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Bayesian Projected Normal Regression Models for Circular Data
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joliencremers/bpnreg
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The goal of bpnreg is to fit Bayesian projected normal regression modelsfor circular data.
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")
To cite the package ‘bpnreg’ in publications use:
Jolien Cremers (2021). bpnreg: Bayesian Projected Normal RegressionModels 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 projectednormal 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 = 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
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