Implements a naive classifier using soft discretization.
sdnLearn(data, cls, clslevs = NULL, ncats = 3, nodeCats = NULL, quant="uniform", std=TRUE)
sdnPredict(model, data, std=TRUE)
sdnEvaluate(train, test, ncats = 3, nodeCats = NULL, std=FALSE)a numerical matrix in row-genes format
a numerical matrix in row-genes format
a numerical matrix in row-genes format
a factor or integer, the sample labels
an optional vector of labels, should include training data's labels
an integer, the number of categories per node
a list, custom node categories
quantization method
a logical, should the data rows be standardized
a list of components for the training model
sdnPredict returns the log-ratio of the class conditional probabilities for each test observation. sdnEvaluate handles 2-class problems and returns the prediction accuracy and predicted classes.
The model contains a vector of gene names geneset, a vector of sample labels clslevs, class catNetworks: nets, a list of node categories nodeCats and a training quantization model quant.