# RE: [R] Hoaglin Outlier Method

From: Liaw, Andy <andy_liaw_at_merck.com>
Date: Sat 23 Apr 2005 - 01:47:34 EST

That looks just like how `outliers' are determined in boxplots. You can use the output of boxplot.stats() to compute the limits.

[EDA purists would tell you that those shound be letter values (or `F' for fourths), not quartiles.]

HTH,
Andy

> -----Original Message-----
> From: r-help-bounces@stat.math.ethz.ch
> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of
> Sent: Friday, April 22, 2005 11:23 AM
> To: r-help@stat.math.ethz.ch
> Subject: [R] Hoaglin Outlier Method
>
>
>
>
>
>
> I am a new user of R so please bear with me. I have reviewed
> some R books,
> FAQs and such but the volume of material is great. I am in
> the process of
> porting my current SAS and SVS Script code to Lotus Approach, R and
> WordPerfect.
>
> My question is, can you help me determine the best R method
> to implement
> the Hoaglin Outlier Method? It is used in the Appendix A and
> B of the fo
>
> The sample data from Appendix A for determining outliers in R:
> T314Data <-
> structure(list(Lab = as.integer(c(1:60)), X = c(4.89, 3.82, 2.57, 2.3,
> 2.034, 2, 1.97, 1.85,
> 1.85, 1.85, 1.84, 1.82, 1.82, 1.77, 1.76, 1.67, 1.66, 1.63, 1.62,
> 1.62, 1.55, 1.54, 1.54, 1.53, 1.53, 1.44, 1.428, 1.42, 1.39,
> 1.36, 1.35, 1.31, 1.28, 1.24, 1.24, 1.23, 1.22, 1.21, 1.19, 1.18,
> 1.18, 1.18, 1.17, 1.16, 1.13, 1.13, 1.099, 1.09, 1.09, 1.08,
> 1.07, 1.05, 0.98, 0.97, 0.84, 0.808, 0.69, 0.63, 0.6, 0.5), Y
> = c(5.28,
> 3.82, 2.41, 2.32, 2.211, 1.46, 2.24, 1.91, 1.78, 1.63, 1.81,
> 1.92, 1.2, 1.67, 1.28, 1.59, 1.45, 2.06, 1.91, 1.19, 1.26, 1.79,
> 1.39, 1.48, 0.72, 1.29, 1.517, 1.71, 1.12, 1.38, 0.93, 1.36,
> 1.2, 1.23, 0.71, 1.29, 1.26, 1.48, 1.26, 1.33, 1.21, 1.04, 1.57,
> 1.42, 1.08, 1.04, 1.33, 1.33, 1.2, 1.05, 1.24, 0.91, 0.99, 1.06,
> 1.27, 0.702, 0.77, 0.58, 1, 0.38)), .Names = c("Lab", "X", "Y"
> ), class = "data.frame", row.names = c("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", "36", "37", "38",
> "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49",
> "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60"
> ))
>
> >From this point on, I could use your advise. There are several other
> methods for determining outliers in R. I'd rather not
> re-invent the wheel

> or use a brute strength and force method if there is a better
> way in R.
>
> Our usual method for determining outliers is a student's T
> test as in ASTM
> E 178 or when the standard deviation for a lab is 3 or more.
> We normally
> have 120 labs to evaluate for outliers similar what is shown
> in T312Data.
> On occasion, I have used the Wilk-Shapiro W statistic in SAS.
> A point in
> the right direction or an R code example would help greatly.
> After I trim
> the outliers, I will need to show which labs were eliminated but that
> should be fairly trivial.
>
> The reference in Appendix A is:
> Hoaglin, D. C., Iglewicz, B., Tukey, J. W., "Performance of
> Some Resistant
> Rules for Outlier Labeling," Journal
> of the American Statistical Association, Vol. 81, No. 396
> (Dec., 1986), pp.
> 991-999.
>
> The ASTM E 178 reference is:
> Shapiro, S. S., and Wilk, M. B., "An Analysis of Variance Test for
> Non-Normality (Complete Samples)," Biometrika, BIOKA, Vol 52,
> 1965, pp. 591-611.
>
> Kenneth Ray Hobson, P.E.
> Oklahoma DOT - QA & IAS Manager
> 200 N.E. 21st Street
> Oklahoma City, OK 73105-3204
> (405) 522-4985, (405) 522-0552 fax
>
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