# Simulate longitudinal data Simulates longitudinal data with continuous or binary response from models with increasing complexity of covariate-time interactions. ```r 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`: 1. **`model=0`:** Linear with no covariate-time interactions. 2. **`model=1`:** Linear covariate-time interaction. 3. **`model=2`:** Quadratic time-quadratic covariate interaction. 4. **`model=3`:** Quadratic time-quadratic two-way covariate interaction. For details see Pande et al. (2017). ## Returns 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(s) 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 ```r ## 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) ```

simLong function