simu: 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
)

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).

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.

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)
dat <- simu(n=50,p=30,model="linear")

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