Description Usage Arguments Value Examples

View source: R/simulate_funmediation_example.R

Simulates a dataset for demonstrating the funmediation function.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
simulate_funmediation_example(
nsub = 500,
nlevels = 2,
ntimes = 100,
observe_rate = 0.4,
alpha_int = function(t) { return(t^0.5) },
alpha_X = function(t) { return(-(t/2)^0.5) },
beta_M = function(t) { (1/2) * (exp(t) - 1) },
beta_int = 0,
beta_X = 0.2,
sigma_Y = 1,
sigma_M_error = 2,
rho_M_error = 0.8,
simulate_binary_Y = FALSE,
make_covariate_S = FALSE
)
``` |

`nsub` |
Number of subjects |

`nlevels` |
Number of treatment groups or levels on the treatment variable X. Subjects are assumed to be randomly assigned to each level with equal probability (i.e., the probability per level is 1/nlevel). Default is 2 for a randomized controlled trial with a control group X=0 and an experimental group X=1. There should not be less than 2 or more than 5 groups for purposes of this function. |

`ntimes` |
Number of potential times that could be observed on each subject |

`observe_rate` |
Proportion of potential times on which there are actually observations. Not all times are observed; this is assumed to be completely random and to be done by design to reduce participant burden. |

`alpha_int` |
Function representing the time-varying mean of mediator variable for the level of treatment with all treatment dummy codes X set to 0 (e.g., the control group). |

`alpha_X` |
Function representing the time-varying effect of X on the mediator (if there are two treatment levels) or a list of nlevels-1 functions representing the effect of receiving each nonzero level of X rather than control (if there are more than two treatment levels). |

`beta_M` |
Function representing the functional coefficient for cumulative (scalar-on-function) effect of the mediator M on the treatment Y adjusting for the treatment X |

`beta_int` |
Mean of Y if the X is zero and M is the 0 function |

`beta_X` |
Numeric value representing the direct effect of X on Y after adjusting for M (if there are two treatment levels) or a vector of nlevels-1 numeric values (if there are more than two treatment levels) |

`sigma_Y` |
Error standard deviation of the outcome Y (conditional on treatment and mediator trajectory) |

`sigma_M_error` |
Error standard deviation of the mediator M (conditional on treatment and time) |

`rho_M_error` |
Autoregressive correlation coefficient of the error in the mediator M, from one observation to the next |

`simulate_binary_Y` |
Whether Y should be generated from a binary logistic (TRUE) or Gaussian (FALSE) model |

`make_covariate_S` |
Whether to generate a random binary covariate S at the subject (i.e., time-invariant) level. It will be generated to have zero population-level relationship to the outcome. |

A list with the following components:

- time_grid
The time grid for interpreting functional coefficients.

- true_alpha_int
True value of the time-varying alpha_int parameter, representing the time-specific mean of the mediator M when the treatment value X is 0.

- true_alpha_X
True value of the time-varying alpha_X parameter, representing the effect of X on M. This is a single number if nlevels=2, or a vector of effects if nlevels>2.

- true_beta_int
True value of the beta_M parameter, representing the mean of the outcome Y when X=0 and M=0.

- true_beta_M
True value of the beta_M parameter, representing the functional effect of treatment on the outcome Y.

- true_beta_X
True value of the beta_X parameter, representing the effect of treatment on the outcome Y adjusting for the mediator. This is a single function if nlevels=2, or a vector of functions if nlevels>2.

- true_indirect
True value of the indirect parameter, representing the indirect (mediated) effect of treatment on the outcome Y. This is a single number if nlevels=2, or a vector of effects if nlevels>2.

- dataset
The simulated longitudinal dataset in long form.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
set.seed(123)
# Simplest way to call the function:
simulation_all_defaults <- simulate_funmediation_example()
summary(simulation_all_defaults)
head(simulation_all_defaults)
# Changing the sample size to be larger:
simulation_larger <- simulate_funmediation_example(nsub=10000)
summary(simulation_larger)
# Changing the effect of the mediator to be null:
simulation_null <- simulate_funmediation_example(beta_M=function(t) {return(0*t)})
summary(simulation_null)
# Simulating a exposure variable with three levels (two dichotomous dummy codes)
simulation_three_group <- simulate_funmediation_example(nlevels=3,
alpha_X = list(function(t) {return(.1*t)},
function(t) {return(-(t/2)^.5)}),
beta_X = c(-.2,.2))
print(summary(simulation_three_group));
``` |

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