Simulate Data
To test out the regsem package, we will first simulate data
library(lavaan);library(regsem)
## This is lavaan 0.6-15
## lavaan is FREE software! Please report any bugs.
## Loading required package: Rcpp
## Loading required package: Rsolnp
sim.mod <- "
f1 =~ 1*y1 + 1*y2 + 1*y3+ 1*y4 + 1*y5
f1 ~ 0*x1 + 0*x2 + 0*x3 + 0*x4 + 0*x5 + 0.2*x6 + 0.5*x7 + 0.8*x8
f1~~1*f1"
dat.sim = simulateData(sim.mod,sample.nobs=100,seed=12)
Run the Model with Lavaan
And then run the model with lavaan so we can better see the structure.
run.mod <- "
f1 =~ NA*y1 + y2 + y3+ y4 + y5
f1 ~ c1*x1 + c2*x2 + c3*x3 + c4*x4 + c5*x5 + c6*x6 + c7*x7 + c8*x8
f1~~1*f1
"
lav.out <- sem(run.mod,dat.sim,fixed.x=FALSE)
#summary(lav.out)
parameterestimates(lav.out)[6:13,] # just look at regressions
## lhs op rhs label est se z pvalue ci.lower ci.upper
## 6 f1 ~ x1 c1 -0.019 0.111 -0.172 0.863 -0.237 0.198
## 7 f1 ~ x2 c2 -0.102 0.111 -0.917 0.359 -0.320 0.116
## 8 f1 ~ x3 c3 0.022 0.113 0.193 0.847 -0.199 0.243
## 9 f1 ~ x4 c4 -0.075 0.122 -0.612 0.540 -0.313 0.164
## 10 f1 ~ x5 c5 -0.096 0.112 -0.857 0.391 -0.316 0.124
## 11 f1 ~ x6 c6 0.160 0.130 1.234 0.217 -0.094 0.415
## 12 f1 ~ x7 c7 0.334 0.148 2.263 0.024 0.045 0.624
## 13 f1 ~ x8 c8 0.696 0.135 5.163 0.000 0.432 0.960
Plot the Model
semPlot::semPaths(lav.out)
One of the difficult pieces in using the cv_regsem function is that the penalty has to be calibrated for each particular problem. In running this code, I first tested the default, but this was too small given that there were some parameters > 0.4. After plotting this initial run, I saw that some parameters weren’t penalized enough, therefore, I increased the penalty jump to 0.05 and with 30 different values this tested a model (at a high penalty) that had all estimates as zero. In some cases it isn’t necessary to test a sequence of penalties that would set “large” parameters to zero, as either the model could fail to converge then, or there is not uncertainty about those parameters inclusion.
reg.out <- cv_regsem(lav.out,n.lambda=30,type="lasso",jump=0.04,
pars_pen=c("c1","c2","c3","c4","c5","c6","c7","c8"))
In specifying this model, we use the parameter labels to tell cv_regsem which of the parameters to penalize. Equivalently, we could have used the extractMatrices function to identify which parameters to penalize.
# not run
extractMatrices(lav.out)["A"]
Additionally, we can specify which parameters are penalized by type: “regressions”, “loadings”, or both c(“regressions”,“loadings”). Note though that this penalizes all parameters of this type. If you desire to penalize a subset of parameters, use either the parameter name or number format for pars_pen.
Next, we can get a summary of the models tested.
summary(reg.out)
## CV regsem Object
## Number of parameters regularized: 8
## Lambda ranging from 0 to 0.96
## Lowest Fit Lambda: 0.24
## Metric: BIC
## Number Converged: 25
Plot the parameter trajectories
plot(reg.out)
Here we can see that we used a large enough penalty to set all parameter estimates to zero. However, the best fitting model came early on, when only a couple parameters were zero.
regsem defaults to using the BIC to choose a final model. This shows up in the final_pars object as well as the lines in the plot. This can be changed with the metric argument.
Understand better what went on with the fit
head(reg.out$fits,10)
## lambda conv rmsea BIC chisq
## [1,] 0.00 0 0.06069 3959.819 50.49164
## [2,] 0.04 0 0.05553 3951.021 50.90404
## [3,] 0.08 0 0.05785 3952.039 51.92170
## [4,] 0.12 0 0.06130 3953.624 53.50699
## [5,] 0.16 0 0.06304 3951.247 55.73492
## [6,] 0.20 0 0.06675 3953.155 57.64243
## [7,] 0.24 0 0.06331 3941.757 60.06064
## [8,] 0.28 0 0.06684 3943.714 62.01715
## [9,] 0.32 0 0.07332 3950.653 64.35092
## [10,] 0.36 0 0.07119 3943.165 66.07319
One thing to check is the “conv” column. This refers to convergence, with 0 meaning the model converged.
And see what the best fitting parameter estimates are.
reg.out$final_pars[1:13] # don't show variances/covariances
## f1 -> y1 f1 -> y2 f1 -> y3 f1 -> y4 f1 -> y5 x1 -> f1 x2 -> f1 x3 -> f1
## 1.065 0.949 1.033 1.119 1.039 0.000 0.000 0.000
## x4 -> f1 x5 -> f1 x6 -> f1 x7 -> f1 x8 -> f1
## 0.000 -0.001 0.000 0.106 0.481
In this final model, we set the regression paths for x2,x3, x4, and x5 to zero. We make a mistake for x1, but we also correctly identify x6, x7, and x8 as true paths .Maximum likelihood estimation with lavaan had p-values > 0.05 for the parameters simulated as zero, but also did not identify the true path of 0.2 as significant (< 0.05).