conf.mat.plot {BDgraph}R Documentation

Plot Confusion Matrix


Plot a Confusion Matrix.


conf.mat.plot( pred, actual, cutoff = 0.5, conf.level = 0, margin = 1, 
                          color = c( "#ff83a8", "#83ff9b" ), ... )



An adjacency matrix corresponding to an estimated graph. It can be an object with S3 class "bdgraph" from function bdgraph. It can be an object of S3 class "ssgraph", from the function ssgraph::ssgraph() of R package ssgraph::ssgraph().


An adjacency matrix corresponding to the actual graph structure in which a_{ij}=1 if there is a link between notes i and j, otherwise a_{ij}=0. It can be an object with S3 class "sim" from function bdgraph.sim. It can be an object with S3 class "graph" from function graph.sim. It can be a factor, numeric or character vector of responses (true class), typically encoded with 0 (controls) and 1 (cases). Only two classes can be used in a ROC curve.


cutoff value for the case that pred is vector of probabilites. The default is 0.5.


confidence level used for the confidence rings on the odds ratios. Must be a single nonnegative number less than 1; if set to 0 (the default), confidence rings are suppressed.


a numeric vector with the margins to equate. Must be one of 1 (the default), 2, or c(1, 2), which corresponds to standardizing the row, column, or both margins in each 2 by 2 table. Only used if std equals "margins".


a vector of length 2 specifying the colors to use for the smaller and larger diagonals of each 2 by 2 table.


options to be passed to fourfoldplot.


Reza Mohammadi

See Also

conf.mat, compare, roc, bdgraph


## Not run: 
set.seed( 100 )

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )

# Running sampling algorithm based on GGMs 
sample.ggm <- bdgraph( data = data.sim, method = "ggm", iter = 10000 )

# Confusion Matrix for GGM method
conf.mat.plot( pred = sample.ggm, actual = data.sim )

## End(Not run)

[Package BDgraph version 2.64 Index]