R help - Sep 2008 - Options for viewing / using results from lm

Hello,
I would like to state what I am (trying) to do. I have data set. It has 5749
rows (including the header) and 23 columns.  The data contains values
related to spatial aspects of the 412 landscapes (over various years). I
will be making 2 groups from the data based on spatial extent. I will then
be performing a quadratic lm for each extent by percent forest vs 1 of 8
other metrics. For a total of 16 (2 extents * 8 metrics) quad lm runs. I
will be doing this 'manually'. I hope this is the best way to do these
analyses.

e.g.
ed.qlm.s <- lm(data=small, pfor~ED+I(pfor^2))
pd.qlm.s <- lm(data=small, pfor~PD+I(pfor^2))
...
ed.qlm.l <- lm(data=large, pfor~ED+I(pfor^2))
pd.qlm.l <- lm(data=large, pfor~PD+I(pfor^2))
etc.

I am ultimately intested in the residuals and how they compare amongst
various delineations fo the data.

Q1: I would like to view the residuals for each run.  I think this might be
better done in a another program. I have the read the R import/export
manual. However, using it and trying to use the cat, list, sink, or write
functions I am still lost. What is the best way to export the residual (and
or other available data from lm) data for viewing elsewhere? A table?

Q2: How can I take the residuals and create an object(s) for further
analysis.

I'd appreciate any comments or suggestions including 'read the
manual' but
if thats the case perhaps with a little direction.

Thank you kindly,
Cheers,
M Just

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I haven't seen a response to this yet, so I will give my $0.25US worth
(which is not worth that much anymore ).  :-)
> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of Michael Just
> Sent: Tuesday, September 23, 2008 11:01 AM
> To: r-help at r-project.org
> Subject: [R] Options for viewing / using results from lm
> 
> Hello,
> I would like to state what I am (trying) to do. I have data 
> set. It has 5749
> rows (including the header) and 23 columns.  The data contains values
> related to spatial aspects of the 412 landscapes (over 
> various years). I
> will be making 2 groups from the data based on spatial 
> extent. I will then
> be performing a quadratic lm for each extent by percent 
> forest vs 1 of 8
> other metrics. For a total of 16 (2 extents * 8 metrics) quad 
> lm runs. I
> will be doing this 'manually'. I hope this is the best way to do
these
> analyses.
> 
> e.g.
> ed.qlm.s <- lm(data=small, pfor~ED+I(pfor^2))
> pd.qlm.s <- lm(data=small, pfor~PD+I(pfor^2))
> ...
> ed.qlm.l <- lm(data=large, pfor~ED+I(pfor^2))
> pd.qlm.l <- lm(data=large, pfor~PD+I(pfor^2))
> etc.
> 
> I am ultimately intested in the residuals and how they compare amongst
> various delineations fo the data.
> 
> Q1: I would like to view the residuals for each run.  I think 
> this might be
> better done in a another program. I have the read the R import/export
> manual. However, using it and trying to use the cat, list, 
> sink, or write
> functions I am still lost. What is the best way to export the 
> residual (and
> or other available data from lm) data for viewing elsewhere? A table?
Why do you think it is better done in another program?  Keeping it in R saves
you from the exporting, which you say you are having trouble with.
> 
> Q2: How can I take the residuals and create an object(s) for further
> analysis.
>
See ?residuals.

Try the following:

x <- sample(1:20, 100, replace=TRUE)
y <- rnorm(100)
fit.lm <- lm(y ~ x)
plot(residuals(fit.lm))
plot(x,residuals(fit.lm))

 
> I'd appreciate any comments or suggestions including 'read 
> the manual' but
> if thats the case perhaps with a little direction.
> 
> Thank you kindly,
> Cheers,
> M Just
> 
Hope this is helpful,

Dan

Daniel J. Nordlund
Washington State Department of Social and Health Services
Planning, Performance, and Accountability
Research and Data Analysis Division
Olympia, WA  98504-5204

Hello,
>Why do you think it is better done in another program? Keeping it in R
saves you from the exporting, which you say you are having trouble with.

I think it might be better for viewing capabilities. If I had columns of
residuals right next to each other I could spot differences amongst
locations. Right? If I can take the residual values and put them back into
my main data file I can then have the options to use grouping values that
exist there. (Like I asked here:
http://tolstoy.newcastle.edu.au/R/e5/help/08/09/2186.html)
>> Q2: How can I take the residuals and create an object(s) for further
<http://tolstoy.newcastle.edu.au/R/e5/help/08/09/2259.html#2265qlink2>*analysis.
*
**
>See ?residuals.
>Try the following:
>x <- sample(1:20, 100, replace=TRUE)
>y <- rnorm(100)
>fit.lm <- lm(y ~ x)
>plot(residuals(fit.lm))
>plot(x,residuals(fit.lm))

When I was thinking further analysis, for example, could I take the
residuals for all metrics in one extent and compare them to all residuals
for all metrics in another extent? Even though the were created with 16
different models?

In 'plot(residuals(fit.lm))' what is the index 'counting'?


Thank you kindly for you $0.25,

Cheers,

M Just

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