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Simulate longitudinal data

Source: R/simLong.R
simLong.Rd

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

Usage

simLong(
  n = 100,
  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 "Continuous" or "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.

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.

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).

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.

Author

Hemant Ishwaran, Amol Pande and Udaya B. Kogalur

Examples


# \donttest{
##------------------------------------------------------------
##  Response is continuous
##----------------------------------------------------------------------------

## set the number of boosting iterations
M <- 20

## simulation 0: only main effects (x1, x3, x4)
dta <- simLong(n = 20, ntest = 5, 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$y[trn],
          family = "Continuous", M = M)
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pred.0 <- predict(obj.0, dtaL$features[-trn, ], dtaL$time[-trn], dtaL$id[-trn], dtaL$y[-trn])



##------------------------------------------------------------
##  Response is binary
##----------------------------------------------------------------------------

## set the number of boosting iterations
M <- 20

## simulation 0: only main effects (x1, x3, x4)
dta <- simLong(n = 20, ntest = 5, 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$y[trn],
          family = "Binary", M = M)
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pred.0 <- predict(obj.0, dtaL$features[-trn, ], dtaL$time[-trn], dtaL$id[-trn], dtaL$y[-trn])
# }