parameters/ 0000755 0001762 0000144 00000000000 13620060023 012405 5 ustar ligges users parameters/NAMESPACE 0000644 0001762 0000144 00000045510 13620043641 013640 0 ustar ligges users # Generated by roxygen2: do not edit by hand
S3method(as.data.frame,glmmTMB)
S3method(as.data.frame,lm)
S3method(as.data.frame,merMod)
S3method(as.double,n_clusters)
S3method(as.double,n_factors)
S3method(as.numeric,n_clusters)
S3method(as.numeric,n_factors)
S3method(bootstrap_model,default)
S3method(bootstrap_model,merMod)
S3method(check_multimodal,data.frame)
S3method(check_multimodal,numeric)
S3method(ci,BBmm)
S3method(ci,BBreg)
S3method(ci,DirichletRegModel)
S3method(ci,LORgee)
S3method(ci,MixMod)
S3method(ci,aareg)
S3method(ci,betareg)
S3method(ci,biglm)
S3method(ci,bracl)
S3method(ci,brmultinom)
S3method(ci,censReg)
S3method(ci,clm)
S3method(ci,clm2)
S3method(ci,clmm2)
S3method(ci,comlmrob)
S3method(ci,coxme)
S3method(ci,coxph)
S3method(ci,cpglm)
S3method(ci,cpglmm)
S3method(ci,crch)
S3method(ci,crq)
S3method(ci,default)
S3method(ci,effectsize_std_params)
S3method(ci,feglm)
S3method(ci,feis)
S3method(ci,fixest)
S3method(ci,flexsurvreg)
S3method(ci,gam)
S3method(ci,gamlss)
S3method(ci,gamm)
S3method(ci,gamm4)
S3method(ci,gee)
S3method(ci,geeglm)
S3method(ci,glm)
S3method(ci,glmmTMB)
S3method(ci,glmmadmb)
S3method(ci,glmrob)
S3method(ci,glmx)
S3method(ci,gls)
S3method(ci,hurdle)
S3method(ci,ivreg)
S3method(ci,list)
S3method(ci,lm)
S3method(ci,lm_robust)
S3method(ci,lme)
S3method(ci,logistf)
S3method(ci,lrm)
S3method(ci,merMod)
S3method(ci,mixor)
S3method(ci,mlm)
S3method(ci,multinom)
S3method(ci,negbin)
S3method(ci,nlrq)
S3method(ci,ols)
S3method(ci,plm)
S3method(ci,polr)
S3method(ci,psm)
S3method(ci,rma)
S3method(ci,rms)
S3method(ci,rq)
S3method(ci,rqss)
S3method(ci,speedglm)
S3method(ci,speedlm)
S3method(ci,survreg)
S3method(ci,svyglm.glimML)
S3method(ci,svyglm.nb)
S3method(ci,svyglm.zip)
S3method(ci,tobit)
S3method(ci,truncreg)
S3method(ci,vglm)
S3method(ci,zerocount)
S3method(ci,zeroinfl)
S3method(convert_data_to_numeric,character)
S3method(convert_data_to_numeric,data.frame)
S3method(convert_data_to_numeric,double)
S3method(convert_data_to_numeric,factor)
S3method(convert_data_to_numeric,logical)
S3method(convert_data_to_numeric,numeric)
S3method(convert_efa_to_cfa,fa)
S3method(convert_efa_to_cfa,parameters_efa)
S3method(convert_efa_to_cfa,parameters_pca)
S3method(describe_distribution,data.frame)
S3method(describe_distribution,factor)
S3method(describe_distribution,numeric)
S3method(dof_satterthwaite,lme)
S3method(dof_satterthwaite,lmerMod)
S3method(equivalence_test,MixMod)
S3method(equivalence_test,glm)
S3method(equivalence_test,glmmTMB)
S3method(equivalence_test,lm)
S3method(equivalence_test,merMod)
S3method(factor_analysis,data.frame)
S3method(format_parameters,default)
S3method(format_parameters,parameters_model)
S3method(format_parameters,rma)
S3method(kurtosis,data.frame)
S3method(kurtosis,default)
S3method(kurtosis,matrix)
S3method(kurtosis,numeric)
S3method(model_parameters,BFBayesFactor)
S3method(model_parameters,DirichletRegModel)
S3method(model_parameters,FAMD)
S3method(model_parameters,MCMCglmm)
S3method(model_parameters,Mclust)
S3method(model_parameters,MixMod)
S3method(model_parameters,PCA)
S3method(model_parameters,anova)
S3method(model_parameters,aov)
S3method(model_parameters,aovlist)
S3method(model_parameters,befa)
S3method(model_parameters,betareg)
S3method(model_parameters,blavaan)
S3method(model_parameters,bracl)
S3method(model_parameters,brmsfit)
S3method(model_parameters,brmultinom)
S3method(model_parameters,cgam)
S3method(model_parameters,clm2)
S3method(model_parameters,clmm)
S3method(model_parameters,clmm2)
S3method(model_parameters,cpglmm)
S3method(model_parameters,default)
S3method(model_parameters,fa)
S3method(model_parameters,gam)
S3method(model_parameters,gamlss)
S3method(model_parameters,gamm)
S3method(model_parameters,glmmTMB)
S3method(model_parameters,glmx)
S3method(model_parameters,htest)
S3method(model_parameters,hurdle)
S3method(model_parameters,kmeans)
S3method(model_parameters,lavaan)
S3method(model_parameters,list)
S3method(model_parameters,lme)
S3method(model_parameters,mcmc)
S3method(model_parameters,merMod)
S3method(model_parameters,mixor)
S3method(model_parameters,mlm)
S3method(model_parameters,multinom)
S3method(model_parameters,omega)
S3method(model_parameters,parameters_efa)
S3method(model_parameters,parameters_pca)
S3method(model_parameters,principal)
S3method(model_parameters,rlmerMod)
S3method(model_parameters,rma)
S3method(model_parameters,rqss)
S3method(model_parameters,stanmvreg)
S3method(model_parameters,stanreg)
S3method(model_parameters,vgam)
S3method(model_parameters,wbgee)
S3method(model_parameters,wbm)
S3method(model_parameters,zerocount)
S3method(model_parameters,zeroinfl)
S3method(n_parameters,BBmm)
S3method(n_parameters,Gam)
S3method(n_parameters,MCMCglmm)
S3method(n_parameters,MixMod)
S3method(n_parameters,brmsfit)
S3method(n_parameters,coxme)
S3method(n_parameters,cpglmm)
S3method(n_parameters,default)
S3method(n_parameters,gam)
S3method(n_parameters,glimML)
S3method(n_parameters,glmmTMB)
S3method(n_parameters,hurdle)
S3method(n_parameters,lavaan)
S3method(n_parameters,lme)
S3method(n_parameters,merMod)
S3method(n_parameters,mixed)
S3method(n_parameters,multinom)
S3method(n_parameters,rlmerMod)
S3method(n_parameters,sim.merMod)
S3method(n_parameters,stanmvreg)
S3method(n_parameters,stanreg)
S3method(n_parameters,vgam)
S3method(n_parameters,wbm)
S3method(n_parameters,zeroinfl)
S3method(n_parameters,zerotrunc)
S3method(p_value,BBmm)
S3method(p_value,BBreg)
S3method(p_value,BFBayesFactor)
S3method(p_value,DirichletRegModel)
S3method(p_value,Gam)
S3method(p_value,LORgee)
S3method(p_value,MCMCglmm)
S3method(p_value,MixMod)
S3method(p_value,aareg)
S3method(p_value,anova)
S3method(p_value,aov)
S3method(p_value,aovlist)
S3method(p_value,betareg)
S3method(p_value,biglm)
S3method(p_value,bracl)
S3method(p_value,brmsfit)
S3method(p_value,brmultinom)
S3method(p_value,censReg)
S3method(p_value,cgam)
S3method(p_value,clm2)
S3method(p_value,clmm2)
S3method(p_value,complmrob)
S3method(p_value,coxme)
S3method(p_value,coxph)
S3method(p_value,cpglm)
S3method(p_value,cpglmm)
S3method(p_value,crch)
S3method(p_value,crq)
S3method(p_value,data.frame)
S3method(p_value,default)
S3method(p_value,feglm)
S3method(p_value,fixest)
S3method(p_value,flexsurvreg)
S3method(p_value,gam)
S3method(p_value,gamlss)
S3method(p_value,gamm)
S3method(p_value,gamm4)
S3method(p_value,gee)
S3method(p_value,geeglm)
S3method(p_value,glimML)
S3method(p_value,glmmTMB)
S3method(p_value,glmx)
S3method(p_value,gls)
S3method(p_value,gmnl)
S3method(p_value,htest)
S3method(p_value,hurdle)
S3method(p_value,ivreg)
S3method(p_value,list)
S3method(p_value,lm)
S3method(p_value,lm_robust)
S3method(p_value,lme)
S3method(p_value,lmerMod)
S3method(p_value,logistf)
S3method(p_value,lrm)
S3method(p_value,maxLik)
S3method(p_value,merMod)
S3method(p_value,mixor)
S3method(p_value,mlm)
S3method(p_value,multinom)
S3method(p_value,negbin)
S3method(p_value,nlrq)
S3method(p_value,numeric)
S3method(p_value,ols)
S3method(p_value,pggls)
S3method(p_value,pglm)
S3method(p_value,plm)
S3method(p_value,polr)
S3method(p_value,psm)
S3method(p_value,rlm)
S3method(p_value,rlmerMod)
S3method(p_value,rma)
S3method(p_value,rms)
S3method(p_value,rq)
S3method(p_value,rqss)
S3method(p_value,stanreg)
S3method(p_value,survreg)
S3method(p_value,svyglm)
S3method(p_value,svyglm.nb)
S3method(p_value,svyglm.zip)
S3method(p_value,svyolr)
S3method(p_value,tobit)
S3method(p_value,truncreg)
S3method(p_value,vgam)
S3method(p_value,vglm)
S3method(p_value,wbgee)
S3method(p_value,wbm)
S3method(p_value,zerocount)
S3method(p_value,zeroinfl)
S3method(p_value_kenward,lmerMod)
S3method(p_value_satterthwaite,gls)
S3method(p_value_satterthwaite,lme)
S3method(p_value_satterthwaite,lmerMod)
S3method(p_value_wald,cpglmm)
S3method(p_value_wald,merMod)
S3method(p_value_wald,rlmerMod)
S3method(plot,check_clusterstructure)
S3method(plot,cluster_analysis)
S3method(plot,n_clusters)
S3method(plot,n_factors)
S3method(plot,parameters_distribution)
S3method(plot,parameters_efa)
S3method(plot,parameters_model)
S3method(plot,parameters_pca)
S3method(plot,parameters_sem)
S3method(plot,parameters_simulate)
S3method(predict,kmeans)
S3method(predict,parameters_clusters)
S3method(predict,parameters_efa)
S3method(predict,parameters_pca)
S3method(predict,parameters_sem)
S3method(principal_components,data.frame)
S3method(principal_components,lm)
S3method(principal_components,merMod)
S3method(print,cfa_model)
S3method(print,cluster_analysis)
S3method(print,cluster_discrimintation)
S3method(print,equivalence_test_lm)
S3method(print,n_clusters)
S3method(print,n_factors)
S3method(print,parameters_clusters)
S3method(print,parameters_distribution)
S3method(print,parameters_efa)
S3method(print,parameters_efa_summary)
S3method(print,parameters_loadings)
S3method(print,parameters_model)
S3method(print,parameters_omega)
S3method(print,parameters_omega_summary)
S3method(print,parameters_pca)
S3method(print,parameters_pca_summary)
S3method(print,parameters_random)
S3method(print,parameters_sem)
S3method(reduce_parameters,data.frame)
S3method(reduce_parameters,lm)
S3method(reduce_parameters,merMod)
S3method(reshape_loadings,data.frame)
S3method(reshape_loadings,parameters_efa)
S3method(se_satterthwaite,default)
S3method(se_satterthwaite,gls)
S3method(se_satterthwaite,lme)
S3method(select_parameters,lm)
S3method(select_parameters,merMod)
S3method(select_parameters,stanreg)
S3method(simulate_model,LORgee)
S3method(simulate_model,MixMod)
S3method(simulate_model,betareg)
S3method(simulate_model,biglm)
S3method(simulate_model,bracl)
S3method(simulate_model,brmultinom)
S3method(simulate_model,censReg)
S3method(simulate_model,cglm)
S3method(simulate_model,clm)
S3method(simulate_model,clm2)
S3method(simulate_model,clmm2)
S3method(simulate_model,coxme)
S3method(simulate_model,coxph)
S3method(simulate_model,cpglm)
S3method(simulate_model,cpglmm)
S3method(simulate_model,crch)
S3method(simulate_model,crq)
S3method(simulate_model,default)
S3method(simulate_model,feglm)
S3method(simulate_model,feis)
S3method(simulate_model,fixest)
S3method(simulate_model,flexsurvreg)
S3method(simulate_model,gam)
S3method(simulate_model,gamlss)
S3method(simulate_model,gamm)
S3method(simulate_model,gee)
S3method(simulate_model,geeglm)
S3method(simulate_model,glimML)
S3method(simulate_model,glm)
S3method(simulate_model,glmRob)
S3method(simulate_model,glmmTMB)
S3method(simulate_model,glmmadmb)
S3method(simulate_model,glmrob)
S3method(simulate_model,glmx)
S3method(simulate_model,gls)
S3method(simulate_model,hurdle)
S3method(simulate_model,iv_robust)
S3method(simulate_model,ivreg)
S3method(simulate_model,list)
S3method(simulate_model,lm)
S3method(simulate_model,lmRob)
S3method(simulate_model,lm_robust)
S3method(simulate_model,lme)
S3method(simulate_model,lmrob)
S3method(simulate_model,logistf)
S3method(simulate_model,lrm)
S3method(simulate_model,merMod)
S3method(simulate_model,mixor)
S3method(simulate_model,multinom)
S3method(simulate_model,nlrq)
S3method(simulate_model,ols)
S3method(simulate_model,plm)
S3method(simulate_model,polr)
S3method(simulate_model,psm)
S3method(simulate_model,rms)
S3method(simulate_model,rq)
S3method(simulate_model,speedglm)
S3method(simulate_model,speedlm)
S3method(simulate_model,survreg)
S3method(simulate_model,svyglm.nb)
S3method(simulate_model,svyglm.zip)
S3method(simulate_model,tobit)
S3method(simulate_model,truncreg)
S3method(simulate_model,vgam)
S3method(simulate_model,vglm)
S3method(simulate_model,zerocount)
S3method(simulate_model,zeroinfl)
S3method(simulate_parameters,default)
S3method(simulate_parameters,multinom)
S3method(skewness,data.frame)
S3method(skewness,default)
S3method(skewness,matrix)
S3method(skewness,numeric)
S3method(sort,parameters_efa)
S3method(sort,parameters_pca)
S3method(standard_error,BBmm)
S3method(standard_error,BBreg)
S3method(standard_error,DirichletRegModel)
S3method(standard_error,LORgee)
S3method(standard_error,MCMCglmm)
S3method(standard_error,MixMod)
S3method(standard_error,aareg)
S3method(standard_error,anova)
S3method(standard_error,aov)
S3method(standard_error,aovlist)
S3method(standard_error,betareg)
S3method(standard_error,biglm)
S3method(standard_error,bracl)
S3method(standard_error,brmultinom)
S3method(standard_error,censReg)
S3method(standard_error,cgam)
S3method(standard_error,character)
S3method(standard_error,clm2)
S3method(standard_error,clmm2)
S3method(standard_error,complmrob)
S3method(standard_error,coxme)
S3method(standard_error,coxph)
S3method(standard_error,cpglm)
S3method(standard_error,cpglmm)
S3method(standard_error,crch)
S3method(standard_error,crq)
S3method(standard_error,data.frame)
S3method(standard_error,default)
S3method(standard_error,effectsize_std_params)
S3method(standard_error,factor)
S3method(standard_error,feglm)
S3method(standard_error,fixest)
S3method(standard_error,flexsurvreg)
S3method(standard_error,gam)
S3method(standard_error,gamlss)
S3method(standard_error,gamm)
S3method(standard_error,gamm4)
S3method(standard_error,gee)
S3method(standard_error,geeglm)
S3method(standard_error,glimML)
S3method(standard_error,glm)
S3method(standard_error,glmmTMB)
S3method(standard_error,glmx)
S3method(standard_error,gls)
S3method(standard_error,gmnl)
S3method(standard_error,htest)
S3method(standard_error,hurdle)
S3method(standard_error,ivreg)
S3method(standard_error,list)
S3method(standard_error,lm)
S3method(standard_error,lm_robust)
S3method(standard_error,lme)
S3method(standard_error,logistf)
S3method(standard_error,lrm)
S3method(standard_error,merMod)
S3method(standard_error,mixor)
S3method(standard_error,mlm)
S3method(standard_error,multinom)
S3method(standard_error,negbin)
S3method(standard_error,nlrq)
S3method(standard_error,numeric)
S3method(standard_error,ols)
S3method(standard_error,plm)
S3method(standard_error,polr)
S3method(standard_error,psm)
S3method(standard_error,rma)
S3method(standard_error,rms)
S3method(standard_error,rq)
S3method(standard_error,rqss)
S3method(standard_error,survreg)
S3method(standard_error,svyglm)
S3method(standard_error,svyglm.nb)
S3method(standard_error,svyglm.zip)
S3method(standard_error,table)
S3method(standard_error,tobit)
S3method(standard_error,truncreg)
S3method(standard_error,vgam)
S3method(standard_error,vglm)
S3method(standard_error,wbgee)
S3method(standard_error,wbm)
S3method(standard_error,xtabs)
S3method(standard_error,zerocount)
S3method(standard_error,zeroinfl)
S3method(standardize_names,default)
S3method(standardize_names,parameters_model)
S3method(summary,n_clusters)
S3method(summary,n_factors)
S3method(summary,parameters_clusters)
S3method(summary,parameters_efa)
S3method(summary,parameters_omega)
S3method(summary,parameters_pca)
export(DRR)
export(ICA)
export(bootstrap_model)
export(bootstrap_parameters)
export(check_clusterstructure)
export(check_factorstructure)
export(check_kmo)
export(check_multimodal)
export(check_sphericity)
export(ci)
export(ci_betwithin)
export(ci_kenward)
export(ci_ml1)
export(ci_robust)
export(ci_satterthwaite)
export(ci_wald)
export(closest_component)
export(cluster_analysis)
export(cluster_discrimination)
export(cmds)
export(convert_data_to_numeric)
export(convert_efa_to_cfa)
export(data_partition)
export(data_to_numeric)
export(degrees_of_freedom)
export(demean)
export(describe_distribution)
export(dof)
export(dof_betwithin)
export(dof_kenward)
export(dof_ml1)
export(dof_satterthwaite)
export(efa_to_cfa)
export(equivalence_test)
export(factor_analysis)
export(format_algorithm)
export(format_bf)
export(format_model)
export(format_number)
export(format_order)
export(format_p)
export(format_parameters)
export(format_pd)
export(format_rope)
export(get_scores)
export(kurtosis)
export(model_bootstrap)
export(model_parameters)
export(model_simulate)
export(n_clusters)
export(n_factors)
export(n_parameters)
export(p_value)
export(p_value_betwithin)
export(p_value_kenward)
export(p_value_ml1)
export(p_value_robust)
export(p_value_satterthwaite)
export(p_value_wald)
export(parameters)
export(parameters_bootstrap)
export(parameters_reduction)
export(parameters_selection)
export(parameters_simulate)
export(parameters_table)
export(parameters_type)
export(principal_components)
export(random_parameters)
export(reduce_parameters)
export(rescale_weights)
export(reshape_loadings)
export(se_betwithin)
export(se_kenward)
export(se_ml1)
export(se_satterthwaite)
export(select_parameters)
export(simulate_model)
export(simulate_parameters)
export(skewness)
export(smoothness)
export(standard_error)
export(standard_error_robust)
export(standardize_names)
importFrom(bayestestR,bayesfactor_models)
importFrom(bayestestR,bayesian_as_frequentist)
importFrom(bayestestR,ci)
importFrom(bayestestR,convert_pd_to_p)
importFrom(bayestestR,describe_posterior)
importFrom(bayestestR,equivalence_test)
importFrom(bayestestR,p_direction)
importFrom(bayestestR,rope_range)
importFrom(grDevices,colorRampPalette)
importFrom(grDevices,dev.off)
importFrom(grDevices,png)
importFrom(insight,clean_names)
importFrom(insight,clean_parameters)
importFrom(insight,find_algorithm)
importFrom(insight,find_parameters)
importFrom(insight,find_predictors)
importFrom(insight,find_random)
importFrom(insight,find_random_slopes)
importFrom(insight,find_response)
importFrom(insight,find_terms)
importFrom(insight,format_ci)
importFrom(insight,format_table)
importFrom(insight,format_value)
importFrom(insight,get_data)
importFrom(insight,get_parameters)
importFrom(insight,get_priors)
importFrom(insight,get_random)
importFrom(insight,get_statistic)
importFrom(insight,get_varcov)
importFrom(insight,get_variance)
importFrom(insight,has_intercept)
importFrom(insight,model_info)
importFrom(insight,n_obs)
importFrom(insight,print_color)
importFrom(insight,print_colour)
importFrom(methods,slot)
importFrom(stats,ave)
importFrom(stats,coef)
importFrom(stats,complete.cases)
importFrom(stats,confint)
importFrom(stats,cor)
importFrom(stats,cov2cor)
importFrom(stats,cutree)
importFrom(stats,df.residual)
importFrom(stats,dist)
importFrom(stats,hclust)
importFrom(stats,heatmap)
importFrom(stats,kmeans)
importFrom(stats,lm)
importFrom(stats,logLik)
importFrom(stats,model.matrix)
importFrom(stats,na.omit)
importFrom(stats,pchisq)
importFrom(stats,pnorm)
importFrom(stats,prcomp)
importFrom(stats,predict)
importFrom(stats,pt)
importFrom(stats,qnorm)
importFrom(stats,qt)
importFrom(stats,reshape)
importFrom(stats,runif)
importFrom(stats,sd)
importFrom(stats,setNames)
importFrom(stats,sigma)
importFrom(stats,step)
importFrom(stats,update)
importFrom(stats,var)
importFrom(stats,vcov)
importFrom(tools,toTitleCase)
importFrom(utils,capture.output)
importFrom(utils,head)
importFrom(utils,tail)
parameters/README.md 0000644 0001762 0000144 00000023750 13620033002 013670 0 ustar ligges users
# parameters
[](https://cran.r-project.org/package=parameters)
[](https://cran.r-project.org/package=parameters)
[](https://travis-ci.org/easystats/parameters)
***Describe and understand your model’s parameters\!***
`parameters`’ primary goal is to provide utilities for processing the
parameters of various statistical models. Beyond computing
***p*-values**, **CIs**, **Bayesian indices** and other measures for a
wide variety of models, this package implements features like
**bootstrapping** of parameters and models, **feature reduction**
(feature extraction and variable selection).
## Installation
Run the following:
``` r
install.packages("parameters")
```
``` r
library("parameters")
```
## Documentation
[](https://easystats.github.io/parameters/)
[](https://easystats.github.io/blog/posts/)
[](https://easystats.github.io/parameters/reference/index.html)
Click on the buttons above to access the package
[documentation](https://easystats.github.io/parameters/) and the
[easystats blog](https://easystats.github.io/blog/posts/), and check-out
these vignettes:
- [Summary of Model
Parameters](https://easystats.github.io/parameters/articles/model_parameters.html)
- [Standardized Model
Parameters](https://easystats.github.io/parameters/articles/model_parameters_standardized.html)
- [Robust Estimation of Standard Errors, Confidence Intervals and
p-values](https://easystats.github.io/parameters/articles/model_parameters_robust.html)
- [Parameters
selection](https://easystats.github.io/parameters/articles/parameters_selection.html)
- [Feature reduction (PCA, cMDS,
ICA…)](https://easystats.github.io/parameters/articles/parameters_reduction.html)
- [Structural models (EFA, CFA,
SEM…)](https://easystats.github.io/parameters/articles/efa_cfa.html)
# Features
## Model’s parameters description
The
[`model_parameters()`](https://easystats.github.io/parameters/articles/model_parameters.html)
function (that can be accessed via the `parameters()` shortcut) allows
you to extract the parameters and their characteristics from various
models in a consistent way. It can be considered as a lightweight
alternative to [`broom::tidy()`](https://github.com/tidymodels/broom),
with some notable differences:
- The column names of the returned data frame are **specific** to
their content. For instance, the column containing the statistic is
named following the statistic name, i.e., *t*, *z*, etc., instead of
a generic name such as *statistic* (**however**, you can get
standardized (generic) column names using
[`standardize_names()`](https://easystats.github.io/parameters/reference/standardize_names.html)).
- It is able to compute or extract indices not available by default,
such as ***p*-values**, **CIs**, etc.
- It includes **feature engineering** capabilities, including
parameters
[**bootstrapping**](https://easystats.github.io/parameters/reference/bootstrap_parameters.html).
### Classical Regression Models
``` r
model <- lm(Sepal.Width ~ Petal.Length * Species + Petal.Width, data = iris)
# regular model parameters
model_parameters(model)
# Parameter | Coefficient | SE | 95% CI | t | df | p
# ------------------------------------------------------------------------------------------------
# (Intercept) | 2.89 | 0.36 | [ 2.18, 3.60] | 8.01 | 143 | < .001
# Petal.Length | 0.26 | 0.25 | [-0.22, 0.75] | 1.07 | 143 | 0.287
# Species [versicolor] | -1.66 | 0.53 | [-2.71, -0.62] | -3.14 | 143 | 0.002
# Species [virginica] | -1.92 | 0.59 | [-3.08, -0.76] | -3.28 | 143 | 0.001
# Petal.Width | 0.62 | 0.14 | [ 0.34, 0.89] | 4.41 | 143 | < .001
# Petal.Length * Species [versicolor] | -0.09 | 0.26 | [-0.61, 0.42] | -0.36 | 143 | 0.721
# Petal.Length * Species [virginica] | -0.13 | 0.26 | [-0.64, 0.38] | -0.50 | 143 | 0.618
# standardized parameters
model_parameters(model, standardize = "refit")
# Parameter | Coefficient | SE | 95% CI | t | df | p
# ------------------------------------------------------------------------------------------------
# (Intercept) | 3.59 | 1.30 | [ 1.01, 6.17] | 2.75 | 143 | 0.007
# Petal.Length | 1.07 | 1.00 | [-0.91, 3.04] | 1.07 | 143 | 0.287
# Species [versicolor] | -4.62 | 1.31 | [-7.21, -2.03] | -3.53 | 143 | < .001
# Species [virginica] | -5.51 | 1.38 | [-8.23, -2.79] | -4.00 | 143 | < .001
# Petal.Width | 1.08 | 0.24 | [ 0.59, 1.56] | 4.41 | 143 | < .001
# Petal.Length * Species [versicolor] | -0.38 | 1.06 | [-2.48, 1.72] | -0.36 | 143 | 0.721
# Petal.Length * Species [virginica] | -0.52 | 1.04 | [-2.58, 1.54] | -0.50 | 143 | 0.618
```
### Mixed Models
``` r
library(lme4)
model <- lmer(Sepal.Width ~ Petal.Length + (1|Species), data = iris)
# model parameters with CI, df and p-values based on Wald approximation
model_parameters(model)
# Parameter | Coefficient | SE | 95% CI | t | df | p
# ----------------------------------------------------------------------
# (Intercept) | 2.00 | 0.56 | [0.90, 3.10] | 3.56 | 146 | < .001
# Petal.Length | 0.28 | 0.06 | [0.17, 0.40] | 4.75 | 146 | < .001
# model parameters with CI, df and p-values based on Kenward-Roger approximation
model_parameters(model, df_method = "kenward")
# Parameter | Coefficient | SE | 95% CI | t | df | p
# -------------------------------------------------------------------------
# (Intercept) | 2.00 | 0.57 | [0.89, 3.11] | 3.53 | 2.67 | 0.046
# Petal.Length | 0.28 | 0.06 | [0.16, 0.40] | 4.58 | 140.99 | < .001
```
### Structural Models
Besides many types of regression models and packages, it also works for
other types of models, such as [**structural
models**](https://easystats.github.io/parameters/articles/efa_cfa.html)
(EFA, CFA, SEM…).
``` r
library(psych)
model <- psych::fa(attitude, nfactors = 3)
model_parameters(model)
# # Rotated loadings from Principal Component Analysis (oblimin-rotation)
#
# Variable | MR1 | MR2 | MR3 | Complexity | Uniqueness
# ------------------------------------------------------------
# rating | 0.90 | -0.07 | -0.05 | 1.02 | 0.23
# complaints | 0.97 | -0.06 | 0.04 | 1.01 | 0.10
# privileges | 0.44 | 0.25 | -0.05 | 1.64 | 0.65
# learning | 0.47 | 0.54 | -0.28 | 2.51 | 0.24
# raises | 0.55 | 0.43 | 0.25 | 2.35 | 0.23
# critical | 0.16 | 0.17 | 0.48 | 1.46 | 0.67
# advance | -0.11 | 0.91 | 0.07 | 1.04 | 0.22
#
# The 3 latent factors (oblimin rotation) accounted for 66.60% of the total variance of the original data (MR1 = 38.19%, MR2 = 22.69%, MR3 = 5.72%).
```
## Variable and parameters selection
[`parameters_selection()`](https://easystats.github.io/parameters/articles/parameters_selection.html)
can help you quickly select and retain the most relevant predictors
using methods tailored for the model type.
``` r
library(dplyr)
lm(disp ~ ., data = mtcars) %>%
select_parameters() %>%
model_parameters()
# Parameter | Coefficient | SE | 95% CI | t | df | p
# ----------------------------------------------------------------------------
# (Intercept) | 141.70 | 125.67 | [-116.62, 400.02] | 1.13 | 26 | 0.270
# cyl | 13.14 | 7.90 | [ -3.10, 29.38] | 1.66 | 26 | 0.108
# hp | 0.63 | 0.20 | [ 0.22, 1.03] | 3.18 | 26 | 0.004
# wt | 80.45 | 12.22 | [ 55.33, 105.57] | 6.58 | 26 | < .001
# qsec | -14.68 | 6.14 | [ -27.31, -2.05] | -2.39 | 26 | 0.024
# carb | -28.75 | 5.60 | [ -40.28, -17.23] | -5.13 | 26 | < .001
```
## Miscellaneous
This packages also contains a lot of [other useful
functions](https://easystats.github.io/parameters/reference/index.html):
### Describe a Distribution
``` r
data(iris)
describe_distribution(iris)
# Variable | Mean | SD | Min | Max | Skewness | Kurtosis | n | n_Missing
# --------------------------------------------------------------------------------
# Sepal.Length | 5.84 | 0.83 | 4.30 | 7.90 | 0.31 | -0.55 | 150 | 0
# Sepal.Width | 3.06 | 0.44 | 2.00 | 4.40 | 0.32 | 0.23 | 150 | 0
# Petal.Length | 3.76 | 1.77 | 1.00 | 6.90 | -0.27 | -1.40 | 150 | 0
# Petal.Width | 1.20 | 0.76 | 0.10 | 2.50 | -0.10 | -1.34 | 150 | 0
```
### Citation
In order to cite this package, please use the following citation:
- Makowski D, Ben-Shachar M, Lüdecke D (2019). “Describe and
understand your model’s parameters.” CRAN. R package,
.
Corresponding BibTeX entry:
@Article{,
title = {Describe and understand your model's parameters},
author = {Dominique Makowski and Mattan S. Ben-Shachar and Daniel
Lüdecke},
journal = {CRAN},
year = {2019},
note = {R package},
url = {https://github.com/easystats/parameters},
}
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