Can be used to carry out inference on a pair of sibling regions or on a single region.

branchInference(
tree,
branch,
type = "reg",
alpha = 0.05,
sigma_y = NULL,
c = 0,
computeCI = TRUE,
permute = FALSE
)

## Arguments

tree |
An `rpart` object corresponding to the tree that you wish to do inference on. This tree must have been built
with `rpart` parameter `model=TRUE` . |

branch |
A vector of splits describing the branch that you wish to do inference on. You should obtain this using the function
`getBranch()`. Must actually correspond to a branch in `tree` . |

type |
A string that should be set to either `"reg"` (default) or `"sib"` . This specifies whether you are doing
inference on the mean of single region at the end of this branch ("reg"), or doing inference on the difference
between this region and its sibling. |

alpha |
Function will compute an equi-tailed `(1-alpha)` confidence interval. Default is `0.05` . |

sigma_y |
The true standard deviation of the response. If known, this should be passed in. Otherwise, the sample standard deviation
will be used as a conservative estimate. |

c |
The p-value returned will test the null hypothesis that the parameter of interest is equal to c. Currently, only c=0 is a valid
input. |

computeCI |
Boolean that specifies if you would like a confidence interval to be computed. Confidence intervals are much slower to compute than
p-values, and so if you only wish to see a p-value you may want to set this to be false. |

permute |
Boolean. Only relevant if type="reg". If `FALSE` (default), inference is conducted conditional on the event that this exact branch
appeared in the tree. If `TRUE` , inference is conducted conditional on the event that some permutation of this branch appeared in the tree. While the
latter achieves higher power, it can be computationally prohibitive in large trees. |

## Value

An object of class `branch_inference`

that contains a confidence interval, a p-value,
the sample statistic, the conditioning set, and a flag reminding the user if `type="reg"`

or `type="sib"`

.

## Examples

#>
#> Sample statistic: -435.6817
#> 95% confidence interval: -1106.747, 136.5523
#> Type: sib
#> p-value for test that param=0: 0.9002555
#> Conditioning Set:
#> Object of class Intervals_full
#> 2 intervals over R:
#> (-543.236399481531, -428.125574226105)
#> (428.125574226104, 929.97322312961)

branchInference (bls.tree, branch, type="reg", permute=TRUE)

#>
#> Sample statistic: 1294.109
#> 95% confidence interval: 253.7905, 2148.2956
#> Type: reg
#> p-value for test that param=0: 0.01663048
#> Conditioning Set:
#> Object of class Intervals_full
#> 2 intervals over R:
#> (1224.75070250313, 1301.6652990799)
#> (2868.72223626769, 3427.28431563896)