Description Usage Arguments Value Author(s)

Performs error checks on the input for the Umediation function.

1 2 3 4 5 6 7 | ```
ErrorCheck(n = 1000, Atype = "D", Mtype = "C", Ytype = "C",
Ctype = "C", Utype = "C", interact = FALSE, muC = 0, varC = 1,
muU = 0, varU = 1, gamma0 = 0, gammaC = 0, gammaU = 0,
varA = 1, alpha0 = 0, alphaA = 0, alphaC = 0, alphaU = 0,
varM = 1, beta0 = 0, betaA = 0, betaM = 0, betaI = 0,
betaC = 0, betaU = 0, varY = 1, alpha = 0.05, nSim = 300,
nBoot = 500, seed = 1, atreat = 1, acontrol = 0)
``` |

`n` |
is the sample size of the population that is being simulated. |

`Atype` |
is either "C" for continuous, normally distributed exposure A or "D" for dichotomous, binary exposure A (i.e. Atype="D"). |

`Mtype` |
is either "C" continuous, normally distributed mediator M or "D" for dichotomous, binary mediator M (i.e. Mtype="C"). |

`Ytype` |
is either "C" for continuous, normally distributed outcome Y or "D" for dichotomous, binary outcome Y (i.e. Ytype="D"). |

`Ctype` |
is either "C" for continuous, normally distributed measured confounder C or "D" for dichotomous, binary measured confounder C. Ctype can be a single value (i.e. Ctype="C") or a vector for multiple measured confounders (i.e. Ctype=c("C","C","D") ). |

`Utype` |
is either "C" for continuous, normally distributed unmeasured confounder U or "D" for dichotomous, binary unmeasured confounder U. Utype can be a single value (i.e. Utype="C") or a vector for multiple measured confounders (i.e. Utype=c("C","C","D") ). |

`interact` |
Using the flag interact=TRUE allows for an interaction between the exposure A and the mediator M on the outcome Y (i.e. E[Y]=beta0+betaA*A+betaM*M+betaC*C+betaU*U+betaI*A*M). By default, interact=FALSE (i.e. E[Y]=beta0+betaA*A+betaM*M+betaC*C+betaU*U). |

`muC` |
is the mean vector for the measured confounder C. For continuous measured confounder (i.e. Ctype="C"), muC is the mean of C. For dichotomous measured confounder C (i.e. Ctype="D"), muC is the probability C=1. |

`varC` |
is the variance of the measured confounder C when Ctype="C". For multiple measured confounders, the length of varC must match muC and Ctype (i.e. Ctype=c("C","C","D") and muC=c(-0.1,0.2,0.3) and varC=c(1,1,1)) |

`muU` |
is the mean vector for the unmeasured confounder U. For continuous unmeasured confounder (i.e. Utype="C"), muU is the mean of U. For dichotomous unmeasured confounder U (i.e. Utype="D"), muU is the probability U=1. |

`varU` |
is the variance of the unmeasured confounder U when Utype="C". For multiple unmeasured confounders, the length of varU must match muU and Utype (i.e. Utype=c("C","C","D") and muU=c(-0.1,0.2,0.3) and varU=c(1,1,1)) |

`gamma0` |
specifies the intercept for the exposure A (i.e. logit(P(A=1)) or E[A]=gamma0+gammaU*U+gammaC*C). |

`gammaC` |
specifies the relationship between the measured confounder C and the exposure A (i.e. logit(P(A=1)) or E[A]=gamma0+gammaU*U+gammaC*C). |

`gammaU` |
specifies the relationship between the measured confounder U and the exposure A (i.e. logit(P(A=1)) or E[A]=gamma0+gammaU*U+gammaC*C). |

`varA` |
is the variance of the exposure A when Atype="C". Default is varA=1. |

`alpha0` |
specifies the intercept for the mediator M (i.e. Logit(P(M=1)) or E[M]=alpha0+alphaA*A+alphaC*C+alphaU*U). |

`alphaA` |
specifies the relationship between the exposure A and the mediator M (i.e logit(P(M=1)) or E[M]=alpha0+alphaA*A+alphaC*C+alphaU*U). |

`alphaC` |
specifies the relationship between the measured confounder C and the mediator M (i.e. E[M] or logit(P(M=1))=alpha0+alphaA*A+alphaC*C+alphaU*U). |

`alphaU` |
specifies the relationship between the unmeasured confounder U and the mediator M (i.e. E[M] or logit(P(M=1))=alpha0+alphaA*A+alphaC*C+alphaU*U). |

`varM` |
is the variance of the mediator M when Mtype="C". Default is varM=1. |

`beta0` |
specifies the intercept for the outcome Y (i.e. logit(P(Y=1)) or E[Y]=beta0+betaA*A+betaM*M+betaI*A*M+betaC*C+betaU*U). |

`betaA` |
specifies the relationship between the exposure A and the outcome Y (i.e. E[Y] or logit(P(Y=1))=beta0+betaA*A+betaM*M+betaI*A*M+betaC*C+betaU*U). |

`betaM` |
specifies the relationship between the mediator M and the outcome Y (i.e. E[Y] or logit(P(Y=1))=beta0+betaA*A+betaM*M+betaI*A*M+betaC*C+betaU*U). |

`betaI` |
specifies the interaction between the mediator M and the exposure A on the outcome Y (i.e. E[Y] or logit(P(Y=1))= beta0+betaA*A+betaM*M+betaI*A*M+betaC*C+betaU*U) when the flag interact=TRUE. |

`betaC` |
specifies the relationship between the measured confounder C and the outcome Y (i.e. E[Y] or logit(P(Y=1))=beta0+betaA*A+betaM*M+betaI*A*M+betaC*C+betaU*U). |

`betaU` |
specifies the relationship between the unmeasured confounder U and the outcome Y (i.e. E[Y] or logit(P(Y=1))=beta0+betaA*A+betaM*M+betaI*A*M+betaC*C+betaU*U). |

`varY` |
is the variance of the outcome Y when Ytype="C". Default is varY=1. |

`alpha` |
is the significance level. Default value is alpha=0.05. |

`nSim` |
is the number of simulations run for the function. The more simulations run, the more accurate the results, but this will make the function slower. |

`nBoot` |
is the number of Monte Carlo draws for nonparametric bootstrap or quasi-Bayesian approximation for the mediate function. |

`seed` |
sets the seed used for the random generator. |

`atreat` |
sets the treatment group for the exposure A. |

`acontrol` |
sets the control group for the exposure A. |

The function exits with an error message if the error checks are not met.

Sharon Lutz

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