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psfmi  

Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets
View on CRAN: Click here


Download and install psfmi package within the R console
Install from CRAN:
install.packages("psfmi")

Install from Github:
library("remotes")
install_github("cran/psfmi")

Install by package version:
library("remotes")
install_version("psfmi", "1.4.0")



Attach the package and use:
library("psfmi")
Maintained by
Martijn Heymans
[Scholar Profile | Author Map]
First Published: 2019-05-16
Latest Update: 2023-06-17
Description:
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) . Wiel (2009) . Marshall (2009) .
How to cite:
Martijn Heymans (2019). psfmi: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets. R package version 1.4.0, https://cran.r-project.org/web/packages/psfmi. Accessed 05 May. 2025.
Previous versions and publish date:
0.1.0 (2019-05-16 13:50), 0.2.0 (2020-02-03 08:30), 0.5.0 (2020-09-24 14:30), 0.7.1 (2021-01-13 17:40), 1.0.0 (2021-09-23 12:10), 1.1.0 (2022-11-06 16:50), 1.3.0 (2023-06-16 09:10)
Other packages that cited psfmi R package
View psfmi citation profile
Other R packages that psfmi depends, imports, suggests or enhances
Complete documentation for psfmi
Functions, R codes and Examples using the psfmi R package
Some associated functions: MI_boot . MI_cv_naive . RR_diff_prop . anderson . aortadis . bmd . boot_MI . bw_single . chlrform . chol_long . chol_wide . clean_P . coxph_bw . coxph_fw . cv_MI . cv_MI_RR . day2_dataset4_mi . glm_bw . glm_fw . hipstudy . hipstudy_external . hoorn_basic . hoslem_test . infarct . ipdna_md . km_estimates . km_fit . lbp_orig . lbpmi_extval . lbpmicox . lbpmilr . lbpmilr_dev . lungvolume . mammaca . mean_auc_log . men . miceImp . mivalext_lr . nri_cox . nri_est . pool_D2 . pool_D4 . pool_RR . pool_auc . pool_compare_models . pool_intadj . pool_performance . pool_performance_internal . pool_reclassification . psfmi_coxr . psfmi_coxr_bw . psfmi_coxr_fw . psfmi_lm . psfmi_lm_bw . psfmi_lm_fw . psfmi_lr . psfmi_lr_bw . psfmi_lr_fw . psfmi_mm . psfmi_mm_multiparm . psfmi_perform . psfmi_stab . psfmi_validate . risk_coxph . rsq_nagel . rsq_surv . sbp_age . sbp_qas . scaled_brier . smoking . stab_single . weight . 
Some associated R codes: MI_boot.R . MI_cv_naive.R . RR_diff_prop.R . boot_MI.R . bw_single.R . clean_P.R . coxph_bw.R . coxph_fw.R . cv_MI.R . cv_MI_RR.R . glm_bw.R . glm_fw.R . hoslem_test.R . km_estimates.R . km_fit.R . lbpmi_extval.R . mean_auc_log.R . miceImp.R . mivalext_lr.R . nri_cox.R . nri_est.R . pool_D2.R . pool_D4.R . pool_RR.R . pool_auc.R . pool_compare_models.R . pool_intadj.R . pool_performance.R . pool_performance_internal.R . pool_reclassification.R . psfmi_coxr.R . psfmi_coxr_bw.R . psfmi_coxr_fw.R . psfmi_lm.R . psfmi_lm_bw.R . psfmi_lm_fw.R . psfmi_lr.R . psfmi_lr_bw.R . psfmi_lr_fw.R . psfmi_mm.R . psfmi_mm_multiparm.R . psfmi_perform.R . psfmi_stab.R . psfmi_validate.R . risk_coxph.R . rsq_nagel.R . rsq_surv.R . scaled_brier.R . stab_single.R .  Full psfmi package functions and examples
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