Petra,
It looks as though the problem is with your data.
Reading it into 'R' gives---
dat<-read.table("clipboard",header=T,sep="")
dat
Bosque estado lux dosel
1 deciduo pristino 703 88.56
2 deciduo pristino 800 90.64
3 deciduo pristino 150 95.84
4 deciduo pristino 245 87.52
5 deciduo pristino 1300 91.68
6 deciduo activo 1900 26.16
7 deciduo activo 840 59.44
8 deciduo activo 323 69.84
9 deciduo activo 112 75.04
10 deciduo activo 1360 51.12
11 siemprev activo 900 41.76
12 siemprev activo 480 65.68
13 siemprev activo 350 78.16
14 siemprev activo 350 37.60
15 siemprev activo 272 58.40
16 siemprev pristino 100 94.80
17 siemprev pristino 60 95.84
18 siemprev pristino 50 97.92
19 siemprev pristino 270 94.80
20 siemprev pristino 110 97.92
a straight analysis of variance (aov) model gives--
dat.aov<-aov(dosel~estado*Bosque,data=dat)
summary(dat.aov)
Df Sum Sq Mean Sq F value Pr(>F)
estado 1 6931.1 6931.1 41.6455 7.974e-06 ***
Bosque 1 36.6 36.6 0.2197 0.6456
estado:Bosque 1 36.6 36.6 0.2197 0.6456
Residuals 16 2662.9 166.4
showing that Bosque and its interaction with estado do indeed have
the same 'sum of squares' of 36.6
a preliminary exploration of the data's factors shows--
with(dat,tapply(dosel,list(estado,Bosque),mean))
deciduo siemprev
activo 56.320 56.320
pristino 90.848 96.256
with(dat,tapply(dosel,list(estado,Bosque),sd))
deciduo siemprev
activo 19.232972 16.817800
pristino 3.239062 1.577238
This shows that the levels of the factors are highly corelated
the linear model and its anova confirms this--
fit.lm<-lm(dosel~estado*Bosque,data=dat)
summary(fit.lm)
Call:
lm(formula = dosel ~ estado * Bosque, data = dat)
Residuals:
Min 1Q Median 3Q Max
-30.160 -2.548 0.312 3.588 21.840
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.632e+01 5.769e+00 9.762 3.84e-08 ***
estadopristino 3.453e+01 8.159e+00 4.232 0.000635 ***
Bosquesiemprev 1.249e-15 8.159e+00 1.53e-16 1.000000
estadopristino:Bosquesiemprev 5.408e+00 1.154e+01 0.469 0.645622
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.9 on 16 degrees of freedom
Multiple R-Squared: 0.7245, Adjusted R-squared: 0.6729
F-statistic: 14.03 on 3 and 16 DF, p-value: 9.615e-05
Analysis of Variance Table
Response: dosel
Df Sum Sq Mean Sq F value Pr(>F)
estado 1 6931.1 6931.1 41.6455 7.974e-06 ***
Bosque 1 36.6 36.6 0.2197 0.6456
estado:Bosque 1 36.6 36.6 0.2197 0.6456
Residuals 16 2662.9 166.4
the drop function shows that the model would improve by
dropping the interaction term and so reducing the RSS
(by 36.56, being the redundant interaction Sum of Sq)
drop1(fit.lm).The AIC confirms this (the lower the better).
Single term deletions
Model:
dosel ~ estado * Bosque
Df Sum of Sq RSS AIC
<none> 2662.90 105.83
estado:Bosque 1 36.56 2699.46 104.10
The only sig effect of the model is thus between estado levels.
pristino effect being *** sig greater than activo for both levels of
Bosque ( as the tapply table above clearly shows)
It pays to do a preliminary survry of the data.
I hope that helps,
John