Package: psfmi 1.4.0
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>.
Authors:
psfmi_1.4.0.tar.gz
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psfmi_1.4.0.tgz(r-4.4-any)
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psfmi.pdf |psfmi.html✨
psfmi/json (API)
NEWS
# Install 'psfmi' in R: |
install.packages('psfmi', repos = c('https://mwheymans.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mwheymans/psfmi/issues
- anderson - Data from a placebo-controlled RCT with leukemia patients
- aortadis - Dataset of patients with a aortadissection
- bmd - Data of a non-experimental study in more than 300 elderly women
- chlrform - Data about concentration of ß2-microglobuline in urine as indicator for possible damage to the kidney
- chol_long - Long dataset of persons from the The Amsterdam Growth and Health Longitudinal Study
- chol_wide - Wide dataset of persons from the The Amsterdam Growth and Health Longitudinal Study
- day2_dataset4_mi - Dataset of low back pain patients with missing values
- hipstudy - Dataset of elderly patients with a hip fracture
- hipstudy_external - External Dataset of elderly patients with a hip fracture
- hoorn_basic - Dataset of the Hoorn Study
- infarct - Data of a patient-control study regarding the relationship between MI and smoking
- ipdna_md - Example dataset for the psfmi_mm function
- lbp_orig - Example dataset for psfmi_perform function, method boot_MI
- lbpmi_extval - Example dataset of Low Back Pain Patients for external validation
- lbpmicox - Example dataset for psfmi_coxr function
- lbpmilr - Example dataset for psfmi_lr function
- lbpmilr_dev - Example dataset for mivalext_lr function
- lungvolume - Data of the development of lung and heartvolume of unborn babies
- mammaca - Data of a study among women with breast cancer
- men - Data of 613 patients with meningitis
- sbp_age - Dataset with blood pressure measurements
- sbp_qas - Dataset with blood pressure measurements
- smoking - Survival data about smoking
- weight - Dataset of persons from the The Amsterdam Growth and Health Longitudinal Study
cox-regressionimputationimputed-datasetslogisticmultiple-imputationpoolpredictorregressionselectionsplinespline-predictors
Last updated 1 years agofrom:6afb51f1f1. Checks:OK: 5. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 08 2024 |
R-4.5-win | OK | Nov 08 2024 |
R-4.5-linux | OK | Nov 08 2024 |
R-4.4-win | OK | Nov 08 2024 |
R-4.4-mac | OK | Nov 08 2024 |
Exports:boot_MIbw_singleclean_Pcoxph_bwcoxph_fwcv_MIcv_MI_RRglm_bwglm_fwhoslem_testkm_estimateskm_fitmean_auc_logMI_bootMI_cv_naivemiceImpmivalext_lrnri_coxnri_estpool_aucpool_compare_modelspool_D2pool_D4pool_intadjpool_performancepool_performance_internalpool_reclassificationpool_RRpsfmi_coxrpsfmi_coxr_bwpsfmi_coxr_fwpsfmi_lmpsfmi_lm_bwpsfmi_lm_fwpsfmi_lrpsfmi_lr_bwpsfmi_lr_fwpsfmi_mmpsfmi_mm_multiparmpsfmi_performpsfmi_stabpsfmi_validaterisk_coxphRR_diff_proprsq_nagelrsq_survscaled_brierstab_single
Dependencies:abindbackportsbase64encbitbit64bitopsbootbroombslibcachemcarcarDatacaToolscheckmateclicliprclustercodetoolscolorspacecowplotcpp11crayoncvAUCdata.tableDBIDerivdigestdoBydplyrevaluatefansifarverfastmapfontawesomeforcatsforeachforeignFormulafsfurrrfuturegenericsggplot2glmnetglobalsgluegplotsgridExtragtablegtoolshavenhighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjomojquerylibjsonliteKernSmoothknitrlabelinglatticelifecyclelistenvlme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicemicrobenchmarkmimeminqamitmlmitoolsmodelrmultcompmunsellmvtnormnlmenloptrnnetnormnumDerivordinalpanparallellypbkrtestpillarpkgconfigplyrpolsplineprettyunitspROCprogresspurrrquantregR6rappdirsRColorBrewerRcppRcppEigenreadrrlangrmarkdownrmsROCRrpartrsamplerstudioapisandwichsassscalesshapesliderSparseMstringistringrsurvivalTH.datatibbletidyrtidyselecttinytextzdbucminfutf8vctrsviridisviridisLitevroomwarpwithrxfunyamlzoo
Pool Model Performance
Rendered fromPool_Model_Performance.Rmd
usingknitr::rmarkdown
on Nov 08 2024.Last update: 2023-06-17
Started: 2021-09-23
Pooling and Selection of Cox Regression Models
Rendered frompsfmi_CoxModels.Rmd
usingknitr::rmarkdown
on Nov 08 2024.Last update: 2023-06-15
Started: 2020-06-30
Pooling and Selection of Linear Regression Models
Rendered frompsfmi_LinearModels.Rmd
usingknitr::rmarkdown
on Nov 08 2024.Last update: 2021-08-29
Started: 2021-08-29
Pooling and Selection of Logistic Regression Models
Rendered frompsfmi_LogisticModels.Rmd
usingknitr::rmarkdown
on Nov 08 2024.Last update: 2021-08-29
Started: 2020-06-30
Pooling AUC values
Rendered fromPooling_AUC_values.Rmd
usingknitr::rmarkdown
on Nov 08 2024.Last update: 2023-06-17
Started: 2021-09-23
Working together: mice and psfmi
Rendered frompsfmi_mice.Rmd
usingknitr::rmarkdown
on Nov 08 2024.Last update: 2021-08-29
Started: 2020-07-01