a3 {A3} | R Documentation |

This function calculates the A3 results for an arbitrary model construction algorithm (e.g. Linear Regressions, Support Vector Machines or Random Forests). For linear regression models, you may use the `a3.lm`

convenience function.

```
a3(formula, data, model.fn, model.args = list(), ...)
```

`formula` |
the regression formula. |

`data` |
a data frame containing the data to be used in the model fit. |

`model.fn` |
the function to be used to build the model. |

`model.args` |
a list of arguments passed to |

`...` |
additional arguments passed to |

S3 `A3`

object; see `a3.base`

for details

Scott Fortmann-Roe (2015). Consistent and Clear Reporting of Results from Diverse Modeling Techniques: The A3 Method. Journal of Statistical Software, 66(7), 1-23. <http://www.jstatsoft.org/v66/i07/>

```
## Standard linear regression results:
summary(lm(rating ~ ., attitude))
## A3 Results for a Linear Regression model:
# In practice, p.acc should be <= 0.01 in order
# to obtain finer grained p values.
a3(rating ~ ., attitude, lm, p.acc = 0.1)
## A3 Results for a Random Forest model:
# It is important to include the "+0" in the formula
# to eliminate the constant term.
require(randomForest)
a3(rating ~ .+0, attitude, randomForest, p.acc = 0.1)
# Set the ntrees argument of the randomForest function to 100
a3(rating ~ .+0, attitude, randomForest, p.acc = 0.1, model.args = list(ntree = 100))
# Speed up the calculation by doing 5-fold cross-validation.
# This is faster and more conservative (i.e. it should over-estimate error)
a3(rating ~ .+0, attitude, randomForest, n.folds = 5, p.acc = 0.1)
# Use Leave One Out Cross Validation. The least biased approach,
# but, for large data sets, potentially very slow.
a3(rating ~ .+0, attitude, randomForest, n.folds = 0, p.acc = 0.1)
## Use a Support Vector Machine algorithm.
# Just calculate the slopes and R^2 values, do not calculate p values.
require(e1071)
a3(rating ~ .+0, attitude, svm, p.acc = NULL)
```

[Package *A3* version 1.0.0 Index]