R/diff.R
get_avg_activity_diff_based_on_tp_predictions.Rd
This function splits the models to 'good' and 'bad' based on the number of true
positive predictions: num.high TPs (good) vs num.low TPs (bad).
Then, for each network node, it finds the node's average activity in each of
the two classes (a value in the [0,1] interval) and then subtracts the
'bad' average activity value from the good' one, taking into account the
given penalty
factor and the number of models in each respective
model group.
get_avg_activity_diff_based_on_tp_predictions( models.synergies.tp, models.stable.state, num.low, num.high, penalty = 0 )
models.synergies.tp | an integer vector of TP values. The names
attribute holds the models' names and must be a subset of the row names
of the |
---|---|
models.stable.state | a |
num.low | integer. The number of true positives representing the 'bad' model class. |
num.high | integer. The number of true positives representing the 'good'
model class. This number has to be strictly higher than |
penalty | value between 0 and 1 (inclusive). A value of 0 means no penalty and a value of 1 is the strickest possible penalty. Default value is 0. This penalty is used as part of a weighted term to the difference in a value of interest (e.g. activity or link operator difference) between two group of models, to account for the difference in the number of models from each respective model group. |
a numeric vector with values in the [-1,1] interval (minimum and maximum possible average difference) and with the names attribute representing the name of the nodes.
So, if a node has a value close to -1 it means that on average, this node is more inhibited in the 'good' models compared to the 'bad' ones while a value closer to 1 means that the node is more activated in the 'good' models. A value closer to 0 indicates that the activity of that node is not so much different between the 'good' and 'bad' models and so it won't not be a node of interest when searching for indicators of better performance (higher number of true positives) in the good models.
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_mat_based_on_mcc_clustering()
,
get_avg_activity_diff_mat_based_on_specific_synergy_prediction()
,
get_avg_activity_diff_mat_based_on_tp_predictions()
,
get_avg_link_operator_diff_based_on_synergy_set_cmp()
,
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()