stack_imputations: Stack Multiply Imputed Datasets

In ctreeMI: Conditional Inference Trees with Stacked Multiple Imputation

Stack Multiply Imputed Datasets

Description

Concatenates a list of imputed data frames into a single "stacked" data frame. An imputation index column (.imp) is added to identify which imputed dataset each row originated from. This is the first step of the stacked-imputation workflow described in Rodgers et al. (2021) and applied to conditional inference trees in Sherlock et al. (2026).

Usage

stack_imputations(data_list, imp_col = ".imp")

Arguments

data_list	A list of data frames, each the same dimensions, representing M imputed versions of the same dataset.
imp_col	Character string. Name of the imputation-index column added to the stacked data (default ".imp"). Set to NULL to suppress this column.

Value

A single data frame with M * n rows, where n is the number of rows in each imputed dataset.

References

Sherlock, P., et al. (2026). Beyond linear risk: A machine learning approach to understanding perinatal depression in context. Multivariate Behavioral Research, 1-16. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2026.2661244")}

Rodgers, D. M., Jacobucci, R., & Grimm, K. J. (2021). A multiple imputation approach for handling missing data in classification and regression trees. Journal of Behavioral Data Science, 1(1), 127-153. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.35566/jbds/v1n1/p6")}

Examples

df1 <- data.frame(x = 1:5, y = c(2, 4, 6, 8, 10))
df2 <- data.frame(x = 1:5, y = c(2, 3, 6, 9, 10))
stacked <- stack_imputations(list(df1, df2))
nrow(stacked) # 10
table(stacked$.imp)

ctreeMI documentation built on Oct. 12, 2026, 5:08 p.m.