,
Mark Hok Chio Lai [aut] | Title: | Customizing Structural Equation Modelling Plots |
|---|---|
| Description: | Most function focus on specific ways to customize a graph. They use a 'qgraph' output as the first argument, and return a modified 'qgraph' object. This allows the functions to be chained by a pipe operator. |
| Authors: | Shu Fai Cheung [aut, cre] ,
Mark Hok Chio Lai [aut] |
| Maintainer: | Shu Fai Cheung <[email protected]> |
| License: | GPL-3 |
| Version: | 0.3.1 |
| Built: | 2024-11-09 03:22:49 UTC |
| Source: | https://github.com/sfcheung/semptools |
Add a fit object (e.g., 'lavaan' output) to the a 'qgraph' object as an attribute.
add_object(semPaths_plot, object)add_object(semPaths_plot, object)
semPaths_plot |
A
qgraph::qgraph object generated by
|
object |
Should be the object,
such as the output of |
It adds an object to
a qgraph::qgraph object as the
attribute "semptools_fit_object",
to be retrieved by other functions
that need to access the original
output used in semPlot::semPaths()
to create a diagram.
The original qgraph::qgraph object
set to semPaths_plot, with the
attribute "semptools_fit_object"
set to object.
library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 + x06 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 + x03 f4 =~ x11 + x12 + x13 + x14 ' fit <- lavaan::cfa(mod, cfa_example) p <- semPaths(fit, whatLabels = "est", sizeMan = 3.25, node.width = 1, edge.label.cex = .75, mar = c(10, 5, 10, 5), DoNotPlot = TRUE) p <- add_object(p, fit) attr(p, "semptools_fit_object")library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 + x06 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 + x03 f4 =~ x11 + x12 + x13 + x14 ' fit <- lavaan::cfa(mod, cfa_example) p <- semPaths(fit, whatLabels = "est", sizeMan = 3.25, node.width = 1, edge.label.cex = .75, mar = c(10, 5, 10, 5), DoNotPlot = TRUE) p <- add_object(p, fit) attr(p, "semptools_fit_object")
Replace the residual variances of exogenous variables by their R-squares in a qgraph::qgraph object.
add_rsq(semPaths_plot, object, digits = 2L, rsq_string = "R2=", ests = NULL)add_rsq(semPaths_plot, object, digits = 2L, rsq_string = "R2=", ests = NULL)
semPaths_plot |
A qgraph object generated by
|
object |
The object used by semPaths to generate the plot. Use
the same argument name used in |
digits |
Integer indicating number of decimal places for the R-squares. Default is 2L. |
rsq_string |
The string before the
R-squares. Default is |
ests |
A data.frame from the
|
Modify a qgraph::qgraph object generated by
semPaths by setting the labels
of the residuals of endogenous variables to their
R-squares.
Require either the original object used in the semPaths call, or a data frame with the R-square for each endogenous variable.
Currently supports only plots based on lavaan
output.
If the input is a qgraph::qgraph object, the function returns a qgraph based on the original one, with R-squares added. If the input is a list of qgraph objects, the function returns a list of the same length.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) p_pa2 <- add_rsq(p_pa, fit_pa) plot(p_pa2) mod_cfa <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod_cfa, cfa_example) lavaan::parameterEstimates(fit_cfa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_cfa <- semPlot::semPaths(fit_cfa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Place standard errors on a new line p_cfa2 <- add_rsq(p_cfa, fit_cfa) plot(p_cfa2) mod_sem <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' # Can be used with mark_se() and mark_sig() fit_sem <- lavaan::sem(mod_sem, sem_example) lavaan::parameterEstimates(fit_sem)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_sem <- semPlot::semPaths(fit_sem, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Mark significance, and then add standard errors p_sem2 <- mark_sig(p_sem, fit_sem) p_sem3 <- mark_se(p_sem2, fit_sem, sep = "\n") p_sem4 <- add_rsq(p_sem3, fit_sem) plot(p_sem4)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) p_pa2 <- add_rsq(p_pa, fit_pa) plot(p_pa2) mod_cfa <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod_cfa, cfa_example) lavaan::parameterEstimates(fit_cfa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_cfa <- semPlot::semPaths(fit_cfa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Place standard errors on a new line p_cfa2 <- add_rsq(p_cfa, fit_cfa) plot(p_cfa2) mod_sem <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' # Can be used with mark_se() and mark_sig() fit_sem <- lavaan::sem(mod_sem, sem_example) lavaan::parameterEstimates(fit_sem)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_sem <- semPlot::semPaths(fit_sem, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Mark significance, and then add standard errors p_sem2 <- mark_sig(p_sem, fit_sem) p_sem3 <- mark_se(p_sem2, fit_sem, sep = "\n") p_sem4 <- add_rsq(p_sem3, fit_sem) plot(p_sem4)
Use a named vector or named arguments to create a matrix of the directions of indicators of factors.
auto_factor_point_to(factor_layout, ...)auto_factor_point_to(factor_layout, ...)
factor_layout |
Argument description. |
... |
Additional arguments. If
the first argument is not named, then
it should be a named vector of
directions, names being the names of
the factors, and directions can be
one of these values: |
A helper function to make
it easier to create the matrix
used by set_sem_layout() to
indicate where the indicators of
each factor should be positioned.
It works in two modes. If the
first argument is a named vector,
such as c(f1 = "up", f2 = "down"),
then this vector will be used to
create the direction matrix.
If the arguments are named, such as
auto_factor_point_to(factor_layout, f1 = "up", f2 = "down",
then the names are treated as the
factor names, and the values of
the arguments are treated as the
directions.
The matrix created can then be used
for the argument factor_point_to
in set_sem_layout().
A character matrix of the same
dimension as factor_layout. The
cells of factor names are replaced
by the directions to place their
indicators.
factor_layout <- matrix(c("f1", NA, NA, NA, "f3", "f4", "f2", NA, NA), byrow = TRUE, 3, 3) factor_point_to <- auto_factor_point_to(factor_layout, f1 = "left", f2 = "left", f3 = "down", f4 = "down") factor_point_tofactor_layout <- matrix(c("f1", NA, NA, NA, "f3", "f4", "f2", NA, NA), byrow = TRUE, 3, 3) factor_point_to <- auto_factor_point_to(factor_layout, f1 = "left", f2 = "left", f3 = "down", f4 = "down") factor_point_to
Determine the order of indicators and match indicators and factors based on a plot from a 'qgraph' object.
auto_indicator_order(semPaths_plot, add_isolated_manifest = FALSE)auto_indicator_order(semPaths_plot, add_isolated_manifest = FALSE)
semPaths_plot |
A
qgraph::qgraph object generated by
|
add_isolated_manifest |
Logical. Whether observed variables that are not indicators will be included in the output as "factors", each with one indicator (the observed variable). |
It inspects a qgraph::qgraph object and find variables that are the indicators of latent factors.
The output can be used in the
argument indicator_order of
set_cfa_layout() and
set_sem_layout(). It can also be
modified, such as reordered, as
necessary.
If the generated order is used, there
is no need to call this function
manually because set_cfa_layout()
and set_sem_layout() will
automatically call this function, if
indicator_order is not set.
It assumes that observed variables
are represented by squares (shape
set to "square") and latent
variables represented by circles
or ovals (shape set to "circle").
An observed variable is considered as an indicator if there is an arrow pointing to it from a latent variable.
If an indicator loaded on more than one latent variable, it will only be matched to one of them, determined by the order of appearance in the internal storage.
It uses node names, not node labels, in generating the output.
A named character vector. The values are the indicators identified. The names are the latent factors the indicators loaded on.
set_sem_layout() and
set_cfa_layout().
library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 + x06 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 + x03 f4 =~ x11 + x12 + x13 + x14 ' fit <- lavaan::cfa(mod, cfa_example) p <- semPaths(fit, whatLabels = "est", sizeMan = 3.25, node.width = 1, edge.label.cex = .75, mar = c(10, 5, 10, 5), DoNotPlot = TRUE) indicator_order <- auto_indicator_order(p) indicator_order p2 <- set_cfa_layout(p, indicator_order = indicator_order) plot(p2) # set_cfa_layout() will call auto_indicator_order() # automatically if indicator_order is not set. p3 <- set_cfa_layout(p) plot(p3)library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 + x06 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 + x03 f4 =~ x11 + x12 + x13 + x14 ' fit <- lavaan::cfa(mod, cfa_example) p <- semPaths(fit, whatLabels = "est", sizeMan = 3.25, node.width = 1, edge.label.cex = .75, mar = c(10, 5, 10, 5), DoNotPlot = TRUE) indicator_order <- auto_indicator_order(p) indicator_order p2 <- set_cfa_layout(p, indicator_order = indicator_order) plot(p2) # set_cfa_layout() will call auto_indicator_order() # automatically if indicator_order is not set. p3 <- set_cfa_layout(p) plot(p3)
A sample dataset for fitting a confirmatory factor analysis model.
cfa_examplecfa_example
An object of class data.frame with 200 rows and 14 columns.
Fourteen variables (x01 to x14), 200 cases.
Sample model to fit (in lavaan::model.syntax notation)
mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 '
Change the labels of selected nodes.
change_node_label( semPaths_plot, label_list = NULL, label.cex, label.scale, label.prop, label.norm )change_node_label( semPaths_plot, label_list = NULL, label.cex, label.scale, label.prop, label.norm )
semPaths_plot |
A qgraph::qgraph object generated by semPlot::semPaths, or a similar qgraph object modified by other semptools functions. |
label_list |
A list of named lists. Each named list should
have two named values: |
label.cex |
Identical to the same argument in
|
label.scale |
Identical to the same argument in
semPlot::semPaths. A logical value that determine whether labels
wil be scaled (resized) to the nodes they attach to. It has no
default. If not set, then this option in the |
label.prop |
Identical to the same argument in
semPlot::semPaths. A numeric vector of length equal to the number
of nodes. If |
label.norm |
Identical to the same argument in
semPlot::semPaths. It must be a string. All labels as wide as or
narrower than this string will have the same font size, while all
labels wider than this string will be rescaled to have the same
width as this string. It has no default. If not set, then this
option in the |
Modify a qgraph::qgraph object generated by semPlot::semPaths and change the labels of selected nodes.
A qgraph::qgraph based on the original one, with node attributes of selected nodes modified.
library(semPlot) library(lavaan) mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- sem(mod_pa, pa_example) parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_label_list <- list(list(node = "x3", to = "mediator"), list(node = "x4", to = expression(gamma))) p_pa2 <- change_node_label(p_pa, my_label_list) plot(p_pa2)library(semPlot) library(lavaan) mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- sem(mod_pa, pa_example) parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_label_list <- list(list(node = "x3", to = "mediator"), list(node = "x4", to = expression(gamma))) p_pa2 <- change_node_label(p_pa, my_label_list) plot(p_pa2)
Check which parameters in a lavaan output are the residual variance of a dependent variable.
is_dv_residvar(lavaan_out)is_dv_residvar(lavaan_out)
lavaan_out |
A lavaan::lavaan object. |
Check which parameters in a lavaan output are the variance of a dependent variable. Indicators of a latent variable will be excluded.
A boolean vector with length equal to the number of rows in the lavaan output.
mod <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod, pa_example) is_dv_residvar(fit_pa) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::cfa(mod, cfa_example) is_dv_residvar(fit_cfa) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod, sem_example) is_dv_residvar(fit_sem)mod <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod, pa_example) is_dv_residvar(fit_pa) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::cfa(mod, cfa_example) is_dv_residvar(fit_cfa) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod, sem_example) is_dv_residvar(fit_sem)
Keep or drop nodes from an semPlotModel object.
drop_nodes(object, nodes) keep_nodes(object, nodes)drop_nodes(object, nodes) keep_nodes(object, nodes)
object |
An an |
nodes |
A character vector of the nodes to be kept or removed. |
These functions can be used to edit the nodes in an
semPlot::semPlotModel generated by semPlot::semPlotModel().
The edited object can then be passed to semPlot::semPaths() to
generate a path diagram.
Use keep_nodes() to specify the nodes to be kept. All other nodes
will be removed.
Use drop_nodes() to specify the nodes to be dropped. All other
nodes will be kept.
An object of the class semPlot::semPlotModel.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) pm_pa <- semPlot::semPlotModel(fit_pa) semPlot::semPaths(pm_pa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) pm_pa2 <- drop_nodes(pm_pa, c("x3")) semPlot::semPaths(pm_pa2, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) pm_pa3 <- keep_nodes(pm_pa, c("x1", "x3", "x4")) semPlot::semPaths(pm_pa3, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) pm_pa <- semPlot::semPlotModel(fit_pa) semPlot::semPaths(pm_pa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) pm_pa2 <- drop_nodes(pm_pa, c("x3")) semPlot::semPaths(pm_pa2, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) pm_pa3 <- keep_nodes(pm_pa, c("x1", "x3", "x4")) semPlot::semPaths(pm_pa3, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m)
Determine the order of indicators and match indicators and factors based on a 'lavaan' model syntax.
lavaan_indicator_order(model_syntax)lavaan_indicator_order(model_syntax)
model_syntax |
A string that
should be a model specified in
|
It generates a named vector
for the argument indicator_order
of set_cfa_layout() and
set_sem_layout() using a lavaan
model syntax.
A variable is considered an indicator
if it is on the right-hand side
of the operator =~.
If an indicator loaded on more than one latent variable, it will only be matched to one of them, determined by the order of appearance in the internal storage.
A named character vector. The values are the indicators in the model syntax. The names are the latent factors the indicators loaded on.
set_sem_layout() and
set_cfa_layout().
mod <- 'f1 =~ x01 + x02 + x03 + x06 f4 =~ x11 + x12 + x13 + x14 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 + x03 ' lavaan_indicator_order(mod) mod <- 'f1 =~ x01 + x02 + x03 + x06 f3 =~ x08 + x09 + x10 + x03 f2 =~ x04 + x05 + x06 + x07 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f3 ' lavaan_indicator_order(mod)mod <- 'f1 =~ x01 + x02 + x03 + x06 f4 =~ x11 + x12 + x13 + x14 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 + x03 ' lavaan_indicator_order(mod) mod <- 'f1 =~ x01 + x02 + x03 + x06 f3 =~ x08 + x09 + x10 + x03 f2 =~ x04 + x05 + x06 + x07 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f3 ' lavaan_indicator_order(mod)
Create the layout matrix from a list of coordinates for semPaths.
layout_matrix(...)layout_matrix(...)
... |
Each node in the matrix is specified by this form: |
The layout argument in semPlot::semPaths() accepts a
matrix with node labels as the elements, and NA for empty cells.
This function allows user to create the matrix using a list of
coordinates for the node labels.
A layout matrix for the layout argument of
semPlot::semPaths().
# Suppose this is the layout to be created: m0 <- matrix(c("x1", NA, NA, NA, "x2", "x3", NA, NA, NA, "x4", NA, "x5"), byrow = TRUE, 3, 4) # This call will create the same matrix. m1 <- layout_matrix(x1 = c(1, 1), x2 = c(2, 1), x3 = c(2, 2), x4 = c(3, 2), x5 = c(3, 4)) #The two matrices should be identical. m0 == m1# Suppose this is the layout to be created: m0 <- matrix(c("x1", NA, NA, NA, "x2", "x3", NA, NA, NA, "x4", NA, "x5"), byrow = TRUE, 3, 4) # This call will create the same matrix. m1 <- layout_matrix(x1 = c(1, 1), x2 = c(2, 1), x3 = c(2, 2), x4 = c(3, 2), x5 = c(3, 4)) #The two matrices should be identical. m0 == m1
Add standard error estimates, in parentheses, to parameter estimates (edge labels) in a qgraph::qgraph object.
mark_se( semPaths_plot, object, sep = " ", digits = 2L, ests = NULL, std_type = FALSE )mark_se( semPaths_plot, object, sep = " ", digits = 2L, ests = NULL, std_type = FALSE )
semPaths_plot |
A qgraph object generated by
|
object |
The object used by semPaths to generate the plot. Use
the same argument name used in |
sep |
A character string to separate the coefficient and the
standard error (in parentheses). Default to " " (one space). Use
|
digits |
Integer indicating number of decimal places for the appended standard errors. Default is 2L. |
ests |
A data.frame from the
|
std_type |
If standardized solution is used in the plot,
set this either to the type of standardization (e.g., |
Modify a qgraph::qgraph object generated by
semPaths (currently in parentheses) to the
labels. Require either the original object used in the semPaths call,
or a data frame with the standard error for each parameter. The latter
option is for standard errors not computed by lavaan but by
other functions.
Currently supports only plots based on lavaan
output.
This function is a variant of, and can be combined with, the
mark_sig function.
If the input is a qgraph::qgraph object, the function returns a qgraph based on the original one, with standard error estimates appended. If the input is a list of qgraph objects, the function returns a list of the same length.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) p_pa2 <- mark_se(p_pa, fit_pa) plot(p_pa2) mod_cfa <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod_cfa, cfa_example) lavaan::parameterEstimates(fit_cfa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_cfa <- semPlot::semPaths(fit_cfa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Place standard errors on a new line p_cfa2 <- mark_se(p_cfa, fit_cfa, sep = "\n") plot(p_cfa2) mod_sem <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod_sem, sem_example) lavaan::parameterEstimates(fit_sem)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_sem <- semPlot::semPaths(fit_sem, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Mark significance, and then add standard errors p_sem2 <- mark_sig(p_sem, fit_sem) p_sem3 <- mark_se(p_sem2, fit_sem, sep = "\n") plot(p_sem3)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) p_pa2 <- mark_se(p_pa, fit_pa) plot(p_pa2) mod_cfa <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod_cfa, cfa_example) lavaan::parameterEstimates(fit_cfa)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_cfa <- semPlot::semPaths(fit_cfa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Place standard errors on a new line p_cfa2 <- mark_se(p_cfa, fit_cfa, sep = "\n") plot(p_cfa2) mod_sem <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod_sem, sem_example) lavaan::parameterEstimates(fit_sem)[ , c("lhs", "op", "rhs", "est", "pvalue", "se")] p_sem <- semPlot::semPaths(fit_sem, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0) # Mark significance, and then add standard errors p_sem2 <- mark_sig(p_sem, fit_sem) p_sem3 <- mark_se(p_sem2, fit_sem, sep = "\n") plot(p_sem3)
Mark parameter estimates (edge labels) based on p-value.
mark_sig( semPaths_plot, object, alphas = c(`*` = 0.05, `**` = 0.01, `***` = 0.001), ests = NULL, std_type = FALSE )mark_sig( semPaths_plot, object, alphas = c(`*` = 0.05, `**` = 0.01, `***` = 0.001), ests = NULL, std_type = FALSE )
semPaths_plot |
A qgraph::qgraph object generated by semPaths, or a similar qgraph object modified by other semptools functions. |
object |
The object used by semPaths to generate the plot. Use
the same argument name used in semPaths to make the meaning of this
argument obvious. Currently only object of class
|
alphas |
A named numeric vector. Each element is the cutoff (level of significance), and the name of it is the symbol to be used if p-value is less than this cutoff. The default is c("" = .05, "" = .01, "" = .001). |
ests |
A data.frame from the
|
std_type |
If standardized solution is used in the plot,
set this either to the type of standardization (e.g., |
Modify a qgraph::qgraph object generated by semPaths and add marks (currently asterisk, "*") to the labels based on their p-values. Require either the original object used in the semPaths call, or a data frame with the p-values for each parameter. The latter option is for p-values not computed by lavaan but by other functions.
Currently supports only plots based on lavaan output.
A qgraph::qgraph based on the original one, with marks appended to edge labels based on their p-values.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) p_pa2 <- mark_sig(p_pa, fit_pa) plot(p_pa2) mod_cfa <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod_cfa, cfa_example) lavaan::parameterEstimates(fit_cfa)[, c("lhs", "op", "rhs", "est", "pvalue")] p_cfa <- semPlot::semPaths(fit_cfa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0) p_cfa2 <- mark_sig(p_cfa, fit_cfa) plot(p_cfa2) mod_sem <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod_sem, sem_example) lavaan::parameterEstimates(fit_sem)[, c("lhs", "op", "rhs", "est", "pvalue")] p_sem <- semPlot::semPaths(fit_sem, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0) p_sem2 <- mark_sig(p_sem, fit_sem) plot(p_sem2)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) p_pa2 <- mark_sig(p_pa, fit_pa) plot(p_pa2) mod_cfa <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod_cfa, cfa_example) lavaan::parameterEstimates(fit_cfa)[, c("lhs", "op", "rhs", "est", "pvalue")] p_cfa <- semPlot::semPaths(fit_cfa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0) p_cfa2 <- mark_sig(p_cfa, fit_cfa) plot(p_cfa2) mod_sem <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod_sem, sem_example) lavaan::parameterEstimates(fit_sem)[, c("lhs", "op", "rhs", "est", "pvalue")] p_sem <- semPlot::semPaths(fit_sem, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0) p_sem2 <- mark_sig(p_sem, fit_sem) plot(p_sem2)
A sample dataset for fitting a path analysis model.
pa_examplepa_example
An object of class data.frame with 100 rows and 4 columns.
Four variables (x1 to x4), 100 cases.
Sample model to fit (in lavaan::model.syntax notation)
mod <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 '
A sample dataset for fitting a path analysis model, with three control variables.
pa_example_3covspa_example_3covs
An object of class data.frame with 100 rows and 7 columns.
Four variables (x1 to x4), and three control variables
(cov1, cov2, cov3), 100 cases.
Sample model to fit (in lavaan::model.syntax notation)
mod <- ' x3 ~ x1 + x2 + cov1 +cov2 + cov3 x4 ~ x1 + x3 + cov1 +cov2 + cov3 '
Rescale the layout of a
qgraph object, such as the output
of semptools functions that modify
the output of semPlot::semPaths().
rescale_layout(semPaths_plot, x_min = -1, x_max = 1, y_min = -1, y_max = 1)rescale_layout(semPaths_plot, x_min = -1, x_max = 1, y_min = -1, y_max = 1)
semPaths_plot |
A
qgraph::qgraph object generated by
|
x_min, x_max, y_min, y_max
|
The
ranges of x-coordinates and
y-coordinates after rescaling.
Default is -1 for |
The plot generated by some
functions, such as
set_sem_layout(), may have the area
underused for some model. This
function rescale the layout matrix,
just like what the rescale argument
of semPlot::semPaths() does.
A qgraph::qgraph based on the original one, with the layout matrix rescaled.
library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + x04 f4 ~ f3 + x05' fit_sem <- sem(mod, sem_example) p <- semPaths(fit_sem, whatLabels="est", sizeMan = 5, nCharNodes = 0, nCharEdges = 0, edge.width = 0.8, node.width = 0.7, edge.label.cex = 0.6, mar = c(10, 10, 10, 10), DoNotPlot = TRUE) plot(p) indicator_order <- c("x04", "x05", "x01", "x02", "x03", "x11", "x12", "x13", "x14", "x08", "x09", "x10") indicator_factor <- c("x04", "x05", "f1", "f1", "f1", "f4", "f4", "f4", "f4", "f3", "f3", "f3") factor_layout <- matrix(c( "f1", "f3", "f4", "x04", "x05", NA), byrow = TRUE, 2, 3) factor_point_to <- matrix(c("left", "up", "right", NA, NA, NA), byrow = TRUE, 2, 3) p2 <- set_sem_layout(p, indicator_order = indicator_order, indicator_factor = indicator_factor, factor_layout = factor_layout, factor_point_to = factor_point_to) # The original plot with too much unused area plot(p2) rect(-1, -1, 1, 1) rect(-1.5, -1.5, 1.5, 1.5) # Expand the plot p3 <- p2 p3 <- rescale_layout(p3) plot(p3) rect(-1, -1, 1, 1) rect(-1.5, -1.5, 1.5, 1.5)library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + x04 f4 ~ f3 + x05' fit_sem <- sem(mod, sem_example) p <- semPaths(fit_sem, whatLabels="est", sizeMan = 5, nCharNodes = 0, nCharEdges = 0, edge.width = 0.8, node.width = 0.7, edge.label.cex = 0.6, mar = c(10, 10, 10, 10), DoNotPlot = TRUE) plot(p) indicator_order <- c("x04", "x05", "x01", "x02", "x03", "x11", "x12", "x13", "x14", "x08", "x09", "x10") indicator_factor <- c("x04", "x05", "f1", "f1", "f1", "f4", "f4", "f4", "f4", "f3", "f3", "f3") factor_layout <- matrix(c( "f1", "f3", "f4", "x04", "x05", NA), byrow = TRUE, 2, 3) factor_point_to <- matrix(c("left", "up", "right", NA, NA, NA), byrow = TRUE, 2, 3) p2 <- set_sem_layout(p, indicator_order = indicator_order, indicator_factor = indicator_factor, factor_layout = factor_layout, factor_point_to = factor_point_to) # The original plot with too much unused area plot(p2) rect(-1, -1, 1, 1) rect(-1.5, -1.5, 1.5, 1.5) # Expand the plot p3 <- p2 p3 <- rescale_layout(p3) plot(p3) rect(-1, -1, 1, 1) rect(-1.5, -1.5, 1.5, 1.5)
Rotate the residuals of selected nodes.
rotate_resid(semPaths_plot, rotate_resid_list = NULL)rotate_resid(semPaths_plot, rotate_resid_list = NULL)
semPaths_plot |
A qgraph::qgraph object generated by semPlot::semPaths, or a similar qgraph object modified by other semptools functions. |
rotate_resid_list |
A named vector or a list of named list. For
a named vector, the name of an element is the node for which its
residual is to be rotated, and the value is the degree to rotate.
The 12 o'clock position is zero degree. Positive degree denotes
clockwise rotation, and negative degree denotes anticlockwise
rotation. For example, |
Modify a qgraph::qgraph object generated by semPlot::semPaths and rotate the residuals of selected nodes. Currently only supports "ram" and similar styles of semPlot::semPaths.
A qgraph::qgraph object based on the original one, with
loopRotation attributes of selected nodes modified.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_rotate_resid_vector <- c(x3 = 45, x4 = -45) p_pa2v <- rotate_resid(p_pa, my_rotate_resid_vector) plot(p_pa2v) my_rotate_resid_list <- list(list(node = "x3", rotate = 45), list(node = "x4", rotate = -45)) p_pa2l <- rotate_resid(p_pa, my_rotate_resid_list) plot(p_pa2l)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_rotate_resid_vector <- c(x3 = 45, x4 = -45) p_pa2v <- rotate_resid(p_pa, my_rotate_resid_vector) plot(p_pa2v) my_rotate_resid_list <- list(list(node = "x3", rotate = 45), list(node = "x4", rotate = -45)) p_pa2l <- rotate_resid(p_pa, my_rotate_resid_list) plot(p_pa2l)
A sample dataset for fitting a latent variable model with two 2nd-order factors.
sem_2nd_order_examplesem_2nd_order_example
An object of class data.frame with 500 rows and 21 columns.
Twenty one variables (x01 to x21), 500 cases.
Sample model to fit (in lavaan::model.syntax notation)
mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f5 =~ x15 + x16 + x17 + x18 f6 =~ x19 + x20 + x21 f21 =~ 1*f1 + f3 + f4 f22 =~ 1*f2 + f5 + f6 f22 ~ f21 '
A sample dataset for fitting a latent variable model.
sem_examplesem_example
An object of class data.frame with 200 rows and 14 columns.
Fourteen variables (x01 to x14), 100 cases.
Sample model to fit (in lavaan::model.syntax notation)
mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 '
Configure the layout of factors and adjust other aspects of a CFA graph by semPaths.
set_cfa_layout( semPaths_plot, indicator_order = NULL, indicator_factor = NULL, fcov_curve = 0.4, loading_position = 0.5, point_to = "down" )set_cfa_layout( semPaths_plot, indicator_order = NULL, indicator_factor = NULL, fcov_curve = 0.4, loading_position = 0.5, point_to = "down" )
semPaths_plot |
A qgraph::qgraph object generated by semPaths, or a similar qgraph object modified by other semptools functions. |
indicator_order |
A string vector of the indicators. The order
of the names is the order of the indicators in the graph, when they
are drawn on the bottom of the graph. The indicators should be
grouped by the factors on which they load on. For example, if x1,
x2, x4 load on f2, and x3, x5, x6 load on f1, then vector should be
either c("x1", "x2", "x4", "x3", "x5", "x6") or c("x3", "x5", "x6",
"x1", "x2", "x4"). Indicators within a group can be ordered in any
way. If it is a named vector, its names will be used for the
argument |
indicator_factor |
A string vector of the same length of the
indicator order, storing the name of the factor for which each of
the indicator in indicator_factor loads on. For example, if x1, x2,
x4 load on f2, and x3, x5, x6 load on f1, and indicator_order is
c("x3", "x5", "x6", "x1", "x2", "x4"), then indicator_factor should
be c("f2", "f2", "f2", "f1", "f1", "f1"). If |
fcov_curve |
A number used to set the curvature of the inter-factor covariances. Default is .4. |
loading_position |
The positions of all factor loadings. Default is .5, on the middle of the arrows. Larger the number, closer the loadings to the indicators. Smaller the number, closer the loadings to the factors. |
point_to |
Can be "down", "left", "up", or "right". Specify the direction that the factors "point" to the indicators. Default is "down". |
Modify a qgraph::qgraph object generated by semPaths based on a confirmatory factor analysis model.
A qgraph::qgraph based on the original one, with various aspects of the model modified.
library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod, cfa_example) lavaan::parameterEstimates(fit_cfa)[, c("lhs", "op", "rhs", "est", "pvalue")] p <- semPaths(fit_cfa, whatLabels="est", sizeMan = 2.5, nCharNodes = 0, nCharEdges = 0, edge.width = 0.8, node.width = 0.7, edge.label.cex = 0.6, style = "ram", mar = c(10,10,10,10)) indicator_order <- c("x04", "x05", "x06", "x07", "x01", "x02", "x03", "x11", "x12", "x13", "x14", "x08", "x09", "x10") indicator_factor <- c( "f2", "f2", "f2", "f2", "f1", "f1", "f1", "f4", "f4", "f4", "f4", "f3", "f3", "f3") p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8) plot(p2) # Use a named vector for indicator_order indicator_order2 <- c(f2 = "x04", f2 = "x05", f2 = "x06", f2 = "x07", f1 = "x01", f1 = "x02", f1 = "x03", f4 = "x11", f4 = "x12", f4 = "x13", f4 = "x14", f3 = "x08", f3 = "x09", f3 = "x10") p2 <- set_cfa_layout(p, indicator_order = indicator_order2, fcov_curve = 1.5, loading_position = .8) plot(p2) # Use automatically generated indicator_order and indicator_factor p2 <- set_cfa_layout(p, fcov_curve = 1.5, loading_position = .8) plot(p2) p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8, point_to = "left") plot(p2) p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8, point_to = "up") plot(p2) p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8, point_to = "right") plot(p2)library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 ' fit_cfa <- lavaan::sem(mod, cfa_example) lavaan::parameterEstimates(fit_cfa)[, c("lhs", "op", "rhs", "est", "pvalue")] p <- semPaths(fit_cfa, whatLabels="est", sizeMan = 2.5, nCharNodes = 0, nCharEdges = 0, edge.width = 0.8, node.width = 0.7, edge.label.cex = 0.6, style = "ram", mar = c(10,10,10,10)) indicator_order <- c("x04", "x05", "x06", "x07", "x01", "x02", "x03", "x11", "x12", "x13", "x14", "x08", "x09", "x10") indicator_factor <- c( "f2", "f2", "f2", "f2", "f1", "f1", "f1", "f4", "f4", "f4", "f4", "f3", "f3", "f3") p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8) plot(p2) # Use a named vector for indicator_order indicator_order2 <- c(f2 = "x04", f2 = "x05", f2 = "x06", f2 = "x07", f1 = "x01", f1 = "x02", f1 = "x03", f4 = "x11", f4 = "x12", f4 = "x13", f4 = "x14", f3 = "x08", f3 = "x09", f3 = "x10") p2 <- set_cfa_layout(p, indicator_order = indicator_order2, fcov_curve = 1.5, loading_position = .8) plot(p2) # Use automatically generated indicator_order and indicator_factor p2 <- set_cfa_layout(p, fcov_curve = 1.5, loading_position = .8) plot(p2) p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8, point_to = "left") plot(p2) p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8, point_to = "up") plot(p2) p2 <- set_cfa_layout(p, indicator_order, indicator_factor, fcov_curve = 1.5, loading_position = .8, point_to = "right") plot(p2)
Set the curve attributes of selected edges.
set_curve(semPaths_plot, curve_list = NULL)set_curve(semPaths_plot, curve_list = NULL)
semPaths_plot |
A qgraph::qgraph object generated by semPlot::semPaths, or a similar qgraph object modified by other semptools functions. |
curve_list |
A named vector or a list of named list. For a
named vector, the name of an element should be the path as
specified by lavaan::model.syntax or as appeared in
|
Modified a qgraph::qgraph object generated by semPlot::semPaths and change the curve attributes of selected edges.
A qgraph::qgraph based on the original one, with curve attributes for selected edges changed.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_curve_vector <- c("x2 ~~ x1" = -1, "x4 ~ x1" = 1) p_pa2v <- set_curve(p_pa, my_curve_vector) plot(p_pa2v) my_curve_list <- list(list(from = "x1", to = "x2", new_curve = -1), list(from = "x1", to = "x4", new_curve = 1)) p_pa2l <- set_curve(p_pa, my_curve_list) plot(p_pa2l)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_curve_vector <- c("x2 ~~ x1" = -1, "x4 ~ x1" = 1) p_pa2v <- set_curve(p_pa, my_curve_vector) plot(p_pa2v) my_curve_list <- list(list(from = "x1", to = "x2", new_curve = -1), list(from = "x1", to = "x4", new_curve = 1)) p_pa2l <- set_curve(p_pa, my_curve_list) plot(p_pa2l)
Set arbitrary attributes of selected edges.
set_edge_attribute(semPaths_plot, values = NULL, attribute_name = NULL)set_edge_attribute(semPaths_plot, values = NULL, attribute_name = NULL)
semPaths_plot |
A qgraph::qgraph object generated by semPlot::semPaths, or a similar qgraph object modified by other semptools functions. |
values |
A named vector or a list of named list. See the Details section on how to set this argument. |
attribute_name |
The name of the attribute to be changed. |
Modify a qgraph::qgraph object generated by semPlot::semPaths and change the selected attributes of selected edges.
This function is designed to be a general one that changes the attributes named by the user. The user needs to make sure that the attribute actually exists, and the values are valid for the named attribute.
values
This argument can be set in two ways.
For a named vector, the name of an
element should be the path as
specified by lavaan::model.syntax
or as appeared in
lavaan::parameterEstimates().
For example, if the attributes to be
changed are the colors of selected
edges, to change the color of the
path regressing y on x, the name
should be "y ~ x". To change the
color of the covariance between x1
and x2, the name should be "x1 ~~ x2". Therefore, c("y ~ x1" = "red", "x1 ~~ x2" = "blue") changes the
colors of the path from x1 to y
and the covariance between x1 and
x2 to "red" and "blue",
respectively.
The order of the two nodes may matter for covariances. Therefore, if the attribute of a covariance is not changed, try switching the order of the two nodes.
For a list of named lists, each named
list should have three named values:
from, to, and new_value. The
attribute of the edge from from to
to will be set to new_value.
The second approach is no longer recommended, though kept for backward compatibility.
A qgraph::qgraph based on the original one, with the selected attributes of selected edges changed.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_values_vector <- c("x2 ~~ x1" = "red", "x4 ~ x1" = "blue") p_pa2v <- set_edge_attribute(p_pa, values = my_values_vector, attribute_name = "color") plot(p_pa2v) my_values_list <- list(list(from = "x1", to = "x2", new_value = "red"), list(from = "x1", to = "x4", new_value = "blue")) p_pa2l <- set_edge_attribute(p_pa, values = my_values_list, attribute_name = "color") plot(p_pa2l)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_values_vector <- c("x2 ~~ x1" = "red", "x4 ~ x1" = "blue") p_pa2v <- set_edge_attribute(p_pa, values = my_values_vector, attribute_name = "color") plot(p_pa2v) my_values_list <- list(list(from = "x1", to = "x2", new_value = "red"), list(from = "x1", to = "x4", new_value = "blue")) p_pa2l <- set_edge_attribute(p_pa, values = my_values_list, attribute_name = "color") plot(p_pa2l)
Set the colors of selected edges.
set_edge_color(semPaths_plot, color_list = NULL)set_edge_color(semPaths_plot, color_list = NULL)
semPaths_plot |
A qgraph::qgraph object generated by semPlot::semPaths, or a similar qgraph object modified by other semptools functions. |
color_list |
A named vector or a list of named list. See the Details section on how to set this argument. |
Modified a qgraph::qgraph object generated by semPlot::semPaths and change the colors of selected edges.
color_list
This argument can be set in two ways.
For a named vector, the name of an
element should be the path as
specified by lavaan::model.syntax
or as appeared in
lavaan::parameterEstimates().
For example, to change the color of the
path regressing y on x, the name
should be "y ~ x". To change the
color of the covariance between x1
and x2, the name should be "x1 ~~ x2". Therefore, c("y ~ x1" = "red", "x1 ~~ x2" = "blue") changes the
colors of the path from x1 to y
and the covariance between x1 and
x2 to "red" and "blue",
respectively.
The order of the two nodes may matter for covariances. Therefore, if the attribute of a covariance is not changed, try switching the order of the two nodes.
For a list of named lists, each named
list should have three named values:
from, to, and new_color. The
attribute of the edge from from to
to will be set to new_color.
The second approach is no longer recommended, though kept for backward compatibility.
A qgraph::qgraph based on the original one, with colors for selected edges changed.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_color_vector <- c("x2 ~~ x1" = "red", "x4 ~ x1" = "blue") p_pa2v <- set_edge_color(p_pa, my_color_vector) plot(p_pa2v) my_color_list <- list(list(from = "x1", to = "x2", new_color = "red"), list(from = "x1", to = "x4", new_color = "blue")) p_pa2l <- set_edge_color(p_pa, my_color_list) plot(p_pa2l)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_color_vector <- c("x2 ~~ x1" = "red", "x4 ~ x1" = "blue") p_pa2v <- set_edge_color(p_pa, my_color_vector) plot(p_pa2v) my_color_list <- list(list(from = "x1", to = "x2", new_color = "red"), list(from = "x1", to = "x4", new_color = "blue")) p_pa2l <- set_edge_color(p_pa, my_color_list) plot(p_pa2l)
Set the positions of edge labels of selected edges.
set_edge_label_position(semPaths_plot, position_list = NULL)set_edge_label_position(semPaths_plot, position_list = NULL)
semPaths_plot |
A qgraph::qgraph object generated by semPlot::semPaths, or a similar qgraph object modified by other semptools functions. |
position_list |
A named vector or a list of named lists. For a
named vector, the name of an element should be the path as
specified by lavaan::model.syntax or as appeared in
|
Modify a qgraph::qgraph object generated by semPlot::semPaths and change the edge label positions of selected edges.
A qgraph::qgraph based on the original one, with edge label positions for selected edges changed.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_position_vector <- c("x3 ~ x2" = .25, "x4 ~ x1" = .75) p_pa2v <- set_edge_label_position(p_pa, my_position_vector) plot(p_pa2v) my_position_list <- list(list(from = "x2", to = "x3", new_position = .25), list(from = "x1", to = "x4", new_position = .75)) p_pa2l <- set_edge_label_position(p_pa, my_position_list) plot(p_pa2l)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_position_vector <- c("x3 ~ x2" = .25, "x4 ~ x1" = .75) p_pa2v <- set_edge_label_position(p_pa, my_position_vector) plot(p_pa2v) my_position_list <- list(list(from = "x2", to = "x3", new_position = .25), list(from = "x1", to = "x4", new_position = .75)) p_pa2l <- set_edge_label_position(p_pa, my_position_list) plot(p_pa2l)
Set arbitrary attributes of selected nodes
set_node_attribute(semPaths_plot, values = NULL, attribute_name = NULL)set_node_attribute(semPaths_plot, values = NULL, attribute_name = NULL)
semPaths_plot |
A qgraph::qgraph object generated by semPlot::semPaths, or a similar qgraph object modified by other semptools functions. |
values |
A named vector or a list of named list. See the Details section on how to set this argument. |
attribute_name |
The name of the attribute to be changed. |
Modify a qgraph::qgraph object generated by semPlot::semPaths and change the selected attributes of selected nodes.
This function is designed to be a general one that changes the attributes named by the user. The user needs to make sure that the attribute actually exists, and the values are valid for the named attribute.
values
This argument can be set in two ways.
For a named vector, the name of an element should be the nodes for which their attributes are to be changed. The names need to the displayed names if plotted, which may be different from the names in mode.
For example, if the attributes to be
changed are the colors of selected
nodes, to change the color of x
is to be changed, the name
should be "x". Therefore,
c("y" = "red", "x" = "red") changes
the colors of the nodes y and x
to "red" and "blue",
respectively.
For a list of named lists, each named
list should have two named values:
node and new_value. The
attribute of node
will be set to new_value.
The second approach is no longer recommended, though kept for backward compatibility.
A qgraph::qgraph based on the original one, with the selected attributes of selected nodes changed.
mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_color_vector <- c(x3 = "red", x4 = "blue") p_pa2v <- set_node_attribute(p_pa, my_color_vector, attribute_name = "color") plot(p_pa2v) my_color_list <- list(list(node = "x3", new_value = "green"), list(node = "x4", new_value = "red")) p_pa2l <- set_node_attribute(p_pa, my_color_list, attribute_name = "color") plot(p_pa2l)mod_pa <- 'x1 ~~ x2 x3 ~ x1 + x2 x4 ~ x1 + x3 ' fit_pa <- lavaan::sem(mod_pa, pa_example) lavaan::parameterEstimates(fit_pa)[, c("lhs", "op", "rhs", "est", "pvalue")] m <- matrix(c("x1", NA, NA, NA, "x3", "x4", "x2", NA, NA), byrow = TRUE, 3, 3) p_pa <- semPlot::semPaths(fit_pa, whatLabels="est", style = "ram", nCharNodes = 0, nCharEdges = 0, layout = m) my_color_vector <- c(x3 = "red", x4 = "blue") p_pa2v <- set_node_attribute(p_pa, my_color_vector, attribute_name = "color") plot(p_pa2v) my_color_list <- list(list(node = "x3", new_value = "green"), list(node = "x4", new_value = "red")) p_pa2l <- set_node_attribute(p_pa, my_color_list, attribute_name = "color") plot(p_pa2l)
Configure the layout of factors and adjust other aspects of an SEM graph by semPlot::semPaths.
set_sem_layout( semPaths_plot, indicator_order = NULL, indicator_factor = NULL, factor_layout = NULL, factor_point_to = NULL, indicator_push = NULL, indicator_spread = NULL, loading_position = 0.5 )set_sem_layout( semPaths_plot, indicator_order = NULL, indicator_factor = NULL, factor_layout = NULL, factor_point_to = NULL, indicator_push = NULL, indicator_spread = NULL, loading_position = 0.5 )
semPaths_plot |
A qgraph::qgraph object generated by semPaths, or a similar qgraph object modified by other semptools functions. |
indicator_order |
A string vector of the indicators. The order
of the names is the order of the indicators in the graph, when they
are drawn on the bottom of the graph. The indicators should be
grouped by the factors on which they load on. For example, if x1,
x2, x4 load on f2, and x3, x5, x6 load on f1, then vector should be
either c("x1", "x2", "x4", "x3", "x5", "x6") or c("x3", "x5", "x6",
"x1", "x2", "x4"). Indicators within a group can be ordered in any
way. If it is a named vector, its names will be used for the
argument |
indicator_factor |
A string vector of the same length of the
indicator order, storing the name of the factor for which each of
the indicator in indicator_factor loads on. For example, if x1, x2,
x4 load on f2, and x3, x5, x6 load on f1, and indicator_order is
c("x3", "x5", "x6", "x1", "x2", "x4"), then indicator_factor should
be c("f2", "f2", "f2", "f1", "f1", "f1"). If |
factor_layout |
A matrix of arbitrary size. This matrix will serve as a grid for users to specify where each latent factor should be placed approximately on the graph. Each cell should contain NA or the name of a latent factor. The locations of all latent factors must be explicitly specified by this matrix. |
factor_point_to |
Can be a named character vector with
names being the names of factors, or a matrix of the same size as |
indicator_push |
(Optional) This argument is used to adjust the
positions of the indicators of selected latent factors. It can be
named vector or a list of named lists. For a named vector, The name
is the factor of which the indicators will be "pushed", and the
value is how "hard" the push is: the multiplier to the distance
from the factor to the indicators. If this value is 1, then there
is no change. If this value is greater than 1, then the indicators
are pushed away from the latent factor. If this value is less than
1, then the indicators are pulled toward the latent factor. For
example, to push the indicators of |
indicator_spread |
(Optional) This argument is used to adjust
the distance between indicators of selected latent factors. It can
be a named vector or a list of named lists. For a named vector, the
name is the factor of which the indicators will be spread out. The
value is the multiplier to the distance between neighboring
indicators. If this value is equal to 1, there is no change. Larger
than one, the indicators will be "spread" away from each other.
Less than one, the indicators will be placed closer to each others.
For example, to spread the indicators of |
loading_position |
(Optional) Default is .5. This is used
adjust the position of the loadings. If this is one single number,
it will be used to set the positions of all loadings. If it is .5,
the loadings are placed on the center of the arrows. Larger the
number, closer the loadings to the indicators. Smaller the number,
closer to the latent factors. This argument also accepts a named
vector or a list of named lists, allowing users to specify the
positions of loadings for each factor separately. For a named
vector, in each element, the name is the factor whose loadings will
be moved. The value is the positions of its loadings. The default
is .50. We only need to specify the positions for factors to be
changed from .50 to other values. For example, move the loadings of
|
Modify a qgraph::qgraph object generated by semPaths
based on an SEM model with latent factors. Since version 0.2.9.5,
this function natively supports observed exogenous variable.
If a variable is listed in both indicator_order and
indicator_factor, as if it is both a factor and an indicator,
this function will assume that it is an observed exogenous variable.
It will be positioned as a factor according to factor_layout,
but no indicators will be drawn.
For versions older than 0.2.9.5, an observed exogenous variable needs to be specified as an one-indicator factor in the model specification for this function to work.
A qgraph::qgraph based on the original one, with various aspects of the model modified.
library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod, sem_example) lavaan::parameterEstimates(fit_sem)[, c("lhs", "op", "rhs", "est", "pvalue")] p <- semPaths(fit_sem, whatLabels="est", sizeMan = 5, nCharNodes = 0, nCharEdges = 0, edge.width = 0.8, node.width = 0.7, edge.label.cex = 0.6, style = "ram", mar = c(10,10,10,10)) indicator_order <- c("x04", "x05", "x06", "x07", "x01", "x02", "x03", "x11", "x12", "x13", "x14", "x08", "x09", "x10") indicator_factor <- c( "f2", "f2", "f2", "f2", "f1", "f1", "f1", "f4", "f4", "f4", "f4", "f3", "f3", "f3") factor_layout <- matrix(c("f1", NA, NA, NA, "f3", "f4", "f2", NA, NA), byrow = TRUE, 3, 3) factor_point_to <- matrix(c("left", NA, NA, NA, "down", "down", "left", NA, NA), byrow = TRUE, 3, 3) indicator_push <- c(f3 = 2, f4 = 1.5) indicator_spread <- c(f1 = 2, f2 = 2) loading_position <- c(f1 = .5, f2 = .8, f3 = .8) # Pipe operator can be used if desired p2 <- set_sem_layout(p, indicator_order = indicator_order, indicator_factor = indicator_factor, factor_layout = factor_layout, factor_point_to = factor_point_to, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) p2 <- set_curve(p2, c("f2 ~ f1" = -1, "f4 ~ f1" = 1.5)) p2 <- mark_sig(p2, fit_sem) p2 <- mark_se(p2, fit_sem, sep = "\n") plot(p2) # Use a named vector for indicator_order indicator_order2 <- c(f2 = "x04", f2 = "x05", f2 = "x06", f2 = "x07", f1 = "x01", f1 = "x02", f1 = "x03", f4 = "x11", f4 = "x12", f4 = "x13", f4 = "x14", f3 = "x08", f3 = "x09", f3 = "x10") p2 <- set_sem_layout(p, indicator_order = indicator_order2, factor_layout = factor_layout, factor_point_to = factor_point_to, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) plot(p2) # Use automatically generated indicator_order and indicator_factor p2 <- set_sem_layout(p, factor_layout = factor_layout, factor_point_to = factor_point_to, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) plot(p2) # Use named character vector for factor_point_to directions <- c(f1 = "left", f2 = "left", f3 = "down", f4 = "down") p2v2 <- set_sem_layout(p, indicator_order = indicator_order, indicator_factor = indicator_factor, factor_layout = factor_layout, factor_point_to = directions, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) p2v2 <- set_curve(p2v2, c("f2 ~ f1" = -1, "f4 ~ f1" = 1.5)) p2v2 <- mark_sig(p2v2, fit_sem) p2v2 <- mark_se(p2v2, fit_sem, sep = "\n") plot(p2v2) #Lists of named list which are equivalent to the vectors above: #indicator_push <- list(list(node = "f3", push = 2), # list(node = "f4", push = 1.5)) #indicator_spread <- list(list(node = "f1", spread = 2), # list(node = "f2", spread = 2)) #loading_position <- list(list(node = "f1", position = .5), # list(node = "f2", position = .8), # list(node = "f3", position = .8))library(lavaan) library(semPlot) mod <- 'f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 ' fit_sem <- lavaan::sem(mod, sem_example) lavaan::parameterEstimates(fit_sem)[, c("lhs", "op", "rhs", "est", "pvalue")] p <- semPaths(fit_sem, whatLabels="est", sizeMan = 5, nCharNodes = 0, nCharEdges = 0, edge.width = 0.8, node.width = 0.7, edge.label.cex = 0.6, style = "ram", mar = c(10,10,10,10)) indicator_order <- c("x04", "x05", "x06", "x07", "x01", "x02", "x03", "x11", "x12", "x13", "x14", "x08", "x09", "x10") indicator_factor <- c( "f2", "f2", "f2", "f2", "f1", "f1", "f1", "f4", "f4", "f4", "f4", "f3", "f3", "f3") factor_layout <- matrix(c("f1", NA, NA, NA, "f3", "f4", "f2", NA, NA), byrow = TRUE, 3, 3) factor_point_to <- matrix(c("left", NA, NA, NA, "down", "down", "left", NA, NA), byrow = TRUE, 3, 3) indicator_push <- c(f3 = 2, f4 = 1.5) indicator_spread <- c(f1 = 2, f2 = 2) loading_position <- c(f1 = .5, f2 = .8, f3 = .8) # Pipe operator can be used if desired p2 <- set_sem_layout(p, indicator_order = indicator_order, indicator_factor = indicator_factor, factor_layout = factor_layout, factor_point_to = factor_point_to, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) p2 <- set_curve(p2, c("f2 ~ f1" = -1, "f4 ~ f1" = 1.5)) p2 <- mark_sig(p2, fit_sem) p2 <- mark_se(p2, fit_sem, sep = "\n") plot(p2) # Use a named vector for indicator_order indicator_order2 <- c(f2 = "x04", f2 = "x05", f2 = "x06", f2 = "x07", f1 = "x01", f1 = "x02", f1 = "x03", f4 = "x11", f4 = "x12", f4 = "x13", f4 = "x14", f3 = "x08", f3 = "x09", f3 = "x10") p2 <- set_sem_layout(p, indicator_order = indicator_order2, factor_layout = factor_layout, factor_point_to = factor_point_to, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) plot(p2) # Use automatically generated indicator_order and indicator_factor p2 <- set_sem_layout(p, factor_layout = factor_layout, factor_point_to = factor_point_to, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) plot(p2) # Use named character vector for factor_point_to directions <- c(f1 = "left", f2 = "left", f3 = "down", f4 = "down") p2v2 <- set_sem_layout(p, indicator_order = indicator_order, indicator_factor = indicator_factor, factor_layout = factor_layout, factor_point_to = directions, indicator_push = indicator_push, indicator_spread = indicator_spread, loading_position = loading_position) p2v2 <- set_curve(p2v2, c("f2 ~ f1" = -1, "f4 ~ f1" = 1.5)) p2v2 <- mark_sig(p2v2, fit_sem) p2v2 <- mark_se(p2v2, fit_sem, sep = "\n") plot(p2v2) #Lists of named list which are equivalent to the vectors above: #indicator_push <- list(list(node = "f3", push = 2), # list(node = "f4", push = 1.5)) #indicator_spread <- list(list(node = "f1", spread = 2), # list(node = "f2", spread = 2)) #loading_position <- list(list(node = "f1", position = .5), # list(node = "f2", position = .8), # list(node = "f3", position = .8))
Convert a named vector to a list of lists, to be used
by various functions in semptools.
to_list_of_lists(input, name1 = NULL, name2 = NULL, name3 = NULL)to_list_of_lists(input, name1 = NULL, name2 = NULL, name3 = NULL)
input |
A named vector |
name1 |
The name for the first element in the list-in-list.
Default is |
name2 |
The name for the second element in the list-in-list.
Defaultis |
name3 |
The name for the third element in the list-in-list.
Default is |
This function is not to be used by users, but to be used
internally by other functions of semptools.
A list of lists.
x <- c("x1 ~~ x2" = -1, "x4 ~ x1" = 1) to_list_of_lists(x, name1 = "from", name2 = "to", name3 = "new_curve") #list(list(from = "x1", to = "x2", new_curve = -1), # list(from = "x1", to = "x4", new_curve = 1)) y <- c(x1 = 0, x2 = 180, x3 = 140, x4 = 140) to_list_of_lists(y, name1 = "node", name2 = "rotate") #list(list(node = "x1", rotate = 0), # list(node = "x2", rotate = 180), # list(node = "x3", rotate = 140), # list(node = "x4", rotate = 140))x <- c("x1 ~~ x2" = -1, "x4 ~ x1" = 1) to_list_of_lists(x, name1 = "from", name2 = "to", name3 = "new_curve") #list(list(from = "x1", to = "x2", new_curve = -1), # list(from = "x1", to = "x4", new_curve = 1)) y <- c(x1 = 0, x2 = 180, x3 = 140, x4 = 140) to_list_of_lists(y, name1 = "node", name2 = "rotate") #list(list(node = "x1", rotate = 0), # list(node = "x2", rotate = 180), # list(node = "x3", rotate = 140), # list(node = "x4", rotate = 140))