# residual.plot

From mobForest v1.3.1
by Kasey Jones

##### Produces two plots: a) histogram of residuals, b) predicted Vs residuals. This feature is applicable only when linear regression is considered as the node model.

Residuals are computed as difference between the predicted values of outcome (summarized across all trees) and observed values of outcome. The residual plots are typical when the fitted values are obtained through linear regression but not when logistic or Poisson regression is considered as a node model. Therefore, the residual plots are produced only when linear regression is considered. For logistic or Poisson models, a message is printed saying "Residual Plot not produced when logistic of Poisson regression is considered as the node model".

##### Usage

`residual.plot(object, breaks = 50)`

##### Arguments

- object
An object of class 'mobforest.output'

- breaks
Integer for number of breaks in histogram

##### Examples

```
# NOT RUN {
library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
# Recursive partitioning based on linear regression model medv ~ lstat with 3
# trees. 1 core/processor used.
rfout <- mobforest.analysis(as.formula(medv ~ lstat), c("rad", "tax", "crim"),
mobforest_controls = mobforest.control(ntree = 3, mtry = 2, replace = T,
alpha = 0.05, bonferroni = T, minsplit = 25), data = BostonHousing,
processors = 1, model = linearModel, seed = 1111)
# get predictive performance estimates and produce a performance plot
residualPlot(rfout)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package mobForest, version 1.3.1, License: GPL (>= 2)*

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