# logistic.acc

From mobForest v1.3.1
by Kasey Jones

##### Contingency table: Predicted vs. Observed Outcomes

This function takes predicted probabilities (for out of bag cases) obtained through logistic regression-based tree models and converts them into binary classes (based on specified probability threshold). The predicted classifications are then compared to actual binary response.

##### Usage

`logistic.acc(response, predicted, prob_thresh = 0.5)`

##### Arguments

- response
A vector of binary classes of out-of-cases for a given tree.

- predicted
A vector of predicted probabilities of out-of-cases using same tree.

- prob_thresh
Probability threshold for classification (default = .5).

##### Examples

```
# NOT RUN {
# We should get 15 predictions correct and miss 5
response <- matrix(c(rep(0,10), rep(1,10)))
predicted <- c(rep(.1,15), rep(.8,5))
logistic.acc(response, predicted, .5)
# }
```

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

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