Neural Networks with a Principal Component Step
Run PCA on a dataset, then use it in a neural network model
## S3 method for class 'default': pcaNNet(x, y, thresh = 0.99, ...) ## S3 method for class 'formula': pcaNNet(formula, data, weights, ..., thresh = .99, subset, na.action, contrasts = NULL)
## S3 method for class 'pcaNNet': predict(object, newdata, type = c("raw", "class"), ...)
The function first will run principal component analysis on the data. The cumulative percentage of variance is computed for each principal component. The function uses the
thresh argument to determine how many components must be retained to capture this amount of variance in the predictors.
The principal components are then used in a neural network model.
When predicting samples, the new data are similarly transformed using the information from the PCA analysis on the training data and then predicted. Because the variance of each predictor is used in the PCA analysis, the code does a quick check to make sure that each predictor has at least two distinct values. If a predictor has one unique value, it is removed prior to the analysis.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
data(BloodBrain) modelFit <- pcaNNet(bbbDescr[, 1:10], logBBB, size = 5, linout = TRUE, trace = FALSE) modelFit predict(modelFit, bbbDescr[, 1:10])