bpnreg
Bayesian Projected Normal Regression Models for Circular Data
Fitting Bayesian multiple and mixed-effect regression models for
circular data based on the projected normal distribution. Both continuous
and categorical predictors can be included. Sampling from the posterior is
performed via an MCMC algorithm. Posterior descriptives of all parameters,
model fit statistics and Bayes factors for hypothesis tests for inequality
constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018)
bpnreg
The goal of bpnreg is to fit Bayesian projected normal regression models for circular data.
Installation
The R-package bpnreg can be installed from CRAN as follows:
install.packages("bpnreg")
You can install a beta-version of bpnreg from github with:
# install.packages("devtools")
devtools::install_github("joliencremers/bpnreg")
Citation
To cite the package ‘bpnreg’ in publications use:
Jolien Cremers (2021). bpnreg: Bayesian Projected Normal Regression Models for Circular Data. R package version 2.0.2. https://CRAN.R-project.org/package=bpnreg
Example
This is a basic example which shows you how to run a Bayesian projected normal regression model:
library(bpnreg)
bpnr(Phaserad ~ Cond + AvAmp, Motor, its = 100)
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#> Projected Normal Regression
#>
#> Model
#>
#> Call:
#> bpnr(pred.I = Phaserad ~ Cond + AvAmp, data = Motor, its = 100)
#>
#> MCMC:
#> iterations = 100
#> burn-in = 1
#> lag = 1
#>
#> Model Fit:
#> Statistic Parameters
#> lppd -57.22945 8.000000
#> DIC 127.66465 6.768024
#> DIC.alt 124.17298 5.022188
#> WAIC1 127.33436 6.437733
#> WAIC2 128.65389 7.097498
#>
#>
#> Linear Coefficients
#>
#> Component I:
#> mean mode sd LB HPD UB HPD
#> (Intercept) 1.319611894 1.39128370 0.45635201 0.33485506 2.03794238
#> Condsemi.imp -0.522451171 -0.47667290 0.57057933 -1.55833243 0.50939839
#> Condimp -0.650053029 -0.99688228 0.64741848 -2.00362696 0.53197461
#> AvAmp -0.009320081 -0.01808984 0.01296947 -0.03096035 0.01524266
#>
#> Component II:
#> mean mode sd LB HPD UB HPD
#> (Intercept) 1.37081341 1.057909990 0.43448499 0.5256653 2.265534446
#> Condsemi.imp -1.13529041 -1.508829276 0.60583443 -2.2586284 0.029840305
#> Condimp -0.93550260 -1.263941265 0.62075876 -2.3158274 -0.009041090
#> AvAmp -0.01016616 -0.003931414 0.01062028 -0.0285245 0.008526117
#>
#>
#> Circular Coefficients
#>
#> Continuous variables:
#> mean ax mode ax sd ax LB ax UB ax
#> 116.31973 76.25854 562.60196 -154.19115 219.74298
#>
#> mean ac mode ac sd ac LB ac UB ac
#> 1.0746179 2.2543777 1.1994513 -0.8224601 2.4169745
#>
#> mean bc mode bc sd bc LB bc UB bc
#> -0.034814814 -0.006854753 0.499046459 -0.767238134 0.666230333
#>
#> mean AS mode AS sd AS LB AS UB AS
#> 4.875002e-04 6.466495e-05 5.442953e-03 -1.160784e-02 2.842468e-03
#>
#> mean SAM mode SAM sd SAM LB SAM UB SAM
#> 1.437848e-03 1.305745e-04 1.940441e-02 3.180594e-08 3.466995e-03
#>
#> mean SSDO mode SSDO sd SSDO LB SSSO UB SSDO
#> -0.05101017 1.88339563 1.99577431 -2.77725635 2.64369230
#>
#> Categorical variables:
#>
#> Means:
#> mean mode sd LB UB
#> (Intercept) 0.8119255 0.8675846 0.1957991 0.4326112 1.2082844
#> Condsemi.imp 0.2962062 0.3373583 0.3399843 -0.4996824 0.8360214
#> Condimp 0.5851581 0.4454521 0.4819606 -0.4032866 1.4047517
#> Condsemi.impCondimp -1.3273542 -2.0443304 1.1135480 -2.8870086 1.4407720
#>
#> Differences:
#> mean mode sd LB UB
#> Condsemi.imp 0.5152442 0.4826193 0.4033441 -0.2197928 1.286760
#> Condimp 0.2261741 0.3480214 0.5484078 -0.8033373 1.395936
#> Condsemi.impCondimp 2.2043432 2.8593855 1.0362019 -0.4035095 3.855837