Generate a tidy_layout for a SEM graph.

# S3 method for lavaan
get_layout(x, ..., layout_algorithm = "layout_as_tree")

get_layout(x, ...)

# S3 method for default
get_layout(x, ..., rows = NULL)

Arguments

x

An object for which a method exists; currently, methods exist for character, lavaan, and mplus.model objects.

...

Character arguments corresponding to layout elements. Use node names, empty strings (""), or NA values.

layout_algorithm

Optional argument for fit model objects. Character string, indicating which igraph layout algorithm to apply to position the nodes. Defaults to "layout_as_tree"; see details for more options.

rows

Numeric, indicating the number of rows of the graph.

Value

Object of class 'tidy_layout'

Details

There are three ways to generate a layout:

  1. Specify the layout in the call to get_layout() by providing node names and the number of rows to create a layout matrix. Empty strings ("") or NA can be used for empty cells. See Example 1.

  2. Call get_layout() on a model object or tidy_results object. It will use the function layout_as_tree, or any other layout function from the igraph package, to generate a rudimentary layout. See Example 2.

  3. Instead of using get_layout(), just use a matrix or data.frame with your layout. For example, specify the layout in a spreadsheet program, and load it into R (see Example 3). Or, copy the layout to the clipboard from your spreadsheet program, and load it from the clipboard (see Example 4)

The layout algorithms imported from igraph are: c("layout_as_star", "layout_as_tree", "layout_in_circle", "layout_nicely", "layout_on_grid", "layout_randomly", "layout_with_dh", "layout_with_fr", "layout_with_gem", "layout_with_graphopt", "layout_with_kk", "layout_with_lgl", "layout_with_mds"). These can be used by specifying the optional argument layout_algorithm = "".

Examples

# Example 1 get_layout("c", NA, "d", NA, "e", NA, rows = 2)
#> [,1] [,2] [,3] #> [1,] "c" NA "d" #> [2,] NA "e" NA #> attr(,"class") #> [1] "layout_matrix" "matrix" "array"
# Example 2 library(lavaan) fit <- cfa(' visual =~ x1 + x2 + x3 ', data = HolzingerSwineford1939[1:50, ])
#> Warning: lavaan WARNING: some estimated ov variances are negative
get_layout(fit)
#> [,1] [,2] [,3] #> [1,] NA "visual" NA #> [2,] "x1" "x2" "x3" #> attr(,"class") #> [1] "layout_matrix" "matrix" "array"
if (FALSE) { # Example 3 # Here, we first write the layout to .csv, but you could create it in a # spreadsheet program, and save the spreadsheet to .csv: write.csv(matrix(c("c", "", "d", "", "e", ""), nrow = 2, byrow = TRUE), file = file.path(tempdir(), "example3.csv"), row.names = FALSE) # Now, we load the .csv: read.csv(file.path(tempdir(), "example3.csv")) # Example 4 # For this example, make your layout in a spreadsheet program, select it, and # copy to clipboard. Reading from the clipboard works differently in Windows # and Mac. For this example, I used Microsoft Excel. # On Windows, run: read.table("clipboard", sep = "\t") # On Mac, run: read.table(pipe("pbpaste"), sep="\t") }