simulate_ptglmm: Simulate data from the Poisson-Tweedie generalized linear...

In ptmixed: Poisson-Tweedie Generalized Linear Mixed Model

Simulate data from the Poisson-Tweedie generalized linear mixed model

Description

Simulates a dataset comprising t repeated measurements for n subjects from a Poisson-Tweedie GLMM. Subjects are assumed to belong to two different groups. The linear predictor comprises an intercept, main effects of group and of time, and the interaction between time and group; a random intercept; and, optionally, a normally-distributed offset term.

Usage

simulate_ptglmm(n = 20, t = 5, seed = 1, beta = c(3, 0, 0, 0.4),
  D = 1.5, a = -1, sigma2 = 0.8^2, offset = F)

Arguments

n	number of subjects
t	number of time points (0, 1, ..., t-1)
seed	seed for random number generation
beta	vector of regression coefficients, to be specified in this order: intercept, group main effect, time main effect, group*time interaction
D	dispersion parameter of the Poisson-Tweedie distribution (D > 1)
a	power parameter of the Poisson-Tweedie distribution (a < 1)
sigma2	Variance of the subject-specific random intercept
offset	Logical value. If TRUE, a normally-distributed offset is added to the linear predictor.

Value

A list containing the following elements: a dataframe (data) containing the response y, the subject id, the group indicator and time; a vector with the true random intercept values (true.randint).

Author(s)

Mirko Signorelli

References

Signorelli, M., Spitali, P., Tsonaka, R. (2021). Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data. Statistical Modelling, 21 (6), 520-545. URL: https://doi.org/10.1177/1471082X20936017

Examples

# simulate a simple, small dataset
example1 = simulate_ptglmm(n = 5, t = 2)
example1$data
# the function allows to set several different parameters
example2 = simulate_ptglmm(n = 20, t = 5, seed = 1,
beta = c(2.2, 1.2, 0.3, -0.5), D = 1.8, a = 0.5,
sigma2 = 0.7, offset = TRUE)
# view the distribution of the response variable:
pmf(example2$data$y)
# visualize the data with a trajectory plot:
make.spaghetti(x = time, y = y, id = id,
group = group, data = example2$data)

ptmixed documentation built on Aug. 18, 2022, 5:06 p.m.