node_negative_binomial {simDAG} | R Documentation |
Data from the parents is used to generate the node using negative binomial regression by applying the betas to the design matrix and sampling from the rnbinom
function.
node_negative_binomial(data, parents, formula=NULL, betas,
intercept, theta)
data |
A |
parents |
A character vector specifying the names of the parents that this particular child node has. If non-linear combinations or interaction effects should be included, the user may specify the |
formula |
An optional |
betas |
A numeric vector with length equal to |
intercept |
A single number specifying the intercept that should be used when generating the node. |
theta |
A single number specifying the theta parameter ( |
This function uses the linear predictor defined by the betas
and the input design matrix to sample from a subject-specific negative binomial distribution. It does to by calculating the linear predictor using the data
, betas
and intercept
, exponentiating it and passing it to the mu
argument of the rnbinom
function of the stats package.
Returns a numeric vector of length nrow(data)
.
Robin Denz
empty_dag
, node
, node_td
, sim_from_dag
, sim_discrete_time
library(simDAG)
set.seed(124554)
dag <- empty_dag() +
node("age", type="rnorm", mean=50, sd=4) +
node("sex", type="rbernoulli", p=0.5) +
node("smoking", type="negative_binomial", parents=c("sex", "age"),
betas=c(1.1, 0.4), intercept=-2, theta=0.05)
sim_dat <- sim_from_dag(dag=dag, n_sim=100)