mlts_sim {mlts}

Simulate data from mlts model

Description

Simulate data from mlts model

Usage

mlts_sim(
  model,
  default = FALSE,
  N,
  TP,
  burn.in = 50,
  seed = NULL,
  seed.true = 1,
  btw.var.sds = NULL
)

Arguments

model

data.frame. Output of mlts_model.

default

logical. If set to TRUE, default prior specifications are added.

N

integer Number of observational units.

TP

integer. Number of measurements per observational unit.

burn.in

integer. Length of ‘burn-in’ period.

seed

integer. Seed used for data generation.

seed.true

integer. Separate seed used for sampling of true population parameters values from plausible ranges for stationary time series.

btw.var.sds

named numeric vector. Provide standard deviation(s) for all exogenous between-level variable(s) specified in model, e.g. (btw.var.sds = c("covariate1" = 1), to set the SD of the variable "covariate1" to 1). Mean values of the respective variable(s) will be set to 0 per default.

Details

A function to generate data from an output of mlts_model.

Value

An object of class "mlts_simdata". The object is a list containing the following components:

model

the model object passed to mlts_sim with true parameter values used in the data generation added in the column true.val

data

a long format data.frame of the generated time series data

RE.pars

a matrix of cluster-specific true values used in the data generation

Examples

# build a simple vector-autoregressive mlts model with two time-series variables
var_model <- mlts_model(q = 2)

# simulate data from this model with default true values
# (true values are randomly drawn from normal distribution)
var_data <- mlts_sim(
  model = var_model,
  N = 50, TP = 30, # number of units and number of measurements per unit
  default = TRUE # use default parameter values
)

# the data set is stored in .$data
head(var_data$data)

# individual parameter values are stored in .$RE.pars
head(var_data$RE.pars)

# if the mltssim-object is used in mlts_fit(), true values
# are added to the fitted object
fit <- mlts_fit(
  model = var_model,
  data = var_data,
  id = "ID", ts = c("Y1", "Y2"), time = "time"
)

# inspect model with true values
head(fit$pop.pars.summary)