simLong {BoostMLR}  R Documentation 
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"))
n 
Requested training sample size. 
ntest 
Requested test sample size. 
N 
Parameter controlling number of time points per subject. 
rho 
Correlation parameter. 
model 
Requested simulation model. 
phi 
Variance of measurement error. 
q_x 
Number of noise covariates. 
q_y 
Number of noise responses. 
type 
Type of correlation matrix. 
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
nonlinear relationship between response and covariates and
covariatetime interaction.
An invisible list with the following components:
dtaL 
List containing the simulated data in the following order:

dta 
Simulated data given as a data frame. 
trn 
Index of 
Amol Pande and Hemant Ishwaran
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.