Author: Shu Fai Cheung & Mark Hok Chio Lai
Date: 2026-07-20

# Keep or Drop Selected Variables

#### Shu Fai Cheung & Mark Hok Chio Lai

#### 2026-07-20

# Introduction

In psychological research, it is common for a path analytic model to have several control variables. They need to be included in the analysis. However, for generating the plot, it is acceptable to omit them as long as results regarding these control variables are reported in other forms (e.g., a table of all parameter estimates), and keep only the main variables in the plot.

Two helper functions, `drop_nodes` and `keep_nodes`, from the package semptools (CRAN page), were developed for this purpose. When an SEM output object, such as the output from `lavaan`, is passed to `semPlot::semPaths`, it is first processed by `semPlot::semPlotModel`. In other words, `semPlot::semPaths` itself does not interpret the SEM output. It actually use the output of `semPlot::semPlotModel` to generate the plot. These helper functions modify the output of `semPlot::semPlotModel` to be used by `semPlot::semPaths`.

# Example

Suppose we have a model with four main variables, `x1`, `x2`, `x3`, and `x4`, and three control variables, `cov1`, `cov2`, and `cov3`. The sample data, `pa_example_3covs`, is in the package `semptools`.

This is the analysis in `lavaan`:

    library(lavaan)
    #> This is lavaan 0.7-2.3110
    #> lavaan is FREE software! Please report any bugs.
    mod <-
      'x3 ~  x1 + x2 + cov1 + cov2 + cov3
       x4 ~  x1 + x3 + cov1 + cov2 + cov3
      '
    fit <- lavaan::sem(mod, pa_example_3covs)

We can plot the full model using `semPlot::semPaths` and use `layout` and `layout_matrix` to control the positions of all seven variables (please refer to the vignette on `layou_matrix` on how to do this):

    library(semPlot)
    library(semptools)
    m <- layout_matrix(x1 = c(1, 1),
                       x2 = c(3, 1),
                       x3 = c(2, 2),
                       x4 = c(2, 3),
                       cov1 = c(4, 1),
                       cov2 = c(5, 1),
                       cov3 = c(6, 1))
    p_pa <- semPaths(fit, whatLabels = "est",
               sizeMan = 10,
               edge.label.cex = .5,
               style = "ram",
               nCharNodes = 0, nCharEdges = 0,
               layout = m)

Suppose we want to remove `cov1`, `cov2`, and `cov3`. We do this by calling `semPlot::semPlotModel` directly, and modify it by `keep_nodes` or `drop_nodes`.

We can drop `cov1`, `cov2`, and `cov3` by `drop_nodes`:

    pm_no_covs <- semptools::drop_nodes(
                    object = semPlotModel(fit),
                    nodes = c("cov1", "cov2", "cov3"))

The first argument, `object`, should be the output of `semPlot::semPlotModel`. In the example, `semPlotModel(fit)` is used to call `semPlot::semPlotModel` to process `fit` and then pass the results immediately to `drop_nodes`.

The second argument, `nodes`, is a character vector with the names of the variables to be dropped.

With just two arguments, the argument names can be omitted:

    pm_no_covs <- semptools::drop_nodes(
                    semPlotModel(fit),
                    c("cov1", "cov2", "cov3"))

We can then use `semPlot::semPaths` to plot this modified model:

    m_no_covs <- layout_matrix(x1 = c(1, 1),
                               x2 = c(3, 1),
                               x3 = c(2, 2),
                               x4 = c(2, 3))
    pa_no_covs <- semPaths(pm_no_covs, whatLabels = "est",
                        sizeMan = 10,
                        edge.label.cex = .5,
                        style = "ram",
                        nCharNodes = 0, nCharEdges = 0,
                        layout = m_no_covs)

Note that in the call to `semPlot::semPaths`, the modified output of `semPlot::semPlotModel`, `pm_no_covs`, is used instead of `fit`, the output of `lavaan`.

Alternatively, we can also specify the variables to keep using `keep_nodes`. It is used in a similar way, except that the variables specified in `nodes` will be kept, and all variables not specified will be removed.

    pm_only_xs <- semptools::keep_nodes(
                    semPlotModel(fit),
                    c("x1", "x2", "x3", "x4"))
    pa_only_xs <- semPaths(pm_only_xs, whatLabels = "est",
                        sizeMan = 10,
                        edge.label.cex = .5,
                        style = "ram",
                        nCharNodes = 0, nCharEdges = 0,
                        layout = m_no_covs)

This plot is identical to the previous plot.

The plot generated by `semPlot::semPaths` with selected nodes dropped or kept can then be passed to other `semptools` functions for further processing.

# Keep Or Drop? It Depends

Which function to use depends on which one is easier to specify. For example, if there are a lot of contorl variables but only a few main variables, then it is more efficient to use `keep_nodes` and specify the few main variables. If the number of control variables is substantially less than the number of main variables, then it is more efficient to use `drop_nodes` and specify only the few control variables to drop.