simu: Simulate data following log-ratio model

In FLORAL: Fit Log-Ratio Lasso Regression for Compositional Data

Simulate data following log-ratio model

Description

Simulate a dataset from log-ratio model.

Usage

simu(
  n = 100,
  p = 200,
  model = "linear",
  weak = 4,
  strong = 6,
  weaksize = 0.125,
  strongsize = 0.25,
  pct.sparsity = 0.5,
  rho = 0,
  timedep_slope = NULL,
  timedep_cor = NULL,
  geetype = "gaussian",
  m = 4,
  corstr = "exchangeable",
  sdvec = NULL,
  rhogee = 0.8,
  geeslope = 2.5,
  longitudinal_stability = TRUE,
  ncov = 0,
  betacov = 0,
  intercept = FALSE
)

Arguments

n	An integer of sample size
p	An integer of number of features (taxa).
model	Type of models associated with outcome variable, can be "linear", "binomial", "cox", "finegray", "gee" (scalar outcome with time-dependent features), or "timedep" (survival endpoint with time-dependent features).
weak	Number of features with weak effect size.
strong	Number of features with strong effect size.
weaksize	Actual effect size for weak effect size. Must be positive.
strongsize	Actual effect size for strong effect size. Must be positive.
pct.sparsity	Percentage of zero counts for each sample.
rho	Parameter controlling the correlated structure between taxa. Ranges between 0 and 1.
timedep_slope	If model is "timedep", this parameter specifies the slope for the feature trajectories. Please refer to the Simulation section of the manuscript for more details.
timedep_cor	If model is "timedep", this parameter specifies the sample-wise correlations between longitudinal features. Please refer to the Simulation section of the manuscript for more details.
geetype	If model is "gee", geetype is the type of GEE outcomes. Now support "gaussian" and "binomial".
m	If model is "gee", m is the number of repeated measurements per subject.
corstr	If model is "gee", corstr is the working correlation structure. Now support "independence", "exchangeable", and "AR-1".
sdvec	If model is "gee" and geetype is "gaussian", sdvec is the vector of standard deviations of each outcome variable.
rhogee	If model is "gee", rhogee is the correlation parameter between longitudinal outcomes under the selected working correlation structure.
geeslope	If model is "gee", geeslope is the linear time effect.
longitudinal_stability	If model is "timedep", this is a binary indicator which determines whether the trajectories are more stable (TRUE) or more volatile (FALSE).
ncov	Number of covariates that are not compositional features.
betacov	Coefficients corresponding to the covariates that are not compositional features.
intercept	Boolean. If TRUE, then a random intercept will be generated in the model. Only works for linear or binomial models.

Value

A list with simulated count matrix xcount, log1p-transformed count matrix x, outcome (continuous y, continuous centered y0, binary y, or survival t, d), true coefficient vector beta, list of non-zero features idx, value of intercept intercept (if applicable).

Author(s)

Teng Fei. Email: [e-mail]

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

set.seed(23420)
dat <- simu(n=50,p=30,model="linear")

FLORAL documentation built on Aug. 8, 2025, 7:51 p.m.