Re: [R] Two factors -> nurical data dependency analyzing

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Mon 20 Feb 2006 - 03:26:40 EST

I carefully tested my suggestions in the current version of R, 2.2.1, before posting. They DO work, and as you have not even told us your version of R, we have no idea what you have broken on your R installation.

On Sun, 19 Feb 2006, Evgeniy Kachalin wrote:

> Prof Brian Ripley ?????:
>> On Sun, 19 Feb 2006, Evgeniy Kachalin wrote:
>>
>>> Hello, dear R users.
>>>
>>> What is the easiest and the most visualli understandable way to analize
>>> dependency of numerical variable on two factors?
>>
>> interaction.plot() is a good start.
>>
>>> Is the
>>> boxplot(y~f1+f2) the good way? It seems that this formula does not work.
>>
>> No, nor is it documented to: the help page is there to help you. You need a
>> single factor as the grouping, so make one via an interaction.
>> boxplot(y ~ f1:f2) should work. E.g.
>>
>> library(MASS)
>> boxplot(FL ~ sex:sp, data=crabs)
> Does not work:
> Ошибка в if (any(out[nna])) stats[c(1, 5)] <- range(x[!out], na.rm = TRUE) :
> пропущенное значение, а нужно TRUE/FALSE
> Вдобавок: Warning messages:
> 1: + not meaningful for factors in: Ops.factor(x[floor(d)], x[ceiling(d)])
> 2: < not meaningful for factors in: Ops.factor(x, (stats[2] - coef * iqr))
> 3: > not meaningful for factors in: Ops.factor(x, (stats[4] + coef * iqr))
>
> Hm...
>
>> Another idea is to use lattice's bwplot. E.g.
>>
>> library(lattice)
>> bwplot(FL ~ sex | sp, data=crabs)
>>
>>
> That's not the point. The scales may differ significantly, also this is not
> conviniet for many factors.
>
>

-- 
Brian D. Ripley,                  ripley@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595


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Received on Mon Feb 20 03:29:50 2006

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