node_conditional_distr {simDAG} | R Documentation |
Simulate a Node by Sampling from Different Distributions based on Strata
Description
This function can be used to generate any kind of dichotomous, categorical or numeric variables dependent on one or more categorical variables by randomly sampling from user-defined distributions in each strata defined by the nodes parents.
Usage
node_conditional_distr(data, parents, distr, default_distr=NULL,
default_distr_args=list(), default_val=NA_real_,
coerce2numeric=TRUE, check_inputs=TRUE)
Arguments
data |
A |
parents |
A character vector specifying the names of the parents that this particular child node has. |
distr |
A named list where each element corresponds to one stratum defined by parents. If only one name is given in |
default_distr |
A function that should be used to generate values for all strata that are not explicitly mentioned in the |
default_distr_args |
A named list of arguments which are passed to the function defined by the |
default_val |
A single value which is used as an output for strata that are not mentioned in |
coerce2numeric |
A single logical value specifying whether to try to coerce the resulting variable to numeric or not. |
check_inputs |
A single logical value specifying whether to perform input checks or not. May be set to |
Details
Utilizing the user-defined distribution in each stratum of parents
(supplied using the distr
argument), this function simply calls the user-defined function with the arguments given by the user to generate a new variable. This allows the new variable to consist of a mix of different distributions, based on categorical parents
.
Formal Description:
Formally, the data generation process can be described as a series of conditional equations. For example, suppose that there is just one parent node sex
with the levels male
and female
with the goal of creating a continuous outcome that has a normal distribution of N(10, 3)
for males and N(7, 2)
for females. The conditional equation is then:
Y \sim \begin{cases}
N(10, 3), & \text{if } \texttt{sex="male"} \\
N(7, 2), & \text{if } \texttt{sex="female"} \\
\end{cases},
If there are more than two variables, the conditional distribution would be stratified by the intersection of all subgroups defined by the variables.
Value
Returns a numeric vector of length nrow(data)
.
Author(s)
Robin Denz
See Also
empty_dag
, node
, node_td
, sim_from_dag
, sim_discrete_time
Examples
library(simDAG)
set.seed(42)
#### with one parent node ####
# define conditional distributions
distr <- list(male=list("rnorm", mean=100, sd=5),
female=list("rcategorical", probs=c(0.1, 0.2, 0.7)))
# define DAG
dag <- empty_dag() +
node("sex", type="rcategorical", labels=c("male", "female"),
coerce2factor=TRUE, probs=c(0.4, 0.6)) +
node("chemo", type="rbernoulli", p=0.5) +
node("A", type="conditional_distr", parents="sex", distr=distr)
# generate data
data <- sim_from_dag(dag=dag, n_sim=1000)
#### with two parent nodes ####
# define conditional distributions with interaction between parents
distr <- list(male.FALSE=list("rnorm", mean=100, sd=5),
male.TRUE=list("rnorm", mean=100, sd=20),
female.FALSE=list("rbernoulli", p=0.5),
female.TRUE=list("rcategorical", probs=c(0.1, 0.2, 0.7)))
# define DAG
dag <- empty_dag() +
node("sex", type="rcategorical", labels=c("male", "female"),
coerce2factor=TRUE, probs=c(0.4, 0.6)) +
node("chemo", type="rbernoulli", p=0.5) +
node("A", type="conditional_distr", parents=c("sex", "chemo"), distr=distr)
# generate data
data <- sim_from_dag(dag=dag, n_sim=1000)