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Dylan Beaudette

2006-Jan-26 19:11 UTC

[R] understanding patterns in categorical vs. continuous data

Greetings,

I have a set of bivariate data: one variable (vegetation type) which is 
categorical, and one (computed annual insolation) which is continuous. 
Plotting veg_type ~ insolation produces a nice overview of the patterns that 
I can see in the source data. However, due to the large number of samples 
(1,000), and the apparent "spread" in the distribution of a single
vegetation
type over a range of insolation values- I having a hard time quantitatively 
describing the relationship between the two variables. 

Here is a link to a sample graph:
http://casoilresource.lawr.ucdavis.edu/drupal/node/162

Since the data along each vegetation type "line" is not a distribution
in the
traditional sense, I am having problems applying descriptive statistical 
methods. Conceptually, I would like to some how describe the variation with 
insolation, along each vegetation type "line".

Any guidance, or suggested reading material would be greatly appreciated.


-- 
Dylan Beaudette
Soils and Biogeochemistry Graduate Group
University of California at Davis
530.754.7341

Berton Gunter

2006-Jan-26 19:25 UTC

head link

[R] understanding patterns in categorical vs. continuous data

UC Davis has a statistical department, I would suggest you get consulting
help from them. Do they have a consulting service?

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
 
"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box
 
 
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Dylan Beaudette
> Sent: Thursday, January 26, 2006 11:11 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] understanding patterns in categorical vs. continuous data
> 
> Greetings,
> 
> I have a set of bivariate data: one variable (vegetation 
> type) which is 
> categorical, and one (computed annual insolation) which is 
> continuous. 
> Plotting veg_type ~ insolation produces a nice overview of 
> the patterns that 
> I can see in the source data. However, due to the large 
> number of samples 
> (1,000), and the apparent "spread" in the distribution of a 
> single vegetation 
> type over a range of insolation values- I having a hard time 
> quantitatively 
> describing the relationship between the two variables. 
> 
> Here is a link to a sample graph:
> http://casoilresource.lawr.ucdavis.edu/drupal/node/162
> 
> Since the data along each vegetation type "line" is not a 
> distribution in the 
> traditional sense, I am having problems applying descriptive 
> statistical 
> methods. Conceptually, I would like to some how describe the 
> variation with 
> insolation, along each vegetation type "line".
> 
> Any guidance, or suggested reading material would be greatly 
> appreciated.
> 
> 
> -- 
> Dylan Beaudette
> Soils and Biogeochemistry Graduate Group
> University of California at Davis
> 530.754.7341
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html
>

Dave Roberts

2006-Jan-26 19:48 UTC

head link

[R] understanding patterns in categorical vs. continuous data

You might prefer boxplot(insolation~veg_type) as a graphic.  That will 
give you quantiles.  To get the actual numeric values you could

for (i in levels(veg_type)) {
    print(i)
    quantile(insolation[veg_type==i])
}

see ?quantile for more help.

Dylan Beaudette wrote:> Greetings,
> 
> I have a set of bivariate data: one variable (vegetation type) which is 
> categorical, and one (computed annual insolation) which is continuous. 
> Plotting veg_type ~ insolation produces a nice overview of the patterns
that
> I can see in the source data. However, due to the large number of samples 
> (1,000), and the apparent "spread" in the distribution of a
single vegetation
> type over a range of insolation values- I having a hard time quantitatively
> describing the relationship between the two variables. 
> 
> Here is a link to a sample graph:
> http://casoilresource.lawr.ucdavis.edu/drupal/node/162
> 
> Since the data along each vegetation type "line" is not a
distribution in the
> traditional sense, I am having problems applying descriptive statistical 
> methods. Conceptually, I would like to some how describe the variation with
> insolation, along each vegetation type "line".
> 
> Any guidance, or suggested reading material would be greatly appreciated.
> 
> 

-- 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
David W. Roberts                                     office 406-994-4548
Professor and Head                                      FAX 406-994-3190
Department of Ecology                         email droberts at montana.edu
Montana State University
Bozeman, MT 59717-3460

Gabor Grothendieck

2006-Jan-26 20:03 UTC

head link

[R] understanding patterns in categorical vs. continuous data

Would this do?

boxplot(Sepal.Length ~ Species, iris, horizontal = TRUE)
library(Hmisc)
summary(Sepal.Length ~ Species, iris, fun = summary)


On 1/26/06, Dylan Beaudette <dylan.beaudette at gmail.com>
wrote:> Greetings,
>
> I have a set of bivariate data: one variable (vegetation type) which is
> categorical, and one (computed annual insolation) which is continuous.
> Plotting veg_type ~ insolation produces a nice overview of the patterns
that
> I can see in the source data. However, due to the large number of samples
> (1,000), and the apparent "spread" in the distribution of a
single vegetation
> type over a range of insolation values- I having a hard time quantitatively
> describing the relationship between the two variables.
>
> Here is a link to a sample graph:
> http://casoilresource.lawr.ucdavis.edu/drupal/node/162
>
> Since the data along each vegetation type "line" is not a
distribution in the
> traditional sense, I am having problems applying descriptive statistical
> methods. Conceptually, I would like to some how describe the variation with
> insolation, along each vegetation type "line".
>
> Any guidance, or suggested reading material would be greatly appreciated.
>
>
> --
> Dylan Beaudette
> Soils and Biogeochemistry Graduate Group
> University of California at Davis
> 530.754.7341
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
>

Liaw, Andy

2006-Jan-27 03:07 UTC

head link

[R] understanding patterns in categorical vs. continuous data

From: Dave Roberts> 
> You might prefer boxplot(insolation~veg_type) as a graphic.  
> That will 
> give you quantiles.  To get the actual numeric values you could
> 
> for (i in levels(veg_type)) {
>     print(i)
>     quantile(insolation[veg_type==i])
> }
> 
> see ?quantile for more help.
If you want the five-number summaries plotted in the boxplots, just look at
the returned object of boxplot():
> g <- factor(rep(1:3, 10))
> y <- rnorm(30)
> res <- boxplot(y ~ g)
> str(res)List of 6
 $ stats: num [1:5, 1:3] -1.135 -0.757 -0.536  0.499  0.996 ...
 $ n    : num [1:3] 10 10 10
 $ conf : num [1:2, 1:3] -1.1639  0.0918 -0.5208  1.6546 -1.2487 ...
 $ out  : num(0) 
 $ group: num(0) 
 $ names: chr [1:3] "1" "2" "3"

If you just want to compute the summaries without the boxplots, use
fivenum():
> tapply(y, g, fivenum)$"1"
[1] -1.1352456 -0.7571895 -0.5360496  0.4994445  0.9956749

$"2"
[1] -1.1408493 -0.3751730  0.5668747  1.8018146  2.0019303

$"3"
[1] -2.2309983 -0.9333305 -0.3402786  0.8849042  0.9833057

... and if you really want the quantiles, you can do that, too:
> tapply(y, g, quantile)$"1"
        0%        25%        50%        75%       100% 
-1.1352456 -0.7391977 -0.5360496  0.3378861  0.9956749 

$"2"
        0%        25%        50%        75%       100% 
-1.1408493 -0.3039648  0.5668747  1.6669879  2.0019303 

$"3"
        0%        25%        50%        75%       100% 
-2.2309983 -0.8389260 -0.3402786  0.6746950  0.9833057 

... but note how the quartiles and hinges are not necessarily the same.

Andy
 > Dylan Beaudette wrote:
> > Greetings,
> > 
> > I have a set of bivariate data: one variable (vegetation 
> type) which is 
> > categorical, and one (computed annual insolation) which is 
> continuous. 
> > Plotting veg_type ~ insolation produces a nice overview of 
> the patterns that 
> > I can see in the source data. However, due to the large 
> number of samples 
> > (1,000), and the apparent "spread" in the distribution of a 
> single vegetation 
> > type over a range of insolation values- I having a hard 
> time quantitatively 
> > describing the relationship between the two variables. 
> > 
> > Here is a link to a sample graph:
> > http://casoilresource.lawr.ucdavis.edu/drupal/node/162
> > 
> > Since the data along each vegetation type "line" is not a 
> distribution in the 
> > traditional sense, I am having problems applying 
> descriptive statistical 
> > methods. Conceptually, I would like to some how describe 
> the variation with 
> > insolation, along each vegetation type "line".
> > 
> > Any guidance, or suggested reading material would be 
> greatly appreciated.
> > 
> > 
> 
> 
> -- 
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> ~~~~~~~~~~
> David W. Roberts                                     office 
> 406-994-4548
> Professor and Head                                      FAX 
> 406-994-3190
> Department of Ecology                         email 
> droberts at montana.edu
> Montana State University
> Bozeman, MT 59717-3460
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html
> 
>

Dylan Beaudette

2006-Jan-27 21:09 UTC

head link

[R] understanding patterns in categorical vs. continuous data

Thanks to all for the helpful suggestions, I was able to get good start from 
there.

Cheers,

Dylan

On Thursday 26 January 2006 12:03 pm, Gabor Grothendieck
wrote:> Would this do?
>
> boxplot(Sepal.Length ~ Species, iris, horizontal = TRUE)
> library(Hmisc)
> summary(Sepal.Length ~ Species, iris, fun = summary)
>
> On 1/26/06, Dylan Beaudette <dylan.beaudette at gmail.com> wrote:
> > Greetings,
> >
> > I have a set of bivariate data: one variable (vegetation type) which
is
> > categorical, and one (computed annual insolation) which is continuous.
> > Plotting veg_type ~ insolation produces a nice overview of the
patterns
> > that I can see in the source data. However, due to the large number of
> > samples (1,000), and the apparent "spread" in the
distribution of a
> > single vegetation type over a range of insolation values- I having a
hard
> > time quantitatively describing the relationship between the two
> > variables.
> >
> > Here is a link to a sample graph:
> > http://casoilresource.lawr.ucdavis.edu/drupal/node/162
> >
> > Since the data along each vegetation type "line" is not a
distribution in
> > the traditional sense, I am having problems applying descriptive
> > statistical methods. Conceptually, I would like to some how describe
the
> > variation with insolation, along each vegetation type
"line".
> >
> > Any guidance, or suggested reading material would be greatly
appreciated.
> >
> >
> > --
> > Dylan Beaudette
> > Soils and Biogeochemistry Graduate Group
> > University of California at Davis
> > 530.754.7341
> >
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> > http://www.R-project.org/posting-guide.html
-- 
Dylan Beaudette
Soils and Biogeochemistry Graduate Group
University of California at Davis
530.754.7341

R help - Jan 2006 - understanding patterns in categorical vs. continuous data

[R] understanding patterns in categorical vs. continuous data

[R] understanding patterns in categorical vs. continuous data

[R] understanding patterns in categorical vs. continuous data

[R] understanding patterns in categorical vs. continuous data

[R] understanding patterns in categorical vs. continuous data

[R] understanding patterns in categorical vs. continuous data