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 (2020). bpnreg: Bayesian Projected Normal Regression Models for Circular Data. R package version 1.0.3. 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)
#> Warning: package 'bpnreg' was built under R version 4.0.2
bpnr(Phaserad ~ Cond + AvAmp, Motor)
#> Projected Normal Regression
#>
#> Model
#>
#> Call:
#> bpnr(pred.I = Phaserad ~ Cond + AvAmp, data = Motor)
#>
#> MCMC:
#> iterations = 1000
#> burn-in = 1
#> lag = 1
#>
#> Model Fit:
#> Statistic Parameters
#> lppd -57.02276 8.000000
#> DIC 129.91767 7.933199
#> DIC.alt 129.16896 7.558843
#> WAIC 129.92344 7.938965
#> WAIC2 131.73043 8.842460
#>
#>
#> Linear Coefficients
#>
#> Component I:
#> mean mode sd LB HPD UB HPD
#> (Intercept) 1.35903284 1.31054078 0.45916211 0.51176151 2.26408763
#> Condsemi.imp -0.51431167 -0.38356351 0.65112849 -1.76181870 0.77224926
#> Condimp -0.63880458 -0.74159122 0.66793394 -1.86754185 0.71109682
#> AvAmp -0.01055016 -0.01139835 0.01218486 -0.03623167 0.01170322
#>
#> Component II:
#> mean mode sd LB HPD UB HPD
#> (Intercept) 1.42272991 1.27049889 0.42518913 0.59913060 2.23085396
#> Condsemi.imp -1.17555420 -1.07575082 0.58198521 -2.31181884 -0.04772718
#> Condimp -0.97477439 -1.16513093 0.61236345 -2.16960668 0.15627423
#> AvAmp -0.01120924 -0.01173855 0.01088949 -0.03060563 0.01049163
#>
#>
#> Circular Coefficients
#>
#> Continuous variables:
#> mean ax mode ax sd ax LB ax UB ax
#> 81.92119 66.77753 106.93783 -126.42475 287.94441
#>
#> mean ac mode ac sd ac LB ac UB ac
#> 0.9607303 2.1672257 1.2672149 -0.8206028 2.4573048
#>
#> mean bc mode bc sd bc LB bc UB bc
#> -0.001954087 0.011505774 0.030594802 -0.038497492 0.025762454
#>
#> mean AS mode AS sd AS LB AS UB AS
#> -0.017532826 -0.007563145 0.309320733 -0.124976392 0.130372158
#>
#> mean SAM mode SAM sd SAM LB SAM UB SAM
#> -0.03039514 -0.00759563 0.38996867 -0.25949211 0.22435792
#>
#> mean SSDO mode SSDO sd SSDO LB SSSO UB SSDO
#> -0.07185253 -2.05247080 2.04637805 -2.81847543 2.77529945
#>
#> Categorical variables:
#>
#> Means:
#> mean mode sd LB UB
#> (Intercept) 0.8133872 0.7973453 0.1984700 0.4171888 1.188380
#> Condsemi.imp 0.2843519 0.2298160 0.4338476 -0.5892295 1.117094
#> Condimp 0.5580093 0.5334042 0.4576417 -0.4067679 1.387852
#> Condsemi.impCondimp -1.2346729 -1.0865211 1.0882791 3.0123533 1.194460
#>
#> Differences:
#> mean mode sd LB UB
#> Condsemi.imp 0.5327909 0.5016397 0.5005797 -0.5136915 1.425091
#> Condimp 0.2591943 0.0723281 0.5398564 -0.7832167 1.294051
#> Condsemi.impCondimp 2.1411008 2.4533343 1.0282856 -0.4460970 3.959229