mark_se | R Documentation |
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
)
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)
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