The goal of bpnreg is to fit Bayesian projected normal regression models for circular data.
The R-package bpnreg can be installed from CRAN as follows:
You can install a beta-version of bpnreg from github with:
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
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