This function splits the Matthews correlation coefficient (MCC) scores
of the models to specific groups using the Ckmeans.1d.dp
package for the clustering (groups are denoted by ids, e.g. NaN,1,2,3, etc.
where a larger id corresponds to a group of models with higher MCC scores)
and for each pairwise
combination of group id matchings (e.g. (0,1), (1,3), etc.), it uses the
get_avg_activity_diff_based_on_mcc_clustering
function, comparing thus all groups of models that belong to different
MCC classes.
get_avg_activity_diff_mat_based_on_mcc_clustering(models.mcc,
models.stable.state, num.of.mcc.classes, include.NaN.mcc.class)a numeric vector of Matthews Correlation Coefficient (MCC)
scores, one for each model. The names attribute holds the models' names.
Can be the result of using the function calculate_models_mcc.
a matrix (nxm) with n models and m nodes. The row
names of the matrix specify the models' names (same order as in the models.mcc
parameter) whereas the column names specify the name of the network nodes
(gene, proteins, etc.). Possible values for each model-node element
are either 0 (inactive node) or 1 (active node).
numeric. A positive integer larger than 2 that signifies the number of mcc classes (groups) that we should split the models MCC values (excluding the 'NaN' values).
logical. Should the models that have NaN MCC value (e.g. TP+FP = 0, models that predicted no synergies at all) be classified together in one class - the 'NaN MCC Class' - and compared with the other model classes in the analysis? If TRUE, then the number of total MCC classes will be num.of.mcc.classes + 1.
a matrix whose rows are vectors of average node activity state differences between two groups of models where the classification was based on the models' MCC values. Rows represent the different classification group matchings, e.g. (1,2) means the models that belonged to the 1st group of MCC values vs the models that belonged to the 2nd group. The columns represent the network's node names. Values are in the [-1,1] interval.
Other average data difference functions: get_avg_activity_diff_based_on_mcc_clustering,
get_avg_activity_diff_based_on_specific_synergy_prediction,
get_avg_activity_diff_based_on_synergy_set_cmp,
get_avg_activity_diff_based_on_tp_predictions,
get_avg_activity_diff_mat_based_on_specific_synergy_prediction,
get_avg_activity_diff_mat_based_on_tp_predictions,
get_avg_link_operator_diff_mat_based_on_mcc_clustering,
get_avg_link_operator_diff_mat_based_on_specific_synergy_prediction,
get_avg_link_operator_diff_mat_based_on_tp_predictions