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).

## Examples

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