README.md

In vpnsctl/bbreg: Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data

The bbreg package

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Dev badges:

The bbreg package deals with regression models with response variables being continuous and bounded. It currently provides implementation of two regression models: bessel regression https://arxiv.org/abs/2003.05157 and beta regression https://doi.org/10.1080/0266476042000214501. For both of these models, the estimation is done with the EM-algorithm. The EM-algorithm approach for beta regression was developed in https://doi.org/10.1080/00949655.2017.1350679.

Installing the bbreg package from CRAN:

install.packages("bbreg")

Installing the bbreg package from this repository:

To install the bbreg package from this repository, just run the command:

#install.packages("devtools")
devtools::install_github("vpnsctl/bbreg")

To install the bbreg package from this repository with vignette, run the command:

#install.packages("devtools")
devtools::install_github("vpnsctl/bbreg", build_vignettes = TRUE)

This repository will always contain the most recent version of the bbreg package.

Vignette

The bbreg package has a vignette. Check the most recent version of the vignette at https://rpubs.com/alexandrebsimas/intro-bbreg

Basic usage

The usage of the bbreg package is analogous to the usage of standard regression functions and packages in R:

library(bbreg)
fit <- bbreg(agreement ~ priming + eliciting, data = WT)
fit
#> 
#> Bessel regression via EM - Model selected via Discrimination test (DBB)
#> 
#> Call: 
#> bbreg(agreement ~ priming + eliciting | 1)
#> 
#> Coefficients modeling the mean (with logit link):
#> (intercept)     priming   eliciting 
#>  -1.1537851  -0.2548633   0.3392742 
#> Coefficients modeling the precision (with identity link):
#> (intercept) 
#>    4.924825

summary(fit)
#> 
#> Bessel regression via EM - Model selected via Discrimination test (DBB):
#> Call:
#> bbreg(agreement ~ priming + eliciting | 1)
#> Number of iterations of the EM algorithm = 297
#> 
#>  Results of the discrimination test DBB:
#>     sum(z2/n) sum(quasi_mu)    |D_bessel|      |D_beta| 
#>        0.0853       52.6201        0.0004        0.0030 
#> 
#>  Pearson residuals:
#>      RSS      Min       1Q   Median       3Q      Max 
#> 331.9432  -1.7387  -0.6606  -0.3847   0.5562   4.5786 
#> 
#>  Coefficients modeling the mean (with logit link):
#>             Estimate Std.error z-value Pr(>|z|)    
#> (intercept) -1.15379   0.05251 -21.971  < 2e-16 ***
#> priming     -0.25486   0.05733  -4.446 8.77e-06 ***
#> eliciting    0.33927   0.05846   5.804 6.49e-09 ***
#> 
#>  Coefficients modeling the precision (with identity link):
#>             Estimate Std.error z-value Pr(>|z|)    
#> (intercept)   4.9248    0.4493   10.96   <2e-16 ***
#> g(phi) = 0.1316
#> ---
#>  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

For further details we refer the reader to the vignette whose link can be found above.

vpnsctl/bbreg documentation built on March 14, 2021, 12:11 a.m.