postResample

From caret v4.20 by Max Kuhn

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)
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.

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.

Value

A vector of performance estimates.

Examples

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

Documentation reproduced from package caret, version 4.20, License: GPL-2

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