fda.usc: influence.fregre.fd – R documentation

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influence.fregre.fd

Functional influence measures

Description

Once estimated the functional regression model with scalar response, influence.fregre.fd function is used to obtain the functional influence measures.

Usage

## S3 method for class 'fregre.fd'
influence(model, ...)

Arguments

`model`	`fregre.pc`, `fregre.basis` or `fregre.basis.cv` object.
`...`	Further arguments passed to or from other methods.

Details

Identify influential observations in the functional linear model in which the predictor is functional and the response is scalar. Three statistics are introduced for measuring the influence: Distance Cook Prediction DCP, Distance Cook Estimation DCE and Distance DP respectively.

Value

Return:

DCP Cook's Distance for Prediction.
DCE Cook's Distance for Estimation.
DP Distance.

Note

influence.fdata deprecated.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es

References

Febrero-Bande, M., Galeano, P. and Gonzalez-Manteiga, W. (2010). Measures of influence for the functional linear model with scalar response. Journal of Multivariate Analysis 101, 327-339.

Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/

Examples

## Not run: 
data(tecator)
x=tecator$absorp.fdata[1:129]
y=tecator$y$Fat[1:129]

res1=fregre.pc(x,y,1:5)  
# time consuming
res.infl1=influence(res1)  
res2=fregre.basis(x,y)  
res.infl2=influence(res2)  

res<-res1
res.infl<-res.infl1
mat=cbind(y,res$fitted.values,res.infl$DCP,res.infl$DCE,res.infl$DP)
colnames(mat)=c("Resp.","Pred.","DCP","DCE","DP")
pairs(mat)

## End(Not run)

fda.usc

Functional Data Analysis and Utilities for Statistical Computing

v2.0.2

GPL-2

Authors

Manuel Febrero Bande [aut], Manuel Oviedo de la Fuente [aut, cre], Pedro Galeano [ctb], Alicia Nieto [ctb], Eduardo Garcia-Portugues [ctb]

Initial release

2020-02-17