plot.ldblm {dbstats}R Documentation

Plots for objects of clases ldblm or ldbglm


Three plots (selected by which) are available: a plot of fitted values vs response, a plot of residuals vs fitted and the optimal bandwidth h of "OCV", "GCV", "AIC" or "BIC" criterion (only if one of these four methods have been chosen in the ldblm function). By default, only the first and the second are provided.


## S3 method for class 'ldblm'



an object of class ldblm or ldbglm.


if a subset of the plots is required, specify a subset of the numbers 1:3.


number of points to be labelled in each plot, starting with the most extreme.


an overall title for the plot. Only if one of the three plots is selected.


other parameters to be passed through to plotting functions.


Boj, Eva <>, Caballe, Adria <>, Delicado, Pedro <> and Fortiana, Josep <>


Boj E, Delicado P, Fortiana J (2010). Distance-based local linear regression for functional predictors. Computational Statistics and Data Analysis 54, 429-437.

Boj E, Grane A, Fortiana J, Claramunt MM (2007). Selection of predictors in distance-based regression. Communications in Statistics B - Simulation and Computation 36, 87-98.

Cuadras CM, Arenas C, Fortiana J (1996). Some computational aspects of a distance-based model for prediction. Communications in Statistics B - Simulation and Computation 25, 593-609.

Cuadras C, Arenas C (1990). A distance-based regression model for prediction with mixed data. Communications in Statistics A - Theory and Methods 19, 2261-2279.

Cuadras CM (1989). Distance analysis in discrimination and classification using both continuous and categorical variables. In: Y. Dodge (ed.), Statistical Data Analysis and Inference. Amsterdam, The Netherlands: North-Holland Publishing Co., pp. 459-473.

Belsley, D. A., Kuh, E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

See Also

ldblm for local distance-based linear models.
ldbglm for local distance-based generalized linear models.


# example to use of the ldblm function
n <- 100
p <- 1
k <- 5

Z <- matrix(rnorm(n*p),nrow=n)
b1 <- matrix(runif(p)*k,nrow=p)
b2 <- matrix(runif(p)*k,nrow=p)
b3 <- matrix(runif(p)*k,nrow=p)

s <- 1
e <- rnorm(n)*s

y <- Z%*%b1 + Z^2%*%b2 +Z^3%*%b3 + e

D2 <- as.matrix(dist(Z))^2
class(D2) <- "D2"

ldblm1 <- ldblm(D2,y=y,kind.of.kernel=1,method.h="AIC",noh=5,h.knn=NULL)

[Package dbstats version 2.0.1 Index]