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)
Arguments
| 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
|
Value
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.
Details
Modify a qgraph::qgraph object generated by
semPaths (currently in parentheses) to the
labels. Require the original object used in the
semPaths call.
Currently supports only plots based on lavaan
output.
This function is a variant of, and can be combined with, the
mark_sig function.
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", "se")]#> lhs op rhs est pvalue se #> 1 x1 ~~ x2 0.005 0.957 0.097 #> 2 x3 ~ x1 0.537 0.000 0.097 #> 3 x3 ~ x2 0.376 0.000 0.093 #> 4 x4 ~ x1 0.111 0.382 0.127 #> 5 x4 ~ x3 0.629 0.000 0.108 #> 6 x3 ~~ x3 0.874 0.000 0.124 #> 7 x4 ~~ x4 1.194 0.000 0.169 #> 8 x1 ~~ x1 0.933 0.000 0.132 #> 9 x2 ~~ x2 1.017 0.000 0.144m <- 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)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")]#> lhs op rhs est pvalue se #> 1 f1 =~ x01 1.000 NA 0.000 #> 2 f1 =~ x02 1.097 0.000 0.137 #> 3 f1 =~ x03 1.247 0.000 0.154 #> 4 f2 =~ x04 1.000 NA 0.000 #> 5 f2 =~ x05 -0.040 0.587 0.073 #> 6 f2 =~ x06 1.098 0.000 0.132 #> 7 f2 =~ x07 0.771 0.000 0.099 #> 8 f3 =~ x08 1.000 NA 0.000 #> 9 f3 =~ x09 0.937 0.000 0.148 #> 10 f3 =~ x10 1.785 0.000 0.262 #> 11 f4 =~ x11 1.000 NA 0.000 #> 12 f4 =~ x12 0.949 0.000 0.134 #> 13 f4 =~ x13 -0.077 0.356 0.083 #> 14 f4 =~ x14 1.184 0.000 0.161 #> 15 x01 ~~ x01 0.969 0.000 0.129 #> 16 x02 ~~ x02 0.853 0.000 0.130 #> 17 x03 ~~ x03 0.976 0.000 0.159 #> 18 x04 ~~ x04 0.725 0.000 0.130 #> 19 x05 ~~ x05 0.954 0.000 0.095 #> 20 x06 ~~ x06 1.161 0.000 0.176 #> 21 x07 ~~ x07 0.903 0.000 0.114 #> 22 x08 ~~ x08 1.026 0.000 0.125 #> 23 x09 ~~ x09 1.119 0.000 0.129 #> 24 x10 ~~ x10 0.566 0.009 0.218 #> 25 x11 ~~ x11 1.231 0.000 0.163 #> 26 x12 ~~ x12 1.032 0.000 0.141 #> 27 x13 ~~ x13 0.990 0.000 0.099 #> 28 x14 ~~ x14 0.985 0.000 0.172 #> 29 f1 ~~ f1 0.855 0.000 0.176 #> 30 f2 ~~ f2 1.119 0.000 0.201 #> 31 f3 ~~ f3 0.585 0.000 0.143 #> 32 f4 ~~ f4 0.943 0.000 0.209 #> 33 f1 ~~ f2 -0.173 0.059 0.092 #> 34 f1 ~~ f3 0.387 0.000 0.089 #> 35 f1 ~~ f4 -0.178 0.041 0.087 #> 36 f2 ~~ f3 -0.112 0.132 0.074 #> 37 f2 ~~ f4 0.593 0.000 0.122 #> 38 f3 ~~ f4 -0.181 0.014 0.074p_cfa <- semPlot::semPaths(fit_cfa, whatLabels = "est", style = "ram", nCharNodes = 0, nCharEdges = 0)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")]#> lhs op rhs est pvalue se #> 1 f1 =~ x01 1.000 NA 0.000 #> 2 f1 =~ x02 1.124 0.000 0.166 #> 3 f1 =~ x03 1.310 0.000 0.191 #> 4 f2 =~ x04 1.000 NA 0.000 #> 5 f2 =~ x05 0.079 0.205 0.062 #> 6 f2 =~ x06 1.120 0.000 0.121 #> 7 f2 =~ x07 0.736 0.000 0.093 #> 8 f3 =~ x08 1.000 NA 0.000 #> 9 f3 =~ x09 0.819 0.000 0.084 #> 10 f3 =~ x10 1.230 0.000 0.112 #> 11 f4 =~ x11 1.000 NA 0.000 #> 12 f4 =~ x12 0.773 0.000 0.068 #> 13 f4 =~ x13 0.064 0.160 0.046 #> 14 f4 =~ x14 0.928 0.000 0.073 #> 15 f3 ~ f1 0.612 0.000 0.131 #> 16 f3 ~ f2 0.584 0.000 0.093 #> 17 f4 ~ f1 -0.542 0.001 0.170 #> 18 f4 ~ f3 0.980 0.000 0.127 #> 19 x01 ~~ x01 1.055 0.000 0.138 #> 20 x02 ~~ x02 1.015 0.000 0.149 #> 21 x03 ~~ x03 1.028 0.000 0.178 #> 22 x04 ~~ x04 0.933 0.000 0.144 #> 23 x05 ~~ x05 0.795 0.000 0.080 #> 24 x06 ~~ x06 0.771 0.000 0.156 #> 25 x07 ~~ x07 1.071 0.000 0.126 #> 26 x08 ~~ x08 0.976 0.000 0.134 #> 27 x09 ~~ x09 0.937 0.000 0.115 #> 28 x10 ~~ x10 1.164 0.000 0.177 #> 29 x11 ~~ x11 1.008 0.000 0.161 #> 30 x12 ~~ x12 1.033 0.000 0.131 #> 31 x13 ~~ x13 0.846 0.000 0.085 #> 32 x14 ~~ x14 0.807 0.000 0.135 #> 33 f1 ~~ f1 0.714 0.000 0.168 #> 34 f2 ~~ f2 1.277 0.000 0.229 #> 35 f3 ~~ f3 0.759 0.000 0.151 #> 36 f4 ~~ f4 1.188 0.000 0.221 #> 37 f1 ~~ f2 0.016 0.856 0.088p_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)
