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 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.

sep

A character string to separate the coefficient and the standard error (in parentheses). Default to " " (one space). Use "\n" to enforce a line break.

digits

Integer indicating number of decimal places for the appended standard errors. Default is 2L.

ests

A data.frame from the parameterEstimates function. Only used when object is not specified.

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 0.144
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")]
#> 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 0.074
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")]
#> 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 0.088
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)