PRNGODBinNormal: Pseudo-Random Number Generation of Overdispersed Correlated...
In lalondetl/PRNG:

Description Usage Arguments Examples

View source: R/PRNGODBinNormal.R

This function produces pseudo-random numbers for responses for correlated (longitudinal) data. The responses are generated as either binary (Bernoulli) data or count (Binomial), using a normal random effect to induce autocorrelation. These random effects are then passed through the systematic component to a Binomial-Beta pseudo-random number generator from the VGAM package. Therefore there are two instances of random effects: in the normal random effects, and through the Binomial-Beta pseudo-random number generation. The predictors must be provided as a vector. The function returns a response vector, labeled "Outcomes".

1	PRNGODBinNormal(M, mvec, mu_r, s_r, D, beta0, beta1, x, seed)

`M`	The number of groups in the resulting data.
`mvec`	A vector indicating the length of each group in the data.
`mu_r`	The mean of the normal random effect.
`s_r`	The standard deviation of the normal random effect.
`D`	A vector of denominators for each response; a vector of 1's indicates Bernoulli data.
`beta0`	The true intercept in the systematic component of the model.
`beta1`	The true slope in the systematic component of the model.
`x`	A vector of predictor values.
`seed`	The seed for data generation.