R: Summarizing selected compositional features over multiple...

mcv.FLORAL {FLORAL}

R Documentation

Summarizing selected compositional features over multiple cross validations

Description

Summarizing FLORAL outputs from multiple random k-fold cross validations

Usage

mcv.FLORAL(
  mcv = 10,
  ncore = 1,
  seed = NULL,
  x,
  y,
  ncov = 0,
  family = "gaussian",
  longitudinal = FALSE,
  id = NULL,
  tobs = NULL,
  failcode = NULL,
  length.lambda = 100,
  lambda.min.ratio = NULL,
  ncov.lambda.weight = 0,
  a = 1,
  mu = 1,
  ncv = 5,
  intercept = FALSE,
  step2 = TRUE,
  progress = TRUE,
  plot = TRUE
)

Arguments

`mcv`	Number of random 'ncv'-fold cross-validation to be performed.
`ncore`	Number of cores used for parallel computation. Default is to use only 1 core.
`seed`	A random seed for reproducibility of the results. By default the seed is the numeric form of `Sys.Date()`.
`x`	Feature matrix, where rows specify subjects and columns specify features. The first `ncov` columns should be patient characteristics and the rest columns are microbiome absolute counts corresponding to various taxa. If `x` contains longitudinal data, the rows must be sorted in the same order of the subject IDs used in `y`.
`y`	Outcome. For a continuous or binary outcome, `y` is a vector. For survival outcome, `y` is a `Surv` object.
`ncov`	An integer indicating the number of first `ncov` columns in `x` that will not be subject to the zero-sum constraint.
`family`	Available options are `gaussian`, `binomial`, `cox`, `finegray`.
`longitudinal`	`TRUE` or `FALSE`, indicating whether longitudinal data matrix is specified for input `x`. (Still under development. Please use with caution)
`id`	If `longitudinal` is `TRUE`, `id` specifies subject IDs corresponding to the rows of input `x`.
`tobs`	If `longitudinal` is `TRUE`, `tobs` specifies time points corresponding to the rows of input `x`.
`failcode`	If `family = finegray`, `failcode` specifies the failure type of interest. This must be a positive integer.
`length.lambda`	Number of penalty parameters used in the path
`lambda.min.ratio`	Ratio between the minimum and maximum choice of lambda. Default is `NULL`, where the ratio is chosen as 1e-2.
`ncov.lambda.weight`	Weight of the penalty lambda applied to the first `ncov` covariates. Default is 0 such that the first `ncov` covariates are not penalized.
`a`	A scalar between 0 and 1: `a` is the weight for lasso penalty while `1-a` is the weight for ridge penalty.
`mu`	Value of penalty for the augmented Lagrangian
`ncv`	Folds of cross-validation. Use `NULL` if cross-validation is not wanted.
`intercept`	`TRUE` or `FALSE`, indicating whether an intercept should be estimated.
`step2`	`TRUE` or `FALSE`, indicating whether a second-stage feature selection for specific ratios should be performed for the features selected by the main lasso algorithm. Will only be performed if cross validation is enabled.
`progress`	`TRUE` or `FALSE`, indicating whether printing progress bar as the algorithm runs.
`plot`	`TRUE` or `FALSE`, indicating whether returning summary plots of selection probability for taxa features.

Value

A list with relative frequencies of a certain feature being selected over mcv ncv-fold cross-validations.

Author(s)

Teng Fei. Email: feit1@mskcc.org

References

Fei T, Funnell T, Waters N, Raj SS et al. Scalable Log-ratio Lasso Regression Enhances Microbiome Feature Selection for Predictive Models. bioRxiv 2023.05.02.538599.

Examples


set.seed(23420)

dat <- simu(n=50,p=30,model="linear")
fit <- mcv.FLORAL(mcv=2,ncore=1,x=dat$xcount,y=dat$y,ncv=2,progress=FALSE,step2=TRUE,plot=FALSE)

[Package FLORAL version 0.2.0 Index]