EN_predict {DMTL} | R Documentation |

## Predictive Modeling using Elastic Net

### Description

This function trains a Elastic Net regressor using the training data
provided and predict response for the test features. This implementation
depends on the `glmnet`

package.

### Usage

```
EN_predict(
x_train,
y_train,
x_test,
lims,
optimize = FALSE,
alpha = 0.8,
seed = NULL,
verbose = FALSE,
parallel = FALSE
)
```

### Arguments

`x_train` |
Training features for designing the EN regressor. |

`y_train` |
Training response for designing the EN regressor. |

`x_test` |
Test features for which response values are to be predicted.
If |

`lims` |
Vector providing the range of the response values for modeling. If missing, these values are estimated from the training response. |

`optimize` |
Flag for model tuning. If |

`alpha` |
EN mixing parameter with |

`seed` |
Seed for random number generator (for reproducible outcomes).
Defaults to |

`verbose` |
Flag for printing the tuning progress when |

`parallel` |
Flag for allowing parallel processing when performing grid
search |

### Value

If `x_test`

is missing, the trained EN regressor.

If `x_test`

is provided, the predicted values using the model.

### Note

The response values are filtered to be bound by range in `lims`

.

### Examples

```
set.seed(86420)
x <- matrix(rnorm(3000, 0.2, 1.2), ncol = 3); colnames(x) <- paste0("x", 1:3)
y <- 0.3*x[, 1] + 0.1*x[, 2] - x[, 3] + rnorm(1000, 0, 0.05)
## Get the model only...
model <- EN_predict(x_train = x[1:800, ], y_train = y[1:800], alpha = 0.6)
## Get predictive performance...
y_pred <- EN_predict(x_train = x[1:800, ], y_train = y[1:800], x_test = x[801:1000, ])
y_test <- y[801:1000]
print(performance(y_test, y_pred, measures = "RSQ"))
```

*DMTL*version 0.1.2 Index]