On May 18, 2010, at 12:07 PM, Arantzazu Blanco Bernardeau wrote:
Hello
Well, the problem is, that arcilla is the percentage of clay in the
soil sample. So, for linear model, I need to work with that number
or value. Now, R thinks that arcilla (arcilla means clay in
spanish), is a factor, and gives me the value as a factor, so the
output of the linear model is
Call:
lm(formula = formula, data = caperf)
Would help if you also displayed the value for "formula", so we might
have an idea what you are calling your "y"-variable .... and it would
be wise not to continue to name your formulas "formula."
require(fortunes)
fortune("dog")
What happens when you create a new variable in caperf with the numeric
equivalant of the arcilla levels?
caperf$claynum <- as.numeric(as.character(arcilla))
lm(y ~ claynum + limo + CO_gkg1 + C03Ca , data=caperf)
--
David.
Residuals:
Min 1Q Median 3Q Max
-1.466e+01 -1.376e-15 1.780e-16 2.038e-15 1.279e+01
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.68964 6.33889 0.267 0.790221
arcilla0.9 1.90228 8.90888 0.214 0.831239
arcilla10 1.26371 7.96734 0.159 0.874212
arcilla10.3 15.70081 9.05141 1.735 0.085090 .
arcilla10.4 7.27517 7.72806 0.941 0.348183
arcilla10.45 7.03879 9.02600 0.780 0.436853
arcilla10.5 2.41241 8.90827 0.271 0.786954
arcilla10.65 15.44298 9.03879 1.709 0.089838 .
arcilla10.7 19.35651 9.04675 2.140 0.034185 *
arcilla10.9 3.55947 9.18501 0.388 0.698974
[...]
arcilla9.9 6.31949 7.35724 0.859 0.391892
arcilla#N/A 24.17959 8.87201 2.725 0.007274 **
limo 0.24920 0.04605 5.412 2.76e-07 ***
CO_gkg1 0.21015 0.03931 5.346 3.73e-07 ***
C03Ca 0.01711 0.02727 0.628 0.531337
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.249 on 135 degrees of freedom
(50 observations deleted due to missingness)
Multiple R-squared: 0.9736, Adjusted R-squared: 0.9014
F-statistic: 13.47 on 370 and 135 DF, p-value: < 2.2e-16
So, in the desired linear model, arcilla should be just a line, with
the valors of the linear model.
I hope you understand better more. If not, I could make an english
version of the file to send, so you can try the commands.
Thanks a lot for your help!
Arantzazu Blanco Bernardeau
Dpto de Química Agrícola, Geología y Edafología
Universidad de Murcia-Campus de Espinardo
----------------------------------------
Date: Tue, 18 May 2010 11:54:20 -0400
Subject: Re: [R] (no subject)
From: mailinglist.honey...@gmail.com
To: aramu...@hotmail.com
CC: r-help@r-project.org
Hi,
Sorry, I'm not really getting what going on here ... perhaps having
more domain knowledge would help me make better sense of our
question.
In particular:
On Tue, May 18, 2010 at 11:35 AM, Arantzazu Blanco Bernardeau
wrote:
Hello
I have a data array with soil variables (caperf), in which the
variable "clay" is factor (as I see entering str(caperf)) . I need
to do a regression model, so I need to have arcilla (=clay) as a
numeric variable. For that I have entered
as.numeric(as.character(arcilla))
and even entering
'as.numeric(levels(arcilla))[arcilla]'
The above code doesn't make sense to me ...
Perhaps cleaning up your question and providing some reproducible
example we can use to help show you the light (just describing what a
variable has isn't enough -- give us minimal code we can paste into R
that reproduces your problem).
Alternatively, depending no what your "levels" mean, you might want
to
recode your data using "dummy variables" (I'm not sure if that's the
official term) .. this is what I mean:
http://dss.princeton.edu/online_help/analysis/dummy_variables.htm
In your example, let's say you have four levels for "clay" ... maybe
"soft", "hard", "smooth", "red"
Instead of only using 1 variable with values 1-4, you would recode
this into 4 variables with values 0,1
So, if one example has a value of "smooth" for clay. Instead of
coding it like:
clay: 3
You would do:
soft: 0
hard: 0
smooth: 1
red : 0
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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