caret (version 4.36)

# postResample: Calculates performance across resamples

## Description

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.

## Usage

```postResample(pred, obs)
defaultSummary(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 hte observed and predicted outcomes
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`.

## Value

• A vector of performance estimates.

## 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`.

Other functions can be used via the `summaryFunction` argument of `trainControl`. Custom functions must have the same arguments as`defaultSummary`.

## See Also

`trainControl`

## Examples

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