stepwise_selection {dsem} | R Documentation |
Simulate dsem
Description
Plot from a fitted dsem
model
Usage
stepwise_selection(
model_options,
model_shared,
options_initial = c(),
quiet = FALSE,
criterion = AIC,
...
)
Arguments
model_options |
character-vector containing sem elements that could be included or dropped depending upon their parsimony |
model_shared |
character-vector containing sem elements that must be included regardless of parsimony |
options_initial |
character-vector containing some (possible empty)
subset of |
quiet |
whether to avoid displaying progress to terminal |
criterion |
function that computes the information criterion to be
minimized, typically using |
... |
arguments passed to |
Details
This function conducts stepwise (i.e., forwards and backwards) model selection using marginal AIC, while forcing some model elements to be included and selecting among others.
Value
An object (list) that includes:
- model
the string with the selected SEM model
- record
a list showing the AIC and whether each
model_options
is included or not
Examples
# Simulate x -> y -> z
set.seed(101)
x = rnorm(100)
y = 0.5*x + rnorm(100)
z = 1*y + rnorm(100)
tsdata = ts(data.frame(x=x, y=y, z=z))
# define candidates
model_options = c(
"y -> z, 0, y_to_z",
"x -> z, 0, x_to_z"
)
# define paths that are required
model_shared = "
x -> y, 0, x_to_y
"
# Do selection
step = stepwise_selection(
model_options = model_options,
model_shared = model_shared,
tsdata = tsdata,
quiet = TRUE
)
# Check selected model
cat(step$model)