[cre, aut],
Iris Eekhout [ctb]Pooling, backward and forward selection of 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 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 bootstrapping and cluster bootstrapping. The package further contains functions to pool the model performance as ROC/AUC, R-squares, scaled Brier score and calibration plots for logistic regression models. Internal validation can be done 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. 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: | 0.7.1 |
| Depends: | R (≥ 4.0.0) |
| Imports: | ggplot2 (≥ 3.3.2), norm (≥ 1.0-9.5), survival (≥ 3.1-12), mitools (≥ 2.4), pROC (≥ 1.16.2), rms (≥ 6.1-0), ResourceSelection (≥ 0.3-5), magrittr (≥ 2.0.1), rsample (≥ 0.0.8), mice (≥ 3.12.0), mitml (≥ 0.3-7), cvAUC (≥ 1.1.0), dplyr (≥ 1.0.2), purrr (≥ 0.3.4), tidyr (≥ 1.1.2), tibble (≥ 3.0.4), stringr (≥ 1.4.0), lme4 (≥ 1.1-26), miceadds (≥ 3.10-28), car (≥ 3.0-10) |
| Suggests: | foreign (≥ 0.8-80), knitr, rmarkdown, testthat, bookdown, readr |
| Published: | 2021-01-13 |
| Author: | Martijn Heymans
[cre, aut],
Iris Eekhout [ctb] |
| Maintainer: | Martijn Heymans <mw.heymans at amsterdamumc.nl> |
| BugReports: | https://github.com/mwheymans/psfmi/issues/ |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://mwheymans.github.io/psfmi/ |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | MissingData |
| CRAN checks: | psfmi results |
| Reference manual: | psfmi.pdf |
| Vignettes: |
Multiple Imputation and Bootstrapping - Method MI_boot Multiple Imputation and Cross-validation - The MI_cv_naive method Multiple Imputation and Bootstrapping - Method boot_MI Multiple Imputation and Cross-validation - method cv_MI Multiple Imputation and Cross-validation - Method cv_MI_RR Workflow to Develop and Validate a Prediction model in Multiply Imputed data Pooling and Selection of Cox Regression Models Pooling and Selection of Logistic Regression Models Stability analysis after Multiple Imputation Working together: mice and psfmi |
| Package source: | psfmi_0.7.1.tar.gz |
| Windows binaries: | r-devel: psfmi_0.7.1.zip, r-release: psfmi_0.7.1.zip, r-oldrel: psfmi_0.5.0.zip |
| macOS binaries: | r-release: psfmi_0.7.1.tgz, r-oldrel: psfmi_0.5.0.tgz |
| Old sources: | psfmi archive |
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