influence.fregre.fd

0th

Percentile

Functional influence measures

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

Keywords
outliers
Usage
# S3 method for 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 $\mbox{pe}\tilde{\mbox{n}}\mbox{a}$ DP respectively.

Value

Return:

• DCP Cook's Distance for Prediction.

• DCE Cook's Distance for Estimation.

• DP $\mbox{Pe}\tilde{\mbox{n}}\mbox{a's}$ Distance.

Note

influence.fdata deprecated.

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/

See Also as: fregre.pc, fregre.basis, influence_quan

Aliases
• influence.fregre.fd
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)
# }

Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

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