mvrnorm_corr_gen {microbiomeDASim} | R Documentation |
For this methodology we assume that we draw a set of n
independent each
with q_{i} observations.
mvrnorm_corr_gen( n, obs, t, mu, sigma, rho, corr_str = c("ar1", "compound", "ind"), zero_trunc = TRUE )
n |
integer scalar representing the total number of individuals |
obs |
vector of length |
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. |
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
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")