prediction
(Package: neuralnet) :
Summarizes the output of the neural network, the data and the fitted values of glm objects (if available)
prediction, a method for objects of class nn, typically produced by neuralnet. In a first step, the dataframe will be amended by a mean response, the mean of all responses corresponding to the same covariate-vector. The calculated data.error is the error function between the original response and the new mean response. In a second step, all duplicate rows will be erased to get a quick overview of the data. To obtain an overview of the results of the neural network and the glm objects, the covariate matrix will be bound to the output of the neural network and the fitted values of the glm object(if available) and will be reduced by all duplicate rows.
neuralnet is used to train neural networks using backpropagation, resilient backpropagation (RPROP) with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version (GRPROP) by Anastasiadis et al. (2005). The function allows flexible settings through custom-choice of error and activation function. Furthermore the calculation of generalized weights (Intrator O. and Intrator N., 1993) is implemented.
Training of neural networks using the backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.
gwplot
(Package: neuralnet) :
Plot method for generalized weights
gwplot, a method for objects of class nn, typically produced by neuralnet. Plots the generalized weights (Intrator and Intrator, 1993) for one specific covariate and one response variable.
confidence.interval, a method for objects of class nn, typically produced by neuralnet. Calculates confidence intervals of the weights (White, 1989) and the network information criteria NIC (Murata et al. 1994). All confidence intervals are calculated under the assumption of a local identification of the given neural network. If this assumption is violated, the results will not be reasonable. Please make also sure that the chosen error function equals the negative log-likelihood function, otherwise the results are not meaningfull, too.
compute
(Package: neuralnet) :
Computation of a given neural network for given covariate vectors
compute, a method for objects of class nn, typically produced by neuralnet. Computes the outputs of all neurons for specific arbitrary covariate vectors given a trained neural network. Please make sure that the order of the covariates is the same in the new matrix or dataframe as in the original neural network.
providing 
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