Description Usage Arguments Details Value Examples

View source: R/Gen.Alg.Consensus.R

GA-Mantel is a fully multivariate method that uses a genetic algorithm to search over possible taxa subsets using the Mantel correlation as the scoring measure for assessing the quality of any given taxa subset.

1 2 | ```
Gen.Alg.Consensus(data, covars, consensus = .5, numRuns = 10,
parallel = FALSE, cores = 3, ...)
``` |

`data` |
A matrix of taxonomic counts(columns) for each sample(rows). |

`covars` |
A matrix of covariates(columns) for each sample(rows). |

`consensus` |
The required fraction (0, 1] of solutions containing an edge in order to keep it. |

`numRuns` |
Number of runs to do. In practice the number of runs needed varies based on data set size and the GA parameters set. |

`parallel` |
When this is 'TRUE' it allows for parallel calculation of the bootstraps. Requires the package |

`cores` |
The number of parallel processes to run if parallel is 'TRUE'. |

`...` |
Other arguments for the GA function see Gen.Alg |

Use a GA consensus approach to find taxa that separate subjects based on group membership or set of covariates if you cannot run the GA long enough to get a final solution.

A list containing

`solutions` |
The best solution from each run. |

`consSol` |
The consensus solution. |

`selectedIndex` |
The selected taxa by column number. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## Not run:
data(saliva)
data(throat)
### Combine the data into a single data frame
group.data <- list(saliva, throat)
group.data <- formatDataSets(group.data)
data <- do.call("rbind", group.data)
### Normalize the data by subject
dataNorm <- t(apply(data, 1, function(x){x/sum(x)}))
### Set covars to just be group membership
memb <- c(rep(0, nrow(saliva)), rep(1, nrow(throat)))
covars <- matrix(memb, length(memb), 1)
### We use low numbers for speed. The exact numbers to use depend
### on the data being used, but generally the higher iters and popSize
### the longer it will take to run. earlyStop is then used to stop the
### run early if the results aren't improving.
iters <- 500
popSize <- 200
earlyStop <- 250
numRuns <- 3
gaRes <- Gen.Alg.Consensus(dataNorm, covars, .5, numRuns, FALSE, 3,
iters, popSize, earlyStop)
## End(Not run)
``` |

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