vankesteren / tensorsem

Structural Equation Modeling using Torch

Structural Equation Modeling using Torch

Description

An R package for structural equation modeling using Torch. This package is meant for researchers who know their way around SEM, Torch, and lavaan.

Note: tensorsem used to be a combined R and python package, with model specification in R and estimation in python. To view and download version 1, go to v1.0

Structural equation modeling is implemented as a fully functional torch nn module. A short example optimization loop would be:

library(tensorsem)

# model syntax (we use lavaan)
syntax <- "
  # three-factor model
  visual  =~ x1 + x2 + x3
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9
"

# create a data object as a torch tensor
dat <- torch_tensor(
  data = scale(HolzingerSwineford1939[,7:15], scale = FALSE),
  requires_grad = FALSE
)

# initialize the SEM model object
model <- torch_sem(syntax) 

# initialize the optimizer
opt <- optim_adam(model$parameters, lr = 0.01)

# optimize
for (epoch in 1:500) {
  opt$zero_grad()  # reset the gradients of the parameters
  Sigma <- model()  # compute the model-implied covariance matrix
  
  # compute the negative log-likelihood
  dist <- distr_multivariate_normal(loc = model$mu, covariance_matrix = Sigma)
  loss <- -dist$log_prob(dat)$sum()
  loss$backward()  # compute gradients and store them in parameter tensors
  opt$step()  # take a step in the negative gradient direction using adam
}

# show parameter table
model$partable()

   id     lhs op     rhs user block group free ustart exo label plabel start   est se
1   1  visual =~      x1    1     1     1    1     NA   0         .p1.     1 0.900 NA
2   2  visual =~      x2    1     1     1    2     NA   0         .p2.     1 0.498 NA
3   3  visual =~      x3    1     1     1    3     NA   0         .p3.     1 0.656 NA
4   4 textual =~      x4    1     1     1    4     NA   0         .p4.     1 0.990 NA
5   5 textual =~      x5    1     1     1    5     NA   0         .p5.     1 1.102 NA
6   6 textual =~      x6    1     1     1    6     NA   0         .p6.     1 0.917 NA
7   7   speed =~      x7    1     1     1    7     NA   0         .p7.     1 0.619 NA
8   8   speed =~      x8    1     1     1    8     NA   0         .p8.     1 0.731 NA
9   9   speed =~      x9    1     1     1    9     NA   0         .p9.     1 0.670 NA
10 10      x1 ~~      x1    0     1     1   10     NA   0        .p10.     1 0.549 NA
11 11      x2 ~~      x2    0     1     1   11     NA   0        .p11.     1 1.134 NA
12 12      x3 ~~      x3    0     1     1   12     NA   0        .p12.     1 0.844 NA
13 13      x4 ~~      x4    0     1     1   13     NA   0        .p13.     1 0.371 NA
14 14      x5 ~~      x5    0     1     1   14     NA   0        .p14.     1 0.446 NA
15 15      x6 ~~      x6    0     1     1   15     NA   0        .p15.     1 0.356 NA
16 16      x7 ~~      x7    0     1     1   16     NA   0        .p16.     1 0.799 NA
17 17      x8 ~~      x8    0     1     1   17     NA   0        .p17.     1 0.488 NA
18 18      x9 ~~      x9    0     1     1   18     NA   0        .p18.     1 0.566 NA
19 19  visual ~~  visual    0     1     1    0      1   0        .p19.     1 1.000  0
20 20 textual ~~ textual    0     1     1    0      1   0        .p20.     1 1.000  0
21 21   speed ~~   speed    0     1     1    0      1   0        .p21.     1 1.000  0
22 22  visual ~~ textual    0     1     1   19     NA   0        .p22.     0 0.459 NA
23 23  visual ~~   speed    0     1     1   20     NA   0        .p23.     0 0.471 NA
24 24 textual ~~   speed    0     1     1   21     NA   0        .p24.     0 0.283 NA

Installation

To install the latest version of tensorsem, run the following:

1. Install torch as per the instructions here: https://github.com/mlverse/torch
2. Install the R package from this repository:

   remotes::install_github("vankesteren/tensorsem")

Usage

See the examples directory for some usage examples and documentation.

tensorsem's Issues

not an issue more a question

Why did you move from tensorflow to pytorch?

TensorFlow 2.0 plans

Hi. The tensorsem paper that you put out was an interesting read. Do you have any plans to port this package over to TensorFlow 2.0?

On a related note, do you have a roadmap or plan of what functionality you'd like to add next?

I'm curious because this seems like it'd be a worthwhile project to contribute to.