# postResample

0th

Percentile

##### 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)
##### Arguments
pred
A vector of numeric data (could be a factor)
obs
A vector of numeric data (could be a factor)
##### Details

This function 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.

##### Value

• A vector of performance estimates.

##### See Also

resampleSummary

• postResample
##### Examples
predicted <-  matrix(rnorm(50), ncol = 5)
observed <- rnorm(10)
apply(predicted, 2, postResample, obs = observed)
Documentation reproduced from package caret, version 3.21, License: GPL-2

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