Description Usage Arguments Details Value Author(s) References
Simulates longitudinal data from multivariate and univariate longitudinal response model.
1 2 3 4 5 6 7 8 9 |
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
non-linear relationship between response and covariates and
covariate-time 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.
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