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)
pred |
A vector of numeric data (could be a factor) |
obs |
A vector of numeric data (could be a factor) |
This function 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.
A vector of performance estimates.
Max Kuhn
predicted <- matrix(rnorm(50), ncol = 5) observed <- rnorm(10) apply(predicted, 2, postResample, obs = observed)