Description Usage Arguments Details Author(s) References See Also Examples
plot
method and panel functions for tvcm
objects.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## S3 method for class 'tvcm'
plot(x, type = c("default", "coef",
"simple", "partdep", "cv"),
main, part = NULL, drop_terminal = TRUE,
tnex, newpage = TRUE, ask = NULL,
pop = TRUE, gp = gpar(), ...)
panel_partdep(object, parm = NULL,
var = NULL, ask = NULL,
prob = NULL, neval = 50, add = FALSE,
etalab = c("int", "char", "eta"), ...)
panel_coef(object, parm = NULL,
id = TRUE, nobs = TRUE,
exp = FALSE,
plot_gp = list(),
margins, yadj = 0.1,
mean = FALSE, mean_gp = list(),
conf.int = FALSE, conf.int_gp = list(),
abbreviate = TRUE, etalab = c("int", "char", "eta"), ...)

x, object 
An object of class 
type 
the type of the plot. Available types are

main 
character. A main title for the plot. 
drop_terminal 
a logical indicating whether all terminal nodes
should be plotted at the bottom. See also

tnex 
a numeric value giving the terminal node extension in relation to the inner nodes. By default the value is computed adaptively to the tree size. 
newpage 
a logical indicating whether 
pop 
a logical whether the viewport tree should be popped before return. 
gp 
graphical parameters. See 
part 
integer or letter. The partition i.e. varying coefficient component to be plotted. 
parm 
character vector ( 
var 
character vector. Indicates the partitioning variables to be visualized. 
ask 
logical. Whether an input should be asked before printing the next panel. 
prob 
a probability between 0 and 1. Gives the size of the random subsample over which the coefficients are averaged. May be smaller than 1 if the sample is large. 
neval 
the maximal number of distinct values of the variable to be evaluated. 
add 
logical. Whether the panel is to be added into an active plot. 
id 
logical. Whether the node id should be displayed. 
nobs 
logical. Whether the number of observations in each node should be displayed. 
exp 
logical. Whether the labels in the yaxes should be the exponential of coefficients. 
plot_gp 
a list of graphical parameters for the panels. Includes
components 
margins 
a numeric vector 
yadj 
a numeric scalar larger than zero that increases the margin above the panel. May be useful if the edge labels are covered by the coefficient panels. 
mean 
logical. Whether the average coefficients over the population should be visualized. 
mean_gp 
list with graphical parameters for plotting the mean
coefficients. Includes a component 
conf.int 
logical. Whether confidence intervals should be visualized. These are indicative values only. They do not account for the uncertainty of model selection procedure. 
conf.int_gp 
a list of graphical parameters for the confidence
intervals applied to 
abbreviate 
logical scalar. Whether labels of coefficients should be abbreviated. 
etalab 
character. Whether categoryspecific effects should be
labeled by integers of categories (default), the labels of the
categories ( 
... 
additional arguments passed to

The plot functions allow the diagnosis of fitted tvcm
objects. type = "default"
, type = "coef"
and
type = "simple"
show the tree structure and coefficients in
each node. type = "partdep"
plots partial dependency plots, see
Hastie et al. (2001), section 10.13.2. Finally, type = "cv"
shows, if available, the results from crossvalidation.
The functions panel_partdep
and
panel_coef
are exported to show the additional
arguments that can be passed to ...
of a
plot
call.
Notice that userdefined plots can be generated by the use of the
plot.party
function, see partykit.
Reto Buergin
Hastie, T., R. Tibshirani and J. Friedman (2001). The Elements of Statistical Learning (2 ed.). New York, USA: SpringerVerlag.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  ##  #
## Dummy example:
##
## Plotting the types "coef" and "partdep" for a 'tvcm' object fitted
## on the artificial data 'vcrpart_2'.
##  #
data(vcrpart_2)
## fit the model
model < tvcglm(y ~ vc(z1, z2, by = x1, intercept = TRUE) + x2,
data = vcrpart_2, family = gaussian(),
control = tvcm_control(maxwidth = 3, minbucket = 5L))
## plot type "coef"
plot(model, "coef")
## add various (stupid) plot parameters
plot(model, "coef",
plot_gp = list(type = "p", pch = 2, ylim = c(4, 4),
label = c("par1", "par2"), gp = gpar(col = "blue")),
conf.int_gp = list(angle = 45, length = unit(2, "mm"),
ends = "last", type = "closed"),
mean_gp = list(pch = 16,
gp = gpar(fontsize = 16, cex = 2, col = "red")))
## separate plots with separate plot parameters
plot(model, "coef", parm = list("(Intercept)", "x1"), tnex = 2,
plot_gp = list(list(gp = gpar(col = "red")),
list(gp = gpar(col = "blue"))),
mean_gp = list(list(gp = gpar(col = "green")),
list(gp = gpar(col = "yellow"))))
## plot type "partdep"
par(mfrow = c(1, 2))
plot(model, "partdep", var = "z1", ask = FALSE)

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