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)
twoClassSummary(data, lev = NULL, model = NULL)
R2(pred, obs, formula = "corr", na.rm = FALSE)
RMSE(pred, obs, na.rm = FALSE)

Arguments

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, $R^2$ is calculated wither using as the square of the correlation between the observed and predicted outcomes when form = "corr". when form = "traditional", $$R^2 = 1-\frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y}_i)^2}$$

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 asdefaultSummary.

References

Kvalseth. Cautionary note about $R^2$. American Statistician (1985) vol. 39 (4) pp. 279-285

Examples

Run this code

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

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