mice.impute.rfpred.emp {RfEmpImp}

Univariate sampler function for continuous variables using the empirical error distributions

Description

Please note that functions with names starting with "mice.impute" are exported to be visible for the mice sampler functions. Please do not call these functions directly unless you know exactly what you are doing.

For continuous variables only.

This function is for RfPred.Emp multiple imputation method, adapter for mice samplers. In the mice() function, set method = "rfpred.emp" to call it.

The function performs multiple imputation based on the empirical distribution of out-of-bag prediction errors of random forests.

Usage

mice.impute.rfpred.emp(
  y,
  ry,
  x,
  wy = NULL,
  num.trees.cont = 10,
  sym.dist = TRUE,
  alpha.emp = 0,
  pre.boot = TRUE,
  num.threads = NULL,
  ...
)

Arguments

y

Vector to be imputed.

ry

Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.

x

Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.

wy

Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.

num.trees.cont

Number of trees to build for continuous variables. The default is num.trees = 10.

sym.dist

If TRUE, the empirical distribution of out-of-bag prediction errors will be assumed to be symmetric; if FALSE, the empirical distribution will be kept intact. The default is sym.dist = TRUE. This option is invalid when emp.err.cont = FALSE.

alpha.emp

The "significance level" for the empirical distribution of out-of-bag prediction errors, can be used for prevention for outliers (useful for highly skewed variables). For example, set alpha = 0.05 to use 95% confidence level. The default is alpha.emp = 0.0, and the empirical distribution of out-of-bag prediction errors will be kept intact. This option is invalid when emp.err.cont = FALSE.

pre.boot

If TRUE, bootstrapping prior to imputation will be performed to perform 'proper' multiple imputation, for accommodating sampling variation in estimating population regression parameters (see Shah et al. 2014). It should be noted that if TRUE, this option is in effect even if the number of trees is set to one.

num.threads

Number of threads for parallel computing. The default is num.threads = NULL and all the processors available can be used.

...

Other arguments to pass down.

num.trees

Number of trees to build. The default is num.trees = 10.

Details

RfPred.Emp imputation sampler.

Value

Vector with imputed data, same type as y, and of length sum(wy).

Author(s)

Shangzhi Hong

References

Hong, Shangzhi, et al. "Multiple imputation using chained random forests." Preprint, submitted April 30, 2020. https://arxiv.org/abs/2004.14823.

Zhang, Haozhe, et al. "Random Forest Prediction Intervals." The American Statistician (2019): 1-20.

Shah, Anoop D., et al. "Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study." American journal of epidemiology 179.6 (2014): 764-774.

Malley, James D., et al. "Probability machines." Methods of information in medicine 51.01 (2012): 74-81.

Examples

# Users can set method = "rfpred.emp" in call to mice to use this method
data("airquality")
impObj <- mice(airquality, method = "rfpred.emp", m = 5,
maxit = 5, maxcor = 1.0, eps = 0,
remove.collinear = FALSE, remove.constant = FALSE,
printFlag = FALSE)