wrapper: The main warpper function called by the user. LATER THE USER...

In matthiasspeidel/hmi: Hierarchical Multiple Imputation

Description

The user has to passes to the function his data. Optionally he pass his analysis model formula so that hmi runs the imputation model in line with his analysis model formula. And of course he can specify some parameters for the imputation routine (like the number of imputations and iterations and the burn in percentage.) The standard usage should be that the user gives his complete dataset and his analysis model. But he also could just give y, X and Z and the cluser ID.

The user has to passes to the function his data. Optionally he pass his analysis model formula so that hmi runs the imputation model in line with his analysis model formula. And of course he can specify some parameters for the imputation routine (like the number of imputations and iterations and the burn in percentage.) The standard usage should be that the user gives his complete dataset and his analysis model. But he also could just give y, X and Z and the cluser ID.

The user has to passes to the function his data. Optionally he pass his analysis model formula so that hmi runs the imputation model in line with his analysis model formula. And of course he can specify some parameters for the imputation routine (like the number of imputations and iterations and the burn in percentage.) The standard usage should be that the user gives his complete dataset and his analysis model. But he also could just give y, X and Z and the cluser ID.

Usage

wrapper(data, model_formula = NULL, parameter_list = NULL, n.iter = 100,
  M = 10, n.chains = 3, burn.in = 1/3, max.iter = 5000)

wrapper(data, model_formula = NULL, parameter_list = NULL, n.iter = 100,
  M = 10, n.chains = 3, burn.in = 1/3, max.iter = 5000)

wrapper(data, model_formula = NULL, parameter_list = NULL, n.iter = 100,
  M = 10, n.chains = 3, burn.in = 1/3, max.iter = 5000)

Arguments

data	A matrix or (better) a data.frame with all variables appearing in model_formula.
model_formula	A formula used for the analysis model.
parameter_list	A LIST OF PARAMETERS
n.iter	An integer defining the number of iterations that should be run in each bunch of iterations.
M	An integer defining the number of imputations that should be made.
n.chains	An integer defining the number of Markov chains to be made.
burn.in	A numeric between 0 and 1 defining the percentage of draws from the gibbs sampler that should be discarded as burn in.
max.iter	An integer defining the maximum number of iterations that should be run in total.
impsyn	SHALL VALUES BE IMPUTED OR SYNTHETICISED?
parameter.list	A LIST OF PARAMETERS
data	A matrix or (better) a data.frame with all variables appearing in model_formula.
model_formula	A formula used for the analysis model.
M	An integer defining the number of imputations that should be made.
data	A matrix or (better) a data.frame with all variables appearing in model_formula.
model_formula	A formula used for the analysis model.
parameter_list	A LIST OF PARAMETERS
n.iter	An integer defining the number of iterations that should be run in each bunch of iterations.
M	An integer defining the number of imputations that should be made.
n.chains	An integer defining the number of Markov chains to be made.
burn.in	A numeric between 0 and 1 defining the percentage of draws from the gibbs sampler that should be discarded as burn in.

Value

A data.frame. It consists of the original data and m additional variables with the imputed values.

A data.frame. It consists of the original data and m additional variables with the imputed values.

A data.frame. It consists of the original data and m additional variables with the imputed values.

matthiasspeidel/hmi documentation built on Aug. 18, 2020, 4:37 p.m.