seq.dr.mediator: The estimator for the sequential mediation that utilizes data...

In mcl868/causalinmisdata: Causal inference in missing data

The estimator for the sequential mediation that utilizes data with missing observations

Usage

seq.dr.mediator(mmodels, exposure, int, covariates, regList, augList=NULL, data, ...)

Arguments

mmodels	Models corresponding to response.
exposure	The time-varying exposure.
int	The exposure of interest for the mediation analysis.
covariates	The ordered sequence of the variables of the interest without the response.
regList	The list consist of the models to estimate the probabilities for the missingness in data. See the function missing.pattern.
augList	The list consist of the models of the Augmentations space. All the models are linear by default (augList=NULL)
data	Data.
...

Value

coef	The coefficients from the analysis of the direct effect and the indirect effects.
mmodels	The mmodels that have been used for modeling data.
N	The sample size of data.
NCC	The sample size of complete cases of data. In case of no missing values NCC is equal to N.
exposure	The exposure of the analysis.
regList	The list consist of the models to estimate the probabilities for the missingness in data. See the function missing.pattern.
augList	The list consist of the models of the Augmentations space.
count	The number of the observed variables in integers.
CoefList	The coefficients form the regression models (exist if list.out is equal to TRUE).

Author(s)

Thomas Maltesen [e-mail]

References

put references to the literature/web site here

Examples

## Not run: 
p<-function(x)exp(x)/(1+exp(x))

## End(Not run)

mcl868/causalinmisdata documentation built on March 5, 2024, 8:22 a.m.