predict.dblm {dbstats}R Documentation

Predicted values for a dblm object


predict.dblm returns the predicted values, obtained by evaluating the distance regression function in the new data (newdata). newdata can be the values of the explanatory variables of these new cases, the squared distances between these new individuals and the originals ones, or rows of new doubly weighted and centered inner products matrix G.


## S3 method for class 'dblm'



an object of class dblm. Result of dblm.


data.frame or matrix which contains the values of Z (if type.var="Z". The squared distances between k new individuals and the original n individuals (only if type.var="D2"). Finally, the G inner products matrix (if type.var="G").


set de type.var of newdata. Can be "Z" if newdata contains the values of the explanatory variables, "D2" if contains the squared distances matrix or "G" if contains the inner products matrix.


arguments passed to or from other methods to the low level.


predict.dblm produces a vector of predictions for the k new individuals.


Look at which way (or type.var) was made the dblm call. The parameter type.var must be consistent with the data type that is introduced to dblm.


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.

See Also

dblm for distance-based linear models.


# prediction of new observations newdata
n <- 100
p <- 3
k <- 5

Z <- matrix(rnorm(n*p),nrow=n)
b <- matrix(runif(p)*k,nrow=p)
s <- 1
e <- rnorm(n)*s
y <- Z%*%b + e

D <- dist(Z)
D2 <- disttoD2(D)
D2_train <- D2[1:90,1:90]

dblm1 <- dblm(D2_train,y[1:90])

newdata <- D2[91:100,1:90]

[Package dbstats version 2.0.2 Index]