Simulate a Node by Sampling from Different Distributions based on Strata

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 data.table (or something that can be coerced to a data.table) containing all columns specified by parents.
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 parents, this means that there should be one element for possible values of the variable given in parents. If the node has multiple parents, there needs to be one element for possible combinations of parents (see examples). The values of those elements should be a list themselves, with the first argument being a callable function (such as rnorm, rcategorical, ...) and the rest should be named arguments of that function. Any function can be used, as long as it returns a vector of n values, with n being an argument of the function. n is set internally based on the stratum size and cannot be set by the user. If this list does not contain one element for each possible strata defined by parents, the default_val or default_distr arguments will be used.
default_distr: A function that should be used to generate values for all strata that are not explicitly mentioned in the distr argument, or NULL (default). If NULL, the default_val argument will be used to fill the missing strata with values. A function passed to this argument should contain the argument n, which should define the number of samples to generate. It should return a vector with n values. Some examples are (again), rnorm or rbernoulli.
default_distr_args: A named list of arguments which are passed to the function defined by the default_distr argument. Ignored if default_distr is NULL.
default_val: A single value which is used as an output for strata that are not mentioned in distr. Ignored if default_distr is not NULL.
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 TRUE to speed up things a little if you are sure your input is correct.

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.

Author

Robin Denz

Value

Returns a numeric vector of length nrow(data).

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"),
       output="factor", 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"),
       output="factor", 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)