sim_dat_types: Simulate Conditional Longitudinal Data

In CJangelo/SPR: Tools to Learn Statistical Programming in R

Description

This is a conditional model with a random intercept

Usage

sim_dat_types(
  N = 1000,
  data.type = NULL,
  number.groups = 2,
  number.timepoints = 4,
  reg.formula = NULL,
  Beta = NULL,
  sigma2 = NULL,
  phi = NULL,
  theta = NULL,
  zip = NULL,
  thresholds = NULL,
  k = NULL,
  subject.var = 1
)

Arguments

data.type	Specify the type of data you want to generate. Select one from the following: 'Binomial', 'Ordinal', 'Beta', 'Gaussian', 'NegBinom', 'ZIP', 'ZINB', 'Multinom'. This must be a character vector of length 1.
reg.formula	this is a regression formula to pass to the data generation; null is formula(~ Group + Time + Time*Group),
Beta	this is the Beta for the regression equation; numeric matrix of Beta values OR a scalar value != 0 that will be the final value of the interaction parameters, default is all(Beta == 0) for Type I error simulations
sigma2	the residual error in Gaussian data
phi	parameter in Beta distribution
theta	size parameter in NB
zip	zero inflation parameter
thresholds	thresholds in ordinal data
k	number of categories in ordinal/nominal
subject.var	the subject-level variance, also known as the variance associated with the random intercept

Details

Select the data type you want to generate - this is best suited for fitting data in glmmTMB() among other R packages that are based on the conditional model with a random intercept

Value

returns a dataframe containing the simulated data

CJangelo/SPR documentation built on Feb. 24, 2022, 5:02 a.m.