Nothing

```
# Goal: Compute the risk score of a given patient sample
# Arguments: newdata.vec = vector of test data (1 observation)
# postprob.vec = postprob from bic.surv
# mle.mat = mle matrix from bic.surv (models = rows, variables = columns)
# Output: A patient sample risk score
predictBicSurv <- function(newdata.vec, postprob.vec, mle.mat) {
# First compute the risk score for each of the models
# Risk score for each model = sum(coefficient in model k * corresponding expression level in newdata.vec)
risk.score.vec <- apply (mle.mat, 1, function(x) sum(x * newdata.vec))
# Patient sample risk score under BMA = sum (postprob for model k * risk score for model k)
# over all the selected models
retprob <- sum (postprob.vec * risk.score.vec)
retprob
}
```

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