Simulate longitudinal data — simLong • boostmtree

Simulates longitudinal data with continuous or binary response from models with increasing complexity of covariate-time interactions.

Usage

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,
        ...)

Arguments

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 y. Use any one from {"Continuous", "Binary"} based on the scale of y.
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.

Details

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).

Value

An invisible list with the following components:

dtaL: List containing the simulated data in the following order: features, time, id and y.
dta: Simulated data given as a data frame.
trn: Index of id values identifying the training data.
f.true: Formula of the simulation model.

Author

Hemant Ishwaran, Amol Pande and Udaya B. Kogalur

References

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.

Examples

if (FALSE) { # \dontrun{
##------------------------------------------------------------
##  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])
} # }