Gain insight into your models!
The goal of insight is to provide tools to help an easy, intuitive and consistent access 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 modeling-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.
A statistical model is an object describing the relationship between variables. Although there are a lot of different types of models, each with their specificities, most of them also share some common components. The goal of insight
is to help you retrieve these components.
Such components are:
x + I(x^2)
, there is only the term x
.x + I(x^2)
has two objects with two different sets of data values, and thus are treated as two variables.Isn’t the predictors, the terms and the parameters the same thing?
In some cases, yes. But not in all cases. Find out more by clicking here to access the documentation.
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")
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.
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.
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)
If this package helped you, please consider citing as follows: