imp_count_single: The function for imputation of binary variables.

Description Usage Arguments Value

View source: R/hmi_imp_count_single.R

Description

The function is called by the wrapper.

Usage

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

Arguments

y_imp

A vector with the variable to impute.

X_imp

A data.frame with the fixed effects variables.

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.

Value

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.