plotSbm.BipartiteSBM_fit: plotSbm.BipartiteSBM_fit Method

In shinySbm: 'shiny' Application to Use the Stochastic Block Model

plotSbm.BipartiteSBM_fit Method

Description

plotSbm method for BipartiteSBM_fit object

Usage

## S3 method for class 'BipartiteSBM_fit'
plotSbm(
  x,
  ordered = FALSE,
  transpose = FALSE,
  labels = NULL,
  plotOptions = list()
)

Arguments

x	an Sbm model of class '"BipartiteSBM_fit"'
ordered	Boolean. Set TRUE if the matrix should be reordered (Default is FALSE)
transpose	Boolean. Set TRUE to invert columns and rows to flatten a long matrix (Default is FALSE)
labels	named list (names should be: '"col"' and '"row"') of characters describing columns and rows component (Default is NULL)
plotOptions	list providing options. See details below.

Details

The list of parameters plotOptions for the matrix plot is

• "showValues": Boolean. Set TRUE to see the real values. Default value is TRUE

• "showPredictions": Boolean. Set TRUE to see the predicted values. Default value is TRUE

• "title": Title in characters. Will be printed at the bottom of the matrix. Default value is NULL

• "colPred": Color of the predicted values, the small values will be more transparent. Default value is "red"

• "colValue": Color of the real values, the small values will close to white. Default value is "black"

• "showLegend": Should a legend be printed ? TRUE or FALSE, default: FALSE

• "interactionName": Name of connection in legend default: "Connection"

Value

a ggplot object corresponding to the matrix plot inside the app. Groups the network matrix is organized by blocks, the small tiles are for individuals connections. The big tiles between red lines are for block connectivity

Examples

# my_sbm_bi <- sbm::estimateBipartiteSBM(sbm::fungusTreeNetwork$fungus_tree,
#                                        model = 'bernoulli')
my_sbm_bi <- FungusTreeNetwork$sbmResults$fungus_tree

plotSbm(my_sbm_bi,
  ordered = TRUE, transpose = TRUE,
  plotOptions = list(title = "An example Matrix")
)

shinySbm documentation built on Sept. 8, 2023, 5:06 p.m.