`plot`

method for `mob`

objects with
extended facilities for plugging in panel functions.

1 2 | ```
## S3 method for class 'mob'
plot(x, terminal_panel = node_bivplot, tnex = NULL, ...)
``` |

`x` |
an object of class |

`terminal_panel` |
a panel function or panel-generating function of
class |

`tnex` |
a numeric value giving the terminal node extension in relation to the inner nodes. |

`...` |
further arguments passed to |

This `plot`

method for `mob`

objects simply calls the
`plot.BinaryTree`

method, setting a different `terminal_panel`

function by default (`node_bivplot`

) and `tnex`

value.

`node_bivplot`

, `node_scatterplot`

,
`plot.BinaryTree`

, `mob`

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 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | ```
set.seed(290875)
if(require("mlbench")) {
## recursive partitioning of a linear regression model
## load data
data("BostonHousing", package = "mlbench")
## and transform variables appropriately (for a linear regression)
BostonHousing$lstat <- log(BostonHousing$lstat)
BostonHousing$rm <- BostonHousing$rm^2
## as well as partitioning variables (for fluctuation testing)
BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1,
labels = c("no", "yes"))
BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE)
## partition the linear regression model medv ~ lstat + rm
## with respect to all remaining variables:
fm <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis +
rad + tax + crim + b + ptratio,
control = mob_control(minsplit = 40), data = BostonHousing,
model = linearModel)
## visualize medv ~ lstat and medv ~ rm
plot(fm)
## visualize only one of the two regressors
plot(fm, tp_args = list(which = "lstat"), tnex = 2)
plot(fm, tp_args = list(which = 2), tnex = 2)
## omit fitted mean lines
plot(fm, tp_args = list(fitmean = FALSE))
## mixed numerical and categorical regressors
fm2 <- mob(medv ~ lstat + rm + chas | zn + indus + nox + age +
dis + rad,
control = mob_control(minsplit = 100), data = BostonHousing,
model = linearModel)
plot(fm2)
## recursive partitioning of a logistic regression model
data("PimaIndiansDiabetes", package = "mlbench")
fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps +
insulin + mass + pedigree + age,
data = PimaIndiansDiabetes, model = glinearModel,
family = binomial())
## default plot: spinograms with breaks from five point summary
plot(fmPID)
## use the breaks from hist() instead
plot(fmPID, tp_args = list(fivenum = FALSE))
## user-defined breaks
plot(fmPID, tp_args = list(breaks = 0:4 * 50))
## CD plots instead of spinograms
plot(fmPID, tp_args = list(cdplot = TRUE))
## different smoothing bandwidth
plot(fmPID, tp_args = list(cdplot = TRUE, bw = 15))
}
``` |

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