create_missings: Create Missings for Approximation of Continuous Time Dynamic...

In mlts: Multilevel Latent Time Series Models with 'R' and 'Stan'

Create Missings for Approximation of Continuous Time Dynamic Models (new version)

Description

Create Missings for Approximation of Continuous Time Dynamic Models (new version)

Usage

create_missings(data, tinterval, id, time, btw_vars = NULL)

Arguments

data	An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model.
tinterval	The step interval for approximation for a continuous time DSEM. The smaller the step interval, the better the approximation.
id	The variable in data that identifies the person or observational unit (as character).
time	The variable in data that contains the (continuous) time (as string).
btw_vars	The names of between-level variables in the data to be added in newly created rows with NAs.

Value

A data.frame with missings imputed for use in mlts_fit.

Examples

# create some data for example
data <- data.frame(
  id = rep(c(1, 2), each = 4),
  time = c(0, 3, 4, 6,
           1, 4, 5, 7)
)

# create missings to approximate continuous time process
create_missings(
  data = data, id = "id", time = "time",
  tinterval = 1 # use time interval of 1 minute
)

mlts documentation built on June 27, 2024, 5:13 p.m.