insight
Easy Access to Model Information for Various Model Objects
A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.
Gain insight into your models!
The goal of insight is to provide tools to help an easy, intuitive and consistent accesss to information contained in various models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. Although there are generic functions to get information and data from models, many modelling-functions from different packages do not provide such methods to access these information. The insight package aims at closing this gap by providing functions that work for (almost) any model.
Installation
Run the following to install the latest GitHub-version of insight:
install.packages("devtools")devtools::install_github("easystats/insight")Or install the latest stable release from CRAN:
install.packages("insight")Documentation and Support
Please visit https://easystats.github.io/insight/ for documentation. In case you want to file an issue or contribute in another way to the package, please follow this guide. For questions about the functionality, you may either contact me via email or also file an issue.
Functions
The syntax of insight mainly revolves around two types of functions. One is to find the names of the things (find_*), and the second is to actually get the things (get_). The things can be the following:
On top of that, the model_info() function runs many checks to help you classify and understand the nature of your model.
List of Supported Packages and Models
AER (ivreg, tobit), afex (mixed), base (aov, aovlist, lm, glm), BayesFactor (BFBayesFactor), betareg (betareg), biglm (biglm, bigglm), blme (blmer, bglmer), brms (brmsfit), censReg, crch, countreg (zerontrunc), coxme, estimatr (lm_robust, iv_robust), feisr (feis), gam (Gam), gamm4 , gamlss, gbm, gee, geepack (geeglm), GLMMadaptive (MixMod), glmmTMB (glmmTMB), gmnl, lfe (felm), lme4 (lmer, glmer, nlmer, glmer.nb), MASS (glmmPQL, polr), mgcv (gam, gamm), multgee (LORgee), nnet (multinom), nlme (lme, gls), ordinal (clm, clm2, clmm), plm, pscl (zeroinf, hurdle), quantreg (rq, crq, rqss), rms (lsr, ols, psm), robust (glmRob, lmRob), robustbase (glmrob, lmrob), robustlmm (rlmer), rstanarm (stanreg, stanmvreg), speedlm (speedlm, speedglm), survey, survival (coxph, survreg), truncreg (truncreg), VGAM (vgam, vglm)
- Didn't find a model? File an issue and request additional model-support in insight!
Credits
If this package helped you, please consider citing as follows:
- Lüdecke D, Makowski D (2019). insight: Easy Access to Model Information for Various Model Objects. R package. https://easystats.github.io/insight/.
News
insight 0.3.0
New supported model classes
biglmandbigglm(biglm),feis(feisr),gbm(gbm),BFBayesFactor(BayesFactor),psm(rms),LORgee(multgee),censReg(censReg),ols(rms),speedlmandspeedglm(speedglm),svyolr(survey)
New functions
is_nullmodel()to check if model is a null-model (intercept-only), i.e. if the conditional part of the model has no predictors.has_intercept()to check if model has an intercept.
Breaking Changes
- Functions like
find_predictors()orfind_terms()returnNULLfor null-models (intercept-only models). Useis_nullmodel()to check if your model only has an intercept-parameter (but no predictors). get_variance()no longer stops if random effects variance cannot be calculated. Rather, the return-value for$var.randomwill beNULL.
Changes to functions
get_variance()now computes the full variance for mixed models with zero-inflation component.get_priors()now returns the default-prior that was defined for all parameters of a class, if certain parameters have no specific prior.find_parameters()gets aflatten-argument, to either return results as list or as simple vector.find_variables()gets aflatten-argument, to either return results as list or as simple vector.
Bug fixes
get_data()did not work when model formula contained a function with namespace-prefix (likelm(Sepal.Length ~ splines::bs(Petal.Width, df=4)) (#93).get_priors()failed for stanreg-models, when one or more priors had no adjusted scales (#74).find_random()failed for mixed models with multiple responses.get_random()failed for brmsfit and stanreg models.get_parameters()andfind_parameters()did not work forMixMod-objects without zero-inflation component, whencomponent = "all"(the default).find_formula()did not work forplm-models without instrumental variables.find_formula()returned random effects as conditional part of the formula for null-models (only intercept in fixed parts) (#87).- Fixed issue with invalid notation of instrumental-variables formula in
felm-models for R-devel on Linux. - Fixed issue with
get_data()for gee models, where incomplete cases were not removed from the data. - Fixed potential issue with
get_data()for null-models (only intercept in fixed parts) from models of classglmmTMB,brmsfit,MixModandrstanarm(#91). find_variables()no longer returns (multiple)"1"for random effects.
insight 0.2.0
General
- Better handling of
AsIs-variables with division-operation as dependent variables, e.g. if outcome was defined asI(income/frequency), especially forfind_response()andget_data(). - Revised package-functions related to
felm-models due to breaking changes in the lfe-package.
New supported model classes
iv_robust(estimatr),crch(crch),gamlss(gamlss),lmrobandglmrob(robustbase, #64),rq,rqssandcrq(quantreg),rlmer(robustlmm),mixed(afex),tobit(AER) andsurvreg(survival).
New functions
get_variance(), to calculate the variance components from mixed models of classmerMod,glmmTMB,MixMod,rlmer,mixed,lmeandstanreg(#52). Furthermore, convenient shortcuts to return the related components directly, likeget_variance_random()orget_variance_residual().find_algorithm(), to get information about sampling algorithms and optimizers, and for Bayesian models also about chains and iterations (#38).find_random_slopes(), which returns the names of the random slopes of mixed models.get_priors(), to get a summary of priors used for a model (#39).is_model()to check whether an object is a (supported) regression model (#69).all_models_equal()to check whether objects are all (supported) regression models and of same class.print_color()(resp.print_colour()) to print coloured output to the console. Mainly implemented to reduce package dependencies.
Changes to functions
find_parameters()andget_parameters()get aparameters-argument forbrmsfitandstanregmodels, to allow selection of specific parameters that should be returned (#55).find_parameters()andget_parameters()now also return simplex parameters of monotic effects (brms only) and smooth terms (e.g. for gam-models).find_terms()andfind_predictors()no longer return constants, in particularpi(#26).- For
glsandlmeobjects, functions likefind_formula()etc. also return the correlation component (#19). model_infonow returns$is_tweediefor models from tweedie-families.
Bug fixes
find_parameters()andget_parameters()did not preserve coefficients of monotonic category-specific effects from brmsfit-objects.- Fixed bug that sometimes returned more elements for
find_predictors()orget_parameters()than requested. - Fixed bug in
get_data()for MixMod-objects when response variable was defined viacbind(). - Fixed bug in
get_response()for models that usedcbind()with a substraction (e.g.cbind(success, total - success)). In such cases, values for second column (in this example:total) were the substracted valuestotal - success, not the original values fromtotal.
insight 0.1.2
General
- Initial release.