linear_quadVAR_network {quadVAR}R Documentation

Linearize a quadVAR object to produce a network.

Description

A quadVAR object is nonlinear, which means that the relationship between variables are not the same across different values of the variables. This function linearizes a quadVAR object by specifying the values of the variables that the linearized model will be based on, to facilitate interpretation. The linearized model is then expressed in an adjacency matrix, which can be used to produce a network.

Usage

linear_quadVAR_network(model, value = NULL, value_standardized = TRUE)

## S3 method for class 'linear_quadVAR_network'
plot(x, interactive = FALSE, ...)

Arguments

model

A quadVAR object.

value

A numeric vector of length 1 or the same as the number of nodes, that specifies the values of the variables that the linearized model will be based on. If the length is 1, the same value will be used for all variables. The default value is NULL, in which case the value will be set to 0 in calculation, which means (if value_standardized = TRUE) the linearized model will be based on the mean values of all variables.

value_standardized

A logical value that specifies whether the input value is standardized or not. If TRUE, the input value will be regarded as standardized value, i.e., mean + value * sd (e.g., 0 is the mean, 1 is mean + sd, ...). If FALSE, the input value will regarded as in the raw scale of the input data. If the raw dataset was already standardized, this parameter does not have an effect. The default value is TRUE.

x

A linear_quadVAR_network object.

interactive

Whether to produce an interactive plot using shiny (in which the user can change the values of variables interactively) or a static plot using qgraph::qgraph(). Default is FALSE.

...

Other arguments passed to qgraph::qgraph().

Value

A linear_quadVAR_network with the following elements:

Methods (by generic)

References

The idea of this linearization function is inspired by Kroc, E., & Olvera Astivia, O. L. (2023). The case for the curve: Parametric regression with second- and third-order polynomial functions of predictors should be routine. Psychological Methods. https://doi.org/10.1037/met0000629


[Package quadVAR version 0.1.2 Index]