gen_norm_microbiome_obs {microbiomeDASim} | R Documentation

## Generate Longitduinal Differential Abundance from Multivariate Normal with Observed Data

### Description

Generate Longitduinal Differential Abundance from Multivariate Normal with Observed Data

### Usage

    gen_norm_microbiome_obs(
      features = 10,
      diff_abun_features = 5,
      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
    )

### Arguments

`features` | numeric value specifying the number of features/microbes to simulate. Default is 10.
`diff_abun_features` | numeric value specifying the number of differentially abundant features. Default is 5.
`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

### Value

This function returns a list with the following objects

`Y` The full simulated feature sample matrix where each row represent a feature and each column a sample. Note that the differential and non-differential bugs are marked by row.names

### Examples

    set.seed(011520)
    id_list <- lapply(seq_len(60), function(i){
    obs <- sample(5:10, 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)

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

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

    gen_norm_microbiome_obs(features=4, diff_abun_features=2,
    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, zero_trunc=TRUE)

[Package microbiomeDASim version 1.1.7 Index]