On Sat, 16 Aug 2008, Brown, Heidi wrote:
Having spent the last few weeks trying to decipher R, I feel I may
finally be getting somewhere, but i'M still in need of some advice and
all my tutors seem to be on holiday!
Basically a bit of background, I have data collected on a population of
Lizards which includes age,sex, and body condition. I collected data
myself this year and I have data previously collected from 1999, 2002
and 2005. My plan is to compare this data to identify if there has been
any change in body condition since the first sample in 1999. I have run
my data through R using the following:
What is 'year'? I am wondering if it should have been a factor. If it
has been entered as numeric the results are relative to year dot (0) and
the intercept is meaningless.
You mention ANCOVA here but I see no {C}ovariates (unless you have good
reason to treat 'year' as numeric), and you do not even show an ANOVA
table. Take a look at MASS (the book) chapter 6 and use step() or similar
to do backwards model selection following its examples. In particular you
need to respect the hierarchy of terms, so you can only remove
age:sex:year at the first stage.
This sort of thing is best done in an interactive consultation with a
statistician (or a biologist with MSc-level statistical training): until
the full model properly reflects the design the R output presented is not
useful.
mos1<-lm(ci~age*sex*year)
summary(mos1)
and R has gven me the results
Call:
lm(formula = ci ~ age * sex * year)
Residuals:
Min 1Q Median 3Q Max
-0.156304 -0.036740 0.002953 0.039081 0.213696
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.538260 4.956850 1.924 0.0556 .
ageJ -15.943787 11.211551 -1.422 0.1564
sexM -11.844042 6.195258 -1.912 0.0572 .
year -0.004657 0.002474 -1.883 0.0611 .
ageJ:sexM 18.887391 13.657536 1.383 0.1681
ageJ:year 0.007923 0.005590 1.417 0.1578
sexM:year 0.005977 0.003091 1.934 0.0545 .
ageJ:sexM:year -0.009458 0.006809 -1.389 0.1663
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06299 on 218 degrees of freedom
Multiple R-squared: 0.6109, Adjusted R-squared: 0.5984
F-statistic: 48.89 on 7 and 218 DF, p-value: < 2.2e-16
Firstly I'm a bit bemused, I think my head has turned to mush the last
few weeks and I'm struggling to decipher the results, am I right in
thinking the intercept Adult Females?? and secondly I have Been told to
update the model to produce the minimal adequate model. By doing this do
I need to remove the least significant from the above list ie age:sex:?
mos2<-update(mos1,~.-age:sex)
summary(mos2)
Call:
lm(formula = ci ~ age + sex + year + age:year + sex:year + age:sex:year)
Residuals:
Min 1Q Median 3Q Max
-0.161296 -0.040699 0.001092 0.038537 0.208704
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.050e+00 4.628e+00 1.523 0.129
ageJ -3.216e+00 6.416e+00 -0.501 0.617
sexM -7.958e+00 5.533e+00 -1.438 0.152
year -3.416e-03 2.310e-03 -1.479 0.141
ageJ:year 1.577e-03 3.199e-03 0.493 0.622
sexM:year 4.038e-03 2.761e-03 1.463 0.145
ageJ:sexM:year -4.132e-05 9.762e-06 -4.233 3.40e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06312 on 219 degrees of freedom
Multiple R-squared: 0.6075, Adjusted R-squared: 0.5967
F-statistic: 56.49 on 6 and 219 DF, p-value: < 2.2e-16
Basically how do i know once the minimal adequate model has been
reached? how many times should I remove categories and update the model?
Any help will be greatly appreciated and if more information is required
then let me know!!
Cheers
H
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