Description Usage Arguments Details Value Author(s) References See Also Examples

This functions works identical to `boxplot.stats`

.
It is typically called by another function to gather the statistics
necessary for producing box plots, but may be invoked separately.

1 | ```
qbxp.stats(x, coef = 1.5, do.conf = TRUE, do.out = TRUE, type = 7)
``` |

`x` |
a numeric vector for which the boxplot will be constructed
( |

`coef` |
it determines how far the plot ‘whiskers’ extend out
from the box. If |

`do.conf` |
logical; if |

`do.out` |
logical; if |

`type` |
an integer between 1 and 9 selecting one of nine quantile
algorithms; for more details see |

The notches (if requested) extend to `+/-1.58 IQR/sqrt(n)`

.
This seems to be based on the same calculations as the formula with 1.57 in
Chambers *et al.* (1983, p. 62), given in McGill *et al.*
(1978, p. 16). They are based on asymptotic normality of the median
and roughly equal sample sizes for the two medians being compared, and
are said to be rather insensitive to the underlying distributions of
the samples. The idea appears to be to give roughly a 95% confidence
interval for the difference in two medians.

List with named components as follows:

`stats` |
a vector of length 5, containing the extreme of the lower whisker, the first quartile, the median, the third quartile and the extreme of the upper whisker. |

`n` |
the number of non- |

`conf` |
the lower and upper extremes of the ‘notch’
( |

`out` |
the values of any data points which lie beyond the
extremes of the whiskers ( |

Note that `$stats`

and `$conf`

are sorted in *in*creasing
order, unlike S, and that `$n`

and `$out`

include any
`+- Inf`

values.

Matthias Kohl Matthias.Kohl@stamats.de

Tukey, J. W. (1977) *Exploratory Data Analysis.* Section 2C.

McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box
plots. *The American Statistician* **32**, 12–16.

Velleman, P. F. and Hoaglin, D. C. (1981) *Applications, Basics
and Computing of Exploratory Data Analysis.* Duxbury Press.

Emerson, J. D and Strenio, J. (1983). Boxplots and batch comparison.
Chapter 3 of *Understanding Robust and Exploratory Data
Analysis*, eds. D. C. Hoaglin, F. Mosteller and J. W. Tukey. Wiley.

Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983)
*Graphical Methods for Data Analysis.* Wadsworth \& Brooks/Cole.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## adapted example from boxplot.stats
x <- c(1:100, 1000)
(b1 <- qbxp.stats(x))
(b2 <- qbxp.stats(x, do.conf=FALSE, do.out=FALSE))
stopifnot(b1$stats == b2$stats) # do.out=F is still robust
qbxp.stats(x, coef = 3, do.conf=FALSE)
## no outlier treatment:
qbxp.stats(x, coef = 0)
qbxp.stats(c(x, NA)) # slight change : n is 101
(r <- qbxp.stats(c(x, -1:1/0)))
stopifnot(r$out == c(1000, -Inf, Inf))
``` |

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