Variable Importance Measure for A FSM Model

var_imp calculates the importance of the covariates of the model.

var_imp(state_mat, action_vec, data, outcome, period, measure)

Arguments

state_mat	Numeric matrix with rows as states and columns as predictors.
action_vec	Numeric vector indicating what action to take for each state.
data	Data frame that has "period" and "outcome" columns and rest of cols are predictors, ranging from one to three predictors. All of the (3-5 columns) should be named.
outcome	Numeric vector same length as the number of rows as data.
period	Numeric vector same length as the number of rows as data.
measure	Optional length one character vector that is either: "accuracy", "sens", "spec", or "ppv". This specifies what measure of predictive performance to use for training and evaluating the model. The default measure is "accuracy". However, accuracy can be a problematic measure when the classes are imbalanced in the samples, i.e. if a class the model is trying to predict is very rare. Alternatives to accuracy are available that illuminate different aspects of predictive power. Sensitivity answers the question, `` given that a result is truly an event, what is the probability that the model will predict an event?'' Specificity answers the question, ``given that a result is truly not an event, what is the probability that the model will predict a negative?'' Positive predictive value answers, ``what is the percent of predicted positives that are actually positive?''

Value

Numeric vector the same length as the number of columns of the provided state matrix (the number of predictors in the model) with relative importance scores for each predictor.

Details

Takes the state matrix and action vector from an already evolved model and the fitness function and data used to evolve the model (or this could be test data), flips the values of each of the elements in the state matrix and measures the change in fitness (prediction of data) relative to the original model. Then these changes are summed across columns to provide the importance of each of the columns of the state matrix.