FLORAL: Fit Log-ratio lasso regression for compositional covariates

Description

Conduct log-ratio lasso regression for continuous, binary and survival outcomes.

Usage

FLORAL(
  x,
  y,
  ncov = 0,
  family = "gaussian",
  longitudinal = FALSE,
  id = NULL,
  tobs = NULL,
  failcode = NULL,
  corstr = "exchangeable",
  scalefix = FALSE,
  scalevalue = 1,
  pseudo = 1,
  length.lambda = 100,
  lambda.min.ratio = NULL,
  ncov.lambda.weight = 0,
  a = 1,
  mu = 1,
  pfilter = 0,
  maxiter = 100,
  ncv = 5,
  ncore = 1,
  intercept = FALSE,
  foldid = NULL,
  step2 = TRUE,
  progress = TRUE,
  plot = TRUE
)

Value

A list with path-specific estimates (beta), path (lambda), and others. Details can be found in README.md.

Arguments

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. (Longitudinal=TRUE and family="cox" or "finegray" will fit a time-dependent covariate model. Longitudinal=TRUE and family="gaussian" or "binomial" will fit a GEE model.)
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.
corstr: If a GEE model is specified, then corstr is the corresponding working correlation structure. Options are independence, exchangeable, AR-1 and unstructured.
scalefix: TRUE or FALSE, indicating whether the scale parameter is estimated or fixed if a GEE model is specified.
scalevalue: Specify the scale parameter if scalefix=TRUE.
pseudo: Pseudo count to be added to x before taking log-transformation. If unspecified, then the log-transformation will not be performed.
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
pfilter: A pre-specified threshold to force coefficients with absolute values less than pfilter times the maximum value of absolute coefficient as zeros in the GEE model. Default is zero, such that all coefficients will be reported.
maxiter: Number of iterations needed for the outer loop of the augmented Lagrangian algorithm.
ncv: Folds of cross-validation. Use NULL if cross-validation is not wanted.
ncore: Number of cores for parallel computing for cross-validation. Default is 1.
intercept: TRUE or FALSE, indicating whether an intercept should be estimated.
foldid: A vector of fold indicator. Default is NULL.
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 plots of model fitting.

Author

Teng Fei. Email: feit1@mskcc.org

References

Fei T, Funnell T, Waters N, Raj SS et al. Enhanced Feature Selection for Microbiome Data using FLORAL: Scalable Log-ratio Lasso Regression bioRxiv 2023.05.02.538599.

Examples

Run this code


set.seed(23420)

# Continuous outcome
dat <- simu(n=50,p=30,model="linear")
fit <- FLORAL(dat$xcount,dat$y,family="gaussian",ncv=2,progress=FALSE,step2=TRUE)

# Binary outcome
# dat <- simu(n=50,p=30,model="binomial")
# fit <- FLORAL(dat$xcount,dat$y,family="binomial",progress=FALSE,step2=TRUE)

# Survival outcome
# dat <- simu(n=50,p=30,model="cox")
# fit <- FLORAL(dat$xcount,survival::Surv(dat$t,dat$d),family="cox",progress=FALSE,step2=TRUE)

# Competing risks outcome
# dat <- simu(n=50,p=30,model="finegray")
# fit <- FLORAL(dat$xcount,survival::Surv(dat$t,dat$d,type="mstate"),failcode=1,
#               family="finegray",progress=FALSE,step2=FALSE)

# Longitudinal continuous outcome
#  dat <- simu(n=50,p=30,model="gee",geetype="gaussian",m=3,corstr="exchangeable",sdvec=rep(1,3))
#  fit <- FLORAL(x=cbind(dat$tvec, dat$xcount),y=dat$y,id=dat$id,family="gaussian",
#                ncov=1,longitudinal = TRUE,corstr = "exchangeable",lambda.min.ratio=1e-3,
#                progress=FALSE,step2=FALSE)

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