expres {sdnet} | R Documentation |
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)
data |
a numerical |
train |
a numerical |
test |
a numerical |
cls |
a |
clslevs |
an optional vector of labels, should include training data's labels |
ncats |
an |
nodeCats |
a |
quant |
quantization method |
std |
a |
model |
a |
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
.
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.
N. Balov