mice.impute.rfpred.norm: Univariate sampler function for continuous variables for...

In RfEmpImp: Multiple Imputation using Chained Random Forests

Univariate sampler function for continuous variables for prediction-based imputation, assuming normality for prediction errors of random forest

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.Norm multiple imputation method, adapter for mice samplers. In the mice() function, set method = "rfpred.norm" to call it.

The function performs multiple imputation based on normality assumption using out-of-bag mean squared error as the estimate for the variance.

Usage

mice.impute.rfpred.norm(
  y,
  ry,
  x,
  wy = NULL,
  num.trees.cont = 10,
  norm.err.cont = TRUE,
  alpha.oob = 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.
norm.err.cont	Use normality assumption for prediction errors of random forests. The default is norm.err.cont = TRUE, and normality will be assumed for the distribution for the prediction errors, the variance estimate equals to overall out-of-bag prediction error, i.e. out-of-bag mean squared error (see Shah et al. 2014). If FALSE, then the predictions of random forest are used.
alpha.oob	The "significance level" for individual out-of-bag prediction errors used for the calculation for out-of-bag mean squared error, useful when presence of extreme values. For example, set alpha = 0.05 to use 95% confidence level. The default is alpha.oob = 0.0, and all the individual out-of-bag prediction errors will be kept intact.
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.

Details

RfPred.Norm imputation sampler.

Value

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

Author(s)

Shangzhi Hong

References

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.

Examples

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

RfEmpImp documentation built on Oct. 20, 2022, 9:06 a.m.