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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