| Title: | Ensemble Conditional Trees for Missing Data Imputation |
|---|---|
| Description: | Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) <doi:10.1186/1471-2105-8-25>). |
| Authors: | Imad El Badisy [aut, cre], Roch Giorgi [ctb] |
| Maintainer: | Imad El Badisy <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.0.8 |
| Built: | 2025-03-04 03:31:51 UTC |
| Source: | https://github.com/ielbadisy/misscforest |
Introducing a proportion of missing values given a data.frame under Missing Completely at Random mechanism (MCAR).
generateNA(dat, pmiss = 0.2, seed = 123)generateNA(dat, pmiss = 0.2, seed = 123)
dat |
complete data.frame. |
pmiss |
proportion of NA. |
seed |
seed value to ensure reproducibility. |
data.frame with the desired proportion of missing values.
This function is made only for experimental purpose. Whithout specifying the columns (i.e. variables), missing values are introduced to all columns of the dataset.
data(iris) # introduce 30% of NA irisNA <- generateNA(iris, 0.3) # check the proportion of NA mean(is.na(irisNA))data(iris) # introduce 30% of NA irisNA <- generateNA(iris, 0.3) # check the proportion of NA mean(is.na(irisNA))
Single imputation based on the Ensemble Conditional Trees Cforest algorithm.
missCforest( dat, formula = . ~ ., ntree = 100L, minsplit = 20L, minbucket = 7L, alpha = 0.05, cores = 1 )missCforest( dat, formula = . ~ ., ntree = 100L, minsplit = 20L, minbucket = 7L, alpha = 0.05, cores = 1 )
dat |
|
formula |
|
ntree |
number of trees to grow for the forest. |
minsplit |
minimum sum of weights in a node in order to be considered for splitting in a single tree. |
minbucket |
minimum sum of weights in a terminal node of a single tree. |
alpha |
statistical significance level (alpha). |
cores |
number of cores to use or in most cases how many child processes will be run simultaneously. This option is initialized at 4 to ensure fast execution. |
complete (i.e. imputed) data.frame.
Formula for defining the imputation model is of the form
[imputed_variables ~ predictors]
The variables to be imputed are specified on the left-side and
the predictors to be used for imputation are specified on the right-side of the formula.
The user can specify a customized imputation model using the formula argument.
By default, latter is set to [. ~ .] which corresponds to the situation where all variables that contain missing values will be imputed by the rest of variables.
missCforest can be used for numerical, categorical, or mixed-type data imputation. Missing values are imputed through ensemble prediction using Conditional Inference Trees (Ctree) as base learners (Hothorn, Hornik, and Zeileis 2006). Ctree is a non-parametric class of regression and classification trees embedding recursive partitioning into the theory of conditional inference (Strasser and Weber 1999). The missCforest algorithm redefines the imputation problem as a prediction one using single imputation approach. Iteratively, missing values are predicted based on the the complete cases set updated at each iteration. No stopping criterion is pre-defined, the imputation process ends when the missing data are all imputed. This algorithm is robust to outliers and gives a particular attention to the association structure between covariates (i.e. variables used for imputation) and th outcome (i.e. variable to be imputed) since the recursive partitioning of Conditional Trees is based on the multiple tests procedures.
Hothorn T, Hornik K, Zeileis A (2006). "Unbiased Recursive Partitioning: A Conditional Inference Framework" Journal of Computational and Graphical Statistics, 15(3), 651–674.
Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, 8(1), 1-21.
Strasser H, Weber C (1999). "On the Asymptotic Theory of Permutation Statistics." Mathematical Methods of Statistics, 8, 220–250.
library(missCforest) # import the iris dataset data(iris) # introduce randomly 30% of NA to variables irisNA <- generateNA(iris, 0.3) summary(irisNA) # impute all the missing values using all the possible combinations of the imputation model formula irisImp <- missCforest(irisNA, .~.) summary(irisImp)library(missCforest) # import the iris dataset data(iris) # introduce randomly 30% of NA to variables irisNA <- generateNA(iris, 0.3) summary(irisNA) # impute all the missing values using all the possible combinations of the imputation model formula irisImp <- missCforest(irisNA, .~.) summary(irisImp)