plot.pnt: Plot method for S3 class "pnt" objects.

In psychNET: Psychometric Networks for Intensive Longitudinal Data

Description

Generic that plots an object of class "pnt".

Usage

## S3 method for class 'pnt'
plot(x, type, person, community, ...)

Arguments

x	: An object resulted from the psychNET function.
type	: String argument, which controls the type of plot that will be returned. The available options are: "temporal" (for temporal network), "contemporaneous" (for contemporaneous network), "between" (for between subjects network), and "both" (both temporal and contemporaneous networks)
person	: This can be a single number or vector of numbers that denotes the person index for which plots will be returned. This argument is used only when the model fitted is a Multi-level VAR (i.e. the argument model in the psychNET function equals to "MLVAR".)
community	: Logical argument. When TRUE, the function fits a spinglass community detection algorithm with negative weights where only the present edges are taken into account. !!WARNING!! this can make the plot.pnt function to be very slow.
...	: Other arguments to be passed on to the plot.igraph function. Use this with care since some arguments in are already specified internally.

Details

For This function is used to visualize networks estimated via the psychNET function. See additional details of the function psychNET.

Value

The value returned by the plot is a list where its elements are two lists named temporal and contemporaneous respectively. These lists contain objects of class igraph and qgraph that can be used by the user to create tailor made plots.

Author(s)

Spyros E. Balafas (author, creator), Sanne Booij, Marco A. Grzegorczyk, Hanneke Wardenaar-Wigman, Ernst C. Wit

Maintainer: Spyros E. Balafas ([e-mail])

References

Lutkepohl, H. (2006). New Introduction to Multiple Time Series Analysis. Springer, New York.

Basu, S., Michailidis, G. (2015). Regularized estimation in sparse high-dimensional time series models. Ann. Statist. 43, no. 4, 1535-1567.

Abegaz, F., Wit, E. (2013). Sparse time series chain graphical models for reconstructing genetic networks. Biostatistics. 14, 3: 586-599.

Haslbeck, J., Waldorp, L. J. (2016). mgm: Structure Estimation for time-varying Mixed Graphical Models in high-dimensional Data.

Nicholson, W. B., Bien, J., Matteson, D. S. (2017). High Dimensional Forecasting via Interpretable Vector Autoregression..

Wilms, I., Basu, S., Bien, J., Matteson D. S. (2017). Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages.

Epskamp, S., Waldorp, L. J., Mottus, R., Borsboom, D. (2016). The Gaussian Graphical Model in Cross-sectional and Time-series Data.

psychNET documentation built on April 14, 2020, 6:39 p.m.