Description Usage Arguments Details Examples

Plotting functionalities for objects of class `fitGGM`

or `mixGGM`

.

1 2 3 4 5 6 7 8 |

`x` |
An object of class |

`what` |
The type of plot to be produced. If |

`layout` |
Layout of the graph, either circular (default) or random. |

`colors` |
A vector of user defined colors |

`symb` |
A vector of user defined symbols |

`dimens` |
A vector giving the integer dimensions of the desired variables for multivariate data in case of |

`...` |
Other arguments. |

These functions are used to visualize graph association structures and clustering results for single and mixtures of Gaussian covariance and concentration models.

In the case of `what = "graph"`

, the graph of a Gaussian covariance graph model is bi-directed, while the graph of a Gaussian concentration model is un-directed. Thickness of the edges is proportional to the estimated association parameters.

See "Examples" for various cases.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ```
# covariance graph
data(mtcars)
x <- mtcars[,c(1,3:7)]
R <- cor(x)
graph <- ( abs(R) < 0.5 )*1
diag(graph) <- 0
fit1 <- fitGGM(data = x, graph = graph)
plot(fit1)
plot(fit1, what = "adjacency")
# concentration graph
data(swiss)
V <- ncol(swiss)
graph <- matrix( c(0,1,0,1,1,1,
1,0,1,1,0,0,
0,1,0,1,1,0,
1,1,1,0,1,0,
1,0,1,1,0,0,
1,0,0,0,0,0), V,V, byrow = TRUE )
fit2 <- fitGGM(swiss, graph = graph, model = "concentration")
plot(fit2)
plot(fit2, layout = "random")
plot(fit2, what = "adjacency")
## Not run:
# mixture of Gaussian concentration graph models
data(banknote, package = "mclust")
mod3 <- mixGGM(banknote[,-1], model = "concentration", K = 2)
plot(mod3, what = "graph")
plot(mod3, what = "adjacency")
plot(mod3, what = "classification")
plot(mod3, what = "classification", dimens = c(1,4,5))
plot(mod3, what = "common")
# mixture of Gaussian covariance graph models
data(wine, package = "gclus")
mod4 <- mixGGM(wine[,-1], model = "covariance", K = 3)
clb <- c("#999999", "#E69F00", "#56B4E9") # colorblind friendly palette
plot(mod4, what = "graph", colors = clb)
plot(mod4, what = "adjacency", colors = clb)
plot(mod4, what = "classification", colors = clb, dimens = c(1,7,8,12))
plot(mod4, what = "common")
## End(Not run)
``` |

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