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Package: modelbased 0.17.0.13
Dominique Makowski
modelbased: Estimation of Model-Based Predictions, Contrasts and Means
Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see 'insight::supported_models()'.
Authors:
modelbased_0.17.0.13.tar.gz
modelbased_0.17.0.13.zip(r-4.7-any)modelbased_0.17.0.13.zip(r-4.6-any)modelbased_0.17.0.13.zip(r-4.5-any)
modelbased_0.17.0.13.tgz(r-4.6-any)modelbased_0.17.0.13.tgz(r-4.5-any)
modelbased_0.17.0.13.tar.gz(r-4.7-any)modelbased_0.17.0.13.tar.gz(r-4.6-any)
modelbased_0.17.0.13.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
modelbased/json (API)
| # Install 'modelbased' in R: |
| install.packages('modelbased', repos = c('https://easystats.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/easystats/modelbased/issues
Pkgdown/docs site:https://easystats.github.io
Datasets:
- coffee_data - Sample dataset from a course about analysis of factorial designs
- efc - Sample dataset from the EFC Survey
- fish - Sample data set
- puppy_love - More puppy therapy data
- stigma - Sample dataset from patients with somatic symptons
contrast-analysiscontrastseasystatsestimateggplot2hacktoberfestmarginalmarginal-effectsmeanspredict
Last updated from:bd28738335. Checks:7 OK, 2 ERROR. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 214 | ||
| source / vignettes | OK | 262 | ||
| linux-release | OK | 247 | ||
| macos-release | OK | 123 | ||
| macos-oldrel | ERROR | 162 | ||
| windows-devel | OK | 251 | ||
| windows-release | OK | 256 | ||
| windows-oldrel | ERROR | 284 | ||
| wasm-release | OK | 179 |
Exports:collapse_by_groupdescribe_nonlineardisplayestimate_contrastsestimate_expectationestimate_grouplevelestimate_linkestimate_meansestimate_predictionestimate_relationestimate_slopesestimate_smoothfind_inversionsget_emcontrastsget_emmeansget_emtrendsget_marginalcontrastsget_marginalmeansget_marginaltrendspool_contrastspool_predictionspool_slopesprint_htmlprint_mdreshape_grouplevelresidualize_over_gridsmoothingstandardizeunstandardizevisualisation_recipezero_crossings
Dependencies:bayestestRdatawizardinsightparameters
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Converting modelbased-objects into raw data frames | as.data.frame.estimate_contrasts |
| Sample dataset from a course about analysis of factorial designs | coffee_data |
| Collapse raw data by random effect groups | collapse_by_group |
| Describe the smooth term (for GAMs) or non-linear predictors | describe_nonlinear describe_nonlinear.data.frame estimate_smooth |
| Printing modelbased-objects | display.estimate_contrasts format.estimate_contrasts print.estimate_contrasts |
| Sample dataset from the EFC Survey | efc |
| Estimate Marginal Contrasts | estimate_contrasts estimate_contrasts.default |
| Model-based predictions | estimate_expectation estimate_link estimate_prediction estimate_relation |
| Group-specific parameters of mixed models random effects | estimate_grouplevel estimate_grouplevel.brmsfit estimate_grouplevel.default reshape_grouplevel reshape_grouplevel.estimate_grouplevel |
| Estimate Marginal Means (Model-based average at each factor level) | estimate_means |
| Estimate Marginal Effects | estimate_slopes |
| Sample data set | fish |
| Consistent API for 'emmeans' and 'marginaleffects' | get_emcontrasts get_emmeans get_emtrends get_marginalcontrasts get_marginalmeans get_marginaltrends |
| Global options from the modelbased package | modelbased-options |
| Automated plotting for 'modelbased' objects | plot.estimate_means plot.estimate_predicted tinyplot.estimate_means visualisation_recipe.estimate_grouplevel visualisation_recipe.estimate_predicted visualisation_recipe.estimate_slopes |
| Pool contrasts and comparisons from 'estimate_contrasts()' | pool_contrasts |
| Pool Predictions and Estimated Marginal Means | pool_predictions pool_slopes |
| More puppy therapy data | puppy_love |
| Compute partial residuals from a data grid | residualize_over_grid residualize_over_grid.data.frame |
| Smoothing a vector or a time series | smoothing |
| Sample dataset from patients with somatic symptons | stigma |
| Find zero-crossings and inversion points | find_inversions zero_crossings |