Description
Bayesian Projected Normal Regression Models for Circular Data.
Description
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) <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) <doi:10.1016/j.csda.2012.07.025>.
README.md
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