imp_cont_multi: The function for hierarchical imputation of continuous...
In hmi: Hierarchical Multiple Imputation

The function is called by the wrapper.

imp_cont_multi(
  y_imp,
  X_imp,
  Z_imp,
  clID,
  nitt = 22000,
  burnin = 2000,
  thin = 20,
  pvalue = 0.2,
  k = Inf
)

`y_imp`	A vector with the variable to impute.
`X_imp`	A data.frame with the fixed effects variables.
`Z_imp`	A data.frame with the random effects variables.
`clID`	A vector with the cluster ID.
`nitt`	An integer defining number of MCMC iterations (see MCMCglmm).
`burnin`	burnin A numeric value between 0 and 1 for the desired percentage of Gibbs samples that shall be regarded as burnin.
`thin`	An integer to set the thinning interval range. If thin = 1, every iteration of the Gibbs-sampling chain will be kept. For highly autocorrelated chains, that are only examined by few iterations (say less than 1000).
`pvalue`	A numeric between 0 and 1 denoting the threshold of p-values a variable in the imputation model should not exceed. If they do, they are excluded from the imputation model.
`k`	An integer defining the allowed maximum of levels in a factor covariate.

A list with 1. 'y_ret' the n x 1 data.frame with the original and imputed values. 2. 'Sol' the Gibbs-samples for the fixed effects parameters. 3. 'VCV' the Gibbs-samples for variance parameters.

hmi documentation built on Oct. 23, 2020, 7:31 p.m.