2 覓伎 企慨蠍 #
use tempdb;
--drop table 煙
create table 煙
(
糾 int
, 蟆一 int
, int
);
insert 煙 values (85, 3, 65);
insert 煙 values (74, 7, 50);
insert 煙 values (76, 5, 55);
insert 煙 values (90, 1, 65);
insert 煙 values (85, 3, 55);
insert 煙 values (87, 3, 70);
insert 煙 values (94, 1, 65);
insert 煙 values (98, 2, 70);
insert 煙 values (81, 4, 55);
insert 煙 values (91, 2, 70);
insert 煙 values (76, 3, 50);
insert 煙 values (74, 4, 55);
> library("RODBC")
> conn <- odbcConnect("26")
> data <- sqlQuery(conn, "SELECT 糾, , 蟆一 FROM tempdb.dbo.煙")
> a <- aov(糾 ~ + 蟆一, data)
> a
Call:
aov(formula = 糾 ~ + 蟆一, data = data)
Terms:
蟆一 Residuals
Sum of Squares 541.6927 61.9657 124.5916
Deg. of Freedom 1 1 9
Residual standard error: 3.720687
Estimated effects may be unbalanced
> summary(a)
Df Sum Sq Mean Sq F value Pr(>F)
1 541.69 541.69 39.1297 0.0001487 ***
蟆一 1 61.97 61.97 4.4762 0.0634803 .
Residuals 9 124.59 13.84
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
>
蟆郁骸伎
- 螳
- p-value = 0.0001487襦 譴 0.05覲企 朱襦 襴所れ 觧 伎襯 谿場 覈詩. 蠏碁覩襦 糾 谿願 .
- 蟆一 p-value = 0.0634803襦 譴 0.05覲企 覩襦 襴所れ 觧 伎襯 谿場. 蠏碁覩襦 蠏覓願れ 讌讌. 蠏碁覩襦 糾 蟆一 磯殊 譬讌一 り .
3 殊 覿磯 #
c1 <- c(3.6, 4.1, 4.0)
c2 <- c(3.1, 3.2, 3.9)
c3 <- c(3.2, 3.5, 3.5)
c4 <- c(3.5, 3.8, 3.9)
data <- data.frame(c1,c2,c3,c4)
#一危磯ゼ 襷れ伎 . stack()
data <- stack(data)
summary(aov(values ~ ind, data))
蟆郁骸 れ螻 螳.
Df Sum Sq Mean Sq F value Pr(>F)
ind 3 0.56250 0.18750 2.25 0.1598
Residuals 8 0.66667 0.08333
蟆郁骸 伎
- 螳
- p-value = 0.1598 朱 襴所れ 觧 1譬 る 襯 0.05覲企 . 蠏碁覩襦 蠏覓願れ 讌讌.
- 讀, 3螳 蠏碁９ 蠏 谿企 .
4 覦覲旧 れ 覿 覿 #
ろ <- c(rep("1",4), rep("2",4), rep("3",4))
<- c(rep(c("A","B","C","D"),3))
蟯豸′ <- c(3.6,3.1,3.2,3.5,4.1,3.2,3.5,3.8,4.0,3.9,3.5,3.8)
data <- data.frame(蟯豸′, ろ, )
data
summary(aov(蟯豸′ ~ + ろ), data)
蟆郁骸 れ螻 螳.
> data
蟯豸′ ろ
1 3.6 1 A
2 3.1 1 B
3 3.2 1 C
4 3.5 1 D
5 4.1 2 A
6 3.2 2 B
7 3.5 2 C
8 3.8 2 D
9 4.0 3 A
10 3.9 3 B
11 3.5 3 C
12 3.8 3 D
> summary(aov(蟯豸′ ~ + ろ), data)
Df Sum Sq Mean Sq F value Pr(>F)
(Intercept) 1 155.520 155.520 4241.4545 8.813e-10 ***
3 0.540 0.180 4.9091 0.04692 *
ろ 2 0.420 0.210 5.7273 0.04062 *
Residuals 6 0.220 0.037
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
variable '' converted to a factor
2: In model.matrix.default(mt, mf, contrasts) :
variable 'ろ' converted to a factor
3: In Ops.factor(left) : ! 語 伎 覓伎覩誤
4: In Ops.factor(left) : ! 語 伎 覓伎覩誤
>
蟆郁骸 伎
- 螳
- p-value = 0.04692企襦 0.05覲企 . 讀, 襴所れ 觧 蟇企讌襯 谿場 覈詩. 蠏覓願 蠍郁. 蠏碁覩襦 覲襦 蟯豸♀ 蠏 谿願 .
- ろれ p-value= 0.04062企襦 0.05覲企 . 讀, 襴所れ 觧 蟇企讌襯 谿場 覈詩. 蠏覓願 蠍郁. 蠏碁覩襦 ろる襦 蟯豸♀ 蠏 谿願 .
5 覦覲旧 れ 覿磯 #
"覦覲旧 "企 詞 れ螻 螳 詞企. 讀, 譴覲給 一危郁 詩. ("select distinct 蠍, 讌"螻 "select 蠍, 讌" るゴ.)
讌 <-c(rep("",4), rep("譴覿",4), rep("覿",4))
蠍 <- rep(c("","","",""), 3)
蟯豸′ <- c(74,78,70,74,78,74,68,72,68,72,60,64)
data <- data.frame(蟯豸′, 讌, 蠍)
> data
蟯豸′ 讌 蠍
1 74
2 78
3 70
4 74
5 78 譴覿
6 74 譴覿
7 68 譴覿
8 72 譴覿
9 68 覿
10 72 覿
11 60 覿
12 64 覿
覦覲旧 れ 覿磯 れ 蟆曙, 螳 襾殊 蟲語 讌 危ｴ覲伎 . 蟲語 蟲語(interaction plot) 伎覃 . R 蟆曙 れ螻 螳 覃 .
par(mfrow=c(1,2))
interaction.plot(讌,蠍,蟯豸′)
interaction.plot(蠍,讌,蟯豸′)
蟆郁骸 れ螻 螳. 襷 蟲語 覦る 蠏碁螳 蟲谿 覿覿 蠍企.
蟲語 蟆 讌襷 蠏碁 覯 蟲語 讌 危ｴ覲企襦 .
summary(aov(蟯豸′ ~ 蠍 + 讌 + 蠍:讌, data))
蟆郁骸 螳.
Df Sum Sq Mean Sq F value Pr(>F)
(Intercept) 1 60492 60492 7561.5 1.558e-10 ***
蠍 1 108 108 13.5 0.01040 *
讌 2 152 76 9.5 0.01382 *
蠍:讌 2 8 4 0.5 0.62974
Residuals 6 48 8
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
variable '蠍' converted to a factor
2: In model.matrix.default(mt, mf, contrasts) :
variable '讌' converted to a factor
3: In Ops.factor(left) : ! 語 伎 覓伎覩誤
4: In Ops.factor(left) : ! 語 伎 覓伎覩誤
蟆郁骸伎
- 螳
- 蠍 p-value = 0.01040 襦 譴 0.05覲企 朱襦 襴所れ 觧 伎襯 谿場 覈詩. 襴所れ 讌讌螻, 蠏覓願れ 蠍郁. 讀, 蠍一 磯殊 蟯豸′(螳蟆) 谿願 .
- 讌 p-value = 0.01382 襦 譴 0.05覲企 朱襦 襴所れ 觧 伎襯 谿場 覈詩. 襴所れ 讌讌螻, 蠏覓願れ 蠍郁. 讀, 讌 磯殊 蟯豸′(螳蟆) 谿願 .
- 蠍:讌 p-value = 0.629742 襦 譴 0.05覲企 覩襦 襴所れ 觧 伎襯 谿場. 蠏覓願れ 讌讌. 讀, 蠍一 讌 覲牛螻殊 蟯豸′(螳蟆) 谿企 . (蟲語 )
6 觜覈 覦覯 #
- 襭れ梗-襴 蟆讀(kruskcal-wallis rank sum test)
- 觜覈: 覲, 蠏 螳 蠍 企れ 蟆曙
- kruskcal.test() 伎
> # 蠏碁９ 蠍郁讌 襷覈 煙 豸′ 蟆郁骸
> x <- c(2.9, 3.0, 2.5, 2.6, 3.2) #
> y <- c(3.8, 2.7, 4.0, 2.4) # 蠍磯讌
> z <- c(2.8, 3.4, 3.7, 2.2, 2.0) #覃危讀
> data <- data.frame(x,y,z)
危 data.frame(x, y, z) : 語 るジ 伎襯 覩誤 5, 4
> kruskal.test(data)
Kruskal-Wallis rank sum test
data: data
Kruskal-Wallis chi-squared = 0.7714, df = 2, p-value = 0.68
>
蟆郁骸伎
- 螳
- p-value = 0.68企襦 譴 0.05 襴所れ 觧. 蠏碁覩襦 蠏覓願 讌讌
- 讀, 3蠏碁９ 覿讌蟇一 襷覈 煙 谿願 .
れ螻 螳 蠏碁９ 襷れ伎 . (g螳 蠏碁９企)
s <- c(x,y,z)
g <- rep(1:3, c(length(x),length(y),length(z)))
data <- data.frame(s,g)
kruskal.test(s,g)
> s <- c(x,y,z)
> g <- rep(1:3, c(length(x),length(y),length(z)))
> data <- data.frame(s,g)
> kruskal.test(s,g)
Kruskal-Wallis rank sum test
data: s and g
Kruskal-Wallis chi-squared = 0.7714, df = 2, p-value = 0.68
>
7 襴譴.. #
{ろ螻覯, 覦燕, 覩殊} 3.1
#3-1
x <- read.table(header=T, text="
factorLevel characteristicValue
A1 8.44
A1 8.36
A1 8.28
A2 8.59
A2 8.91
A2 8.6
A3 9.34
A3 9.41
A3 9.69
A4 8.92
A4 8.92
A4 8.74")
head(x)
aov.out <- aov(characteristicValue~factorLevel, data=x)
summary(aov.out)
drop1(aov.out,~.,test="F")
model.tables(aov.out, type="means", se=T)
model.tables(aov.out, type="effects", se=T)
TukeyHSD(aov.out)
plot(TukeyHSD(aov.out))
library(gplots)
plotmeans(characteristicValue~factorLevel, data=x)
lm.out <- lm(characteristicValue~factorLevel, data=x)
summary(lm.out)
summary.aov(lm.out)
#焔 蟆
bartlett.test(characteristicValue~factorLevel, data=x)
fligner.test(characteristicValue~factorLevel, data=x)
#library(HH)
#hov(characteristicValue~factorLevel, data=x)
#library(lawstat)
#levene.test(characteristicValue~factorLevel, data=x)
