Description Usage Arguments Value
View source: R/hmi_imp_binary_multi.R
The function is called by the wrapper.
1 2 3 4 5 6 7 8 9 10 11 | imp_binary_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.
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