AMK {DSWE} | R Documentation |

## Additive Multiplicative Kernel Regression

### Description

An additive multiplicative kernel regression based on Lee et al. (2015).

### Usage

```
AMK(
trainX,
trainY,
testX,
bw = "dpi_gap",
nMultiCov = 3,
fixedCov = c(1, 2),
cirCov = NA
)
```

### Arguments

`trainX` |
a matrix or dataframe of input variable values in the training dataset. |

`trainY` |
a numeric vector for response values in the training dataset. |

`testX` |
a matrix or dataframe of test input variable values to compute predictions. |

`bw` |
a numeric vector or a character input for bandwidth. If character, bandwidth computed internally; the input should be either |

`nMultiCov` |
an integer or a character input specifying the number of multiplicative covariates in each additive term. Default is 3 (same as Lee et al., 2015). The character inputs can be: |

`fixedCov` |
an integer vector specifying the fixed covariates column number(s), default value is |

`cirCov` |
an integer vector specifying the circular covariates column number(s) in |

### Details

This function is based on Lee et al. (2015). Main features are:

Flexible number of multiplicative covariates in each additive term, which can be set using

`nMultiCov`

.Flexible number and columns for fixed covariates, which can be set using

`fixedCov`

. The default option`c(1,2)`

sets the first two columns as fixed covariates in each additive term.Handling the data with gaps when the direct plug-in estimator used in Lee et al. fails to return a finite bandwidth. This is set using the option

`bw = 'dpi_gap'`

for bandwidth estimation.

### Value

a numeric vector for predictions at the data points in `testX`

.

### References

Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” Journal of the American Statistical Association, Vol. 110, pp. 56-67, doi:10.1080/01621459.2014.977385.

### Examples

```
data = data1
trainX = as.matrix(data[c(1:100),2])
trainY = data[c(1:100),7]
testX = as.matrix(data[c(101:110),2])
AMK_prediction = AMK(trainX, trainY, testX, bw = 'dpi_gap', cirCov = NA)
```

*DSWE*version 1.8.2 Index]