Description Usage Arguments Details Value Source Examples

This function fits a Bayesian circular mixed-effects model based on the projected normal distribution.

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`pred.I` |
model equation for effects of component 1. |

`data` |
the dataframe used for analysis. |

`pred.II` |
model equation for effects of component 2. |

`its` |
output iterations of the MCMC sampler. |

`burn` |
number of burn-in iterations. |

`n.lag` |
amount of lag for the iterations and burn-in. |

`seed` |
user-specified random seed. |

Because the model is based on the projected normal distribution, a
model equation has to be given for the fixed and random effects of the two
components. By default the model equation of the second component
`pred.II`

is set to be equal to that of the first component. For more
information about the projected normal distribution see Presnell, Morrisson
& Littell (1998).

A tutorial on how to use this function can be found in Cremers & Klugkist (2017, working paper). More details on the sampling algorithm and interpretation of the coefficients from the model can be found in Nu<c3><b1>ez-Antonio & Gutti<c3><a9>rrez-Pe<c3><b1>a (2014) and Cremers & Klugkist (2017, working paper).

A `bpnme`

object, which can be further analyzed using the
associated functions `traceplot.bpnme`

,
`BFc.bpnme`

, `coef_lin.bpnme`

,
`coef_circ.bpnme`

, `coef_ran.bpnme`

,
`residuals.bpnme`

, `predict.bpnme`

,
`fit.bpnme`

and `print.bpnme`

.

A `bpnr`

object contains the following elements (some elements are not
returned if not applicable)

`Beta.I`

A matrix of posterior samples for the fixed effects coefficients for the first component.

`Beta.II`

A matrix of posterior samples for the fixed effects coefficients for the second component.

`B.I`

An array of posterior samples for the random effects coefficients for the first component.

`B.II`

An array of posterior samples for the random effects coefficients for the second component.

`VCovI`

An array of posterior samples for the random effect variances of the first component.

`VCovII`

An array of posterior samples for the random effect variances of the second component.

`predictiva`

A list containing the posterior density values for all timepoints of individuals in the dataset for all iterations. The rowsums of this matrix are the likelihood values for all iterations

`circular.ri`

A vector of posterior samples for the circular random intercepts.

`N`

Number of observed cases.

`its`

Number of output iterations.

`n.lag`

One in

`n.lag`

iterations will be saved as output iteration. Set lag to 1 to save all iterations (default).`burn`

Burn-in time for the MCMC sampler.

`p1`

Number of fixed effect parameters predicting the first component.

`p2`

Number of fixed effect parameters predicting the second component.

`q1`

Number of random effect parameters predicting the first component.

`q2`

Number of random effect parameters predicting the second component.

`a.x`

A matrix of posterior samples for

`a.x`

which describes the location of the inflection point of the regression curve on the axis of the predictor.`a.c`

A matrix of posterior samples for

`a.c`

which describes the location of the inflection point of the regression curve on the axis of the circular outcome.`b.c`

A matrix of posterior samples for

`b.c`

which describes the slope of the tangent line at the inflection point.`SAM`

A matrix of posterior samples for the circular regression slopes at the mean.

`AS`

A matrix of posterior samples for the average slopes of the circular regression.

`SSDO`

A matrix of posterior samples for the signed shortest distance to the origin.

`circ.diff`

A matrix of posterior samples for the circular difference found between levels of categorical variables and the intercept.

`cRSnum`

A string indicating whether there are continuous variables with a random slope

`cRScat`

A string indicating whether there are categorical variables with a random slope

`cRS`

A string indicating whether there are categorical or continuous variables with a random slope

`cRI`

A vector of posterior samples of the mean resultant length of the circular random intercept, a measure of concentration.

`Call`

The matched call.

`lin.coef.I`

The mean, mode, standard deviation and 95 posterior density of the linear fixed effect coefficients for

`B1`

.`lin.coef.II`

The mean, mode, standard deviation and 95 confidence interval of the highest posterior density of the linear fixed effect coefficients for

`B2`

.`circ.coef`

The mean, mode, standard deviation and 95 density for

`a.x`

,`a.c`

,`SSDO`

, and the circular fixed effect coefficients`b.c`

,`AS`

, and`SAM`

`circ.coef.cat`

The mean, mode, standard deviation and 95 confidence interval of the highest posterior density the circular difference between levels of categorical variables and the intercept.

`circ.coef.means`

The mean, mode, standard deviation and 95 confidence interval of the highest posterior density of circular means of the categorical variables.

`model.fit`

A list of information criteria for assessment of model fit.

`lin.res.varrand.I`

The mean, mode, standard deviation and 95 variances of the random intercepts and slopes of component I.

`lin.res.varrand.II`

The mean, mode, standard deviation and 95 of component II.

`circ.res.varrand`

The mean, mode, standard deviation and 95 random intercepts and slopes.

`mm`

A list of information, model matrices, sample size, etc. on the specified model.

Cremers, J., Mainhard, M.T. & Klugkist, I. (in press). Assessing a Bayesian Embedding Approach to Circular Regression Models. Methodology

Cremers, J., & Klugkist, I. (2017). How to analyze circular data: A tutorial for projected normal regression models. Under review.

Cremers, J., & Klugkist, I. (2017). Longitudinal circular modelling of circumplex measurements for teacher behavior. Working paper.

Nu<c3><b1>ez-Antonio, G. & Guti<c3><a9>rrez-Pe<c3><b1>a, E. (2014). A Bayesian model for longitudinal circular data based on the projected normal distribution. Computational Statistics and Data Analysis, 71, 506-519.

Presnell, B., Morrison, S.P. & Littell, R.C. (1998). Projected multivariate linear models for directional data. Journal of the American Statistical Association, 93 (443), 1068 - 1077.

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bpnreg documentation built on May 2, 2019, 6:37 a.m.

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