Today’s post is on my favorite, “where have you been all my statistical life?” package, semPlot. When I took my first structural equation modeling course 11 years ago, for our final project, we had to use one of the models we learned in the course on a set of data and write up a report. I remember spending way too much time creating the diagram to accompany my report – if memory serves, this is the first time I ever requested an extension on a project or paper, because it took even more time than I realized it would.

Those days are done, thanks to this amazing package, which takes your fitted model and generates a diagram. There are many ways to customize the appearance, which I’ll get into shortly. But first, let’s grab some data to use for examples. For my Statistics Sunday post, I’ll go through using semPlot with the CFA and LVPA from previous A to Z posts. So for today, I’ll use some of the sample datasets that come with lavaan, my favorite SEM package.

library(lavaan)

## This is lavaan 0.5-23.1097

## lavaan is BETA software! Please report any bugs.

data(PoliticalDemocracy)

This dataset is Industrialization and Political Democracy dataset, which contains measures of political democracy and industrialization in developing countries:

• y1 = Expert ratings of the freedom of the press in 1960
• y2 = Freedom of political opposition in 1960
• y3 = Fairness of elections in 1960
• y4 = Effectiveness of the elected legislature in 1960
• y5 = Expert ratings of the freedom of the press in 1965
• y6 = Freedom of political opposition in 1965
• y7 = Fairness of elections in 1965
• y8 = Effectiveness of the elected legislature in 1965
• x1 = Gross national product per capita in 1960
• x2 = Inanimate energy consumption per capital in 1960
• x3 = Percentage of the labor force in industry in 1960

Essentially, the x variables measure industrialization. The y variables measure political democracy at two time points. We can fit a simple measurement model using the 1960 political democracy variables.

Free.Model <- 'Fr60 =~ y1 + y2 + y3 + y4'
fit <- sem(Free.Model, data=PoliticalDemocracy)
summary(fit, standardized=TRUE, fit.measures=TRUE)

## lavaan (0.5-23.1097) converged normally after  26 iterations
## 
##   Number of observations                            75
## 
##   Estimator                                         ML
##   Minimum Function Test Statistic               10.006
##   Degrees of freedom                                 2
##   P-value (Chi-square)                           0.007
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic              159.183
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.948
##   Tucker-Lewis Index (TLI)                       0.843
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -704.138
##   Loglikelihood unrestricted model (H1)       -699.135
## 
##   Number of free parameters                          8
##   Akaike (AIC)                                1424.275
##   Bayesian (BIC)                              1442.815
##   Sample-size adjusted Bayesian (BIC)         1417.601
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.231
##   90 Percent Confidence Interval          0.103  0.382
##   P-value RMSEA <= 0.05                          0.014
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.046
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Standard Errors                             Standard
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Fr60 =~                                                               
##     y1                1.000                               2.133    0.819
##     y2                1.404    0.197    7.119    0.000    2.993    0.763
##     y3                1.089    0.167    6.529    0.000    2.322    0.712
##     y4                1.370    0.167    8.228    0.000    2.922    0.878
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .y1                2.239    0.512    4.371    0.000    2.239    0.330
##    .y2                6.412    1.293    4.960    0.000    6.412    0.417
##    .y3                5.229    0.990    5.281    0.000    5.229    0.492
##    .y4                2.530    0.765    3.306    0.001    2.530    0.229
##     Fr60              4.548    1.106    4.112    0.000    1.000    1.000

While the RMSEA and TLI show poor fit, CFI and SRMR show good fit. We’ll ignore fit measures for now, though, and instead show how semPlot can be used to create a diagram of this model.

library(semPlot)
semPaths(fit)

semPaths(fit)
title("Political Democracy in 1960", line=3)