psfmi: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>.

Version: 1.4.0
Depends: R (≥ 4.0.0)
Imports: ggplot2, norm, survival, mitools, pROC, rms, magrittr, rsample, mice, mitml, cvAUC, dplyr, purrr, tidyr, tibble, stringr, lme4, car
Suggests: foreign (≥ 0.8-80), knitr, rmarkdown, testthat (≥ 3.0.0), bookdown, readr, gtools, covr
Published: 2023-06-17
DOI: 10.32614/CRAN.package.psfmi
Author: Martijn Heymans ORCID iD [cre, aut], Iris Eekhout [ctb]
Maintainer: Martijn Heymans <mw.heymans at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
In views: MissingData
CRAN checks: psfmi results


Reference manual: psfmi.pdf
Vignettes: Pool Model Performance
Pooling AUC values
Pooling and Selection of Cox Regression Models
Pooling and Selection of Linear Regression Models
Pooling and Selection of Logistic Regression Models
Working together: mice and psfmi


Package source: psfmi_1.4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): psfmi_1.4.0.tgz, r-oldrel (arm64): psfmi_1.4.0.tgz, r-release (x86_64): psfmi_1.4.0.tgz, r-oldrel (x86_64): psfmi_1.4.0.tgz
Old sources: psfmi archive


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