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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