plot.mlVAR: Plot Method for mlVAR

Description Usage Arguments Author(s)

View source: R/S3Methods.R

Description

The function plot.mlVAR plots estimated model coefficients as networks using qgraph. These can be three networks: temporal, contemporaneous and between-subjects effects, of which the latter two can be plotted as a correlation or a partial correlation network.

Usage

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  ## S3 method for class 'mlVAR'
plot(x, type = c("temporal", "contemporaneous", "between"),
                 lag = 1, partial = TRUE, SD = FALSE, subject, order,
                 nonsig = c("default", "show", "hide", "dashed"), rule
                 = c("or", "and"), alpha = 0.05, onlySig = FALSE,
                 layout = "spring", verbose = TRUE, ...)
  ## S3 method for class 'mlVARsim'
plot(x, ...)

Arguments

x

An mlVAR object.

type

What network to plot?

lag

The lag to use when type = "temporal"

partial

Logical, should partial correlation matrices be plotted instead of correlation methods? Only used if type is "contemporaneous" or "between". Defaults to TRUE.

SD

Logical. Plot the standard-deviation of random effects instead of the fixed effect estimate?

subject

Subject number. If not missing, will plot the network of a specific subject instead.

order

An optional character vector used to set the order of nodes in the network.

nonsig

How to handle non-significant edges? Default will hide non-significant edges when p-values are available (fixed effects, partial correlations and temporal effects).

rule

How to choose significance in node-wise estimated GGMs (contemporaneous and between-subjects). "or" selects an edge as being significant if one node predicting the other is significant, and "and" requires both predictions to be significant.

alpha

Alpha level to test for significance

onlySig

Deprecated argument only used for backward competability.

layout

The layout argument used by qgraph

verbose

Logical, should message be printed to the console?

...

Arguments sent to qgraph

Author(s)

Sacha Epskamp (mail@sachaepskamp.com)


mlVAR documentation built on Oct. 25, 2021, 5:07 p.m.

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