mvrnorm_sim_obs: Simulate Microbiome Longitudinal Data from Multivariate...
In microbiomeDASim: Microbiome Differential Abundance Simulation

Description Usage Arguments Value Examples

This function is used in the gen_norm_microbiome_obs call.

mvrnorm_sim_obs(
  id,
  time,
  group,
  ref,
  control_mean,
  sigma,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE
)

`id`	vector of length `N` that identifies repeated measurements for each unit
`time`	vector of length `N` that determines when values will be sampled for each unit
`group`	factor vector with two levels indicating the group assignment for each respective id
`ref`	character value identifying which group value to treat as control and which value to treat as treatment
`control_mean`	numeric value specifying the mean value for control subjects. all control subjects are assummed to have the same population mean value.
`sigma`	numeric value specifying the global population standard deviation for both control and treated individuals.
`rho`	value for the correlation parameter. must be between [0, 1]. see `mvrnorm_corr_gen` for details.
`corr_str`	correlation structure selected. see `mvrnorm_corr_gen` for details.
`func_form`	character value specifying the functional form for the longitduinal mean trend. see `mean_trend` for details.
`beta`	vector value specifying the parameters for the differential abundance function. see `mean_trend` for details.
`IP`	vector specifying any inflection points. depends on the type of functional form specified. see `mean_trend` for details. by default this is set to NULL.
`dis_plot`	logical argument on whether to plot the simulated data or not. by default plotting is turned off.
`plot_trend`	specifies whether to plot the true mean trend. see `mean_trend` for details.
`zero_trunc`	logical indicator designating whether simulated outcomes should be zero truncated. default is set to TRUE

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

set.seed(011520)
id_list <- lapply(seq_len(30), function(i){
obs <- sample(seq_len(10), size=1)
id_rep <- rep(i, obs)
})

time_interval <- c(0, 10)
time_list <- lapply(id_list, function(x){
time_len <- length(x)
times <- runif(time_len, min=time_interval[1], max=time_interval[2])
times <- times[order(times)]
})

group_list <- lapply(id_list, function(x){
group_len <- length(x)
tx_ind <- sample(seq_len(2), 1)
tx_group <- ifelse(tx_ind==1, "Control", "Treatment")
groups <- rep(tx_group, group_len)
})
id <- unlist(id_list)
group <- factor(unlist(group_list), levels = c("Control", "Treatment"))
time <- unlist(time_list)

# N=173 total repeated measurements
length(id)

# 15 control and 15 treated subjects
table(group[unique(id)])

# control times
ct <- unlist(lapply(unique(id[group=="Control"]), function(x){
length(id[id==x])
}))

#treatment times
tt <- unlist(lapply(unique(id[group=="Treatment"]), function(x){
length(id[id==x])
}))

# on average the treatment group has one more observation than control
mean(ct)
mean(tt)

mvrnorm_sim_obs(id=id, time=time, group=group, ref="Control", control_mean=2,
               sigma=1, rho=0.7, corr_str="compound", func_form="L_up",
               beta=1, IP=5, plot_trend=TRUE, dis_plot=TRUE, zero_trunc=TRUE)