Description Usage Arguments Value Details See Also

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.

1 2 3 4 5 6 7 | ```
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 |

`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()`

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