Description Usage Arguments Value Examples

computes the change in prediction error from permuting variables.

1 2 3 4 | ```
permutationImportance(data, vars, y, model, nperm = 100L,
predict.fun = function(object, newdata) predict(object, newdata =
newdata), loss.fun = function(x, y) defaultLoss(x, y),
contrast.fun = function(x, y) x - y)
``` |

`data` |
a |

`vars` |
a character vector specifying columns of |

`y` |
a character vector giving the name of the target/outcome variable. |

`model` |
an object with a predict method which returns a vector or matrix. presumably this object represents a model fit. |

`nperm` |
positive integer giving the number of times to permute the indicated variables (default is 100). |

`predict.fun` |
what function to generate predictions using |

`loss.fun` |
what loss function to use to measure prediction errors. default is mean squared-error for ordered predictions and mean misclassification error for unordered prediction errors. this function must take two arguments, “x” and “y”, which operate on the output of |

`contrast.fun` |
what function to use to contrast the permuted and unpermuted predictions. default is the difference. this function takes two arguments “x” and “y”, which are the output of the |

a numeric vector or matrix, depending on `contrast.fun`

and `loss.fun`

, giving the change in prediction error from `nperm`

permutations of `vars`

.

1 2 3 4 5 6 | ```
X = replicate(3, rnorm(100))
y = X %*% runif(3)
data = data.frame(X, y)
fit = lm(y ~ -1 + X1 + X2 + X3, data)
permutationImportance(data, "X1", "y", fit)
``` |

```
[1] 0.2970126
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.