Calculate the Matthews correlation coefficient for each model
calculate_models_mcc(
observed.model.predictions,
unobserved.model.predictions,
number.of.drug.comb.tested
)
data.frame
object with rows the models
and columns the drug combinations that were found as synergistic
(positive results). Possible values for each model-drug combination
element are either 0 (no synergy predicted), 1 (synergy was
predicted) or NA (couldn't find stable states in either the drug
combination inhibited model or in any of the two single-drug inhibited models)
data.frame
object with rows the models
and columns the drug combinations that were found as non-synergistic
(negative results). Possible values for each model-drug combination
element are either 0 (no synergy predicted), 1 (synergy was
predicted) or NA (couldn't find stable states in either the drug
combination inhibited model or in any of the two single-drug inhibited models)
numeric. The total number of drug
combinations tested, which should be equal to the sum of the columns of the
observed.model.predictions
and the unobserved.model.predictions
.
a numeric vector of MCC values, each value being in the [-1,1]
interval. The names attribute holds the models' names
if applicable (i.e. the input data.frames
have rownames).
Other confusion matrix calculation functions:
calculate_mcc()
,
calculate_models_synergies_fn()
,
calculate_models_synergies_fp()
,
calculate_models_synergies_tn()
,
calculate_models_synergies_tp()