sffs: SFFS switch function

Description

A function to choose which sffs function to run. There are six sffs algorithms for choosing.

Usage

sffs(profile_data, sens, sp = 1, max_k = 2, loo = TRUE, class = 2,
  averaging = "one.sided", weighted = FALSE, verbosity = FALSE)

Arguments

profile_data

drug-target interaction data which is a matrix with drugs as row indexes and targets as column indexes.

sens

a drug sensitivity vector.

an integer to specify the starting point for the sffs search algorithm. The number cannot exceed the total number of targets in the drug-target interaction data. By default, the starting point is the first target, namely, sp = 1.

max_k

an integer to sepcify the maximum number of targets that can be selected by the sffs algorithm. By default, max_k = 2. In practice it should not be over than 10 as the number of target combinations will increase exponentially.

loo

a logical value indicating whether to use the leave-one-out cross-validation in the model selection process. By default, loo = TRUE.

class

an integer to specify the number of classes in the drug-target interaction data. For a binary drug-target interaction data, class = 2. For a multi-class drug-target interaction data, class should be the number of classes.

averaging

a parameter to specify which one of the averaging algorithms will be applied in the model construction. By default, averaging = "one.sided", which is the original model construction algorithm. When averaging = "two.sided", a modified averaging algorithm

weighted

a parameter to specify if the similarity between the queried target set and its subsets/supersets is considered as a weight factor in the averaging. When weighted =T RUE, the similarity is considered as a weight factor such that those related target set

verbosity

a boolean value to decide if the information should be displayed. If it is TRUE, the information will be displayed while the model is running. Otherwise, the information will not be displayed. By default, it is FALSE.

Value

A list containing the following components:
timmaa list contains: the predicted efficacy for target combinations, prediction error and predicted drug sensitivity
k_selthe indexes for selected targets

Examples

Run this code

data(tyner_interaction_binary)
data(tyner_sensitivity)
results<-sffs(tyner_interaction_binary, tyner_sensitivity[, 1], max_k = 8)

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