Description Usage Arguments Value Examples
View source: R/get.pred.values.R
Get predictions summarized across trees for out-of-bag cases or all cases for cases from new test data
1 | get.pred.values(rf, OOB = T, newdata = F)
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rf |
An object of class |
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. |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## 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]
## End(Not run)
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