Simulates longitudinal data from multivariate and univariate longitudinal response model.
simLong(n = 100,
ntest = 0,
N = 5,
rho = 0.8,
model = c(1, 2),
phi = 1,
q_x = 0,
q_y = 0,
type = c("corCompSym", "corAR1", "corSymm", "iid"))Requested training sample size.
Requested test sample size.
Parameter controlling number of time points per subject.
Correlation parameter.
Requested simulation model.
Variance of measurement error.
Number of noise covariates.
Number of noise responses.
Type of correlation matrix.
An invisible list with the following components:
List containing the simulated data in the following order:
features, time, id and y.
Simulated data given as a data frame.
Index of id values identifying the training data.
Simulates longitudinal data from multivariate and univariate longitudinal response model. We consider following 2 models:
model=1: Simpler linear model consist of three
longitudinal responses, y1, y2, and y3 and
four covariates x1, x2, x3, and x4.
Response y1 is associated with x1 and x4.
Response y2 is associated with x2 and x4.
Response y3 is associated with x3 and x4.
model=2: Relatively complex model consist of
single longitudinal response and four covariates. Model includes
non-linear relationship between response and covariates and
covariate-time interaction.
Pande A., Li L., Rajeswaran J., Ehrlinger J., Kogalur U.B., Blackstone E.H., Ishwaran H. (2017). Boosted multivariate trees for longitudinal data, Machine Learning, 106(2): 277--305.