Description Usage Arguments Details Examples
The function visualise shows a graphical representation of the found
HMMA. Only the initial distribution and transition distribution are shown.
Each state contains a Bayesian network, these would be unreadble when
displayed within each state. The bnlearn
graphviz.plot
method can be used to display the
bayesian networks.
1 2 3 4 5 6 7 8 9 
model 
The model. 
minProb 
The minimum probability of an arc (initial or transition) to be drawn in the visualisation (see details). 
numDigitsRound 
The amount of decimals that should be presented in the graph. 
relateWidthWithWeight 
An edge with a high weight will be drawn with a thicker line compared to an edge with a lower weight. 
widthType 
The thickness function for the lines. Defaults to linear
function. When 
minWidth 
The minimum thickness for a line. Only used when

maxWidth 
The maximum thickness for a line. Only used when

Per default, all transitions (initial and transition) are drawn on the
canvas. For larger HMMAs, the visualisation can become cluttered with arcs
that have a low probability. By setting the minProb
, an arc is only
drawn when it is equal to or greater than the specified minProb
. The
default value for minProb
is 0.00 (all arcs are drawn).
To further improve the visualisation, edge line width can be related to the
respective weight of the edge. When relateWeightWithThickness
is
TRUE
, an edge with a higher weight will be drawn with a wider line.
The minimum width of a line and the maximum width can be supplied via the
respective parameters.
The minWidth
is associated with a weight of 0.00, the maxWidth is
related to the weight 1.00. When widthType
is set to 'linear', the
width of a line is linearly related to the weight. When widthType
is
set to 'sigmoid', the width of a line is determined via a sigmoid of the
weight.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # First, we need a model that we want to visualise, we create
# one using the learnModel function.
fit < learnModel(data = hmmaExampleData, amountOfStates = 3)
# To visualise the states and transitions, we use the visualise method.
# Only lines with a weight of 0.10 are drawn.
visualise(fit, minProb = 0.10)
# When it is not desired to relate the width of a line with its weight,
# this can be disabled:
visualise(fit, minProb = 0.10, relateWidthWithWeight = FALSE)
# Finally, it is possible to use a sigmoid instead of a linear relation:
visualise(fit, minProb = 0.10, widthType = 'sigmoid')
# To visualise the BNs within the states, use the code below
library(bnlearn)
graphviz.plot(fit$parms.emission[[1]])

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