How do I include p-value and R-square for the estimates in semPaths?
I am using semPaths (semPlot package) to draw my structural equation models. After some trial and error, I have a pretty good script to show what I want. Except, I haven’t been able to figure out how to include the p-value/significance levels of the estimates/regression coefficients in the figure.
Can/how can I include significance levels either as e.g. p-value in the edge labels below the estimate or as a broken line for insignificance or …? I am also interested in including the R-square, but not as critically as the significance level.
This is the script I am using so far:
semPaths(fitmod.bac.class2,
what = "std",
whatLabels = "std",
style="ram",
edge.label.cex = 1.3,
layout = 'tree',
intercepts=FALSE,
residuals=FALSE,
nodeLabels = c("Negati-\nvicutes","cand_class\n_MB_A2_108", "CO2", "Bacilli","Ignavi-\nbacteria","C/N", "pH","Water\ncontent"),
sizeMan=7 )
Example of one of the SemPath outputs
In this example the following are not significant:
- Ignavibacteria -> First_C_CO2_ugC_gC_day, p = 0.096
- pH -> Ignavibacteria, p = 0.151
- cand_class_MB_A2_108 <-> Bacilli correlation, p = 0.054
I am a R-user and not really a coder, so I might just be missing a crucial point in the arguments.
I am testing a lot of different models at the moment, and would really like not to have to draw them all up by hand.
update: Using semPlotModel: Am I right in understanding that semPlotModel doesn’t include the significance levels from the sem function (see my script and output below)? I am specifically looking to include the P(>|z|) for regressions and covariance. Is it just me that is missing that, or is it not included? If it is not included, my solution is simply just to custom the edge labels.
{model.NA.UP.bac.class2 <- '
#LATANT VARIABLES
#REGRESSIONS
#soil organic carbon quality
c_Negativicutes ~ CN
#microorganisms
First_C_CO2_ugC_gC_day ~ c_Bacilli
First_C_CO2_ugC_gC_day ~ c_Ignavibacteria
First_C_CO2_ugC_gC_day ~ c_cand_class_MB_A2_108
First_C_CO2_ugC_gC_day ~ c_Negativicutes
#pH
c_Bacilli ~pH
c_Ignavibacteria ~pH
c_cand_class_MB_A2_108~pH
c_Negativicutes ~pH
#COVARIANCE
initial_water ~~ CN
c_cand_class_MB_A2_108 ~~ c_Bacilli
'
fitmod.bac.class2 <- sem(model.NA.UP.bac.class2, data=datapNA.UP.log, missing="ml", meanstructure=TRUE, fixed.x=FALSE, std.lv=FALSE, std.ov=FALSE)
summary(fitmod.bac.class2, standardized=TRUE, fit.measures=TRUE, rsq=TRUE)
out <- capture.output(summary(fitmod.bac.class2, standardized=TRUE, fit.measures=TRUE, rsq=TRUE))
}
Output:
lavaan 0.6-5 ended normally after 188 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 28
Number of observations 30
Number of missing patterns 1
Model Test User Model:
Test statistic 17.816
Degrees of freedom 16
P-value (Chi-square) 0.335
Model Test Baseline Model:
Test statistic 101.570
Degrees of freedom 28
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.975
Tucker-Lewis Index (TLI) 0.957
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) 472.465
Loglikelihood unrestricted model (H1) 481.373
Akaike (AIC) -888.930
Bayesian (BIC) -849.697
Sample-size adjusted Bayesian (BIC) -936.875
Root Mean Square Error of Approximation:
RMSEA 0.062
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.185
P-value RMSEA <= 0.05 0.414
Standardized Root Mean Square Residual:
SRMR 0.107
Parameter Estimates:
Information Observed
Observed information based on Hessian
Standard errors Standard
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
c_Negativicutes ~
CN 0.419 0.143 2.939 0.003 0.419 0.416
c_cand_class_MB_A2_108 ~
CN -0.433 0.160 -2.707 0.007 -0.433 -0.394
First_C_CO2_ugC_gC_day ~
c_Bacilli 0.525 0.128 4.092 0.000 0.525 0.496
c_Ignavibacter 0.207 0.124 1.667 0.096 0.207 0.195
c_c__MB_A2_108 0.310 0.125 2.475 0.013 0.310 0.301
c_Negativicuts 0.304 0.137 2.220 0.026 0.304 0.271
c_Bacilli ~
pH 0.624 0.135 4.604 0.000 0.624 0.643
c_Ignavibacteria ~
pH 0.245 0.171 1.436 0.151 0.245 0.254
c_cand_class_MB_A2_108 ~
pH 0.393 0.151 2.597 0.009 0.393 0.394
c_Negativicutes ~
pH 0.435 0.129 3.361 0.001 0.435 0.476
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
CN ~~
initial_water 0.001 0.000 2.679 0.007 0.001 0.561
.c_cand_class_MB_A2_108 ~~
.c_Bacilli -0.000 0.000 -1.923 0.054 -0.000 -0.388
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.c_Negativicuts 0.145 0.198 0.734 0.463 0.145 3.826
.c_c__MB_A2_108 1.038 0.226 4.594 0.000 1.038 25.076
.Frs_C_CO2_C_C_ -0.346 0.233 -1.485 0.137 -0.346 -8.115
.c_Bacilli 0.376 0.135 2.778 0.005 0.376 9.340
.c_Ignavibacter 0.754 0.170 4.424 0.000 0.754 18.796
CN 0.998 0.007 145.158 0.000 0.998 26.502
pH 0.998 0.008 131.642 0.000 0.998 24.034
initial_water 0.998 0.008 125.994 0.000 0.998 23.003
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.c_Negativicuts 0.001 0.000 3.873 0.000 0.001 0.600
.c_c__MB_A2_108 0.001 0.000 3.833 0.000 0.001 0.689
.Frs_C_CO2_C_C_ 0.001 0.000 3.873 0.000 0.001 0.408
.c_Bacilli 0.001 0.000 3.873 0.000 0.001 0.586
.c_Ignavibacter 0.002 0.000 3.873 0.000 0.002 0.936
CN 0.001 0.000 3.873 0.000 0.001 1.000
initial_water 0.002 0.000 3.873 0.000 0.002 1.000
pH 0.002 0.000 3.873 0.000 0.002 1.000
R-Square:
Estimate
c_Negativicuts 0.400
c_c__MB_A2_108 0.311
Frs_C_CO2_C_C_ 0.592
c_Bacilli 0.414
c_Ignavibacter 0.064
Warning message:
In lav_model_hessian(lavmodel = lavmodel, lavsamplestats = lavsamplestats, :
lavaan WARNING: Hessian is not fully symmetric. Max diff = 5.15131396241486e-05
This example is taken from ?semPaths since we don't have your object.
library('semPlot')
modFile <- tempfile(fileext = '.OUT')
download.file('http://sachaepskamp.com/files/mi1.OUT', modFile)
Use semPlotModel to get the object without plotting. There you can inspect what is to be plotted. I just dug around without reading the docs until I found what it seems to be using.
After you run semPlotModel, the object has an element x@Pars which contains the edges, nodes, and the std which is being used for the edge labels in your case. semPaths also has an argument that allows you to make custom edge labels, so you can take the data you need from x@Pars and add your p-values:
x <- semPlotModel(modFile)
x@Pars
# label lhs edge rhs est std group fixed par
# 1 lambda[11]^{(y)} perfIQ -> pc 1.000 0.6219648 Group 1 TRUE 0
# 2 lambda[21]^{(y)} perfIQ -> pa 0.923 0.5664888 Group 1 FALSE 1
# 3 lambda[31]^{(y)} perfIQ -> oa 1.098 0.6550159 Group 1 FALSE 2
# 4 lambda[41]^{(y)} perfIQ -> ma 0.784 0.4609990 Group 1 FALSE 3
# 5 theta[11]^{(epsilon)} pc <-> pc 5.088 0.6131598 Group 1 FALSE 5
# 10 theta[22]^{(epsilon)} pa <-> pa 5.787 0.6790905 Group 1 FALSE 6
# 15 theta[33]^{(epsilon)} oa <-> oa 5.150 0.5709541 Group 1 FALSE 7
# 20 theta[44]^{(epsilon)} ma <-> ma 7.311 0.7874800 Group 1 FALSE 8
# 21 psi[11] perfIQ <-> perfIQ 3.210 1.0000000 Group 1 FALSE 4
# 22 tau[1]^{(y)} int pc 10.500 NA Group 1 FALSE 9
# 23 tau[2]^{(y)} int pa 10.374 NA Group 1 FALSE 10
# 24 tau[3]^{(y)} int oa 10.663 NA Group 1 FALSE 11
# 25 tau[4]^{(y)} int ma 10.371 NA Group 1 FALSE 12
# 11 lambda[11]^{(y)} perfIQ -> pc 1.000 0.6515609 Group 2 TRUE 0
# 27 lambda[21]^{(y)} perfIQ -> pa 0.923 0.5876948 Group 2 FALSE 1
# 31 lambda[31]^{(y)} perfIQ -> oa 1.098 0.6981974 Group 2 FALSE 2
# 41 lambda[41]^{(y)} perfIQ -> ma 0.784 0.4621919 Group 2 FALSE 3
# 51 theta[11]^{(epsilon)} pc <-> pc 5.006 0.5754684 Group 2 FALSE 14
# 101 theta[22]^{(epsilon)} pa <-> pa 5.963 0.6546148 Group 2 FALSE 15
# 151 theta[33]^{(epsilon)} oa <-> oa 4.681 0.5125204 Group 2 FALSE 16
# 201 theta[44]^{(epsilon)} ma <-> ma 8.356 0.7863786 Group 2 FALSE 17
# 211 psi[11] perfIQ <-> perfIQ 3.693 1.0000000 Group 2 FALSE 13
# 221 tau[1]^{(y)} int pc 10.500 NA Group 2 FALSE 9
# 231 tau[2]^{(y)} int pa 10.374 NA Group 2 FALSE 10
# 241 tau[3]^{(y)} int oa 10.663 NA Group 2 FALSE 11
# 251 tau[4]^{(y)} int ma 10.371 NA Group 2 FALSE 12
# 26 alpha[1] int perfIQ -2.469 NA Group 2 FALSE 18
As you can see there are more edge labels than ones that are plotted, and I have no idea how it chooses which to use, so I am just taking the first four from each group (since there are four edges shown and the stds match those. Maybe there is an option to plot all of them or select which ones you need--I haven't read the docs.
## take first four stds from each group, generate some p-values
l <- sapply(split(x@Pars$std, x@Pars$group), function(x) head(x, 4))
set.seed(1)
l <- sprintf('%.3f, p=%s', l, format.pval(runif(length(l)), digits = 2))
l
# [1] "0.622, p=0.27" "0.566, p=0.37" "0.655, p=0.57" "0.461, p=0.91" "0.652, p=0.20" "0.588, p=0.90" "0.698, p=0.94" "0.462, p=0.66"
Then you can plot the object with your new labels, edgeLabels = l
layout(1:2)
semPaths(
x,
edgeLabels = l,
ask = FALSE, title = FALSE,
what = 'std',
whatLabels = 'std',
style = 'ram',
edge.label.cex = 1.3,
layout = 'tree',
intercepts = FALSE,
residuals = FALSE,
sizeMan = 7
)
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