Mark Parameter Estimates (Edge Labels) Based on p-Value

Mark parameter estimates (edge labels) based on p-value.

Usage

mark_sig(
  semPaths_plot,
  object,
  alphas = c(`*` = 0.05, `**` = 0.01, `***` = 0.001),
  ests = NULL,
  std_type = FALSE
)

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).
ests: A data.frame from the parameterEstimates function, or from other function with these columns:? lhs, op, rhs, and pvalue. Only used when object is not specified.
std_type: If standardized solution is used in the plot, set this either to the type of standardization (e.g., "std.all") or to TRUE. It will be passed to lavaan::standardizedSolution() to compute the p-values for the standardized solution. Used only if p-values are not supplied directly through ests.

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 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.

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)