```
best(x, metric, maximize)
oneSE(x, metric, num, maximize)
tolerance(x, metric, tol = 1.5, maximize)
```

x

a data frame of tuning parameters and model results, sorted from least complex models to the mst complex

metric

a string that specifies what summary metric will be used to select the optimal model. By default, possible values are "RMSE" and "Rsquared" for regression and "Accuracy" and "Kappa" for classification. If custom performance metrics are used (via the

maximize

a logical: should the metric be maximized or minimized?

num

the number of resamples (for

`oneSE`

only)tol

the acceptable percent tolerance (for

`tolerance`

only)- an row index

`train`

to select the "optimal" model form a series of models. Each requires the user to select a metric that will be used to judge performance. For regression models, values of `"RMSE"`

and `"Rsquared"`

are applicable. Classification models use either `"Accuracy"`

or `"Kappa"`

(for unbalanced class distributions.By default, `train`

uses `best`

.

`best`

simply chooses the tuning parameter associated with the largest (or lowest for `"RMSE"`

) performance.

`oneSE`

is a rule in the spirit of the "one standard error" rule of Breiman et al (1984), who suggest that the tuning parameter associated with eh best performance may over fit. They suggest that the simplest model within one standard error of the empirically optimal model is the better choice. This assumes that the models can be easily ordered from simplest to most complex (see the Details section below).

`tolerance`

takes the simplest model that is within a percent tolerance of the empirically optimal model. For example, if the largest Kappa value is 0.5 and a simpler model within 3 percent is acceptable, we score the other models using `(x - 0.5)/0.5 * 100`

. The simplest model whose score is not less than 3 is chosen (in this case, a model with a Kappa value of 0.35 is acceptable).

User--defined functions can also be used. The argument `selectionFunction`

in `trainControl`

can be used to pass the function directly or to pass the funciton by name.

`train`

, `trainControl`

# simulate a PLS regression model test <- data.frame( ncomp = 1:5, RMSE = c(3, 1.1, 1.02, 1, 2), RMSESD = .4) best(test, "RMSE", maximize = FALSE) oneSE(test, "RMSE", maximize = FALSE, num = 10) tolerance(test, "RMSE", tol = 3, maximize = FALSE) ### usage example data(BloodBrain) marsGrid <- data.frame( .degree = 1, .nprune = (1:10) * 3) set.seed(1) marsFit <- train( bbbDescr, logBBB, "earth", tuneGrid = marsGrid, trControl = trainControl( method = "cv", number = 10, selectionFunction = "tolerance")) # around 18 terms should yield the smallest CV RMSE