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Simulate data following log-ratio model

Source: R/simu.R
simu.Rd

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,
  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", 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.

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).

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.

Author

Teng Fei. Email: feit1@mskcc.org

Examples


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