simLong | R Documentation |
Simulates longitudinal data with continuous or binary response from models with increasing complexity of covariate-time interactions.
simLong(n, ntest = 0, N = 5, rho = 0.8, type = c("corCompSym", "corAR1", "corSymm", "iid"), model = c(0, 1, 2, 3), family = c("Continuous","Binary"), phi = 1, q = 0, ...)
n |
Requested training sample size. |
ntest |
Requested test sample size. |
N |
Parameter controlling number of time points per subject. |
rho |
Correlation parameter. |
type |
Type of correlation matrix. |
model |
Requested simulation model. |
family |
Family of response |
phi |
Variance of measurement error. |
q |
Number of zero-signal variables (i.e., variables unrelated to y). |
... |
Further arguments passed to or from other methods. |
Simulates longitudinal data with 3 main effects and (possibly) a covariate-time
interaction. Complexity of the model is specified using the option model
:
model=0
: Linear with no covariate-time interactions.
model=1
: Linear covariate-time interaction.
model=2
: Quadratic time-quadratic covariate
interaction.
model=3
: Quadratic time-quadratic two-way
covariate interaction.
For details see Pande et al. (2017).
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 |
f.true |
Formula of the simulation model. |
Hemant Ishwaran, Amol Pande and Udaya B. Kogalur
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.
## Not run: ##------------------------------------------------------------ ## Response is continuous ##---------------------------------------------------------------------------- ## set the number of boosting iterations M <- 500 ## simulation 0: only main effects (x1, x3, x4) dta <- simLong(n = 100, ntest = 100, model = 0, family = "Continuous", q = 5) trn <- dta$trn dtaL <- dta$dtaL dta <- dta$dta obj.0 <- boostmtree(dtaL$features[trn, ], dtaL$time[trn], dtaL$id[trn], dtaL$y[trn], family = "Continuous", M = M) pred.0 <- predict(obj.0, dtaL$features[-trn, ], dtaL$time[-trn], dtaL$id[-trn], dtaL$y[-trn]) ##------------------------------------------------------------ ## Response is binary ##---------------------------------------------------------------------------- ## set the number of boosting iterations M <- 500 ## simulation 0: only main effects (x1, x3, x4) dta <- simLong(n = 100, ntest = 100, model = 0, family = "Binary", q = 5) trn <- dta$trn dtaL <- dta$dtaL dta <- dta$dta obj.0 <- boostmtree(dtaL$features[trn, ], dtaL$time[trn], dtaL$id[trn], dtaL$y[trn], family = "Binary", M = M) pred.0 <- predict(obj.0, dtaL$features[-trn, ], dtaL$time[-trn], dtaL$id[-trn], dtaL$y[-trn]) ## End(Not run)
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