Plotting diffusion models

Usage

# S3 method for class 'diff_model'
plot(x, ..., all_steps = TRUE)

# S3 method for class 'diffs_model'
plot(x, ...)

# S3 method for class 'learn_model'
plot(x, ...)

Arguments

x: A "diff_model" of "diffs_model" class of object. E.g. as a result from manynet::play_diffusion().
...: Other arguments to be passed.
all_steps: Whether all steps should be plotted or just those where there is change in the distributions.

Value

plot.diff_model() returns a bar chart of the number of new infected nodes at each time point, as well as an overlay line plot of the total of infected

Examples

plot(res_manynet_diff)

plot(res_migraph_diff)
#> Warning: pseudoinverse used at -0.015
#> Warning: neighborhood radius 2.015
#> Warning: reciprocal condition number  4.9596e-17
#> Warning: There are other near singularities as well. 4.0602
#> Warning: pseudoinverse used at -0.015
#> Warning: neighborhood radius 2.015
#> Warning: reciprocal condition number  4.9596e-17
#> Warning: There are other near singularities as well. 4.0602
#> Warning: pseudoinverse used at -0.015
#> Warning: neighborhood radius 2.015
#> Warning: reciprocal condition number  4.9596e-17
#> Warning: There are other near singularities as well. 4.0602
#> Warning: pseudoinverse used at -0.015
#> Warning: neighborhood radius 2.015
#> Warning: reciprocal condition number  4.9596e-17
#> Warning: There are other near singularities as well. 4.0602

plot(play_learning(ison_networkers, beliefs = runif(net_nodes(ison_networkers))))