# plot.mlVAR: Plot Method for mlVAR In mlVAR: Multi-Level Vector Autoregression

 plot.mlVAR R Documentation

## Plot Method for mlVAR

### 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

``````  ## 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 May 31, 2023, 6:51 p.m.