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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