PartitionsSelection {GRridge} | R Documentation

## Co-data selection in a Group-regularized ridge regression model

### Description

This function implements a procedure to optimize the use of co-data in a GRridge model. Although there is no harm to include as much as co-data in a GRridge model, ordering and selecting co-data can optimized the performance of a GRridge model. This procedure is similar with forward feature selection in a classical regression model

### Usage

    PartitionsSelection(highdimdata, response, partitions,
                        monotoneFunctions, optl=NULL, innfold=NULL)

### Arguments

`highdimdata` | Matrix or numerical data frame. Contains the primary data of the study. Columns are samples, rows are features.
`response` | Factor, numeric, binary or survival. Response values. The number of response values should equal `ncol(highdimdata)`.
`partitions` | List of lists. Each list component contains a partition of the variables, which is again a list.
`monotoneFunctions` | Vector. Monotone functions from each partition. This argument is necesarily specified. If the jth component of monotone equals TRUE, then the group-penalties are forced to be monotone. If `monotone=NULL` monotony is not imposed for any partition.
`optl` | Global penalty parameter lambda
`innfold` | Integer. The fold for cross-validating the global regularization parameter lambda and for computing cross-validated likelihoods. Defaults LOOCV.

### Value

A list containing (i) the indeces of the selected and ordered partitions and (ii) the optimum lambda penalty from the ridge regression.

### Author(s)

Putri W. Novianti

### See Also

Creating partitions: `CreatePartition`. Group-regularized ridge regression: `grridge`.

### Examples

    # # Load data objects
    # data(dataWurdinger)
    #
    # # Transform the data set to the square root scale
    # dataSqrtWurdinger <- sqrt(datWurdinger_BC)
    #
    # #Standardize the transformed data
    # datStdWurdinger <- t(apply(dataSqrtWurdinger,1,function(x){(x-mean(x))/sd(x)}))
    #
    # # A list of gene names in the primary RNAseq data
    # genesWurdinger <- as.character(annotationWurdinger$geneSymbol)
    #
    # # co-data 1: a partition based on immunologic signature pathway
    # # The initial gene sets (groups) are merged into five new groups, using the "mergeGroups" function
    # immunPathway <- coDataWurdinger$immunologicPathway
    # parImmun <- immunPathway$newClust

    # # co-data 2: a partition based on a list of platelets expressed genes
    # plateletsExprGenes <- coDataWurdinger$plateletgenes
    # # The genes are grouped into either "NormalGenes" or "Non-overlapGenes"
    # is <- intersect(plateletsExprGenes,genesWurdinger)
    # im <- match(is, genesWurdinger)
    # plateletsGenes <- replicate(length(genesWurdinger),"Non-overlapGenes")
    # plateletsGenes[im] <- "NormalGenes"
    # plateletsGenes <- as.factor(plateletsGenes)
    # parPlateletGenes <- CreatePartition(plateletsGenes)
    #
    # # co-data 3: a partition based on chromosomal location.
    # # A list of chromosomal location based on {\tt biomaRt} data bases.
    # ChromosomeWur0 <- as.vector(annotationWurdinger$chromosome)
    # ChromosomeWur <- ChromosomeWur0
    # idC <- which(ChromosomeWur0=="MT" | ChromosomeWur0=="notBiomart" |
    #                ChromosomeWur0=="Un")
    # ChromosomeWur[idC] <- "notMapped"
    # table(ChromosomeWur)
    # parChromosome <- CreatePartition(as.factor(ChromosomeWur))
    #
    # partitionsWurdinger <- list(immunPathway=parImmun,
    #                             plateletsGenes=parPlateletGenes,
    #                             chromosome=parChromosome)
    #
    # #A list of monotone functions from the corresponding partitions
    # monotoneWurdinger <- c(FALSE,FALSE,FALSE)
    #
    # # Start ordering and selecting partitions
    # optPartitions <- PartitionsSelection(datStdWurdinger, respWurdinger,
    #                                      partitions=partitionsWurdinger,
    #                                      monotoneFunctions=monotoneWurdinger)

[Package GRridge version 1.16.0 Index]