From: burak pekin <pekinb01_at_student.uwa.edu.au>
Date: Wed, 26 Mar 2008 17:49:58 +0900

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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