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, mu, sigma, rho, corr_str = c("ar1", "compound", "ind"), zero_trunc = TRUE)
n |
integer scalar representing the total number of individuals |
obs |
integer or vector specifying the number of observations per
indivdiual. If an integer then all indivdiuals are assummed to have the same
number of obsevations. If a vector, then the vector must have length equal
to |
mu |
integer or vector specifying the mean value for individuals.
If an integer then all indivdiuals are assummed to have the same mean.
If a vector, then the vector must have length equal to |
sigma |
numeric scalar or vector specifying the standard deviation for 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
mvrnorm_corr_gen(n=15, obs=4, mu=20, sigma=2, rho=0.9, corr_str="ar1")