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psfmi
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]
[Scholar Profile | Author Map]
All associated links for this package
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
Previous versions and publish date:
Other packages that cited psfmi R package
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Other R packages that psfmi depends,
imports, suggests or enhances
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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