# postResample

##### Calculates performance across resamples

Given two numeric vectors of data, the mean squared error and R-squared are calculated. For two factors, the overall agreement rate and Kappa are determined.

- Keywords
- utilities

##### Usage

```
postResample(pred, obs)
defaultSummary(data, lev = NULL, model = NULL)
twoClassSummary(data, lev = NULL, model = NULL)
```

##### Arguments

- pred
- A vector of numeric data (could be a factor)
- obs
- A vector of numeric data (could be a factor)
- data
- a data frame or matrix with columns
`obs`

and`pred`

for the observed and predicted outcomes. For`twoClassSummary`

, columns should also include predicted probabilities for each class. See the`classProbs`

- lev
- a character vector of factors levels for the response. In regression cases, this would be
`NULL`

. - model
- a character string for the model name (as taken form the
`method`

argument of`train`

.

##### Details

`postResample`

is meant to be used with `apply`

across a matrix. For numeric data
the code checks to see if the standard deviation of either vector is zero. If so, the correlation
between those samples is assigned a value of zero. `NA`

values are ignored everywhere.

Note that many models have more predictors (or parameters) than data points, so the typical mean squared
error denominator (n - p) does not apply. Root mean squared error is calculated using `sqrt(mean((pred - obs)^2`

.
Also, R-squared is calculated as the square of the correlation between the observed and predicted outcomes.

For `defaultSummary`

is the default function to compute performance metrics in `train`

. It is a wrapper around `postResample`

.

`twoClassSummary`

computes sensitivity, specificity and the area under the ROC curve. To use this function, the `classProbs`

argument of `trainControl`

should be `TRUE`

.

Other functions can be used via the `summaryFunction`

argument of `trainControl`

. Custom functions must have the same arguments as`defaultSummary`

.

##### Value

- A vector of performance estimates.

##### See Also

##### Examples

```
predicted <- matrix(rnorm(50), ncol = 5)
observed <- rnorm(10)
apply(predicted, 2, postResample, obs = observed)
```

*Documentation reproduced from package caret, version 4.69, License: GPL-2*