postResample(pred, obs)
defaultSummary(data, lev = NULL, model = NULL)twoClassSummary(data, lev = NULL, model = NULL)
R2(pred, obs, formula = "corr", na.rm = FALSE)
RMSE(pred, obs, na.rm = FALSE)
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, $R^2$ is calculated wither using as the square of the correlation between the observed and predicted outcomes when form = "corr". when form = "traditional",
$$R^2 = 1-\frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y}_i)^2}$$
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.
trainControlpredicted <- matrix(rnorm(50), ncol = 5)
observed <- rnorm(10)
apply(predicted, 2, postResample, obs = observed)Run the code above in your browser using DataLab