gibbs.coef: Gibbs sampler for random coefficient model

In matthiasspeidel/hmi: Hierarchical Multiple Imputation

Description

It generates the Markov chains of the imputation parameters by drawing from their conditional distributions until convergence

It generates the Markov chains of the imputation parameters by drawing from their conditional distributions until convergence

Usage

gibbs.coef(y_gibbs, X_gibbs, Z_gibbs, clID, n.iter = 100, M = 10,
  n.chains = 3, burn.in = 1/3, max.iter = 5000)

gibbs.coef(y_gibbs, X_gibbs, Z_gibbs, clID, n.iter = 100, M = 10,
  n.chains = 3, burn.in = 1/3, max.iter = 5000)

Arguments

y_gibbs	A vector or data.frame with ncol = 1 containing the target variable with the missing values.
X_gibbs	A data.frame containing the covariates influencing y via fixed effects. If rows with missing values in X should also be imputed, put all your variables in a data.frame (or matrix)
Z_gibbs	A data.frame containing the covariates influencing y via random effects
clID	A factor (should come as data.frame or vector) containing the cluster IDs.
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.
y	A vector or data.frame with ncol = 1 containing the target variable with the missing values.
X	A vector a data.frame containing the covariates influencing y via fixed effects. If rows with missing values in X should also be imputed, put all your variables in a data.frame (or matrix)
Z	A vector a data.frame containing the covariates influencing y via random effects
cl.id	A factor (should come as data.frame or vector) containing the cluster IDs.
m	An integer defining the number of imputations that should be made.
n.iter	An integer defining the number of iterations that should be run in each bunch of iterations.
max.iter	An integer defining the maximum number of iterations that should be run in total.
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

It returns a multidimensional vector with the Markov chains containing the imputation parameters needed for imp_multi.

It returns a multidimensional vector with the Markov chains containing the imputation parameters needed for imp_multi.

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