conf.mat {BDgraph} R Documentation

## Confusion Matrix

### Description

Create a Confusion Matrix.

### Usage

```
conf.mat( pred, actual, cutoff = 0.5, proportion = FALSE,
dnn = c( "Prediction", "Actual" ), ... )
```

### Arguments

 `pred ` An adjacency matrix corresponding to an estimated graph. It can be an object with `S3` class `"bdgraph"` from function `bdgraph`. It can be an object of `S3` class `"ssgraph"`, from the function `ssgraph::ssgraph()` of `R` package `ssgraph::ssgraph()`. `actual` An adjacency matrix corresponding to the actual graph structure in which a_{ij}=1 if there is a link between notes i and j, otherwise a_{ij}=0. It can be an object with `S3` class `"sim"` from function `bdgraph.sim`. It can be an object with `S3` class `"graph"` from function `graph.sim`. It can be a factor, numeric or character vector of responses (true class), typically encoded with 0 (controls) and 1 (cases). Only two classes can be used in a ROC curve. `cutoff` cutoff value for the case that `pred` is vector of probabilites. The default is 0.5. `proportion` Logical: FALSE (default) for a confusion matrix with number of cases. TRUE, for a confusion matrix with the proportion of cases. `dnn ` the names to be given to the dimensions in the result (the dimnames names). `... ` further arguments to be passed to `table`.

### Value

the results of `table` on `pred` and `actual`.

### Author(s)

`conf.mat.plot`, `compare`, `roc`, `bdgraph`

### Examples

```## Not run:
set.seed( 100 )

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )

# Running sampling algorithm based on GGMs
sample.ggm <- bdgraph( data = data.sim, method = "ggm", iter = 10000 )

# Confusion Matrix for GGM method
conf.mat( pred = sample.ggm, actual = data.sim )

## End(Not run)
```

[Package BDgraph version 2.64 Index]