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:
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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