Estimate Optimal Number of States of a Finite-state Machine Model

Source: R/evolve_model_cv.R

evolve_model_cv calls evolve_model with varied numbers of states and compares their performance with cross-validation.

evolve_model_cv(data, measure, k, actions, max_states, seed,
        popSize, pcrossover, pmutation, maxiter, run,  parallel,
        verbose, ntimes)

Arguments

data

A data.frame that has columns named "period" and "outcome" (period is the time period that the outcome action was taken), and one to three additional columns, containing predictors. All of the 3-5 columns should be named. The period and outcome columns should be integer vectors and the columns with the predictor variable data should be logical vectors (TRUE, FALSE). If the predictor variable data is not logical, it will coerced to logical with
base::as.logical().
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?''
k

Optional numeric vector length one only relevant if cv==TRUE, specifying number of folds for cross-validation.
actions

Optional numeric vector with the number of actions. If not provided, then actions will be set as the number of unique values in the outcome vector.
max_states

Optional numeric vector length one only relevant if cv==TRUE. It specifies how up to how many states that cross-validation should search through. If not provided, will be set to states + 1.
seed

Optional numeric vector length one.
popSize

Optional numeric vector length one specifying the size of the GA population. A larger number will increase the probability of finding a very good solution but will also increase the computation time. This is passed to the GA::ga() function of the GA package.
pcrossover

Optional numeric vector length one specifying probability of crossover for GA. This is passed to the GA::ga() function of the GA package.
pmutation

Optional numeric vector length one specifying probability of mutation for GA. This is passed to the GA::ga() function of the GA package.
maxiter

Optional numeric vector length one specifying max number of iterations for stopping the GA evolution. A larger number will increase the probability of finding a very good solution but will also increase the computation time. This is passed to the GA::ga() function of the GA package. maxiter is scaled by how many parameters are in the model:
maxiter <- maxiter + ((maxiter*(nBits^2)) / maxiter).
run

Optional numeric vector length one specifying max number of consecutive iterations without improvement in best fitness score for stopping the GA evolution. A larger number will increase the probability of finding a very good solution but will also increase the computation time. This is passed to the GA::ga() function of the GA package.
parallel

Optional logical vector length one. For running the GA evolution in parallel. Depending on the number of cores registered and the memory on your machine, this can make the process much faster, but only works for Unix-based machines that can fork the processes.
verbose

Optional logical vector length one specifying whether helpful messages should be displayed on the user's console or not.
ntimes

Optional integer vector length one specifying the number of times to estimate model. Default is 1 time.

Value

Returns the number of states that maximizes the measure, e.g. accuracy.

References

Luca Scrucca (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. URL https://www.jstatsoft.org/v53/i04/.

Hastie, T., R. Tibshirani, and J. Friedman. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd ed. New York, NY: Springer.