This function is computationally expensive, especially if `CI=TRUE`

and/or `permute=TRUE`

. This function is called internally by `treeval.plot()`

,
as it updates `tree$frame`

to store information (pvalues and confidence intervals) that will be printed by `treeval.plot()`

. If you will be
making several plots while playing around with font size and formatting, it is a good idea to call this function first so that it need not be called
repeatedly by different calls of treeval.plot

inferenceFrame(
tree,
sigma_y = sd(tree$model[, 1]),
CI = TRUE,
alpha = 0.05,
digits = 3,
permute = FALSE
)

## Arguments

tree |
The tree that you will be plotting. |

sigma_y |
The standard deviation of the response. If known, should be provided. Otherwise, a conservative estimate (the sample
standard deviation of the response) is used. |

CI |
Boolean. Should confidence intervals be computed? As confidence intervals are inefficient to compute, this should be set to
`FALSE` if you intend to make a plot that does not display confidence intervals. |

alpha |
If `CI=TRUE` , the confidence intervals that are computed will be `(1-alpha)` confidence intervals. |

digits |
Integer. The number of digits that the p-values and confidence intervals will be rounded to in the later plot. |

permute |
Boolean. If `TRUE` , inference for each region conditions on the event that the region appears in the tree
(all permutations of the branch). This leads to narrower intervals but is computationally expensive. If `FALSE` , inference
for each regon conditions on the event that the particular branch appears in the tree; this is a computationally-efficient alternative.
Only relevant if `CI=TRUE` . |

## Value

An rpart object. Identical to `tree`

expect that now `tree$frame`

has two extra columns; one storing p-values for splits and the other
storing confidence intervals for regions. If this object is passed in to `treeval.plot`

, the plots will be made more efficiently.

## Examples