Description Usage Arguments Value References See Also Examples

The function `elliptical`

is used to fit linear elliptical regression models. This models is specified giving a symbolic description of the systematic and stochastic components.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |

`formula` |
regression model formula of a formula |

`family` |
a description of the error distribution to be used in the model (see |

`data` |
an optional data frame, list or environment containing the variables in the model. |

`dispersion` |
an optional fixed value for dispersion parameter. |

`weights` |
an optional numeric vector of “prior weights” to be used in the fitting process. |

`subset` |
an optional numeric vector specifying a subset of observations to be used in the fitting process. |

`na.action` |
a function which indicates what should happen when the data contain NAs (see |

`method` |
optimization method used to estimate the model parameters. The default method "elliptical.fit" uses Fisher's scoring method. The alternative "model.frame" returns the model frame and does no fitting. |

`control` |
a list of parameters for controlling the fitting process. This is passed by |

`model` |
a logical value indicating whether model frame should be included as a component of the return. |

`x` |
a logical value indicating whether the response vector used in the fitting process should be returned as components of the return. |

`y` |
a logical value indicating whether model matrix used in the fitting process should be returned as components of the return. |

`contrasts` |
an optional list. See the |

`offset` |
this can be used to specify a “prior known component” to be included in the linear predictor during fitting (as in |

`...` |
arguments to be used to form the default control argument if it is not supplied directly. |

returns an object of class “elliptical”, a list with follow components:

`coefficients` |
coefficients of location parameters. |

`dispersion` |
coefficient of dispersion parameter. |

`residuals` |
standardized residuals. |

`fitted.values` |
the fitted mean values. |

`loglik` |
the likelihood logarithm value for the fitted model. |

`Wg` |
values of the function |

`Wgder` |
values for the function |

`v` |
values for the function |

`rank` |
the numeric rank for the fitted model. |

`R` |
the matrix of correlation for the estimated parameters. |

`inter` |
number of iterations of optimization process. |

`scale` |
values of the |

`scaledispersion` |
values of the |

`scalevariance` |
values of the scale variance for the specified distribution. |

`df` |
degree of freedom for t-student distribution. |

`s, r` |
shape parameters for generalized t-student distribution. |

`alpha` |
shape parameter for contaminated normal and generalized logistic distributions. |

`mp` |
shape parameter for generalized logistic distribution. |

`epsi,sigmap` |
dispersion parameters for contaminated normal distribution. |

`k` |
shape parameter for power exponential distribution. |

`Xmodel` |
the model matrix. |

`weights` |
the working weights, that is the weights in the final iteration of optimization process |

`df.residuals` |
the residual degrees of freedom. |

`family` |
the |

`formula` |
the |

`terms` |
the |

`contrasts` |
(where relevant) the contrasts used. |

`control` |
the value of the |

`call` |
the matched call. |

`y` |
the response variable used. |

Cysneiros, F. J. A., Paula, G. A., and Galea, M. (2007). Heteroscedastic symmetrical linear models. Statistics & probability letters, 77(11), 1084-1090. doi: 10.1016/j.spl.2007.01.012

Fang, K. T., Kotz, S. and NG, K. W. (1990, ISBN:9781315897943). Symmetric Multivariate and Related Distributions. London: Chapman and Hall.

`glm`

, `family.elliptical`

, `summary.elliptical`

1 2 3 4 5 6 7 8 9 10 | ```
data(luzdat)
y <- luzdat$y
x1 <- luzdat$x1 ; x1 <- factor(x1) ; x1 <- C(x1,treatment)
x2 <- luzdat$x2
x3 <- (luzdat$x2)^2
luz <- data.frame(y,x1,x2,x3)
elliptical.fitt <- elliptical(y ~ x1+x2+x3, family = Student(df=5)
,data=luz)
elliptical.fitLII <- elliptical(y ~ x1+x2+x3, family = LogisII()
,data=luz)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.