Does circular regression of mean direction for an angular response (P.W. Goedhart).

### Options

`PRINT` = string tokens |
What to print (`model` , `summary` , `estimates` , `fittedvalues` , `monitoring` ); default `mode` , `summ` , `esti` |
---|---|

`FACTORIAL` = scalar |
Limit for expansion of model terms; default 3 |

`RESIDUALS` = variate |
To save the residuals |

`FITTEDVALUES` = variate |
To save the fittedvalues, i.e. the fitted mean directions |

`LEVERAGES` = variate |
To save the leverages |

`ESTIMATES` = variate |
To save estimates of linear parameters |

`SE` = variate |
To save standard errors of the estimates |

`VCOVARIANCE` = symmetric matrix |
To save the variance-covariance matrix of the estimates |

`MU0` = scalar |
To save the estimate of the mean parameter μ_{0} |

`SEMU0` = scalar |
To save the standard error of the estimated mea parameter μ_{0} |

`KAPPA` = scalar |
To save the estimate of the concentration parameter κ of the von Mises distribution |

`SEKAPPA` = scalar |
To save the standard error of the estimated concentration parameter κ |

`_2LOGLIKELIHOOD` = scalar |
To save the value of minus twice the maximized log likelihood |

`DF` = scalar |
To save the residual degrees of freedom |

`ITERATIVEWEIGHTS` = variate |
To save the iterative weights |

`LINEARPREDICTOR` = variate |
To save the linear predictor |

`YADJUSTED` = variate |
To save the adjusted dependent variate |

`I_2LOGLIKELIHOOD` = variate |
To save the contribution of each unit to the value of minus twice the maximized log likelihood |

`MAXCYCLE` = scalar |
Maximum number of iterations for see-saw algorithm; default 30 |

`TOLERANCE` = scalar |
Convergence criterion; default 10^{-5} |

### Parameter

`TERMS` = formula |
List of explanatory variates and factors, or model formula |
---|

### Description

Procedure `RCIRCULAR`

can be used to fit a circular regression model to an angular response. A circular regression model is similar in spirit to a generalized linear model; it employs the von Mises distribution and the arctangent link function. More formally, it is assumed that the angular response follows a von Mises distribution with mean direction μ and concentration parameter κ. The mean direction μ is related to the linear predictor η by means of the link function

μ = μ_{0} + 2 arctan(η)

which maps the real line to the circle. The linear predictor η itself is a linear function of all the regressors in the usual way, except that it does not include a constant term. The circular regression model is fitted by means of an iterative algorithm which employs re-weighted least squares to estimate the linear parameters. A detailed account can be found in Fisher (1993) or Fisher & Lee (1992).

Note that the model is not invariant to linear shifts of explanatory variates. This is because the linear predictor η does not contain a constant term. This can be a serious drawback of the circular regression model. An alternative model without the parameter μ_{0} and including an intercept in the linear predictor is not invariant to rotations of the response, which is even worse. Also note that the estimates on page 161 of Fisher (1993) are for the centred distance explanatory variable.

A call to `RCIRCULAR`

must be preceded by a `MODEL`

statement which defines the angular response variate. Only the first response variate is analysed and options other than `WEIGHTS`

should not be set in the `MODEL`

statement. The `TERMS`

parameter of `RCIRCULAR`

specifies the model to be fitted. Cases with a missing response variate or with a zero weight are excluded from the analysis. The `FACTORIAL`

option operates in the usual way. Printed output is controlled by the `PRINT`

option with the usual settings. Setting `PRINT=summary`

displays the value of minus twice the maximized log likelihood, both for the fitted model and for the null model with only the constant μ_{0}. The difference between the two log likelihood values is also printed with a corresponding probability based on the chi-square distribution using likelihood ratio testing. This tests whether the fitted model is an improvement over the null model. `PRINT=monitoring`

displays monitoring information of the iterative algorithm. The iterative process itself is controlled by the `MAXCYCLE`

option which determines the maximum number of cycles, and by the `TOLERANCE`

option. The iterative process is stopped when the relative difference in minus twice the log likelihood is smaller than the specified tolerance.

Results of the circular regression can be saved by a number of options. The `ESTIMATES`

, `SE`

and `VCOVARIANCE`

options save estimates of the linear parameters, their standard errors and variance-covariance matrix. This never includes the constant parameter. The estimate and standard error of the constant parameter μ_{0} can be saved using options `MU0`

and `SEMU0`

, and those for the concentration parameter κ of the von Mises distribution can be saved using options `KAPPA`

and `SEKAPPA`

. The `_2LOGLIKELIHOOD`

option allows minus twice the maximized log likelihood to be saved, and the `DF`

option saves the residual degrees of freedom. These may be useful for comparing a sequence of nested models fitted by `RCIRCULAR`

using likelihood ratio testing. The `RESIDUALS`

, `FITTEDVALUES`

, `LEVERAGES`

, `ITERATIVEWEIGHTS`

, `LINEARPREDICTOR`

and `YADJUSTED`

options allow you to save the simple residuals, the fitted values (i.e. the fitted mean directions), the leverages of the iterative reweighted least squares algorithm, the linear predictor and an adjusted dependent variate. Finally the contribution of each unit to minus twice the maximized log likelihood can be saved by means of the `I_2LOGLIKELIHOOD`

option.

Options: `PRINT`

, `FACTORIAL`

, `RESIDUALS`

, `FITTEDVALUES`

, `LEVERAGES`

, `ESTIMATES`

, `SE`

, `VCOVARIANCE`

, `MU0`

, `SEMU0`

, `KAPPA`

, `SEKAPPA`

, `_2LOGLIKELIHOOD`

, `DF`

, `ITERATIVEWEIGHTS`

, `LINEARPREDICTOR`

, `YADJUSTED`

, `I_2LOGLIKELIHOOD`

, `MAXCYCLE`

, `TOLERANCE`

.

Parameter: `TERMS`

.

### Method

The model is fitted using the algorithm of Fisher & Lee (1993) and Fisher (1993). The iterative fitting of the model is adapted by adding the linear predictor from the previous cycle to the adjusted y variate. For a weighted circular regression the estimated circular standard error of μ_{0} is calculated using the sum of the weights instead of the degrees of freedom, see equation (6.64) in Fisher (1993). Note that the estimated standard errors for the linear parameters are conditional on the estimates of μ_{0} and κ, and vice versa.

### Action with `RESTRICT`

Only the angular response variate can be restricted. The analysis is restricted accordingly.

### References

Fisher, N.I. & Lee, A.J. (1992). Regression models for an angular response. *Biometrics*, 48, 665-677.

Fisher, N.I. (1993). *Statistical Analysis of Circular Data*. Cambridge University Press, Cambridge.

### See also

Procedures: `CASSOCIATION`

, `CCOMPARE`

, `CDESCRIBE`

, `DCIRCULAR`

, `WINDROSE`

.

Commands for: Regression analysis.

### Example

CAPTION 'RCIRCULAR example',\ !t('Directions moved by 31 periwinkles as a function of distance',\ '(Fisher 1993, Statistical Analysis of Circular Data p.160-162).');\ STYLE=meta,plain VARIATE [NVALUES=31] distance,direction READ distance,direction 107 67 46 66 33 74 67 61 122 58 69 60 43 100 30 89 12 171 25 166 37 98 69 60 5 197 83 98 68 86 38 123 21 165 1 133 71 101 60 105 71 71 71 84 57 75 53 98 38 83 70 71 7 74 48 91 7 38 21 200 27 56 : CALCULATE distance = distance - MEAN(distance) MODEL direction RCIRCULAR [PRINT=#,fittedvalues] distance