Lattice dotplots of the rule conditions or the linear model
coefficients produced by `cubist()`

objects

```
# S3 method for cubist
dotplot(x, data = NULL, what = "splits", committee = NULL, rule = NULL, ...)
```

x

a `cubist()`

object

data

not currently used (here for lattice compatibility)

what

either "splits" or "coefs"

committee

which committees to plot

rule

which rules to plot

…

options to pass to `lattice::dotplot()`

a `lattice::dotplot()`

object

For the splits, a panel is created for each predictor. The
x-axis is the range of the predictor scaled to be between zero
and one and the y-axis has a line for each rule (within each
committee). Areas are colored as based on their region. For
example, if one rule has `var1 < 10`

, the linear for this rule
would be colored. If another rule had the complementary region
of `var1 <= 10`

, it would be on another line and shaded a
different color.

For the coefficient plot, another dotplot is made. The layout is the same except the the x-axis is in the original units and has a dot if the rule used that variable in a linear model.

Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348

Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning (1993) pp. 236-243

Quinlan. **C4.5: Programs For Machine Learning** (1993)
Morgan Kaufmann Publishers Inc. San Francisco, CA

`cubist()`

, `cubistControl()`

,
`predict.cubist()`

, `summary.cubist()`

,
`predict.cubist()`

, `lattice::dotplot()`

# NOT RUN { library(mlbench) data(BostonHousing) ## 1 committee and no instance-based correction, so just an M5 fit: mod1 <- cubist(x = BostonHousing[, -14], y = BostonHousing$medv) dotplot(mod1, what = "splits") dotplot(mod1, what = "coefs") ## Now with 10 committees mod2 <- cubist(x = BostonHousing[, -14], y = BostonHousing$medv, committees = 10) dotplot(mod2, scales = list(y = list(cex = .25))) dotplot(mod2, what = "coefs", between = list(x = 1, y = 1), scales = list(x = list(relation = "free"), y = list(cex = .25))) # }