The `lemXv`

function computes the likelihood cross-validation scores for the observed data with the input bandwidths used for the smoothing matrix. The cross-valiation test and training datasets of the observed cases are generated by k-fold sampling without replacement.

1 2 |

`x` |
Spatial polygons of case data |

`lemObjects` |
List of arrays for the smoothing matrix, and raster stacks for the partition and smoothed offsets |

`Nxv` |
Number of cross-validation datasets |

`ncores` |
Number of cores/threads for parallel processing |

`tol` |
Tolerance for convergence |

`maxIter` |
Maximum number of iterations for convergence |

`randomSeed` |
Seed for random number generator |

`verbose` |
Verbose output |

After using the `lemXv`

function, the last entry of the data frame object is the theoretical cross-validation score when bandwidth is set to infinity.

The `lemXv`

function returns a data frame of specified bandwidths and their cross-validation scores.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
## Not run:
data(kentuckyCounty)
data(kentuckyTract)
ncores = 1 + (.Platform$OS.type == 'unix')
lemRaster = rasterPartition(polyCoarse = kentuckyCounty,
polyFine = kentuckyTract,
cellsCoarse = 6,
cellsFine = 100,
bw = c(10, 12, 15, 17, 20, 25) * 1000,
ncores = ncores,
idFile = 'id.grd',
offsetFile = 'offset.grd',
verbose = TRUE)
lemSmoothMat = smoothingMatrix(rasterObjects = lemRaster,
ncores = ncores,
verbose = TRUE)
lemCv = lemXv(x = kentuckyCounty,
lemObjects = lemSmoothMat,
Nxv = 5,
ncores = ncores,
verbose = TRUE)
bestBw = lemCv$bw[which.min(lemCv$cv)]
plot(lemCv[is.finite(lemCv$bw),],
xlab = 'Bandwidth (m)',
ylab = 'CV',
log = 'x', col = 'red', type = 'o')
abline(v = bestBw, lty = 3)
## End(Not run)
``` |

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