| postResample {caret} | R Documentation |
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.
postResample(pred, obs) defaultSummary(data, lev = NULL, model = NULL)
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. |
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.
A vector of performance estimates.
Max Kuhn
predicted <- matrix(rnorm(50), ncol = 5) observed <- rnorm(10) apply(predicted, 2, postResample, obs = observed)