Package: pdp 0.8.2

Brandon M. Greenwell

pdp: Partial Dependence Plots

A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

Authors:Brandon M. Greenwell [aut, cre]

pdp_0.8.2.tar.gz
pdp_0.8.2.tar.gz(r-4.5-noble)pdp_0.8.2.tar.gz(r-4.4-noble)
pdp_0.8.2.tgz(r-4.4-emscripten)pdp_0.8.2.tgz(r-4.3-emscripten)
pdp.pdf |pdp.html
pdp/json (API)
NEWS

# Install 'pdp' in R:
install.packages('pdp', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/bgreenwell/pdp/issues

Datasets:
  • boston - Boston Housing Data
  • pima - Pima Indians Diabetes Data

7.38 score 9 packages 1.1k scripts 5.4k downloads 5 exports 31 dependencies

Last updated 1 days agofrom:8692d1048b. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-linux-x86_64OKOct 29 2024

Exports:exemplarpartialplotPartialtopPredictorstrellis.last.object

Dependencies:clicodetoolscolorspacefansifarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

pdp-approximate

Rendered frompdp-approximate.Rnwusingutils::Sweaveon Oct 29 2024.

Last update: 2022-05-11
Started: 2022-05-11

pdp-introduction

Rendered frompdp-intro.Rnwusingutils::Sweaveon Oct 29 2024.

Last update: 2022-05-11
Started: 2022-05-11

pdp-link-function

Rendered frompdp-link-function.Rnwusingutils::Sweaveon Oct 29 2024.

Last update: 2022-05-11
Started: 2022-05-11

Readme and manuals

Help Manual

Help pageTopics
Plotting Partial Dependence Functionsautoplot.cice autoplot.ice autoplot.partial
Boston Housing Databoston
Exemplar observationexemplar exemplar.data.frame exemplar.dgCMatrix exemplar.matrix
Partial Dependence Functionspartial partial.default partial.model_fit
Pima Indians Diabetes Datapima
Plotting Partial Dependence FunctionsplotPartial plotPartial.cice plotPartial.ice plotPartial.partial
Extract Most "Important" Predictors (Experimental)topPredictors topPredictors.default topPredictors.train