mvrnorm_corr_gen {microbiomeDASim} | R Documentation

## Generate Multivariate Random Normal Longitudinal Data

### Description

For this methodology we assume that we draw a set of `n` independent each with q_{i} observations.

### Usage

    mvrnorm_corr_gen(
      n,
      obs,
      t,
      mu,
      sigma,
      rho,
      corr_str = c("ar1", "compound", "ind"),
      zero_trunc = TRUE
    )

### Arguments

`n` | integer scalar representing the total number of individuals
`obs` | vector of length `n` specifying the number of observations per indivdiual.
`t` | vector corresponding to the timepoints for each individual.
`mu` | vector specifying the mean value for individuals.
`sigma` | scalar specifying the standard deviation for all observations.
`rho` | numeric scalar value between [0, 1] specifying the amount of correlation between. assumes that the correlation is consistent for all subjects.
`corr_str` | character value specifying the correlation structure. Currently available methods are \'ar1\', \'compound\', and \'ind\' which correspond to first-order autoregressive, compound or equicorrelation, and independence respecitvely.
`zero_trunc` | logical value to specifying whether the generating distribution should come from a multivariate zero truncated normal or an untruncated multivariate normal. by default we assume that zero truncation occurs since this is assummed in our microbiome setting.

### Value

This function returns a list with the following objects:

`df` - data.frame object with complete outcome `Y`, subject ID, time, group, and outcome with missing data

`Y` - vector of complete outcome

`Mu` - vector of complete mean specifications used during simulation

`Sigma` - block diagonal symmetric matrix of complete data used during simulation

`N` - total number of observations

### Examples

    size <- 15
    reps <- 4
    N <- size*reps
    mvrnorm_corr_gen(n=size, obs=rep(reps, size), t=rep(seq_len(4), size),
    mu=rep(1, N), sigma=2, rho=0.9, corr_str="ar1")

[Package microbiomeDASim version 1.1.7 Index]