R Under development (unstable) (2021-10-25 r81099) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> pkgname <- "circglmbayes"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('circglmbayes')
> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("BF.circGLM")
> ### * BF.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BF.circGLM
> ### Title: Obtain Bayes Factors or posterior odds from circGLM objects
> ### Aliases: BF.circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData(truebeta = c(0, .2), truedelta = c(.4, .01))
> m   <- circGLM(th ~ ., dat)
> BF.circGLM(m)
$BF_Beta
   BF(bt>0:bt<0) BF(bt==0:bt=/=0)
l1       0.66389         22.44200
l2     999.00000          0.13476

$PMP_Beta_Ineq
   P(bt>0) P(bt<0)
l1   0.399   0.601
l2   0.999   0.001

$PMP_Beta_Eq
   P(bt==0) P(bt=/=0)
l1  0.95734   0.04266
l2  0.11876   0.88124

$BF_Mean
                 Comparison
[mu_a, mu_b]      BF(mu_a>mu_b:mu_a<mu_b) BF(mu_a==mu_b:(mu_a, mu_b))
  [Reference, c1]                 0.00100                     0.36565
  [Reference, c2]                 0.13250                    10.04688
  [c1, c2]                        8.17431                     5.60131

$PMP_Mean_Ineq
                P(mu_a>mu_b) P(mu_a<mu_b)
[Reference, c1]        0.001        0.999
[Reference, c2]        0.117        0.883
[c1, c2]               0.891        0.109

$PMP_Mean_Eq
                P(mu_a==mu_b) P(mu_a, mu_b)
[Reference, c1]       0.26775       0.73225
[Reference, c2]       0.90948       0.09052
[c1, c2]              0.84851       0.15149

> 
> dat <- generateCircGLMData(nconpred = 0)
> m   <- circGLM(th ~ ., dat)
/data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6076:22: runtime error: reference binding to null pointer of type 'const double'
    #0 0x7f284dd33ded in arma::Mat<double>::at(unsigned int, unsigned int) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6076:10
    #1 0x7f284dd33ded in void arma::subview<double>::inplace_op<arma::op_internal_equ, arma::Op<arma::Col<double>, arma::op_htrans> >(arma::Base<double, arma::Op<arma::Col<double>, arma::op_htrans> > const&, char const*) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:170:28
    #2 0x7f284dce9e0d in void arma::subview<double>::operator=<arma::Op<arma::Col<double>, arma::op_htrans> >(arma::Base<double, arma::Op<arma::Col<double>, arma::op_htrans> > const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:537:3
    #3 0x7f284dce9e0d in void arma::subview_row<double>::operator=<arma::Op<arma::Col<double>, arma::op_htrans> >(arma::Base<double, arma::Op<arma::Col<double>, arma::op_htrans> > const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:4322:16
    #4 0x7f284dce9e0d in circGLMC(arma::Col<double>, arma::Mat<double>, arma::Mat<double>, arma::Col<double>, arma::Mat<double>, arma::Col<double>, int, int, arma::Col<double>, double, double, int, double, bool, int, bool, bool) /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/circGLM.cpp:810:26
    #5 0x7f284dcb2361 in _circglmbayes_circGLMC /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/RcppExports.cpp:190:34
    #6 0x6d92d3 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:679:17
    #7 0x724239 in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1284:11
    #8 0x835a78 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7128:14
    #9 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #10 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #11 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #12 0x842d9f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7096:12
    #13 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #14 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #15 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #16 0x81ed88 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:863:12
    #17 0x8931d6 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2982:8
    #18 0x81e738 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:815:12
    #19 0x94e206 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2
    #20 0x951750 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:316:11
    #21 0x951559 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1129:5
    #22 0x4e247a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #23 0x7f285ca58081 in __libc_start_main (/lib64/libc.so.6+0x27081)
    #24 0x43129d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43129d)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6076:22 in 
/data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6088:10: runtime error: reference binding to null pointer of type 'const double'
    #0 0x7f284dd32ddf in arma::Mat<double>::at(unsigned int, unsigned int) const /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6088:3
    #1 0x7f284dd32ddf in arma::subview<double>::extract(arma::Mat<double>&, arma::subview<double> const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:1613:31
    #2 0x7f284dcea49c in arma::Mat<double>::Mat(arma::subview<double> const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:1973:3
    #3 0x7f284dcea49c in circGLMC(arma::Col<double>, arma::Mat<double>, arma::Mat<double>, arma::Col<double>, arma::Mat<double>, arma::Col<double>, int, int, arma::Col<double>, double, double, int, double, bool, int, bool, bool) /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/circGLM.cpp:818:28
    #4 0x7f284dcb2361 in _circglmbayes_circGLMC /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/RcppExports.cpp:190:34
    #5 0x6d92d3 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:679:17
    #6 0x724239 in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1284:11
    #7 0x835a78 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7128:14
    #8 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #9 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #10 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #11 0x842d9f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7096:12
    #12 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #13 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #14 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #15 0x81ed88 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:863:12
    #16 0x8931d6 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2982:8
    #17 0x81e738 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:815:12
    #18 0x94e206 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2
    #19 0x951750 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:316:11
    #20 0x951559 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1129:5
    #21 0x4e247a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #22 0x7f285ca58081 in __libc_start_main (/lib64/libc.so.6+0x27081)
    #23 0x43129d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43129d)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6088:10 in 
/data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6088:10: runtime error: applying non-zero offset 8 to null pointer
    #0 0x7f284dd32db1 in arma::Mat<double>::at(unsigned int, unsigned int) const /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6088:10
    #1 0x7f284dd32db1 in arma::subview<double>::extract(arma::Mat<double>&, arma::subview<double> const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:1613:31
    #2 0x7f284dcea49c in arma::Mat<double>::Mat(arma::subview<double> const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:1973:3
    #3 0x7f284dcea49c in circGLMC(arma::Col<double>, arma::Mat<double>, arma::Mat<double>, arma::Col<double>, arma::Mat<double>, arma::Col<double>, int, int, arma::Col<double>, double, double, int, double, bool, int, bool, bool) /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/circGLM.cpp:818:28
    #4 0x7f284dcb2361 in _circglmbayes_circGLMC /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/RcppExports.cpp:190:34
    #5 0x6d92d3 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:679:17
    #6 0x724239 in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1284:11
    #7 0x835a78 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7128:14
    #8 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #9 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #10 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #11 0x842d9f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7096:12
    #12 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #13 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #14 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #15 0x81ed88 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:863:12
    #16 0x8931d6 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2982:8
    #17 0x81e738 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:815:12
    #18 0x94e206 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2
    #19 0x951750 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:316:11
    #20 0x951559 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1129:5
    #21 0x4e247a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #22 0x7f285ca58081 in __libc_start_main (/lib64/libc.so.6+0x27081)
    #23 0x43129d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43129d)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6088:10 in 
/data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6076:22: runtime error: applying non-zero offset 8 to null pointer
    #0 0x7f284dd33dc0 in arma::Mat<double>::at(unsigned int, unsigned int) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6076:22
    #1 0x7f284dd33dc0 in void arma::subview<double>::inplace_op<arma::op_internal_equ, arma::Op<arma::Col<double>, arma::op_htrans> >(arma::Base<double, arma::Op<arma::Col<double>, arma::op_htrans> > const&, char const*) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:170:28
    #2 0x7f284dce9e0d in void arma::subview<double>::operator=<arma::Op<arma::Col<double>, arma::op_htrans> >(arma::Base<double, arma::Op<arma::Col<double>, arma::op_htrans> > const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:537:3
    #3 0x7f284dce9e0d in void arma::subview_row<double>::operator=<arma::Op<arma::Col<double>, arma::op_htrans> >(arma::Base<double, arma::Op<arma::Col<double>, arma::op_htrans> > const&) /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/subview_meat.hpp:4322:16
    #4 0x7f284dce9e0d in circGLMC(arma::Col<double>, arma::Mat<double>, arma::Mat<double>, arma::Col<double>, arma::Mat<double>, arma::Col<double>, int, int, arma::Col<double>, double, double, int, double, bool, int, bool, bool) /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/circGLM.cpp:810:26
    #5 0x7f284dcb2361 in _circglmbayes_circGLMC /data/gannet/ripley/R/packages/tests-clang-SAN/circglmbayes/src/RcppExports.cpp:190:34
    #6 0x6d92d3 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:679:17
    #7 0x724239 in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1284:11
    #8 0x835a78 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7128:14
    #9 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #10 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #11 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #12 0x842d9f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7096:12
    #13 0x81e34e in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:740:8
    #14 0x887157 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #15 0x882abf in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1836:16
    #16 0x81ed88 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:863:12
    #17 0x8931d6 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2982:8
    #18 0x81e738 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:815:12
    #19 0x94e206 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2
    #20 0x951750 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:316:11
    #21 0x951559 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1129:5
    #22 0x4e247a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #23 0x7f285ca58081 in __libc_start_main (/lib64/libc.so.6+0x27081)
    #24 0x43129d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43129d)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /data/gannet/ripley/R/test-clang/RcppArmadillo/include/armadillo_bits/Mat_meat.hpp:6076:22 in 
> BF.circGLM(m)
$BF_Beta
NULL

$PMP_Beta_Ineq
     P(bt>0) P(bt<0)

$PMP_Beta_Eq
     P(bt==0) P(bt=/=0)

$BF_Mean
                 Comparison
[mu_a, mu_b]      BF(mu_a>mu_b:mu_a<mu_b) BF(mu_a==mu_b:(mu_a, mu_b))
  [Reference, c1]                 0.00100                     0.00000
  [Reference, c2]                 0.00100                     0.00000
  [c1, c2]                        6.63359                     4.68621

$PMP_Mean_Ineq
                P(mu_a>mu_b) P(mu_a<mu_b)
[Reference, c1]        0.001        0.999
[Reference, c2]        0.001        0.999
[c1, c2]               0.869        0.131

$PMP_Mean_Eq
                P(mu_a==mu_b) P(mu_a, mu_b)
[Reference, c1]       0.00000       1.00000
[Reference, c2]       0.00000       1.00000
[c1, c2]              0.82414       0.17586

> 
> dat <- generateCircGLMData(ncatpred = 0)
> m   <- circGLM(th ~ ., dat)
> BF.circGLM(m)
$BF_Beta
   BF(bt>0:bt<0) BF(bt==0:bt=/=0)
l1          1000            5e-05
l2          1000            0e+00

$PMP_Beta_Ineq
   P(bt>0) P(bt<0)
l1   0.999   0.001
l2   0.999   0.001

$PMP_Beta_Eq
   P(bt==0) P(bt=/=0)
l1    5e-05   0.99995
l2    0e+00   1.00000

$BF_Mean
NULL

$PMP_Mean_Ineq
     P(mu_a>mu_b) P(mu_a<mu_b)

$PMP_Mean_Eq
     P(mu_a==mu_b) P(mu_a, mu_b)

> 
> 
> 
> 
> cleanEx()
> nameEx("IC_compare.circGLM")
> ### * IC_compare.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: IC_compare.circGLM
> ### Title: Compare the information criteria of several circGLM models.
> ### Aliases: IC_compare.circGLM
> 
> ### ** Examples
> 
> Xcat <- c(rep(0, 5), rep(1, 5))
> th <- rvmc(10, 0, 4) + Xcat
> 
> # Compare a model that includes group differences with a model that does not.
> IC_compare.circGLM(circGLM(th = th), circGLM(th = th, X = Xcat))
          circGLM(th = th) circGLM(th = th, X = Xcat)
n_par     2                3                         
lppd      -3.430257        -1.0132                   
AIC_Bayes 10.4832          7.927708                  
DIC       10.41982         6.906482                  
DIC_alt   11.11434         8.174809                  
WAIC1     10.0425          6.80779                   
WAIC2     10.74327         8.174321                  
p_DIC     1.968312         2.489387                  
p_DIC_alt 2.315574         3.123551                  
p_WAIC1   1.590995         2.390695                  
p_WAIC2   1.941376         3.07396                   
> 
> 
> 
> cleanEx()
> nameEx("cglmShiny")
> ### * cglmShiny
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cglmShiny
> ### Title: cglmShiny
> ### Aliases: cglmShiny
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D cglmShiny()
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("circGLM")
> ### * circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: circGLM
> ### Title: Fitting Bayesian circular General Linear Models
> ### Aliases: circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData()
> m   <- circGLM(th ~ ., dat)
> print(m)
Bayesian circular GLM 

Call:
circGLM(formula = th ~ ., data = dat)

MCMC run for 1000 its, 1000 used. 

Coefficients:
          Estimate    SD    LB     UB
Intercept    1.403 0.167 1.138  1.758
Kappa        6.360 2.160 3.564 11.784
l1           0.235 0.055 0.122  0.338
l2           0.227 0.052 0.112  0.317
c1           1.036 0.175 0.672  1.319
c2           1.150 0.210 0.783  1.477

DIC:  37.975 
WAIC: 36.879 
> print(m, type = "all")
                  [,1]
b0_meandir       1.403
kp_mean          7.291
kp_mode          6.360
kp_propacc       1.000
lppd           -13.084
n_par            6.000
ll_th_estpars  -12.535
AIC_Bayes       37.071
p_DIC            6.452
p_DIC_alt       30.648
DIC             37.975
DIC_alt         86.367
p_WAIC1          5.356
p_WAIC2          6.506
WAIC1           36.879
WAIC2           39.180
SavedIts      1000.000
TotalIts      1000.000
thin             1.000
burnin           0.000
r                2.000


$b0_CCI
     Beta_0
LB 1.137513
UB 1.758165

$kp_HDI
       Kappa
LB  3.564206
UB 11.784274

$bt_mean
            l1        l2
[1,] 0.2348029 0.2268319

$bt_CCI
          l1        l2
LB 0.1223484 0.1121267
UB 0.3376940 0.3171914

$bt_propacc
        l1   l2
[1,] 0.654 0.63

$dt_meandir
              c1       c2
MeanDir 1.036014 1.149935

$dt_CCI
          c1        c2
LB 0.6724094 0.7831062
UB 1.3189434 1.4767410

$dt_propacc
                      c1    c2
ProportionAccepted 0.798 0.783

$zt_mean
         l1_zt    l2_zt
[1,] 0.1464302 0.141673

$zt_mdir
         l1_zt     l2_zt
[1,] 0.1464722 0.1417149

$zt_CCI
        l1_zt      l2_zt
LB 0.07750421 0.07108516
UB 0.20732798 0.19553976

$DeltaIneqBayesFactors
   BF(dt>0:dt<0)
c1     1000.0000
c2      141.8571

$BetaIneqBayesFactors
   [,1]
l1  199
l2  999

$BetaSDDBayesFactors
        [,1]
l1 0.2755686
l2 0.1672975

$MuIneqBayesFactors
                       [,1]
[Reference, c1] 0.001000000
[Reference, c2] 0.007049345
[c1, c2]        0.424501425

$TimeTaken
                Time (sec)
Initialization    0.000000
Loop              0.173588
Post-processing   0.005964
Total             0.179552

$SDDBFDensEstMethod
[1] "density"

$BetaBayesFactors
   BF(bt>0:bt<0) BF(bt==0:bt=/=0)
l1           199        0.2755686
l2           999        0.1672975

$MuBayesFactors
                 Comparison
[mu_a, mu_b]      BF(mu_a>mu_b:mu_a<mu_b) BF(mu_a==mu_b:(mu_a, mu_b))
  [Reference, c1]             0.001000000                8.089026e-07
  [Reference, c2]             0.007049345                2.508633e-01
  [c1, c2]                    0.424501425                8.342167e+00



> plot(m, type = "tracestack")
> 
> 
> 
> cleanEx()
> nameEx("coef.circGLM")
> ### * coef.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: coef.circGLM
> ### Title: Extract circGLM Coefficients
> ### Aliases: coef.circGLM
> 
> ### ** Examples
> 
> coef(circGLM(th = rvmc(10, 0, 1)))
          Estimate        SD         LB       UB
Intercept 0.186294 0.3996290 -0.5964590 1.001452
Kappa     1.408409 0.7207282  0.2815604 3.031182
> 
> 
> 
> 
> cleanEx()
> nameEx("estimateDensityBySpline")
> ### * estimateDensityBySpline
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: estimateDensityBySpline
> ### Title: Estimate the density value from a sample by a spline
> ###   interpolation of the kernel density
> ### Aliases: estimateDensityBySpline
> 
> ### ** Examples
> 
> # Compare the estimate from this function with the analytic result.
> estimateDensityBySpline(rnorm(1000), 0.1)
[1] 0.365109
> dnorm(.1)
[1] 0.3969525
> 
> 
> 
> 
> cleanEx()
> nameEx("generateCircGLMData")
> ### * generateCircGLMData
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: generateCircGLMData
> ### Title: Generate data that follows the circular GLM model
> ### Aliases: generateCircGLMData
> 
> ### ** Examples
> 
> 
> # Von Mises data with mean 2, kappa 3.
> generateCircGLMData(truebeta0 = 2, residkappa = 3,
+                     nconpred = 0, ncatpred = 0)
             th
 [1,] 2.2535508
 [2,] 1.9439969
 [3,] 2.9860055
 [4,] 2.5564118
 [5,] 2.3861946
 [6,] 1.8014955
 [7,] 1.7446258
 [8,] 1.6108045
 [9,] 1.4689834
[10,] 2.1728400
[11,] 3.4192557
[12,] 2.4271864
[13,] 0.6056226
[14,] 1.9358793
[15,] 1.4078532
[16,] 1.6719153
[17,] 1.4745910
[18,] 1.6549866
[19,] 0.9292550
[20,] 3.2440974
[21,] 1.8887716
[22,] 2.2264450
[23,] 2.4896893
[24,] 1.2347040
[25,] 1.8004270
[26,] 0.8585888
[27,] 2.1362146
[28,] 2.0940127
[29,] 1.8395926
[30,] 1.4208293
attr(,"truebeta0")
[1] 2
attr(,"truebeta")
numeric(0)
attr(,"truezeta")
numeric(0)
attr(,"residkappa")
[1] 3
attr(,"linkfun")
function (x) 
2 * atan(x)
<environment: 0x6250052d6bb8>
attr(,"u")
[1] 1
> 
> # circGLM data
> generateCircGLMData(n = 20, nconpred = 4, truebeta = c(0, 0.4, 0.2, 0.05))
            th          l1          l2          l3         l4 c1 c2
 [1,] 3.159873  0.39359812 -0.71390422  0.76349910  1.7577461  1  1
 [2,] 1.790513 -1.86400530  1.03519119 -1.19914905  1.6738869  0  0
 [3,] 1.800591  0.56105731 -0.53701162  1.87161232  0.4148988  0  0
 [4,] 2.589869  0.40192407  1.35516448 -0.37492539 -1.1094827  1  0
 [5,] 3.823774  0.15397405  0.60246222  1.92745487  0.1433941  1  0
 [6,] 3.060387  0.09151173  1.40864344 -0.28175564  1.6880076  0  0
 [7,] 2.831283  0.41400948 -1.27506321 -0.87321963 -0.3376806  1  0
 [8,] 2.187062 -0.81318384 -0.18601212  0.18094355  0.9604901  1  0
 [9,] 2.023534 -0.09578015 -1.28446310  0.18315450 -0.8455924  0  0
[10,] 1.616810  0.96481195  0.54018583 -1.62120042  0.8498536  1  0
[11,] 3.420797  1.55628318 -0.04846938  1.26754332 -0.8270787  1  1
[12,] 0.515243  0.26024022 -0.54491219 -1.02887352 -1.1082656  0  0
[13,] 4.147649 -0.09386402  1.60218005  0.50874504 -0.8281518  0  1
[14,] 2.564904 -1.16936405  0.08654757  0.67693715 -0.7393633  0  1
[15,] 2.664601 -1.88091381  1.03464405  0.29999337 -0.7810496  0  0
[16,] 3.374456 -1.01368301  0.76945135  0.02860469  0.9027580  0  0
[17,] 2.278781  1.76359912 -1.15587799  0.36300158  0.2170219  0  1
[18,] 1.779764  1.11105243 -0.95726368 -0.97508453 -0.1828034  1  1
[19,] 3.422839 -0.23164953 -0.13881343 -0.69457214 -0.9602620  1  1
[20,] 2.171759 -0.50961793 -1.59267925 -1.02270920 -0.8883271  1  1
attr(,"truebeta0")
[1] 1.570796
attr(,"truebeta")
[1] 0.00 0.40 0.20 0.05
attr(,"truezeta")
[1] 0.0000000 0.2422379 0.1256659 0.0318045
attr(,"residkappa")
[1] 5
attr(,"linkfun")
function (x) 
2 * atan(x)
<environment: 0x6250052dcfd0>
attr(,"u")
[1] 0.7
> 
> 
> 
> 
> cleanEx()
> nameEx("getPMP")
> ### * getPMP
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: getPMP
> ### Title: Obtain posterior model probabilities
> ### Aliases: getPMP
> 
> ### ** Examples
> 
> getPMP(3)
     [,1] [,2]
[1,] 0.75 0.25
> 
> 
> 
> 
> cleanEx()
> nameEx("mcmc_summary.circGLM")
> ### * mcmc_summary.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mcmc_summary.circGLM
> ### Title: Obtain different central tendencies and CIs from a circGLM
> ###   object
> ### Aliases: mcmc_summary.circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData()
> m   <- circGLM(th ~ ., dat)
> mcmc_summary.circGLM(m)
                        Mean    Median      Mode         SD        LB
b0_chain           1.4032251 1.3867744 1.3462783 0.16479602 1.0583246
dt_chain.1         1.0360140 1.0425598 1.0704851 0.17397796 0.6970144
dt_chain.2         1.1499351 1.1585066 1.1656636 0.20538506 0.8687742
mu_chain.Reference 1.4032251 1.3867744 1.3462783 0.16479602 1.0583246
mu_chain.c1        2.4400934 2.4369877 2.4176785 0.12916160 2.2068948
mu_chain.c2        2.5515165 2.5614283 2.5771693 0.14298417 2.2686354
kp_chain           7.2906145 7.1577524 6.3602355 2.15984364 3.5642062
bt_chain.1         0.2348029 0.2331972 0.2182859 0.05528073 0.1395201
bt_chain.2         0.2268319 0.2297045 0.2310939 0.05170693 0.1177659
                           UB
b0_chain            1.6428448
dt_chain.1          1.3317680
dt_chain.2          1.5277612
mu_chain.Reference  1.6428448
mu_chain.c1         2.7156473
mu_chain.c2         2.8233713
kp_chain           11.7842737
bt_chain.1          0.3429952
bt_chain.2          0.3171914
> 
> 
> 
> 
> cleanEx()
> nameEx("medianDirection")
> ### * medianDirection
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: medianDirection
> ### Title: Compute the median direction
> ### Aliases: medianDirection
> 
> ### ** Examples
> 
> medianDirection(rvmc(30, 0, 2))
[1] -0.1355875
> 
> 
> 
> cleanEx()
> nameEx("modalDirection")
> ### * modalDirection
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: modalDirection
> ### Title: Estimate the modal direction
> ### Aliases: modalDirection
> 
> ### ** Examples
> 
> modalDirection(rvmc(30, 0, 2))
[1] 0.2372185
> 
> 
> 
> 
> cleanEx()
> nameEx("plot.circGLM")
> ### * plot.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.circGLM
> ### Title: Plot circGLM object
> ### Aliases: plot.circGLM
> 
> ### ** Examples
> 
> plot(circGLM(th = rvmc(10, 1, 1)))
> 
> dat <- generateCircGLMData(n = 100, nconpred = 1, ncatpred = 1)
> m   <- circGLM(th ~ ., dat, Q = 100, burnin = 0)
> 
> # Traceplot by default
> plot(m)
> 
> # Traceplot stack
> plot(m, type = "tracestack")
> 
> # Prediction plot
> plot(m, type = "predict")
> 
> # Mean comparisons
> plot(m, type = "meancompare")
> plot(m, type = "meanboxplot")
> 
> 
> 
> 
> cleanEx()
> nameEx("plot_meanboxplot.circGLM")
> ### * plot_meanboxplot.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot_meanboxplot.circGLM
> ### Title: Plot mean comparison boxplot from circGLM objects
> ### Aliases: plot_meanboxplot.circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData(nconpred = 0)
> m   <- circGLM(th ~ ., dat)
> plot_meancompare.circGLM(m)
> 
> 
> 
> cleanEx()
> nameEx("plot_meancompare.circGLM")
> ### * plot_meancompare.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot_meancompare.circGLM
> ### Title: Plot mean comparisons for a circGLM object
> ### Aliases: plot_meancompare.circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData(nconpred = 0)
> m   <- circGLM(th ~ ., dat)
> plot_meancompare.circGLM(m)
> 
> 
> 
> cleanEx()
> nameEx("plot_predict.circGLM")
> ### * plot_predict.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot_predict.circGLM
> ### Title: Create a prediction plot from a circGLM object
> ### Aliases: plot_predict.circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData()
> m   <- circGLM(th ~ ., dat, Q = 100, burnin = 0)
> plot(m, type = "predict")
> 
> 
> 
> 
> cleanEx()
> nameEx("plot_trace.circGLM")
> ### * plot_trace.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot_trace.circGLM
> ### Title: Make traceplots for circGLM
> ### Aliases: plot_trace.circGLM
> 
> ### ** Examples
> 
> plot_trace.circGLM(circGLM(th = rvmc(10, 1, 1)))
> 
> dat <- generateCircGLMData()
> plot(circGLM(th ~., dat), type = "trace")
> 
> 
> 
> 
> cleanEx()
> nameEx("plot_tracestack.circGLM")
> ### * plot_tracestack.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot_tracestack.circGLM
> ### Title: Plot a stack of traceplots for a circGLM object
> ### Aliases: plot_tracestack.circGLM
> 
> ### ** Examples
> 
> plot(circGLM(th = rvmc(100, 0, 1)), type = "tracestack")
> 
> dat <- generateCircGLMData()
> plot(circGLM(th ~ ., dat), type = "tracestack")
> 
> 
> 
> 
> cleanEx()
> nameEx("predict.circGLM")
> ### * predict.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.circGLM
> ### Title: Obtain predictions for the circGLM model
> ### Aliases: predict.circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData()
> m   <- circGLM(th ~ ., dat)
> 
> # Predictions for the original outcome angles.
> predict(m)
           [,1]
 [1,] 3.9250128
 [2,] 2.3563370
 [3,] 1.0848010
 [4,] 2.0428257
 [5,] 1.7342721
 [6,] 1.8170179
 [7,] 2.4583774
 [8,] 3.8119665
 [9,] 4.3522762
[10,] 2.7153047
[11,] 1.9462260
[12,] 1.3391912
[13,] 2.4034700
[14,] 1.6863131
[15,] 2.5003546
[16,] 0.8720388
[17,] 3.6712251
[18,] 3.2005696
[19,] 2.6737512
[20,] 3.2146891
[21,] 3.0004675
[22,] 1.3339587
[23,] 2.6678666
[24,] 0.9887587
[25,] 2.3422885
[26,] 4.5032173
[27,] 3.1883505
[28,] 2.3276179
[29,] 2.5175228
[30,] 1.4209316
> 
> # Predictions for new data
> dat2  <- generateCircGLMData()
> predict(m, newdata = dat2)
           [,1]
 [1,] 2.7077392
 [2,] 3.8976838
 [3,] 1.2127170
 [4,] 3.1270668
 [5,] 2.4764382
 [6,] 3.0070879
 [7,] 2.2088385
 [8,] 2.0742216
 [9,] 2.5184084
[10,] 0.8981935
[11,] 3.3498693
[12,] 2.8197618
[13,] 1.3937256
[14,] 2.3593175
[15,] 3.3621717
[16,] 2.3185661
[17,] 4.8203191
[18,] 2.6429774
[19,] 1.4572450
[20,] 3.0253629
[21,] 2.6795961
[22,] 3.0627812
[23,] 3.6789250
[24,] 2.6576370
[25,] 2.7374487
[26,] 1.8180842
[27,] 2.4097518
[28,] 2.6062387
[29,] 3.2668342
[30,] 2.6621071
> 
> 
> 
> cleanEx()
> nameEx("predict_function.circGLM")
> ### * predict_function.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict_function.circGLM
> ### Title: Obtain a prediction function from a circGLM object
> ### Aliases: predict_function.circGLM
> 
> ### ** Examples
> 
> dat <- generateCircGLMData()
> m   <- circGLM(th ~ ., dat)
> predfun <- predict_function.circGLM(m)
> newd <- generateCircGLMData()
> 
> # Predicted values of the new data.
> predfun(newd)
           [,1]
 [1,] 2.7077392
 [2,] 3.8976838
 [3,] 1.2127170
 [4,] 3.1270668
 [5,] 2.4764382
 [6,] 3.0070879
 [7,] 2.2088385
 [8,] 2.0742216
 [9,] 2.5184084
[10,] 0.8981935
[11,] 3.3498693
[12,] 2.8197618
[13,] 1.3937256
[14,] 2.3593175
[15,] 3.3621717
[16,] 2.3185661
[17,] 4.8203191
[18,] 2.6429774
[19,] 1.4572450
[20,] 3.0253629
[21,] 2.6795961
[22,] 3.0627812
[23,] 3.6789250
[24,] 2.6576370
[25,] 2.7374487
[26,] 1.8180842
[27,] 2.4097518
[28,] 2.6062387
[29,] 3.2668342
[30,] 2.6621071
> 
> 
> 
> cleanEx()
> nameEx("print.circGLM")
> ### * print.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.circGLM
> ### Title: Print circGLM Object
> ### Aliases: print.circGLM
> 
> ### ** Examples
> 
> print(circGLM(th = rvmc(10, 1, 1)))
Bayesian circular GLM 

Call:
circGLM(th = rvmc(10, 1, 1))

MCMC run for 1000 its, 1000 used. 

Coefficients:
          Estimate    SD    LB    UB
Intercept    1.186 0.400 0.404 2.001
Kappa        1.408 0.721 0.282 3.031

DIC:  33.033 
WAIC: 32.85 
> 
> dat <- generateCircGLMData()
> cglmmod <- circGLM(th ~ ., dat)
> 
> print(cglmmod)
Bayesian circular GLM 

Call:
circGLM(formula = th ~ ., data = dat)

MCMC run for 1000 its, 1000 used. 

Coefficients:
          Estimate    SD    LB    UB
Intercept    1.621 0.215 1.183 2.026
Kappa        3.089 0.984 1.762 5.506
l1           0.293 0.071 0.161 0.446
l2           0.147 0.066 0.010 0.287
c1           0.819 0.254 0.327 1.301
c2           0.948 0.209 0.562 1.324

DIC:  60.902 
WAIC: 60.36 
> 
> print(cglmmod, type = "mcmc")

Iterations = 0:999
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 1000 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

                      Mean      SD Naive SE Time-series SE
b0_chain           1.62073 0.21531 0.006809       0.035512
kp_chain           3.58847 0.98371 0.031108       0.038382
bt_chain.1         0.29322 0.07072 0.002236       0.008182
bt_chain.2         0.14700 0.06630 0.002097       0.008893
dt_chain.1         0.81931 0.25415 0.008037       0.044970
dt_chain.2         0.94806 0.20873 0.006601       0.034910
zt_chain.1         0.18080 0.04127 0.001305       0.004763
zt_chain.2         0.09251 0.04098 0.001296       0.005493
mu_chain.Reference 1.62073 0.21531 0.006809       0.035512
mu_chain.c1        2.44004 0.20020 0.006331       0.024705
mu_chain.c2        2.56879 0.15651 0.004949       0.017144

2. Quantiles for each variable:

                       2.5%     25%     50%    75%  97.5%
b0_chain           1.185957 1.47216 1.62643 1.7768 2.0257
kp_chain           1.900448 2.86985 3.49707 4.2031 5.7436
bt_chain.1         0.161478 0.24775 0.28967 0.3385 0.4462
bt_chain.2         0.010815 0.10896 0.14572 0.1866 0.2867
dt_chain.1         0.327714 0.65400 0.81553 0.9972 1.3014
dt_chain.2         0.561832 0.80165 0.94526 1.0852 1.3241
zt_chain.1         0.101920 0.15461 0.17950 0.2078 0.2672
zt_chain.2         0.006884 0.06909 0.09212 0.1174 0.1778
mu_chain.Reference 1.185957 1.47216 1.62643 1.7768 2.0257
mu_chain.c1        2.046727 2.29902 2.43847 2.5785 2.8355
mu_chain.c2        2.251253 2.47067 2.57267 2.6717 2.8671

> 
> print(cglmmod, type = "all")
                  [,1]
b0_meandir       1.621
kp_mean          3.588
kp_mode          3.089
kp_propacc       1.000
lppd           -25.135
n_par            6.000
ll_th_estpars  -24.864
AIC_Bayes       61.728
p_DIC            5.587
p_DIC_alt        8.423
DIC             60.902
DIC_alt         66.575
p_WAIC1          5.045
p_WAIC2          6.019
WAIC1           60.360
WAIC2           62.308
SavedIts      1000.000
TotalIts      1000.000
thin             1.000
burnin           0.000
r                2.000


$b0_CCI
     Beta_0
LB 1.183157
UB 2.025635

$kp_HDI
      Kappa
LB 1.761622
UB 5.505756

$bt_mean
            l1       l2
[1,] 0.2932225 0.146995

$bt_CCI
          l1         l2
LB 0.1614776 0.01047914
UB 0.4461518 0.28669771

$bt_propacc
        l1    l2
[1,] 0.736 0.722

$dt_meandir
              c1        c2
MeanDir 0.819046 0.9478997

$dt_CCI
          c1        c2
LB 0.3272452 0.5618007
UB 1.3008341 1.3239508

$dt_propacc
                      c1    c2
ProportionAccepted 0.808 0.752

$zt_mean
         l1_zt      l2_zt
[1,] 0.1807963 0.09251183

$zt_mdir
      l1_zt      l2_zt
[1,] 0.1808 0.09248954

$zt_CCI
       l1_zt       l2_zt
LB 0.1019201 0.006670981
UB 0.2671570 0.177749730

$DeltaIneqBayesFactors
   BF(dt>0:dt<0)
c1          1000
c2          1000

$BetaIneqBayesFactors
        [,1]
l1 999.00000
l2  70.42857

$BetaSDDBayesFactors
        [,1]
l1 0.0729432
l2 1.8603021

$MuIneqBayesFactors
                     [,1]
[Reference, c1] 0.0010000
[Reference, c2] 0.0010000
[c1, c2]        0.5015015

$TimeTaken
                Time (sec)
Initialization    0.000000
Loop              0.295515
Post-processing   0.008943
Total             0.304458

$SDDBFDensEstMethod
[1] "density"

$BetaBayesFactors
   BF(bt>0:bt<0) BF(bt==0:bt=/=0)
l1     999.00000        0.0729432
l2      70.42857        1.8603021

$MuBayesFactors
                 Comparison
[mu_a, mu_b]      BF(mu_a>mu_b:mu_a<mu_b) BF(mu_a==mu_b:(mu_a, mu_b))
  [Reference, c1]               0.0010000                9.778946e-03
  [Reference, c2]               0.0010000                1.348340e-15
  [c1, c2]                      0.5015015                7.241038e+00



> 
> print(cglmmod, type = "coef")
          Estimate    SD    LB    UB
Intercept    1.621 0.215 1.183 2.026
Kappa        3.089 0.984 1.762 5.506
l1           0.293 0.071 0.161 0.446
l2           0.147 0.066 0.010 0.287
c1           0.819 0.254 0.327 1.301
c2           0.948 0.209 0.562 1.324
> 
> 
> 
> 
> cleanEx()
> nameEx("print_all.circGLM")
> ### * print_all.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print_all.circGLM
> ### Title: Print all results from a circGLM object
> ### Aliases: print_all.circGLM
> 
> ### ** Examples
> 
> print(circGLM(th = rvmc(10, 1, 1)), type = "all")
                  [,1]
b0_meandir       1.186
kp_mean          1.544
kp_mode          1.408
kp_propacc       0.984
lppd           -14.458
n_par            2.000
ll_th_estpars  -14.367
AIC_Bayes       32.733
p_DIC            2.150
p_DIC_alt        2.498
DIC             33.033
DIC_alt         33.730
p_WAIC1          1.967
p_WAIC2          2.336
WAIC1           32.850
WAIC2           33.590
SavedIts      1000.000
TotalIts      1000.000
thin             1.000
burnin           0.000
r                2.000


$b0_CCI
     Beta_0
LB 0.403541
UB 2.001452

$kp_HDI
       Kappa
LB 0.2815604
UB 3.0311821

$TimeTaken
                Time (sec)
Initialization    0.000000
Loop              0.045409
Post-processing   0.003978
Total             0.049387

$SDDBFDensEstMethod
[1] "density"



> 
> dat <- generateCircGLMData()
> cglmmod <- circGLM(th ~ ., dat)
> print(cglmmod, type = "all")
                  [,1]
b0_meandir       1.621
kp_mean          3.588
kp_mode          3.089
kp_propacc       1.000
lppd           -25.135
n_par            6.000
ll_th_estpars  -24.864
AIC_Bayes       61.728
p_DIC            5.587
p_DIC_alt        8.423
DIC             60.902
DIC_alt         66.575
p_WAIC1          5.045
p_WAIC2          6.019
WAIC1           60.360
WAIC2           62.308
SavedIts      1000.000
TotalIts      1000.000
thin             1.000
burnin           0.000
r                2.000


$b0_CCI
     Beta_0
LB 1.183157
UB 2.025635

$kp_HDI
      Kappa
LB 1.761622
UB 5.505756

$bt_mean
            l1       l2
[1,] 0.2932225 0.146995

$bt_CCI
          l1         l2
LB 0.1614776 0.01047914
UB 0.4461518 0.28669771

$bt_propacc
        l1    l2
[1,] 0.736 0.722

$dt_meandir
              c1        c2
MeanDir 0.819046 0.9478997

$dt_CCI
          c1        c2
LB 0.3272452 0.5618007
UB 1.3008341 1.3239508

$dt_propacc
                      c1    c2
ProportionAccepted 0.808 0.752

$zt_mean
         l1_zt      l2_zt
[1,] 0.1807963 0.09251183

$zt_mdir
      l1_zt      l2_zt
[1,] 0.1808 0.09248954

$zt_CCI
       l1_zt       l2_zt
LB 0.1019201 0.006670981
UB 0.2671570 0.177749730

$DeltaIneqBayesFactors
   BF(dt>0:dt<0)
c1          1000
c2          1000

$BetaIneqBayesFactors
        [,1]
l1 999.00000
l2  70.42857

$BetaSDDBayesFactors
        [,1]
l1 0.0729432
l2 1.8603021

$MuIneqBayesFactors
                     [,1]
[Reference, c1] 0.0010000
[Reference, c2] 0.0010000
[c1, c2]        0.5015015

$TimeTaken
                Time (sec)
Initialization    0.000000
Loop              0.276523
Post-processing   0.008948
Total             0.285471

$SDDBFDensEstMethod
[1] "density"

$BetaBayesFactors
   BF(bt>0:bt<0) BF(bt==0:bt=/=0)
l1     999.00000        0.0729432
l2      70.42857        1.8603021

$MuBayesFactors
                 Comparison
[mu_a, mu_b]      BF(mu_a>mu_b:mu_a<mu_b) BF(mu_a==mu_b:(mu_a, mu_b))
  [Reference, c1]               0.0010000                9.778946e-03
  [Reference, c2]               0.0010000                1.348340e-15
  [c1, c2]                      0.5015015                7.241038e+00



> 
> 
> 
> cleanEx()
> nameEx("print_coef.circGLM")
> ### * print_coef.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print_coef.circGLM
> ### Title: Print circGLM coefficients
> ### Aliases: print_coef.circGLM
> 
> ### ** Examples
> 
> print(circGLM(th = rvmc(10, 0, 1)), type = "coef")
          Estimate    SD     LB    UB
Intercept    0.186 0.400 -0.596 1.001
Kappa        1.408 0.721  0.282 3.031
> 
> dat <- generateCircGLMData()
> cglmmod <- circGLM(th = dat[, 1], X = dat[, -1])
> print(cglmmod, type = "coef")
          Estimate    SD    LB    UB
Intercept    1.621 0.215 1.183 2.026
Kappa        3.089 0.984 1.762 5.506
l1           0.293 0.071 0.161 0.446
l2           0.147 0.066 0.010 0.287
c1           0.819 0.254 0.327 1.301
c2           0.948 0.209 0.562 1.324
> 
> 
> 
> cleanEx()
> nameEx("print_mcmc.circGLM")
> ### * print_mcmc.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print_mcmc.circGLM
> ### Title: Print the mcmc results from a circGLM object
> ### Aliases: print_mcmc.circGLM
> 
> ### ** Examples
> 
> print(circGLM(th = rvmc(10, 1, 1)), type = "mcmc", digits = 3)

Iterations = 0:999
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 1000 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

         Mean    SD Naive SE Time-series SE
b0_chain 1.19 0.400   0.0126         0.0126
kp_chain 1.54 0.721   0.0228         0.0248
mu_chain 1.19 0.400   0.0126         0.0126

2. Quantiles for each variable:

          2.5%   25%  50%  75% 97.5%
b0_chain 0.415 0.965 1.19 1.40  2.00
kp_chain 0.354 1.026 1.46 1.96  3.13
mu_chain 0.415 0.965 1.19 1.40  2.00

> 
> dat <- generateCircGLMData()
> cglmmod <- circGLM(th = dat[, 1], X = dat[, -1])
> print(cglmmod, type = "mcmc")

Iterations = 0:999
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 1000 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

                      Mean      SD Naive SE Time-series SE
b0_chain           1.62073 0.21531 0.006809       0.035512
kp_chain           3.58847 0.98371 0.031108       0.038382
bt_chain.1         0.29322 0.07072 0.002236       0.008182
bt_chain.2         0.14700 0.06630 0.002097       0.008893
dt_chain.1         0.81931 0.25415 0.008037       0.044970
dt_chain.2         0.94806 0.20873 0.006601       0.034910
zt_chain.1         0.18080 0.04127 0.001305       0.004763
zt_chain.2         0.09251 0.04098 0.001296       0.005493
mu_chain.Reference 1.62073 0.21531 0.006809       0.035512
mu_chain.c1        2.44004 0.20020 0.006331       0.024705
mu_chain.c2        2.56879 0.15651 0.004949       0.017144

2. Quantiles for each variable:

                       2.5%     25%     50%    75%  97.5%
b0_chain           1.185957 1.47216 1.62643 1.7768 2.0257
kp_chain           1.900448 2.86985 3.49707 4.2031 5.7436
bt_chain.1         0.161478 0.24775 0.28967 0.3385 0.4462
bt_chain.2         0.010815 0.10896 0.14572 0.1866 0.2867
dt_chain.1         0.327714 0.65400 0.81553 0.9972 1.3014
dt_chain.2         0.561832 0.80165 0.94526 1.0852 1.3241
zt_chain.1         0.101920 0.15461 0.17950 0.2078 0.2672
zt_chain.2         0.006884 0.06909 0.09212 0.1174 0.1778
mu_chain.Reference 1.185957 1.47216 1.62643 1.7768 2.0257
mu_chain.c1        2.046727 2.29902 2.43847 2.5785 2.8355
mu_chain.c2        2.251253 2.47067 2.57267 2.6717 2.8671

> 
> 
> 
> cleanEx()
> nameEx("print_text.circGLM")
> ### * print_text.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print_text.circGLM
> ### Title: Print the main results from a 'circGLM' object.
> ### Aliases: print_text.circGLM
> 
> ### ** Examples
> 
> print(circGLM(th = rvmc(10, 1, 1)), type = "text")
Bayesian circular GLM 

Call:
circGLM(th = rvmc(10, 1, 1))

MCMC run for 1000 its, 1000 used. 

Coefficients:
          Estimate    SD    LB    UB
Intercept    1.186 0.400 0.404 2.001
Kappa        1.408 0.721 0.282 3.031

DIC:  33.033 
WAIC: 32.85 
> 
> dat <- generateCircGLMData()
> cglmmod <- circGLM(th = dat[, 1], X = dat[, -1])
> print(cglmmod, type = "text")
Bayesian circular GLM 

Call:
circGLM(th = dat[, 1], X = dat[, -1])

MCMC run for 1000 its, 1000 used. 

Coefficients:
          Estimate    SD    LB    UB
Intercept    1.621 0.215 1.183 2.026
Kappa        3.089 0.984 1.762 5.506
l1           0.293 0.071 0.161 0.446
l2           0.147 0.066 0.010 0.287
c1           0.819 0.254 0.327 1.301
c2           0.948 0.209 0.562 1.324

DIC:  60.902 
WAIC: 60.36 
> 
> 
> 
> cleanEx()
> nameEx("residuals.circGLM")
> ### * residuals.circGLM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: residuals.circGLM
> ### Title: Obtain residuals from a circGLM object
> ### Aliases: residuals.circGLM
> 
> ### ** Examples
> 
> m <- circGLM(th = rvmc(10, 0, 1))
> residuals(m)
           [,1]
 [1,] 0.2377636
 [2,] 2.3745233
 [3,] 0.2808314
 [4,] 1.2874381
 [5,] 0.7125932
 [6,] 0.4516249
 [7,] 0.5195333
 [8,] 0.6133420
 [9,] 0.8289552
[10,] 1.0473453
> 
> # Cosine residuals
> residuals(m, type = "cosine")
            [,1]
 [1,] 0.02813286
 [2,] 1.71994770
 [3,] 0.03917465
 [4,] 0.72041845
 [5,] 0.24333098
 [6,] 0.10026085
 [7,] 0.13194903
 [8,] 0.18227110
 [9,] 0.32435364
[10,] 0.50012799
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  39.725 1.623 47.232 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')