I am trying to obtain adjusted means and standard errors for a three way
ANOVA

 

I have three effects, two continuous; fire frequency and annual
precipitation, and one categorical; soil type in an unbalanced design.

 

I am testing the effect of annual precipition (AP), soil type (ST), and fire
frequency (FF) on stem count (SCt)

 

My data table looks as such:

 


 

ST

FF

AP

SCt


3

Coy

4

888

312


4

Coy

3

911

185


6

Coy

3

937

136


7

Coy

5

1011

42


8

Coy

4

1015

138


9

Cop

4

950

290


11

Cop

4

951

252


16

Coy

4

988

124


17

Coy

5

988

118


20

Coy

5

1000

242


24

Cop

3

901

220


25

Cop

2

929

238


26

Cop

2

954

133


27

Cop

1

934

180


28

Cop

1

938

119


30

Cop

2

918

195

 

My R output for a 3 way ANOVA is as such:

 

> SCt.aov  = aov (SCt ~ AP + ST + FF, data)

> summary ( SCt.aov )

 

            Df Sum Sq Mean Sq F value  Pr(>F)  

AP           1  23696   23696  8.4237   0.01327 *

ST           1    313     313     0.1114   0.74429  

FF           1  21532   21532  7.6544   0.01707 *

Residuals   12  33757    2813                  

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

> 

 

I would like to present my data so that it shows the significance of the p
value for FF after the variability of AP and ST have been taken out, so I
will need R to output the adjusted means and standard errors. This I do not
know how to do. What is the easiest way to do this in R from this analysis?

 

Kind regards,

Burak Pekin

 

 

Burak Pekin

Ecosystem Research Group

School of Plant Biology (M090) 

University of Western Australia

35 Stirling Highway 
Crawley, WA 6009  Australia 
Ph:  +61 08 6488 7923
Fax: +61 08 6488 1001 

 


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