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We start by creating a neural network model (using the package) from simulated data before illustrating use of the algorithm.
The model is created from eight input variables, one response variable, 10000 observations, and an arbitrary correlation matrix that describes relationships between the explanatory variables.
If you’re a regular reader of my blog you’ll know that I’ve spent some time dabbling with neural networks.
As I explained here, I’ve used neural networks in my own research to develop inference into causation.
A method proposed by Garson 1991) identifies the relative importance of explanatory variables for specific response variables in a supervised neural network by deconstructing the model weights.
The basic idea is that the relative importance (or strength of association) of a specific explanatory variable for a specific response variable can be determined by identifying all weighted connections between the nodes of interest.
For example, input (explanatory) variables that have strong positive associations with response variables are expected to have many thick black connections between the layers.
This qualitative interpretation can be very challenging for large models, particularly if the sign of the weights switches after passing the hidden layer.
The rationale for use of an NID is to provide insight into variable importance by visually examining the weights between the layers.This categorization partially describes the role of the analyst during model development.For example, a supervised network is developed from a set of known variables and the end goal of the model is to match the predicted output values with the observed via ‘supervision’ of the training process by the analyst.An obvious difference between a neural network and a regression model is that the number of weights is excessive in the former case.This characteristic is advantageous in that it makes neural networks very flexible for modeling non-linear functions with multiple interactions, although interpretation of the effects of specific variables is of course challenging.