Description Usage Arguments Details Author(s) References See Also Examples

Methods for objects returned by expectile regression functions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
## S3 method for class 'expectreg'
print(x, ...)
## S3 method for class 'expectreg'
summary(object,...)
## S3 method for class 'expectreg'
predict(object, newdata = NULL, with_intercept = T, ...)
## S3 method for class 'expectreg'
x[i]
## S3 method for class 'expectreg'
residuals(object, ...)
## S3 method for class 'expectreg'
resid(object, ...)
## S3 method for class 'expectreg'
fitted(object, ...)
## S3 method for class 'expectreg'
fitted.values(object, ...)
## S3 method for class 'expectreg'
effects(object, ...)
## S3 method for class 'expectreg'
coef(object, ...)
## S3 method for class 'expectreg'
coefficients(object, ...)
## S3 method for class 'expectreg'
confint(object, parm = NULL, level = 0.95, ...)
``` |

`x,object` |
An object of class |

`newdata` |
Optionally, a data frame in which to look for variables with which to predict. |

`with_intercept` |
Should the intercept be added to the prediction of splines? |

`i` |
Covariate numbers to be kept in subset. |

`level` |
Coverage probability of the generated confidence intervals. |

`parm` |
Optionally the confidence intervals may be restricted to certain covariates, to be named in a vector. Otherwise the confidence intervals for the fit are returned. |

`...` |
additional arguments passed over. |

These functions can be used to extract details from fitted models.
`print`

shows a dense representation of the model fit.

`[`

can be used to define a new object with a subset of covariates from the original fit.

`resid`

returns the residuals in order of the response.

`fitted`

returns the overall fitted values *\hat{y}* while `effects`

returns the values
for each covariate in a list.

The function `coef`

extracts the regression coefficients for each covariate listed separately.
For the function `expectreg.boost`

this is not possible.

Fabian Otto- Sobotka

Carl von Ossietzky University Oldenburg

http://www.uni-Oldenburg.de

Elmar spiegel

Georg August University Goettingen
http://www.uni-goettingen.de

Schnabel S and Eilers P (2009)
* Optimal expectile smoothing *
Computational Statistics and Data Analysis, 53:4168-4177

Sobotka F and Kneib T (2010)
* Geoadditive Expectile Regression *
Computational Statistics and Data Analysis,
doi: 10.1016/j.csda.2010.11.015.

`expectreg.ls`

, `expectreg.boost`

, `expectreg.qp`

1 2 3 4 5 6 7 8 9 10 11 12 13 |

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.