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 asdefaultSummary.

Examples

Run this code

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