gen_norm_microbiome: Generate Longitduinal Differential Abundance from...
In microbiomeDASim: Microbiome Differential Abundance Simulation

Description Usage Arguments Value Examples

Generate Longitduinal Differential Abundance from Multivariate Normal

gen_norm_microbiome(
  features = 10,
  diff_abun_features = 5,
  n_control,
  n_treat,
  control_mean,
  sigma,
  num_timepoints,
  t_interval,
  rho,
  corr_str = c("ar1", "compound", "ind"),
  func_form = c("linear", "quadratic", "cubic", "M", "W", "L_up", "L_down"),
  beta,
  IP = NULL,
  missing_pct,
  missing_per_subject,
  miss_val = NA,
  dis_plot = FALSE,
  plot_trend = FALSE,
  zero_trunc = TRUE,
  asynch_time = FALSE
)

`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.
`n_control`	integer value specifying the number of control individuals
`n_treat`	integer value specifying the number of treated individuals
`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.
`num_timepoints`	integer value specifying the number of timepoints per subject.
`t_interval`	numeric vector of length two specifying the interval of time from which to draw observatoins [t_1, t_q]. Assumed to be equally spaced over the interval unless `asynch_time` is set to TRUE.
`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.
`missing_pct`	numeric value that must be between [0, \1] that specifies what percentage of the individuals will have missing values.
`missing_per_subject`	integer value specifying how many observations per subject should be dropped. note that we assume that all individuals must have baseline value, meaning that the maximum number of `missing_per_subject` is equal to `num_timepoints` - 1.
`miss_val`	value used to induce missingness from the simulated data. by default missing values are assummed to be NA but other common choices include 0.
`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
`asynch_time`	logical indicator designed to randomly sample timepoints over a specified interval if set to TRUE. default is FALSE.

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

gen_norm_microbiome(features = 5, diff_abun_features = 2,
               n_control = 10, n_treat = 10, control_mean = 8, sigma = 1,
               num_timepoints = 5, t_interval=c(0, 4), rho = 0.8,
               corr_str = "compound", func_form = "linear", beta =  c(0, 1),
               missing_pct = 0.3, missing_per_subject = 2)