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)

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.

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.

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.

Examples

Run this code

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