Simulate data following log-ratio model

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,
  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", or "finegray".
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.
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

Teng Fei. Email: feit1@mskcc.org

Examples


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