imp_roundedcont: The function to impute rounded continuous variables
In hmi: Hierarchical Multiple Imputation

Description Usage Arguments Value References

For example the income in surveys is often reported rounded by the respondents. See Drechsler, Kiesl and Speidel (2015) for more details.

imp_roundedcont(
  y_df,
  X_imp,
  PSI,
  pvalue = 0.2,
  k = Inf,
  rounding_degrees = NULL
)

`y_df`	A data.frame with the variable to impute.
`X_imp`	A data.frame with the fixed effects variables explaining y_df.
`PSI`	A data.frame with the variables explaining the latent rounding tendency G.
`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.
`rounding_degrees`	A numeric vector with the presumed rounding degrees for Y.

A n x 1 data.frame with the original and imputed values.

Joerg Drechsler, Hans Kiesl, Matthias Speidel (2015): "MI Double Feature: Multiple Imputation to Address Nonresponse and Rounding Errors in Income Questions". Austrian Journal of Statistics Vol. 44, No. 2, http://dx.doi.org/10.17713/ajs.v44i2.77

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