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)
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