plot.dblm {dbstats}  R Documentation 
Six plots (selected by which
) are available: a plot of residual vs
fitted values, the QQplot of normality, a ScaleLocation plot of
sqrt(residuals)
against fitted values. A plot of Cook's distances
versus row labels, a plot of residuals against leverages, and the optimal
effective rank of "OCV"
, "GCV"
, "AIC"
or "BIC"
method (only if one of these four methods have been chosen in function dblm
).
By default, only the first three and 5
are provided.
## S3 method for class 'dblm'
plot(x,which=c(1:3, 5),id.n=3,main="",
cook.levels = c(0.5, 1),cex.id = 0.75,
type.pred=c("link","response"),...)
x 

which 
if a subset of the plots is required, specify a subset of the numbers 1:6. 
id.n 
number of points to be labelled in each plot, starting with the most extreme. 
main 
an overall title for the plot. Only if one of the six plots is selected. 
cook.levels 
levels of Cook's distance at which to draw contours. 
cex.id 
magnification of point labels. 
type.pred 
the type of prediction (required only for a 
... 
other parameters to be passed through to plotting functions. 
The five first plots are very useful to the residual analysis and are
the same that plot.lm
. A plot of residuals against fitted
values sees if the variance is constant. The qqplot checks if the residuals
are normal (see qqnorm
).
The plot between "ScaleLocation"
and the fitted values takes the
square root of the absolute residuals in order to diminish skewness.
The Cook's distance against the row labels, measures the effect of deleting a
given observation (estimate of the influence of a data point). Points with a
large Cook's distance are considered to merit closer examination in the analysis.
Finally, the ResidualLeverage plot also shows the most influence points
(labelled by Cook's distance). See cooks.distance
.
The last plot, allows to view the "OCV"
(just for dblm
), "GCV"
, "AIC"
or "BIC"
criterion according to the used rank in the
dblm
or dbglm
functions, and chosen the minimum. Applies only if
the parameter full.search
its TRUE
.
Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>
Boj E, Delicado P, Fortiana J (2010). Distancebased local linear regression for functional predictors. Computational Statistics and Data Analysis 54, 429437.
Boj E, Grane A, Fortiana J, Claramunt MM (2007). Selection of predictors in distancebased regression. Communications in Statistics B  Simulation and Computation 36, 8798.
Cuadras CM, Arenas C, Fortiana J (1996). Some computational aspects of a distancebased model for prediction. Communications in Statistics B  Simulation and Computation 25, 593609.
Cuadras C, Arenas C (1990). A distancebased regression model for prediction with mixed data. Communications in Statistics A  Theory and Methods 19, 22612279.
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: NorthHolland Publishing Co., pp. 459473.
Belsley, D. A., Kuh, E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.
dblm
for distancebased linear models.
dbglm
for distancebased generalized linear models.
n < 64
p < 4
k < 3
Z < matrix(rnorm(n*p),nrow=n)
b < matrix(runif(p)*k,nrow=p)
s < 1
e < rnorm(n)*s
y < Z%*%b + e
dblm1 < dblm(y~Z,metric="gower",method="GCV", full.search=FALSE)
plot(dblm1)
plot(dblm1,which=4)