Two factors -> nurical data dependency analyzing

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Two factors -> nurical data dependency analyzing

Evgeniy Kachalin
Hello, dear R users.

What is the easiest and the most visualli understandable way to analize
dependency of numerical variable on two factors?
Is the
boxplot(y~f1+f2) the good way? It seems that this formula does not work.

--
Evgeniy

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Re: Two factors -> nurical data dependency analyzing

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)

Another idea is to use lattice's bwplot.  E.g.

library(lattice)
bwplot(FL ~ sex | sp, data=crabs)


--
Brian D. Ripley,                  [hidden email]
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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Re: Two factors -> nurical data dependency analyzing

Evgeniy Kachalin
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:
Îøèáêà â if (any(out[nna])) stats[c(1, 5)] <- range(x[!out], na.rm =
TRUE) :
ïðîïóùåííîå çíà÷åíèå, à íóæíî TRUE/FALSE
Âäîáàâîê: 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.

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Re: Two factors -> nurical data dependency analyzing

Prof Brian Ripley
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:
> Îøèáêà â if (any(out[nna])) stats[c(1, 5)] <- range(x[!out], na.rm = TRUE) :
> ïðîïóùåííîå çíà÷åíèå, à íóæíî TRUE/FALSE
> Âäîáàâîê: 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,                  [hidden email]
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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Re: Two factors -> nurical data dependency analyzing

Evgeniy Kachalin
Prof Brian Ripley ?????:
> 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.
I'm sorry. This works. Thank you.
Are there any other visualisation ways in R? I'm sorry if this question
is not good enough for this mailing list.

--
Evgeniy

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Re: Two factors -> nurical data dependency analyzing

Evgeniy Kachalin
In reply to this post by Prof Brian Ripley
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)
>
> Another idea is to use lattice's bwplot. E.g.
>
> library(lattice)
> bwplot(FL ~ sex | sp, data=crabs)

Ideally it would be a tree of factors with mean as leaves. May be you
could hint me direction to search for a graph.

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