groupPredict: Group Predict In SNFtool: Similarity Network Fusion

Description

This function is used to predict the subtype of new patients.

Usage

 `1` ```groupPredict(train, test, groups, K=20, alpha=0.5, t=20, method=1) ```

Arguments

 `train` Training data. Has the same number of view and columns as test data. `test` Test data. Has the same number of view and columns as training data. `groups` The label for the training data. `K` Number of neighbors. `alpha` Hyperparameter used in constructing similarity network. `t` Number of iterations. `method` A indicator of which method to use to predict the label. method = 0 means to use local and global consistency; method = 1 means to use label propagation.

Value

Returns the prediction of which group the test data belongs to.

Author(s)

Dr. Anna Goldenberg, Bo Wang, Aziz Mezlini, Feyyaz Demir

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```# Provide an example of predicting the new labels with label propagation # Load views into list "dataL" and the cluster assignment into vector "label" data(dataL) data(label) # Create the training and test data n = floor(0.8*length(label)) # number of training cases trainSample = sample.int(length(label), n) train = lapply(dataL, function(x) x[trainSample, ]) # Use the first 150 samples for training test = lapply(dataL, function(x) x[-trainSample, ]) # Test the rest of the data set groups = label[trainSample] # Set the other K = 20 alpha = 0.5 t = 20 method = TRUE # Apply the prediction function to the data newLabel = groupPredict(train,test,groups,K,alpha,t,method) # Compare the prediction accuracy accuracy = sum(label[-trainSample] == newLabel[-c(1:n)])/(length(label) - n) ```

SNFtool documentation built on June 11, 2021, 9:06 a.m.