Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

Bayesian Projected Normal Regression Models for Circular Data

License

NotificationsYou must be signed in to change notification settings

joliencremers/bpnreg

Repository files navigation

CRAN_Status_Badge

The goal of bpnreg is to fit Bayesian projected normal regression modelsfor 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 (2021). bpnreg: Bayesian Projected Normal RegressionModels for Circular Data. R package version 2.0.2.https://CRAN.R-project.org/package=bpnreg

Example

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

About

Bayesian Projected Normal Regression Models for Circular Data

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Contributors3

  •  
  •  
  •  

[8]ページ先頭

©2009-2025 Movatter.jp