plot.dblm {dbstats} | R Documentation |
Plots for objects of clases dblm or dbglm
Description
Six plots (selected by which
) are available: a plot of residual vs
fitted values, the Q-Qplot of normality, a Scale-Location 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.
Usage
## 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"),...)
Arguments
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. |
Details
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 qq-plot checks if the residuals
are normal (see qqnorm
).
The plot between "Scale-Location"
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 Residual-Leverage 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
.
Author(s)
Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>
References
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
dblm
for distance-based linear models.
dbglm
for distance-based generalized linear models.
Examples
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)