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SEM Forests
Andreas Brandmaier
2024-04-08
This example demonstrates how SEM forests can be grown. SEM forests
are ensembles of typically hundreds to thousands of SEM trees. Using
permutation-based variable importance estimates, we can aggregate the
importance of each predictor for improving model fit.
Here, we use the affect dataset and a simple SEM with
only a single observed variable and no latent variables.
Load data
Load affect dataset from the psychTools package. These
are data from two studies conducted in the Personality, Motivation and
Cognition Laboratory at Northwestern University to study affect
dimensionality and the relationship to various personality
dimensions.
library(psychTools)
data(affect)
knitr::kable(head(affect))
| maps |
3 |
18 |
9 |
7 |
10 |
3 |
24 |
22 |
24 |
14 |
26 |
2 |
6 |
5 |
7 |
4 |
NA |
NA |
0.0476190 |
| maps |
3 |
16 |
12 |
5 |
8 |
1 |
41 |
40 |
9 |
13 |
10 |
4 |
4 |
14 |
5 |
5 |
NA |
NA |
0.3333333 |
| maps |
3 |
6 |
5 |
3 |
1 |
2 |
37 |
44 |
1 |
14 |
4 |
2 |
2 |
15 |
3 |
1 |
NA |
NA |
0.1904762 |
| maps |
3 |
12 |
15 |
4 |
6 |
3 |
54 |
40 |
5 |
15 |
1 |
0 |
4 |
15 |
0 |
2 |
NA |
NA |
0.3846154 |
| maps |
3 |
14 |
2 |
5 |
6 |
3 |
39 |
67 |
12 |
20 |
7 |
13 |
14 |
15 |
16 |
13 |
NA |
NA |
0.3809524 |
| maps |
1 |
6 |
15 |
2 |
4 |
5 |
51 |
38 |
9 |
14 |
5 |
1 |
7 |
12 |
2 |
2 |
NA |
NA |
0.2380952 |
affect$Film <- as.factor(affect$Film)
affect$lie <- as.ordered(affect$lie)
affect$imp <- as.ordered(affect$imp)
Create simple model of state anxiety
The following code implements a simple SEM with only a single
manifest variables and two parameters, the mean of state anxiety after
having watched a movie (state2), \(\mu\), and the variance of state anxiety,
\(\sigma^2\).
library(OpenMx)
manifests<-c("state2")
latents<-c()
model <- mxModel("Univariate Normal Model",
type="RAM",
manifestVars = manifests,
latentVars = latents,
mxPath(from="one",to=manifests, free=c(TRUE),
value=c(50.0) , arrows=1, label=c("mu") ),
mxPath(from=manifests,to=manifests, free=c(TRUE),
value=c(100.0) , arrows=2, label=c("sigma2") ),
mxData(affect, type = "raw")
);
result <- mxRun(model)
#> Running Univariate Normal Model with 2 parameters
These are the estimates of the model when run on the entire
sample:
summary(result)
#> Summary of Univariate Normal Model
#>
#> free parameters:
#> name matrix row col Estimate Std.Error A
#> 1 sigma2 S state2 state2 115.05414 12.4793862
#> 2 mu M 1 state2 42.45118 0.8226717
#>
#> Model Statistics:
#> | Parameters | Degrees of Freedom | Fit (-2lnL units)
#> Model: 2 168 1289.158
#> Saturated: 2 168 NA
#> Independence: 2 168 NA
#> Number of observations/statistics: 330/170
#>
#> Information Criteria:
#> | df Penalty | Parameters Penalty | Sample-Size Adjusted
#> AIC: 953.1576 1293.158 1293.194
#> BIC: 314.9100 1300.756 1294.412
#> CFI: NA
#> TLI: 1 (also known as NNFI)
#> RMSEA: 0 [95% CI (NA, NA)]
#> Prob(RMSEA <= 0.05): NA
#> To get additional fit indices, see help(mxRefModels)
#> timestamp: 2024-04-08 14:03:39
#> Wall clock time: 0.03678489 secs
#> optimizer: SLSQP
#> OpenMx version number: 2.21.1
#> Need help? See help(mxSummary)
Forest
Create a forest control object that stores all tuning parameters of
the forest. Note that we use only 5 trees for illustration. Please
increase the number in real applications to several hundreds. To speed
up computation time, consider score-based test for variable selection in
the trees.
control <- semforest_control(num.trees = 5)
print(control)
#> SEM-Forest control:
#> -----------------
#> Number of Trees: 5
#> Sampling: subsample
#> Comparisons per Node: 2
#>
#> SEM-Tree control:
#> ▔▔▔▔▔▔▔▔▔▔
#> ● Splitting Method: fair
#> ● Alpha Level: 1
#> ● Bonferroni Correction:FALSE
#> ● Minimum Number of Cases: 20
#> ● Maximum Tree Depth: NA
#> ● Number of CV Folds: 5
#> ● Exclude Heywood Cases: FALSE
#> ● Test Invariance Alpha Level: NA
#> ● Use all Cases: FALSE
#> ● Verbosity: FALSE
#> ● Progress Bar: TRUE
#> ● Seed: NA
Now, run the forest using the control object:
forest <- semforest( model=model,
data = affect,
control = control,
covariates = c("Study","Film", "state1",
"PA2","NA2","TA2"))
Variable importance
Next, we compute permutation-based variable importance. This may take
some time.
vim <- varimp(forest)
print(vim, sort.values=TRUE)
#> Variable Importance
#> Study Film NA2 PA2 TA2 state1
#> 7.746036 11.281160 27.829022 28.542936 67.803211 99.084569
plot(vim)
From this, we can learn that variables such as NA2
representing negative affect (after the movie), TA2
representing tense arousal (after the movie), and state1
representing the state anxiety before having watched the movie, are the
best predictors of difference in the distribution of state anxiety (in
either mean, variance or both) after having watched the movie.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.