), data_labels))
fit <- sem(HS.model, data = x)
nice_modindices(fit, maximum.number = 10, op = "~~")

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

(regression <- list(
  ageyr = c("visual", "textual", "speed"),
  grade = c("visual", "textual", "speed")
))

HS.model <- write_lavaan(latent = latent, regression = regression)
cat(HS.model)

library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
nice_modindices(fit, maximum.number = 5)
data_labels <- data.frame(
  x1 = "I have good visual perception",
  x2 = "I have good cube perception",
  x3 = "I have good at lozenge perception",
  x4 = "I have paragraph comprehension",
  x5 = "I am good at sentence completion",
  x6 = "I excel at finding the meaning of words",
  x7 = "I am quick at doing mental additions",
  x8 = "I am quick at counting dots",
  x9 = "I am quick at discriminating straight and curved capitals"
)
nice_modindices(fit, maximum.number = 10, labels = data_labels, op = "~~")

x <- HolzingerSwineford1939
x <- sjlabelled::set_label(x, label = c(rep("", 6), data_labels))
fit <- sem(HS.model, data = x)
nice_modindices(fit, maximum.number = 10, op = "~~")

Make a quick `tidySEM` plot

Description

Make a quick and decent-looking tidySEM plot.

Usage

nice_tidySEM(
  fit,
  layout = NULL,
  hide_nonsig_edges = FALSE,
  hide_var = TRUE,
  hide_cov = FALSE,
  hide_mean = TRUE,
  est_std = TRUE,
  label,
  label_location = NULL,
  reduce_items = NULL,
  plot = TRUE,
  ...
)
nice_tidySEM(
  fit,
  layout = NULL,
  hide_nonsig_edges = FALSE,
  hide_var = TRUE,
  hide_cov = FALSE,
  hide_mean = TRUE,
  est_std = TRUE,
  label,
  label_location = NULL,
  reduce_items = NULL,
  plot = TRUE,
  ...
)

Arguments

`fit`	SEM or CFA model fit to plot.
`layout`	A matrix (or data.frame) that describes the structure; see tidySEM::get_layout. If a named list is provided, with names "IV" (independent variables), "M" (mediator), and "DV" (dependent variables), `nice_tidySEM` attempts to write the layout matrix automatically.
`hide_nonsig_edges`	Logical, hides non-significant edges. Defaults to FALSE.
`hide_var`	Logical, hides variances. Defaults to TRUE.
`hide_cov`	Logical, hides co-variances. Defaults to FALSE.
`hide_mean`	Logical, hides means/node labels. Defaults to TRUE.
`est_std`	Logical, whether to use the standardized coefficients. Defaults to TRUE.
`label`	Labels to be used on the plot. As elsewhere in `lavaanExtra`, it is provided as a named list with format `(colname = "label")`.
`label_location`	Location of label along the path, as a percentage (defaults to middle, 0.5).
`reduce_items`	A numeric vector of length 1 (x) or 2 (x & y) defining how much space to trim from the nodes (boxes) of the items defining the latent variables. Can be provided either as `reduce_items = 0.4` (will only affect horizontal space, x), or `reduce_items = c(x = 0.4, y = 0.2)` (will affect both horizontal x and vertical y).
`plot`	Logical, whether to plot the result (default). If `FALSE`, returns the `tidy_sem` object, which can be further edited as needed.
`...`	Arguments to be passed to tidySEM::prepare_graph.

Value

A tidySEM plot, of class ggplot, representing the specified lavaan model.

Illustrations

Examples


# Calculate scale averages
library(lavaan)
data <- HolzingerSwineford1939
data$visual <- rowMeans(data[paste0("x", 1:3)])
data$textual <- rowMeans(data[paste0("x", 4:6)])
data$speed <- rowMeans(data[paste0("x", 7:9)])

# Define our variables
IV <- c("sex", "ageyr", "agemo", "school")
M <- c("visual", "grade")
DV <- c("speed", "textual")

# Define our lavaan lists
mediation <- list(speed = M, textual = M, visual = IV, grade = IV)

# Define indirect object
structure <- list(IV = IV, M = M, DV = DV)

# Write the model, and check it
model <- write_lavaan(mediation, indirect = structure, label = TRUE)
cat(model)

# Fit model
fit <- sem(model, data)

# Plot model

nice_tidySEM(fit, layout = structure)


# Calculate scale averages
library(lavaan)
data <- HolzingerSwineford1939
data$visual <- rowMeans(data[paste0("x", 1:3)])
data$textual <- rowMeans(data[paste0("x", 4:6)])
data$speed <- rowMeans(data[paste0("x", 7:9)])

# Define our variables
IV <- c("sex", "ageyr", "agemo", "school")
M <- c("visual", "grade")
DV <- c("speed", "textual")

# Define our lavaan lists
mediation <- list(speed = M, textual = M, visual = IV, grade = IV)

# Define indirect object
structure <- list(IV = IV, M = M, DV = DV)

# Write the model, and check it
model <- write_lavaan(mediation, indirect = structure, label = TRUE)
cat(model)

# Fit model
fit <- sem(model, data)

# Plot model

nice_tidySEM(fit, layout = structure)

Vector-based lavaan syntax interpreter

Description

Vector-based lavaan syntax interpreter.

Usage

write_lavaan(
  mediation = NULL,
  regression = NULL,
  covariance = NULL,
  indirect = NULL,
  latent = NULL,
  intercept = NULL,
  threshold = NULL,
  constraint.equal = NULL,
  constraint.smaller = NULL,
  constraint.larger = NULL,
  custom = NULL,
  label = FALSE,
  use.letters = FALSE
)
write_lavaan(
  mediation = NULL,
  regression = NULL,
  covariance = NULL,
  indirect = NULL,
  latent = NULL,
  intercept = NULL,
  threshold = NULL,
  constraint.equal = NULL,
  constraint.smaller = NULL,
  constraint.larger = NULL,
  custom = NULL,
  label = FALSE,
  use.letters = FALSE
)

Arguments

`mediation`	Mediation indicators (`~` symbol: "is regressed on"). Differs from argument `regression` because path names can be optionally specified automatically with argument `label`.
`regression`	Regression indicators (`~` symbol: "is regressed on").
`covariance`	(Residual) (co)variance indicators (`⁠~~⁠` symbol: "is correlated with").
`indirect`	Indirect effect indicators (`⁠:=⁠` symbol: "indirect effect defined as"). If a named list is provided, with names "IV" (independent variables), "M" (mediator), and "DV" (dependent variables), `write_lavaan` attempts to write indirect effects automatically. In this case, the `mediation` argument must be specified too.
`latent`	Latent variable indicators (`⁠=~⁠` symbol: "is measured by").
`intercept`	Intercept indicators (`~ 1` symbol: "intercept").
`threshold`	Threshold indicators (`\|` symbol: "threshold").
`constraint.equal`	Equality indicators (`==` symbol).
`constraint.smaller`	Smaller than indicators (`<` symbol).
`constraint.larger`	Greater than indicators (`>` symbol).
`custom`	Custom specifications. Takes a single string just like regular `lavaan` syntax would. Always added at the end of the model.
`label`	Logical, whether to display path names for the mediation argument.
`use.letters`	Logical, for the labels, whether to use letters instead of the variable names.

Value

A character string, representing the specified lavaan model.

Examples


x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

HS.model <- write_lavaan(latent = latent)
cat(HS.model)

library(lavaan)
fit <- lavaan(HS.model,
  data = HolzingerSwineford1939,
  auto.var = TRUE, auto.fix.first = TRUE,
  auto.cov.lv.x = TRUE
)
summary(fit, fit.measures = TRUE)

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = x[7:9]
))

HS.model <- write_lavaan(latent = latent)
cat(HS.model)

library(lavaan)
fit <- lavaan(HS.model,
  data = HolzingerSwineford1939,
  auto.var = TRUE, auto.fix.first = TRUE,
  auto.cov.lv.x = TRUE
)
summary(fit, fit.measures = TRUE)

Package 'lavaanExtra'

Help Index

Fit and plot CFA simultaneously

Description

Usage

Arguments

Value

Illustrations

Examples

Extract relevant covariance/correlation indices from lavaan model

Description

Usage

Arguments

Value

Functions

Examples

Extract relevant user-defined parameter (e.g., indirect or total effects) indices from lavaan model

Description

Usage

Arguments

Value

Examples

Extract relevant indices from lavaan model based on specified operator

Description

Usage

Arguments

Value

Examples

Extract relevant regression indices from lavaan model

Description

Usage

Arguments

Value

Examples

Extract relevant variance indices from lavaan model

Description

Usage

Arguments

Value

Examples

Extract relevant fit indices from lavaan model

Description

Usage

Arguments

Details

Value

References

Examples

Make a quick lavaanPlot

Description

Usage

Arguments

Value

Illustrations

Examples

Extract relevant modification indices along item labels

Description

Usage

Arguments

Value

Examples

Make a quick tidySEM plot

Description

Usage

Arguments

Value

Illustrations

Examples

Vector-based lavaan syntax interpreter

Description

Usage

Arguments

Value

See Also

Examples

Make a quick `lavaanPlot`

Make a quick `tidySEM` plot