report_ctreeMI: Methods Paragraph for a Stacked-Imputation Tree

In ctreeMI: Conditional Inference Trees with Stacked Multiple Imputation

Methods Paragraph for a Stacked-Imputation Tree

Description

Generates a short methods paragraph describing how a ctree_stacked model was fit, populated with the actual values used: the number of imputations, the original and stacked sample sizes, the nominal and applied significance thresholds, and the size and depth of the resulting tree. The paragraph is intended to be edited and pasted into a manuscript, and it standardizes how the Stack/M correction is described across studies.

Usage

report_ctreeMI(object, digits = 3)

Arguments

object	An object of class "ctreeMI" as returned by ctree_stacked.
digits	Integer. Significant digits used when reporting the applied significance threshold.

Details

The generated text describes the imputation and stacking workflow and the Stack/M threshold correction, and states that node sizes are reported on the original scale. It does not describe the imputation model itself, which is specific to the analysis and must be added by the author: the number of iterations, the variables included, and the imputation methods used should be reported alongside this paragraph.

Value

An object of class "ctreeMI_report": a list whose text element is the methods paragraph as a single character string, alongside the underlying quantities (m, n_original, n_stacked, alpha_nominal, alpha_applied, n_terminal, max_depth, formula) so they can be used programmatically. The print method wraps and displays the paragraph.

References

Sherlock, P., Mansolf, M., Hofheimer, J., Hockett, C. W., O'Connor, T. G., Roubinov, D., Graff, J. C., Lai, J.-S., Bush, N. R., Wright, R. J., & Chiu, Y.-H. M. (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")}

See Also

ctree_stacked, node_table

Examples

set.seed(1)
make_df <- function(i) {
  set.seed(i)
  n  <- 200
  x1 <- rnorm(n)
  y  <- 2 * (x1 > 0) + rnorm(n)
  data.frame(y = y, x1 = x1)
}
imp_list <- lapply(1:10, make_df)
fit <- ctree_stacked(y ~ x1, data = imp_list, verbose = FALSE)

# Print the methods paragraph
report_ctreeMI(fit)

# Access the raw text or the underlying quantities
rep <- report_ctreeMI(fit)
rep$n_terminal
substr(rep$text, 1, 60)

ctreeMI documentation built on July 26, 2026, 1:06 a.m.