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 time-invariant 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 time-invariant 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)])
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