plot.mob {party} | R Documentation

## Visualization of MOB Trees

### Description

`plot` method for `mob` objects with extended facilities for plugging in panel functions.

### Usage

    ## S3 method for class 'mob':
    plot(x, terminal_panel = node_bivplot, tnex = NULL, ...)

### Arguments

`x` | an object of class `mob`.
`terminal_panel` | a panel function or panel-generating function of class `"grapcon_generator"`. See `plot.BinaryTree` for more details.
`tnex` | a numeric value giving the terminal node extension in relation to the inner nodes.
`...` | further arguments passed to `plot.BinaryTree`.

### Details

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.

### See Also

`node_bivplot`, `node_scatterplot`, `plot.BinaryTree`, `mob`

### Examples

    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))

    }

[Package party version 0.8-6 Index]