# get.pred.values

From mobForest v1.3.1
by Kasey Jones

##### Get predictions summarized across trees for out-of-bag cases or all cases for cases from new test data

Get predictions summarized across trees for out-of-bag cases or all cases for cases from new test data

##### Usage

`get.pred.values(rf, OOB = T, newdata = F)`

##### Arguments

- rf
An object of class

`mobforest.output`

.- OOB
a logical determining whether to return predictions from the out-of-bag sample or the learning sample (not suggested).

- newdata
a logical determining whether to return predictions from test data. If newdata = TRUE, then OOB argument is ignored.

##### Value

matrix with three columns: 1) Mean Predictions across trees, 2) Standard deviation of predictions across trees, and 3) Residual (mean predicted - observed). The third column is applicable only when linear regression is considered as the node model.

##### 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 = TRUE,
alpha = 0.05, bonferroni = TRUE, minsplit = 25), data = BostonHousing,
processors = 1, model = linearModel, seed = 1111)
# Obtain out-of-bag predicted values
OOB_pred_mat <- get.pred.values(rfout, OOB = TRUE)
OOB_pred = OOB_pred_mat[, 1]
# }
# NOT RUN {
# }
```

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

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