Mark parameter estimates (edge labels) based on p-value.
Usage
mark_sig(
semPaths_plot,
object,
alphas = c(`*` = 0.05, `**` = 0.01, `***` = 0.001)
)Arguments
- 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 lavaan is supported.
- 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).
Value
A qgraph::qgraph based on the original one, with marks appended to edge labels based on their p-values.
Details
Modify a qgraph::qgraph object generated by semPaths and add marks (currently asterisk, "*") to the labels based on their p-values. Require the original object used in the semPaths call.
Currently supports only plots based on lavaan output.
Examples
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")]
#> lhs op rhs est pvalue
#> 1 x1 ~~ x2 0.005 0.957
#> 2 x3 ~ x1 0.537 0.000
#> 3 x3 ~ x2 0.376 0.000
#> 4 x4 ~ x1 0.111 0.382
#> 5 x4 ~ x3 0.629 0.000
#> 6 x3 ~~ x3 0.874 0.000
#> 7 x4 ~~ x4 1.194 0.000
#> 8 x1 ~~ x1 0.933 0.000
#> 9 x2 ~~ x2 1.017 0.000
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")]
#> lhs op rhs est pvalue
#> 1 f1 =~ x01 1.000 NA
#> 2 f1 =~ x02 1.097 0.000
#> 3 f1 =~ x03 1.247 0.000
#> 4 f2 =~ x04 1.000 NA
#> 5 f2 =~ x05 -0.040 0.587
#> 6 f2 =~ x06 1.098 0.000
#> 7 f2 =~ x07 0.771 0.000
#> 8 f3 =~ x08 1.000 NA
#> 9 f3 =~ x09 0.937 0.000
#> 10 f3 =~ x10 1.785 0.000
#> 11 f4 =~ x11 1.000 NA
#> 12 f4 =~ x12 0.949 0.000
#> 13 f4 =~ x13 -0.077 0.356
#> 14 f4 =~ x14 1.184 0.000
#> 15 x01 ~~ x01 0.969 0.000
#> 16 x02 ~~ x02 0.853 0.000
#> 17 x03 ~~ x03 0.976 0.000
#> 18 x04 ~~ x04 0.725 0.000
#> 19 x05 ~~ x05 0.954 0.000
#> 20 x06 ~~ x06 1.161 0.000
#> 21 x07 ~~ x07 0.903 0.000
#> 22 x08 ~~ x08 1.026 0.000
#> 23 x09 ~~ x09 1.119 0.000
#> 24 x10 ~~ x10 0.566 0.009
#> 25 x11 ~~ x11 1.231 0.000
#> 26 x12 ~~ x12 1.032 0.000
#> 27 x13 ~~ x13 0.990 0.000
#> 28 x14 ~~ x14 0.985 0.000
#> 29 f1 ~~ f1 0.855 0.000
#> 30 f2 ~~ f2 1.119 0.000
#> 31 f3 ~~ f3 0.585 0.000
#> 32 f4 ~~ f4 0.943 0.000
#> 33 f1 ~~ f2 -0.173 0.059
#> 34 f1 ~~ f3 0.387 0.000
#> 35 f1 ~~ f4 -0.178 0.041
#> 36 f2 ~~ f3 -0.112 0.132
#> 37 f2 ~~ f4 0.593 0.000
#> 38 f3 ~~ f4 -0.181 0.014
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")]
#> lhs op rhs est pvalue
#> 1 f1 =~ x01 1.000 NA
#> 2 f1 =~ x02 1.124 0.000
#> 3 f1 =~ x03 1.310 0.000
#> 4 f2 =~ x04 1.000 NA
#> 5 f2 =~ x05 0.079 0.205
#> 6 f2 =~ x06 1.120 0.000
#> 7 f2 =~ x07 0.736 0.000
#> 8 f3 =~ x08 1.000 NA
#> 9 f3 =~ x09 0.819 0.000
#> 10 f3 =~ x10 1.230 0.000
#> 11 f4 =~ x11 1.000 NA
#> 12 f4 =~ x12 0.773 0.000
#> 13 f4 =~ x13 0.064 0.160
#> 14 f4 =~ x14 0.928 0.000
#> 15 f3 ~ f1 0.612 0.000
#> 16 f3 ~ f2 0.584 0.000
#> 17 f4 ~ f1 -0.542 0.001
#> 18 f4 ~ f3 0.980 0.000
#> 19 x01 ~~ x01 1.055 0.000
#> 20 x02 ~~ x02 1.015 0.000
#> 21 x03 ~~ x03 1.028 0.000
#> 22 x04 ~~ x04 0.933 0.000
#> 23 x05 ~~ x05 0.795 0.000
#> 24 x06 ~~ x06 0.771 0.000
#> 25 x07 ~~ x07 1.071 0.000
#> 26 x08 ~~ x08 0.976 0.000
#> 27 x09 ~~ x09 0.937 0.000
#> 28 x10 ~~ x10 1.164 0.000
#> 29 x11 ~~ x11 1.008 0.000
#> 30 x12 ~~ x12 1.033 0.000
#> 31 x13 ~~ x13 0.846 0.000
#> 32 x14 ~~ x14 0.807 0.000
#> 33 f1 ~~ f1 0.714 0.000
#> 34 f2 ~~ f2 1.277 0.000
#> 35 f3 ~~ f3 0.759 0.000
#> 36 f4 ~~ f4 1.188 0.000
#> 37 f1 ~~ f2 0.016 0.856
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)
