Description Usage Arguments Value Author(s) Examples

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

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

`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. |

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

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

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)
``` |

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