vsb {BalancedSampling} | R Documentation |

## Variance estimator for spatially balanced samples

### Description

Variance estimator of HT estimator of population total.

### Usage

```
vsb(probs, ys, xs, k = 3L, type = "kdtree2", bucketSize = 40)
```

### Arguments

`probs` |
A vector of length n with inclusion probabilities. |

`ys` |
A vector of length n containing the target variable. |

`xs` |
An n by p matrix of (standardized) auxiliary variables. |

`k` |
The number of neighbours to construct the means around. |

`type` |
The method used in finding nearest neighbours.
Must be one of |

`bucketSize` |
The maximum size of the terminal nodes in the k-d-trees. |

### Details

If `k = 0L`

, the variance estimate is constructed by using all units that
have the minimum distance.

If `k > 0L`

, the variance estimate is constructed by using the `k`

closest
units. If multiple units are located on the border, all are used.

### Value

The variance estimate.

### k-d-trees

The `type`

s "kdtree" creates k-d-trees with terminal node bucket sizes
according to `bucketSize`

.

"kdtree0" creates a k-d-tree using a median split on alternating variables.

"kdtree1" creates a k-d-tree using a median split on the largest range.

"kdtree2" creates a k-d-tree using a sliding-midpoint split.

"notree" does a naive search for the nearest neighbour.

### References

Grafström, A., & Schelin, L. (2014). How to select representative samples. Scandinavian Journal of Statistics, 41(2), 277-290.

### See Also

Other measure:
`sb()`

### Examples

```
## Not run:
set.seed(12345);
N = 1000;
n = 100;
prob = rep(n/N, N);
x = matrix(runif(N * 2), ncol = 2);
y = runif(N);
s = lpm2(prob, x);
vsb(prob[s], y[s], x[s, ]);
vsb(prob[s], y[s], x[s, ], 0L);
## End(Not run)
```

*BalancedSampling*version 2.0.6 Index]