timma (version 1.2.1)

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.

sp

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 will be used. These two variants only differ for the case where the minimization and maximization rules are not simultaneously satisfied. For example, for a queried target set if the supersets but not the subsets can be found in the training data, the one.sided algorithm will take the prediction from the averages on the supersets sensitivities using the minimization rule. The two.sided algorithm, however, will lower the predicted sensitivity by averaging it with 0, which is the theoretical lower boundary of the sensitivities that could be obtained in the subsets.

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 sets will be weighted more in the final predictions.

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:

timma

a list contains: the predicted efficacy for target combinations, prediction error and predicted drug sensitivity

k_sel

the indexes for selected targets

Examples

## Not run: 
# data(tyner_interaction_binary)
# data(tyner_sensitivity)
# results<-sffs(tyner_interaction_binary, tyner_sensitivity[, 1], max_k = 8)
# ## End(Not run)