methods | R Documentation |

Methods for objects returned by expectile regression functions.

## 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.

The function `coef`

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

this is not possible.

`[`

returns a new object of class expectreg 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.

`coef`

returns a list of all regression coefficients separately for each covariate.

Fabian Otto- Sobotka

Carl von Ossietzky University Oldenburg

https://uol.de

Elmar Spiegel

Georg August University Goettingen
https://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`

data(dutchboys) expreg <- expectreg.ls(hgt ~ rb(age,"pspline"),data=dutchboys,smooth="f", expectiles=c(0.05,0.2,0.8,0.95)) print(expreg) coef(expreg) new.d = dutchboys[1:10,] new.d[,2] = 1:10 predict(expreg,newdata=new.d)

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