For the Free Space model, (N) is 2, and for the Shadowing model, (N) ranges from 1.5 to 6.
In the model N is the set of all nodes and ( N_{0} = Nbackslash left{ 1 right} )c is the set of all customers.
In the model, N is determined by the number of parking spaces in the certain zone and the accuracy of solution.
where k is the number of parameters in the model, N is the number of recording rate observations in the time series and lnL is the maximized value of the logarithmic likelihood function (equations 5 and 12).
First, in saying that our new model, N, is a submodel of our original model, M, we mean the domain of N is a subset of the domain of M and that the two models agree on the interpretation of the constants, predicates, relations and functions in our language e.g., for any n1, …, nm in the domain of N and any R in our language, N ⊨ R[n1, …, nm ] ⇔ M ⊨ R[n1, …, nm ].
In our model, n is not a fixed number.
In the overall analysis, we first used stepwise selection to find a model that gives the smallest AIC (Akaike information criterion) = 2* K+ n*ln (SSE/n), where K is the number of parameters in the model; n is the number of observations; and SSE is the residual sum of squares [ 30].
It should be noted that the definition of the second order AIC is: AICc : = - 2 Ln ([Residual Sum of Squares]/ n) +2 Kn/ n-K-1 ), where Ln is a natural log, Kn/ n-K-1number of estimated parameters in the model, n is the sample size [ 21].
The Mallows'Cp statistic was calculated according to the following mathematical model: Cp = SS e /RMS – (n - 2p), where SS e is the residual sum of squares, RMS is the residual mean square of the model, n is the number of observations, and p is the number of independent variables [ 16].
TP is the number of actives retrieved by the model, n is the number of actives and inactive compounds retrieved by the model, A is the number of actives in the test set, and N is the number of all compounds in the test set.
