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œƒf[ƒ^“Η‚έ‚±‚έ
> mm <- read.table("Box2_R.tab")@@¦¨ƒf[ƒ^ƒtƒ@ƒCƒ‹FBox2_R.tabCBox2_R.data
> mm
REP TRT DATA
1 1 S25 5113
2 1 S50 5346
3 1 S75 5272
4 1 S100 5164
5 1 S125 4804
6 1 S150 5254
7 2 S25 5398
8 2 S50 5952
9 2 S75 5713
10 2 S100 4831
11 2 S125 4848
12 2 S150 4542
13 3 S25 5307
14 3 S50 4719
15 3 S75 5483
16 3 S100 4986
17 3 S125 4432
18 3 S150 4919
19 4 S25 4678
20 4 S50 4264
21 4 S75 4749
22 4 S100 4410
23 4 S125 4748
24 4 S150 4098
> summary(mm)
REP TRT DATA
Min. :1.00 S100:4 Min. :4098
1st Qu.:1.75 S125:4 1st Qu.:4709
Median :2.50 S150:4 Median :4884
Mean :2.50 S25 :4 Mean :4960
3rd Qu.:3.25 S50 :4 3rd Qu.:5281
Max. :4.00 S75 :4 Max. :5952

œˆφŽqŽw’θ
> mm$REP <- factor(mm$REP)
> mm$TRT <- factor(mm$TRT)

œBartlettŒŸ’θ
> bartlett.test(mm$DATA~mm$REP)

Bartlett test for homogeneity of variances

data: mm$DATA by mm$REP
Bartlett's K-squared = 5.4282, df = 3, p-value = 0.143

> bartlett.test(mm$DATA~mm$TRT)

Bartlett test for homogeneity of variances

data: mm$DATA by mm$TRT
Bartlett's K-squared = 5.3464, df = 5, p-value = 0.3751

œ‚P—vˆφ—‰ς–@‚Μ•ͺŽU•ͺΝ
> fm <- aov(DATA~REP+TRT, data=mm)
> summary(fm)
Df Sum Sq Mean Sq F value Pr(>F)
REP 3 1944361 648120 5.8622 0.007416 **
TRT 5 1198331 239666 2.1678 0.112809
Residuals 15 1658376 110558
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Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

œ‘½d”δŠr
> pairwise.t.test(mm$DATA, mm$TRT, p.adj="bonferroni")

Pairwise comparisons using t tests with pooled SD

data: mm$DATA and mm$TRT

S100 S125 S150 S25 S50
S125 1 - - - -
S150 1 1 - - -
S25 1 1 1 - -
S50 1 1 1 1 -
S75 1 1 1 1 1

P value adjustment method: bonferroni

> pairwise.t.test(mm$DATA, mm$REP, p.adj="bonferroni")

Pairwise comparisons using t tests with pooled SD

data: mm$DATA and mm$REP

1 2 3
2 1.000 - -
3 1.000 1.000 -
4 0.037 0.021 0.232

P value adjustment method: bonferroni

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