Description Usage Arguments Value
View source: R/hmi_imp_semicont_multi_2018_04-17.R View source: R/hmi_imp_semicont_multi_2018_02_27.R View source: R/hmi_imp_semicont_multi_2017-10-12.R View source: R/hmi_imp_semicont_multi_2017-04-11.R View source: R/hmi_imp_semicont_multi_2017-01-10.R View source: R/hmi_imp_semicont_multi_2016-09-14.R View source: R/hmi_imp_semicont_multi.R
The function is called by the wrapper. We consider data to be "semicontinuous" when
more than 5% of the (non categorical) observations.
For example in surveys a certain portion of people, when asked for their income,
report "0", which clearly violates the assumption of income to be (log-) normally distributed.
1 2 | imp_semicont_multi(y_imp_multi, X_imp_multi, Z_imp_multi, clID, model_formula,
heap = 0, M = 10, nitt = 3000, thin = 10, burnin = 1000)
|
y_imp_multi |
A Vector with the variable to impute. |
X_imp_multi |
A data.frame with the fixed effects variables. |
Z_imp_multi |
A data.frame with the random effects variables. |
clID |
A vector with the cluster ID. |
model_formula |
A |
heap |
A numeric saying to which (single) values the data might be heaped. |
M |
An integer defining the number of imputations that should be made. |
nitt |
An integer defining number of MCMC iterations (see MCMCglmm). |
thin |
An integer defining the thinning interval (see MCMCglmm). |
burnin |
An integer defining the percentage of draws from the gibbs sampler that should be discarded as burn in (see MCMCglmm). |
A n x M matrix. Each column is one of M imputed y-variables.
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