The-Structural-Equation-Model

library(lessSEM)

The lessSEM package comes with a custom implementation of structural equation models (SEM). This implementation supports full-information-maximum-likelihood computation in case of missing data and could also be used by other packages. Identical to regsem lessSEM also builds on lavaan to set up the model. That is, if you are already familiar with lavaan, setting up models with lessSEM should be relatively easy.

We will use the political democracy example from the sem documentation of lavaan in the following:

library(lavaan)
# see ?lavaan::sem
model <- ' 
  # latent variable definitions
     ind60 =~ x1 + x2 + x3
     dem60 =~ y1 + a*y2 + b*y3 + c*y4
     dem65 =~ y5 + a*y6 + b*y7 + c*y8

  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60

  # residual correlations
    y1 ~~ y5
    y2 ~~ y4 + y6
    y3 ~~ y7
    y4 ~~ y8
    y6 ~~ y8
'

lavaanModel <- sem(model, data = PoliticalDemocracy)

From lavaan to lessSEM

To translate the model from lavaan to lessSEM, we have to use the lessSEM:::.SEMFromLavaan function. Importantly, this function is not exported by lessSEM. That is, you must use the three colons as shown above to access this function!

library(lessSEM)

# won't work:
mySEM <- .SEMFromLavaan(lavaanModel = lavaanModel)

# will work:
mySEM <- lessSEM:::.SEMFromLavaan(lavaanModel = lavaanModel)

show(mySEM)
#> Internal C++ model representation of lessSEM
#> Parameters:
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>    2.1796566    1.8182100    1.1907820    1.1745407    1.2509789    1.4713302    0.6004746    0.8650430    0.5825389 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>    1.4402477    2.1829448    0.7115901    0.3627964    1.3717741    0.0813878    0.1204271    0.4666596    1.8546417 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>    7.5813926    4.9556766    3.2245521    2.3130404    4.9681408    3.5600367    3.3076854    0.4485989    3.8753039 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>    0.1644633    5.0543838    4.7921946    3.5576898    5.4646667    4.2564429    6.5631103    4.4525330    5.1362519 
#>         y6~1         y7~1         y8~1 
#>    2.9780741    6.1962639    4.0433897 
#> 
#> Objective value: 3097.6361581071

The lessSEM:::.SEMFromLavaan function comes with some additional arguments to fine tune the initialization of the model.

whichPars: with the whichPars arguments, we can change which parameters are used in the mySEM created above. By default, we will use the estimates (whichPars = "est") of the lavaan model, but we could also use the starting values (whichPars = "start") or supply custom parameter values
fit: When fit = TRUE, lessSEM will fit the model once and compare the fitting function value to that of the lavaanModel. If you supplied parameters other than “est”, this should be set to fit = FALSE
addMeans: Should a mean structure be added? It is currenlty recomended to set this to TRUE
activeSet: This allows for only using part of the data set. This can be useful for cross-validation.
dataSet: This allows for passing a different data set to mySEM. This can be useful for cross-validation.

In most cases, we recommend setting up the model as shown above, with none of the additional arguments being used.

Working with the Rcpp_SEMCpp class

The mySEM object is implemented in C++ to make everything run faster. The underlying class is Rcpp_SEMCpp and was created using the wonderful Rcpp and RcppArmadillo packages.

class(mySEM)
#> [1] "Rcpp_SEMCpp"
#> attr(,"package")
#> [1] "lessSEM"

You can access its elements using the dollar-operator:

mySEM$A
#>            [,1]     [,2]     [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#>  [1,] 0.0000000 0.000000 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [2,] 1.4713302 0.000000 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [3,] 0.6004746 0.865043 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [4,] 1.0000000 0.000000 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [5,] 2.1796566 0.000000 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [6,] 1.8182100 0.000000 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [7,] 0.0000000 1.000000 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [8,] 0.0000000 1.190782 0.000000    0    0    0    0    0    0     0     0     0     0     0
#>  [9,] 0.0000000 1.174541 0.000000    0    0    0    0    0    0     0     0     0     0     0
#> [10,] 0.0000000 1.250979 0.000000    0    0    0    0    0    0     0     0     0     0     0
#> [11,] 0.0000000 0.000000 1.000000    0    0    0    0    0    0     0     0     0     0     0
#> [12,] 0.0000000 0.000000 1.190782    0    0    0    0    0    0     0     0     0     0     0
#> [13,] 0.0000000 0.000000 1.174541    0    0    0    0    0    0     0     0     0     0     0
#> [14,] 0.0000000 0.000000 1.250979    0    0    0    0    0    0     0     0     0     0     0

Note that, identical to regsem, the model is implemented with the RAM notation (McArdle & McDonald, 1984). If you are not familiar with this notation, Fox (2006) provides a short introduction. However, you won’t need to know the details for the time being. Instead, we will focus on how to get and set the parameters, fit the model, get its gradients, etc.

Accessing the Parameters

The parameters of the model can be accessed with the lessSEM:::.getParameters function:

(myParameters <- lessSEM:::.getParameters(mySEM))
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>    2.1796566    1.8182100    1.1907820    1.1745407    1.2509789    1.4713302    0.6004746    0.8650430    0.5825389 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>    1.4402477    2.1829448    0.7115901    0.3627964    1.3717741    0.0813878    0.1204271    0.4666596    1.8546417 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>    7.5813926    4.9556766    3.2245521    2.3130404    4.9681408    3.5600367    3.3076854    0.4485989    3.8753039 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>    0.1644633    5.0543838    4.7921946    3.5576898    5.4646667    4.2564429    6.5631103    4.4525330    5.1362519 
#>         y6~1         y7~1         y8~1 
#>    2.9780741    6.1962639    4.0433897

The naming is identical to that of the lavaanModel. By default, the parameters are returned in the transformed format. This requires some more explanation: In lessSEM we assume that negative variances are outside of the parameter space. That is, negative variances are not allowed (this is different from lavaan!). To ensure that all variances are positive, we use a transformation: Say we are interested in the variance ind60~~ind60. Internally, there is a parameter called x1~~x1 and this parameter has a rawValue and a transformed value (called just value). We can access these values with:

mySEM$getParameters()
#>           label     value   rawValue location isTransformation
#> 1     ind60=~x2 2.1796566  2.1796566  Amatrix            FALSE
#> 2     ind60=~x3 1.8182100  1.8182100  Amatrix            FALSE
#> 3             a 1.1907820  1.1907820  Amatrix            FALSE
#> 4             b 1.1745407  1.1745407  Amatrix            FALSE
#> 5             c 1.2509789  1.2509789  Amatrix            FALSE
#> 6   dem60~ind60 1.4713302  1.4713302  Amatrix            FALSE
#> 7   dem65~ind60 0.6004746  0.6004746  Amatrix            FALSE
#> 8   dem65~dem60 0.8650430  0.8650430  Amatrix            FALSE
#> 9        y1~~y5 0.5825389  0.5825389  Smatrix            FALSE
#> 10       y2~~y4 1.4402477  1.4402477  Smatrix            FALSE
#> 11       y2~~y6 2.1829448  2.1829448  Smatrix            FALSE
#> 12       y3~~y7 0.7115901  0.7115901  Smatrix            FALSE
#> 13       y4~~y8 0.3627964  0.3627964  Smatrix            FALSE
#> 14       y6~~y8 1.3717741  1.3717741  Smatrix            FALSE
#> 15       x1~~x1 0.0813878 -2.5085299  Smatrix            FALSE
#> 16       x2~~x2 0.1204271 -2.1167106  Smatrix            FALSE
#> 17       x3~~x3 0.4666596 -0.7621551  Smatrix            FALSE
#> 18       y1~~y1 1.8546417  0.6176915  Smatrix            FALSE
#> 19       y2~~y2 7.5813926  2.0256969  Smatrix            FALSE
#> 20       y3~~y3 4.9556766  1.6005337  Smatrix            FALSE
#> 21       y4~~y4 3.2245521  1.1707941  Smatrix            FALSE
#> 22       y5~~y5 2.3130404  0.8385629  Smatrix            FALSE
#> 23       y6~~y6 4.9681408  1.6030457  Smatrix            FALSE
#> 24       y7~~y7 3.5600367  1.2697708  Smatrix            FALSE
#> 25       y8~~y8 3.3076854  1.1962487  Smatrix            FALSE
#> 26 ind60~~ind60 0.4485989 -0.8016262  Smatrix            FALSE
#> 27 dem60~~dem60 3.8753039  1.3546241  Smatrix            FALSE
#> 28 dem65~~dem65 0.1644633 -1.8050678  Smatrix            FALSE
#> 29         x1~1 5.0543838  5.0543838  Mvector            FALSE
#> 30         x2~1 4.7921946  4.7921946  Mvector            FALSE
#> 31         x3~1 3.5576898  3.5576898  Mvector            FALSE
#> 32         y1~1 5.4646667  5.4646667  Mvector            FALSE
#> 33         y2~1 4.2564429  4.2564429  Mvector            FALSE
#> 34         y3~1 6.5631103  6.5631103  Mvector            FALSE
#> 35         y4~1 4.4525330  4.4525330  Mvector            FALSE
#> 36         y5~1 5.1362519  5.1362519  Mvector            FALSE
#> 37         y6~1 2.9780741  2.9780741  Mvector            FALSE
#> 38         y7~1 6.1962639  6.1962639  Mvector            FALSE
#> 39         y8~1 4.0433897  4.0433897  Mvector            FALSE

For all parameters which are not variances, the rawValue will be identical to the value. For variances, the rawValue can be any real value. The value itself is then computed as e^rawValue; this ensures that the value is always positive. You can access the raw values as follows:

lessSEM:::.getParameters(mySEM, raw = TRUE)
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>    2.1796566    1.8182100    1.1907820    1.1745407    1.2509789    1.4713302    0.6004746    0.8650430    0.5825389 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>    1.4402477    2.1829448    0.7115901    0.3627964    1.3717741   -2.5085299   -2.1167106   -0.7621551    0.6176915 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>    2.0256969    1.6005337    1.1707941    0.8385629    1.6030457    1.2697708    1.1962487   -0.8016262    1.3546241 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>   -1.8050678    5.0543838    4.7921946    3.5576898    5.4646667    4.2564429    6.5631103    4.4525330    5.1362519 
#>         y6~1         y7~1         y8~1 
#>    2.9780741    6.1962639    4.0433897

Note that the raw value for ind60~~ind60 is negative while the transformed value is positive.

Changing the Parameters

Being able to change the parameters is essential for fitting a model. In lessSEM, this is facilitated by the lessSEM:::.setParameters function:

# first, let's change one of the parameters:
myParameters["a"] <- 1

# now, let's change the parameters of the model
mySEM <- lessSEM:::.setParameters(SEM = mySEM, # the model
                                  labels = names(myParameters), # names of the parameters
                                  values = myParameters, # values of the parameters 
                                  raw = FALSE)

Note that we had to specify if the parameters in myParameters are given in raw format. Here, we already used the transformed parameters, so we set raw = FALSE. Using the raw parameters instead would look as follows:

myParameters <- lessSEM:::.getParameters(mySEM, raw = TRUE)
# first, let's change one of the parameters:
myParameters["a"] <- 1

# now, let's change the parameters of the model
mySEM <- lessSEM:::.setParameters(SEM = mySEM, # the model
                                  labels = names(myParameters), # names of the parameters
                                  values = myParameters, # values of the parameters
                                  raw = TRUE)

Let’s check the parameters:

lessSEM:::.getParameters(mySEM)
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>    2.1796566    1.8182100    1.0000000    1.1745407    1.2509789    1.4713302    0.6004746    0.8650430    0.5825389 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>    1.4402477    2.1829448    0.7115901    0.3627964    1.3717741    0.0813878    0.1204271    0.4666596    1.8546417 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>    7.5813926    4.9556766    3.2245521    2.3130404    4.9681408    3.5600367    3.3076854    0.4485989    3.8753039 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>    0.1644633    5.0543838    4.7921946    3.5576898    5.4646667    4.2564429    6.5631103    4.4525330    5.1362519 
#>         y6~1         y7~1         y8~1 
#>    2.9780741    6.1962639    4.0433897

Note that a now has the value 1.

Fitting the model

To compute the -2-log-likelihood of the model, we use the lessSEM:::.fit function:

mySEM <- lessSEM:::.fit(SEM = mySEM)

The -2-log-likelihood can be accessed with:

mySEM$objectiveValue
#> [1] 3100.741

Computing the gradients

To compute the gradients, use the lessSEM:::.getGradients function. Gradients can be computed for the transformed parameters

lessSEM:::.getGradients(mySEM, raw = FALSE)
#>     ind60=~x2     ind60=~x3             a             b             c   dem60~ind60   dem65~ind60   dem65~dem60        y1~~y5 
#>   0.361097622   0.105564095 -32.837359814   2.453232158  17.076222049  -0.450648533  -1.078272542  -5.357920036   0.004650747 
#>        y2~~y4        y2~~y6        y3~~y7        y4~~y8        y6~~y8        x1~~x1        x2~~x2        x3~~x3        y1~~y1 
#>  -0.157923486  -0.567076177   0.161163293   0.266869495  -0.271533827   0.230054158   0.306685898  -0.093753843  -0.015516535 
#>        y2~~y2        y3~~y3        y4~~y4        y5~~y5        y6~~y6        y7~~y7        y8~~y8  ind60~~ind60  dem60~~dem60 
#>  -0.343353554   0.099359383   0.137753880   0.131454473  -0.330083693   0.073331567   0.148964628  -0.103960291  -0.252921392 
#>  dem65~~dem65          x1~1          x2~1          x3~1          y1~1          y2~1          y3~1          y4~1          y5~1 
#>  -1.955349241   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000 
#>          y6~1          y7~1          y8~1 
#>   0.000000000   0.000000000   0.000000000

or for the raw parameters

lessSEM:::.getGradients(mySEM, raw = TRUE)
#>     ind60=~x2     ind60=~x3             a             b             c   dem60~ind60   dem65~ind60   dem65~dem60        y1~~y5 
#>   0.361097622   0.105564095 -32.837359814   2.453232158  17.076222049  -0.450648533  -1.078272542  -5.357920036   0.004650747 
#>        y2~~y4        y2~~y6        y3~~y7        y4~~y8        y6~~y8        x1~~x1        x2~~x2        x3~~x3        y1~~y1 
#>  -0.157923486  -0.567076177   0.161163293   0.266869495  -0.271533827   0.018723602   0.036933297  -0.043751134  -0.028777612 
#>        y2~~y2        y3~~y3        y4~~y4        y5~~y5        y6~~y6        y7~~y7        y8~~y8  ind60~~ind60  dem60~~dem60 
#>  -2.603098086   0.492392971   0.444194569   0.304059508  -1.639902248   0.261063066   0.492728130  -0.046636468  -0.980147244 
#>  dem65~~dem65          x1~1          x2~1          x3~1          y1~1          y2~1          y3~1          y4~1          y5~1 
#>  -0.321583201   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000   0.000000000 
#>          y6~1          y7~1          y8~1 
#>   0.000000000   0.000000000   0.000000000

Computing the Hessian

To compute the Hessian, use the lessSEM:::.getHessian function. The Hessian can be computed for the transformed parameters

lessSEM:::.getHessian(mySEM, raw = FALSE)

or for the raw parameters

lessSEM:::.getHessian(mySEM, raw = TRUE)

Computing the Scores

To compute the scores (derivative of the -2-log-likelihood for each person), use the lessSEM:::.getScores function. The scores can be computed for the transformed parameters

lessSEM:::.getScores(mySEM, raw = FALSE)

or for the raw parameters

lessSEM:::.getScores(mySEM, raw = TRUE)

Using lessSEM with general purpose optimizers

The most important part about the whole SEM implementation mentioned above is that we can use it flexibly with different optimizers. For instance, we may want to try out the BFGS optimizer from optim.

Important: We highly recommend that you use the raw parameters for any optimization. Using the non-raw parameters can cause errors and unnecessary headaches!

Let’s have a look at the optim function:

args(optim)
#> function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", 
#>     "BFGS", "CG", "L-BFGS-B", "SANN", "Brent"), lower = -Inf, 
#>     upper = Inf, control = list(), hessian = FALSE) 
#> NULL

Note that the function requires a par argument - the parameter estimates - a fn argument - the fitting function - and also allows for the gradients to be passed to the function using the gr argument. We could build such functions based on the lessSEM:::.fit and lessSEM:::.getGradients functions shown above, however for convenience such wrappers are already implemented in lessSEM. The fitting function is called with lessSEM:::.fitFunction and the gradient function is called lessSEM:::.gradientFunction. Both expect a vector with parameters, a SEM, and an argument specifying if the parameters are in raw format.

We can use this in optim as follows:

# let's get the starting values:
par <- lessSEM:::.getParameters(mySEM, raw = TRUE) # important: Use raw = TRUE!

print(par)
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>    2.1796566    1.8182100    1.0000000    1.1745407    1.2509789    1.4713302    0.6004746    0.8650430    0.5825389 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>    1.4402477    2.1829448    0.7115901    0.3627964    1.3717741   -2.5085299   -2.1167106   -0.7621551    0.6176915 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>    2.0256969    1.6005337    1.1707941    0.8385629    1.6030457    1.2697708    1.1962487   -0.8016262    1.3546241 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>   -1.8050678    5.0543838    4.7921946    3.5576898    5.4646667    4.2564429    6.5631103    4.4525330    5.1362519 
#>         y6~1         y7~1         y8~1 
#>    2.9780741    6.1962639    4.0433897

opt <- optim(par = par, 
             fn = lessSEM:::.fitFunction, # use the fitting function wrapper
             gr = lessSEM:::.gradientFunction, # use the gradient function wrapper
             SEM = mySEM, # use the SEM we created above
             raw = TRUE, # make sure to tell the functions that we are using raw parameters
             method = "BFGS" # use the BFGS optimizer
)
print(opt$par)
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>    2.1791276    1.8180458    1.1909397    1.1740909    1.2511328    1.4725867    0.6007137    0.8649836    0.5817910 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>    1.4336940    2.1828828    0.7229781    0.3605874    1.3774602   -2.5093044   -2.1126718   -0.7633056    0.6178333 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>    2.0246995    1.6022529    1.1690474    0.8385775    1.6039528    1.2711405    1.1971146   -0.8013687    1.3542390 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>   -1.8057174    5.0543838    4.7921946    3.5576898    5.4646667    4.2564429    6.5631103    4.4525330    5.1362519 
#>         y6~1         y7~1         y8~1 
#>    2.9780741    6.1962639    4.0433897

Note that the parameter a is now back at the maximum likelihood estimate from before. However, all parameters are still in raw format. To get the transformed parameters, let’s take one more step:

mySEM <- lessSEM:::.setParameters(SEM = mySEM, # the model
                                  labels = names(opt$par), # names of the parameters
                                  values = opt$par, # values of the parameters
                                  raw = TRUE)
print(lessSEM:::.getParameters(mySEM, raw = FALSE))
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>   2.17912764   1.81804575   1.19093968   1.17409087   1.25113276   1.47258673   0.60071368   0.86498364   0.58179103 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>   1.43369401   2.18288278   0.72297808   0.36058737   1.37746019   0.08132479   0.12091448   0.46612308   1.85490471 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>   7.57383439   4.96420359   3.21892497   2.31307420   4.97264951   3.56491599   3.31055101   0.44871438   3.87381195 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>   0.16435650   5.05438384   4.79219463   3.55768979   5.46466667   4.25644288   6.56311025   4.45253304   5.13625192 
#>         y6~1         y7~1         y8~1 
#>   2.97807408   6.19626389   4.04338968

Compare those to the parameter estimates from lavaan:

coef(lavaanModel)
#>    ind60=~x2    ind60=~x3            a            b            c            a            b            c  dem60~ind60 
#>        2.180        1.818        1.191        1.175        1.251        1.191        1.175        1.251        1.471 
#>  dem65~ind60  dem65~dem60       y1~~y5       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1 
#>        0.600        0.865        0.583        1.440        2.183        0.712        0.363        1.372        0.081 
#>       x2~~x2       x3~~x3       y1~~y1       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7 
#>        0.120        0.467        1.855        7.581        4.956        3.225        2.313        4.968        3.560 
#>       y8~~y8 ind60~~ind60 dem60~~dem60 dem65~~dem65 
#>        3.308        0.449        3.875        0.164

Finally, we can compute the standard errors:

lessSEM:::.standardErrors(SEM = mySEM, raw = FALSE)
#>    ind60=~x2    ind60=~x3            a            b            c  dem60~ind60  dem65~ind60  dem65~dem60       y1~~y5 
#>   0.13885220   0.15204330   0.14166120   0.11987057   0.12295637   0.39139697   0.23828914   0.07567860   0.36462027 
#>       y2~~y4       y2~~y6       y3~~y7       y4~~y8       y6~~y8       x1~~x1       x2~~x2       x3~~x3       y1~~y1 
#>   0.68977247   0.73096919   0.62119517   0.46062832   0.57969390   0.01968652   0.06991196   0.08897395   0.45717112 
#>       y2~~y2       y3~~y3       y4~~y4       y5~~y5       y6~~y6       y7~~y7       y8~~y8 ind60~~ind60 dem60~~dem60 
#>   1.34332170   0.96373267   0.74092220   0.48364101   0.89600779   0.73922562   0.71425331   0.08675480   0.88802932 
#> dem65~~dem65         x1~1         x2~1         x3~1         y1~1         y2~1         y3~1         y4~1         y5~1 
#>   0.23331748   0.08406657   0.17326967   0.16121433   0.29892606   0.43891242   0.39404806   0.37957637   0.30446534 
#>         y6~1         y7~1         y8~1 
#>   0.39247640   0.36442149   0.37545879

Let’s compare this to lavaan again:

parameterEstimates(lavaanModel)[,1:6]
#>      lhs op   rhs label   est    se
#> 1  ind60 =~    x1       1.000 0.000
#> 2  ind60 =~    x2       2.180 0.138
#> 3  ind60 =~    x3       1.818 0.152
#> 4  dem60 =~    y1       1.000 0.000
#> 5  dem60 =~    y2     a 1.191 0.139
#> 6  dem60 =~    y3     b 1.175 0.120
#> 7  dem60 =~    y4     c 1.251 0.117
#> 8  dem65 =~    y5       1.000 0.000
#> 9  dem65 =~    y6     a 1.191 0.139
#> 10 dem65 =~    y7     b 1.175 0.120
#> 11 dem65 =~    y8     c 1.251 0.117
#> 12 dem60  ~ ind60       1.471 0.392
#> 13 dem65  ~ ind60       0.600 0.226
#> 14 dem65  ~ dem60       0.865 0.075
#> 15    y1 ~~    y5       0.583 0.356
#> 16    y2 ~~    y4       1.440 0.689
#> 17    y2 ~~    y6       2.183 0.737
#> 18    y3 ~~    y7       0.712 0.611
#> 19    y4 ~~    y8       0.363 0.444
#> 20    y6 ~~    y8       1.372 0.577
#> 21    x1 ~~    x1       0.081 0.019
#> 22    x2 ~~    x2       0.120 0.070
#> 23    x3 ~~    x3       0.467 0.090
#> 24    y1 ~~    y1       1.855 0.433
#> 25    y2 ~~    y2       7.581 1.366
#> 26    y3 ~~    y3       4.956 0.956
#> 27    y4 ~~    y4       3.225 0.723
#> 28    y5 ~~    y5       2.313 0.479
#> 29    y6 ~~    y6       4.968 0.921
#> 30    y7 ~~    y7       3.560 0.710
#> 31    y8 ~~    y8       3.308 0.704
#> 32 ind60 ~~ ind60       0.449 0.087
#> 33 dem60 ~~ dem60       3.875 0.866
#> 34 dem65 ~~ dem65       0.164 0.227

References

Fox, J. (2006). Teacher’s corner: Structural equation modeling with the sem package in R. Structural Equation Modeling: A Multidisciplinary Journal, 13(3), 465–486. https://doi.org/10.1207/s15328007sem1303_7
McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures. British Journal of Mathematical and Statistical Psychology, 37(2), 234–251. https://doi.org/10.1111/j.2044-8317.1984.tb00802.x