Introduction | Model | Parameter interpretation | Median effect | Data requirements | Fitting the models | LBC-ML: single-fit maximum likelihood | LBC-MS: multi-start fitting | LBC-EL and LBC-CM: bootstrap aggregation | Prediction and model evaluation | Built-in NHANES data | Main functions | References
Package: lboxcox 2.0.1
Li Xing
lboxcox: Implementation of Logistic Box-Cox Regression
Implements a logistic Box-Cox model that adds a shape parameter to a routine logistic regression model to flexibly estimate the shape and strength of the relationship between a binary outcome and a continuous predictor, adjusting for covariates and survey weights. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>. This version extends the original 'lboxcox' package (1.1) with numerically stabilized likelihood/gradient calculations, vectorized data preprocessing, and a bootstrap-ensemble estimator.
Authors:
lboxcox_2.0.1.tar.gz
lboxcox_2.0.1.zip(r-4.7-any)lboxcox_2.0.1.zip(r-4.6-any)
lboxcox_2.0.1.tar.gz(r-4.7-any)lboxcox_2.0.1.tar.gz(r-4.6-any)
lboxcox_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
lboxcox/json (API)
| # Install 'lboxcox' in R: |
| install.packages('lboxcox', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- depress - Depression dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:f8d0f31bb0. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 147 | ||
| source / vignettes | OK | 237 | ||
| linux-release | OK | 147 | ||
| windows-devel | OK | 150 | ||
| windows-release | OK | 115 | ||
| wasm-release | OK | 128 |
Exports:devrlbc_maxliklbc_train_alllbc_train_bagginglbc_train_mslboxcox_cv.fitlboxcox_cv.predictlboxcox_maxLik_el.predictlboxcox_maxLik.predictmedian_effect
Dependencies:caretclasscliclockcodetoolscpp11data.tableDBIdiagramdigestdoParalleldplyre1071farverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmaxLikminqamiscToolsmitoolsModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartS7sandwichscalesshapesparsevctrsSQUAREMstringistringrsurveysurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Depression dataset | depress |
| Deviance residual prediction error | devr |
| Fit a logistic Box-Cox model by direct likelihood maximization | lbc_maxlik |
| Bootstrap-ensemble logistic Box-Cox model around 'lbc_train_ms' | lbc_train_all |
| Bootstrap-ensemble logistic Box-Cox model around 'lbc_maxlik' | lbc_train_bagging |
| Fit a logistic Box-Cox model from multiple starting points | lbc_train_ms |
| Select the Box-Cox lambda by cross-validated deviance residual | lboxcox_cv.fit |
| Predict from a 'lboxcox_cv.fit' model | lboxcox_cv.predict |
| Predict from a bootstrap-ensemble logistic Box-Cox model | lboxcox_maxLik_el.predict |
| Predict from a fitted logistic Box-Cox model | lboxcox_maxLik.predict |
| Median exposure-response slope of a fitted logistic Box-Cox model | median_effect |