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
penalty factor and the number of models in each respective
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get_avg_activity_diff_based_on_tp_predictions( models.synergies.tp, models.stable.state, num.low, num.high, penalty = 0 )
an integer vector of TP values. The names
attribute holds the models' names and must be a subset of the row names
integer. The number of true positives representing the 'bad' model class.
integer. The number of true positives representing the 'good'
model class. This number has to be strictly higher than
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:
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