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 2.0.1. 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)
#> Iteration:1
#> Iteration:2
#> Iteration:3
#> Iteration:4
#> Iteration:5
#> Iteration:6
#> Iteration:7
#> Iteration:8
#> Iteration:9
#> Iteration:10
#> Iteration:11
#> Iteration:12
#> Iteration:13
#> Iteration:14
#> Iteration:15
#> Iteration:16
#> Iteration:17
#> Iteration:18
#> Iteration:19
#> Iteration:20
#> Iteration:21
#> Iteration:22
#> Iteration:23
#> Iteration:24
#> Iteration:25
#> Iteration:26
#> Iteration:27
#> Iteration:28
#> Iteration:29
#> Iteration:30
#> Iteration:31
#> Iteration:32
#> Iteration:33
#> Iteration:34
#> Iteration:35
#> Iteration:36
#> Iteration:37
#> Iteration:38
#> Iteration:39
#> Iteration:40
#> Iteration:41
#> Iteration:42
#> Iteration:43
#> Iteration:44
#> Iteration:45
#> Iteration:46
#> Iteration:47
#> Iteration:48
#> Iteration:49
#> Iteration:50
#> Iteration:51
#> Iteration:52
#> Iteration:53
#> Iteration:54
#> Iteration:55
#> Iteration:56
#> Iteration:57
#> Iteration:58
#> Iteration:59
#> Iteration:60
#> Iteration:61
#> Iteration:62
#> Iteration:63
#> Iteration:64
#> Iteration:65
#> Iteration:66
#> Iteration:67
#> Iteration:68
#> Iteration:69
#> Iteration:70
#> Iteration:71
#> Iteration:72
#> Iteration:73
#> Iteration:74
#> Iteration:75
#> Iteration:76
#> Iteration:77
#> Iteration:78
#> Iteration:79
#> Iteration:80
#> Iteration:81
#> Iteration:82
#> Iteration:83
#> Iteration:84
#> Iteration:85
#> Iteration:86
#> Iteration:87
#> Iteration:88
#> Iteration:89
#> Iteration:90
#> Iteration:91
#> Iteration:92
#> Iteration:93
#> Iteration:94
#> Iteration:95
#> Iteration:96
#> Iteration:97
#> Iteration:98
#> Iteration:99
#> Iteration:100
#> Projected Normal Regression
#>
#> Model
#>
#> Call:
#> bpnr(pred.I = Phaserad ~ Cond + AvAmp, data = Motor, its = 100)
#>
#> MCMC:
#> iterations = 100
#> burn-in = 1
#> lag =
#>
#> Model Fit:
#> Statistic Parameters
#> lppd -57.1688 8.000000
#> DIC 127.9570 6.915886
#> DIC.alt 124.5182 5.196498
#> WAIC1 127.7447 6.703544
#> WAIC2 129.1263 7.394339
#>
#>
#> Linear Coefficients
#>
#> Component I:
#> mean mode sd LB HPD UB HPD
#> (Intercept) 1.35790309 1.53919307 0.391924091 0.65691407 2.057654675
#> Condsemi.imp -0.52983534 -0.41612729 0.530374398 -1.50572773 0.426828296
#> Condimp -0.68404666 -0.76754183 0.580782922 -1.65486565 0.289774837
#> AvAmp -0.01179946 -0.01223479 0.009548015 -0.03090843 0.005276706
#>
#> Component II:
#> mean mode sd LB HPD UB HPD
#> (Intercept) 1.42614025 1.079492806 0.416421481 0.6984332 2.2183433
#> Condsemi.imp -1.15627523 -1.063931210 0.538037522 -2.2837229 -0.2885647
#> Condimp -1.01689511 -1.125072141 0.586648246 -1.9668072 0.1881823
#> AvAmp -0.01046688 -0.009172757 0.009881872 -0.0306683 0.0055209
#>
#>
#> Circular Coefficients
#>
#> Continuous variables:
#> mean ax mode ax sd ax LB ax UB ax
#> 102.35258 73.34450 86.63490 24.19556 367.47488
#>
#> mean ac mode ac sd ac LB ac UB ac
#> 0.9268703 1.8524139 1.3298789 -0.7441615 2.4409921
#>
#> mean bc mode bc sd bc LB bc UB bc
#> -0.16793096 0.02375924 1.29982126 -0.28692522 0.45828966
#>
#> mean AS mode AS sd AS LB AS UB AS
#> 4.380087e-04 3.366778e-05 1.555164e-03 -9.855660e-04 5.396278e-03
#>
#> mean SAM mode SAM sd SAM LB SAM UB SAM
#> 2.009564e-04 3.131051e-05 3.626970e-04 7.397841e-06 6.529131e-04
#>
#> mean SSDO mode SSDO sd SSDO LB SSSO UB SSDO
#> -0.1083323 1.7910062 2.0399111 -2.8212582 2.5798523
#>
#> Categorical variables:
#>
#> Means:
#> mean mode sd LB UB
#> (Intercept) 0.8067426 0.8972646 0.1975172 0.4065758 1.1637551
#> Condsemi.imp 0.2985994 0.1569926 0.3678727 -0.4165081 0.9970036
#> Condimp 0.5623415 0.7778834 0.4861090 -0.4705304 1.3894279
#> Condsemi.impCondimp -1.4038001 -0.9012296 1.1367688 2.5048970 0.8284608
#>
#> Differences:
#> mean mode sd LB UB
#> Condsemi.imp 0.5095912 0.3943821 0.4515864 -0.3455296 1.390026
#> Condimp 0.2472478 -0.1522208 0.5688090 -0.9860141 1.138581
#> Condsemi.impCondimp 2.3183579 2.0576422 1.0578694 -0.1311784 4.307274