This function applies a full ENA modeling pipeline to accumulated data. It is a convenience wrapper that chains together normalization, centering, rotation, projection, and optional optimization. Each step can be customized by supplying an alternative function.
Usage
model(
data,
...,
normalize = sphere_norm,
center_with = center,
rotate_with = rotate,
project_with = project,
optimize_with = optimize,
rotate_fun = ena.rotate.by.generalized,
rotate_params = list(),
exclude_zero_networks = is(data, "ena.ordered.set"),
center_to_origin = FALSE
)Arguments
- data
An `ena.set` object, typically the result of `accumulate()`.
- ...
Additional arguments passed to the rotation function specified by `rotate_fun`.
- normalize
A function to normalize the connection counts. Defaults to `sphere_norm`.
- center_with
A function to center the normalized data. Defaults to `center`.
- rotate_with
A function to perform the rotation (e.g., SVD). Defaults to `rotate`.
- project_with
A function to project the points into the rotated space. Defaults to `project`.
- optimize_with
A function to optimize node positions. Defaults to `optimize`. Can be set to `NULL` or `FALSE` to skip.
- rotate_fun
The specific rotation function to be used by `rotate_with`. Defaults to `ena.rotate.by.generalized`.
- rotate_params
A list of additional parameters to pass to the `rotate_fun`.
- exclude_zero_networks
A logical value passed to `center_with`. When `TRUE`, units with all-zero line weights are excluded from the mean computation during centering (but all units are still shifted by that mean). Defaults to `TRUE` automatically for ordered sets (`accumulate(ordered = TRUE)`), `FALSE` otherwise.
- center_to_origin
A logical value. When `TRUE`, the mean of all projected points is subtracted from both the points and the node positions after optimization, placing the centroid of the space at the origin. This is the default behavior for ONA (Ordered Network Analysis). Defaults to `FALSE`.
Examples
data(RS.data)
codes <- c("Data", "Technical.Constraints", "Performance.Parameters",
"Client.and.Consultant.Requests", "Design.Reasoning",
"Collaboration")
units <- c("Condition", "UserName")
horizon <- c("Condition", "GroupName")
enaset <- RS.data |>
accumulate(units, codes, horizon) |>
model()