simLong {boostmtree} R Documentation

## Simulate longitudinal data

### Description

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`:

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).

### 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(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

```## 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)
```

[Package boostmtree version 1.5.0 Index]