Package: tidymv 3.4.2
Stefano Coretta
tidymv: Tidy Model Visualisation for Generalised Additive Models
Provides functions for visualising generalised additive models and getting predicted values using tidy tools from the 'tidyverse' packages.
Authors:
tidymv_3.4.2.tar.gz
tidymv_3.4.2.zip(r-4.5)tidymv_3.4.2.zip(r-4.4)tidymv_3.4.2.zip(r-4.3)
tidymv_3.4.2.tgz(r-4.4-any)tidymv_3.4.2.tgz(r-4.3-any)
tidymv_3.4.2.tar.gz(r-4.5-noble)tidymv_3.4.2.tar.gz(r-4.4-noble)
tidymv_3.4.2.tgz(r-4.4-emscripten)tidymv_3.4.2.tgz(r-4.3-emscripten)
tidymv.pdf |tidymv.html✨
tidymv/json (API)
NEWS
# Install 'tidymv' in R: |
install.packages('tidymv', repos = c('https://stefanocoretta.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/stefanocoretta/tidymv/issues
ggplot2modelsoftwaretidyversevisualization
Last updated 2 years agofrom:0a20b885b8. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-win | OK | Oct 31 2024 |
R-4.5-linux | OK | Oct 31 2024 |
R-4.4-win | OK | Oct 31 2024 |
R-4.4-mac | OK | Oct 31 2024 |
R-4.3-win | OK | Oct 31 2024 |
R-4.3-mac | OK | Oct 31 2024 |
Exports:create_start_eventfind_differencegeom_smooth_ciget_differenceget_gam_predictionsget_smooths_differenceplot_differenceplot_gamsdplot_smoothspredict_gamsummary_data
Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RColorBrewerrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Create a start event column. | create_start_event |
Smooths and confidence intervals. | geom_smooth_ci |
Get predictions from a GAM model. | get_gam_predictions |
Get difference of smooths from a GAM model | get_smooths_difference |
Dataset with two factors | inter_df |
Plot difference smooth from a GAM. | plot_difference |
Plot GAM smooths. | plot_smooths |
Dataset with a Poisson outcome variable | pois_df |
Get predictions from a GAM model. | predict_gam |