Description Usage Arguments Value Examples
Simulate sparse observation from longitudinal compositional data X.
1 2 3 4 5 6 7 8  Model(n, p, m = 0, intercept = TRUE, interval = c(0, 1), ns = 100,
obs_spar = 0.6, discrete = FALSE, SNR = 1, sigma = 2, rho_X,
Corr_X = c("CorrAR", "CorrCS"), rho_W, Corr_W = c("CorrAR", "CorrCS"),
Nzero_group = 4, range_beta = c(0.5, 1), range_beta_c = 1, beta_C,
theta.add = c(1, 2, 5, 6), gamma = 0.5, basis_W = c("bs", "OBasis",
"fourier"), df_W = 5, degree_W = 3, basis_beta = c("bs", "OBasis",
"fourier"), df_beta = 5, degree_beta = 3, insert = c("FALSE", "basis"),
method = c("trapezoidal", "step"))

n 
sample size 
p 
size of compositional predictors falling in S^p 
m 
size of timeinvariant predictors. First 
intercept 
including intercept or not to generate response variable, default is TRUE 
interval 
a length 2 vector indicating time domain. 
ns 

obs_spar 
a percentage used to get sparse ovbservation. Each time point probability 
discrete 
is logical, specifying whether X is generated at different time points.
If 
SNR 
signal to noise ratio. 
sigma, rho_X, Corr_X, rho_W, Corr_W 
linear combination scaler 
Nzero_group 
a even scaler. First 
range_beta 
a sorted vector of length 2 used to generate coefficient matrix 
range_beta_c 
value of coefficients for beta0 and beta_c (coefficients for timeinvariant predictors) 
beta_C 
vectorized coefficients of coefficient matrix for compositional predictors. Could be missing. 
theta.add 
logical or numerical. If numerical, indicating which ones of compositional predictors of high level mean.
If logical, 
gamma 
high level mean groups adding log(p * gamma) before convertint into compositional data, otherwise 0. 
basis_W, df_W, degree_W 
longitudinal compositional data is generated from linear combination of basis Ψ(t), take exponetial and change into compositional data.

basis_beta, df_beta, degree_beta 
coefficinet curve is generate by linear combination of basis Φ(t).

insert 
way to interpolation.
Default is 
method 
method used to approximate integral.
Default is 
a list
data 
a list, a vector 
beta 
a length 
basis.info 
matrix for basis for beta, combining the first column as time sequence. 
data.raw 
a list, 
parameter 
a list of parameters. 
1 2 3 4 5 6 7 8  df_beta = 5
p = 20
Data < Model(n = 50, p = p, m = 2, intercept = TRUE, ns = 50, SNR = 1,
rho_X = 0.1, rho_W = 0.2, df_W = 10, df_beta = df_beta, obs_spar = 0.5)
names(Data$data)
Data.test < Model(n = 50, p = p, m = 2, intercept = TRUE, ns = 50, SNR = 1,
rho_X = 0.1, rho_W = 0.2, df_W = 10, df_beta = df_beta, obs_spar = 0.5,
beta_C = Data$beta[1:(p*df_beta)])

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.