time_varying {rcausim} | R Documentation |
Generate time-varying data
Description
Generate time-varying data
Usage
time_varying(func, data, T_max)
Arguments
func |
Functions, an object class generated by
function_from_edge or function_from_user
functions. All vertices must be defined for their functions. The causal
structure needs to be a directed cyclic graph (DCG), which means loops are
allowed. Use edge_from_function to identify edges given a list
of functions, then draw a causal diagram using the edges data frame (see
vignettes). All arguments within any function must be defined by their
respective functions, except the argument 'n'. The output lengths of vertex
functions must match the input length.
|
data |
Data, a data frame generated by data_from_function
which contains causally-simulated data at t=0. Column names of 'i', 't', and
't_max' are not allowed, which respectively refer to instance, time, and
maximum time.
|
T_max |
Maximum time for every instance, a numeric vector of length
equal to the number of rows in 'data' and must be non-negative and
non-decimal.
|
Value
A data frame which include the simulated data for each vertex as a
column for each time up to maximum time for every instance.
Examples
data(functions)
simulated_data <- data_from_function(functions, n = 100)
function_B <- function(B){
B + 1
}
functions <- define(functions, which = "B", what = function_B)
T_max <- rpois(nrow(simulated_data), lambda = 25)
time_varying(functions, data = simulated_data, T_max = T_max)
[Package
rcausim version 0.1.1
Index]