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1 R 1
2 Excel
3
4 1, 2.6, 2/3, 105.2, , 3, 0, 245 A, B, C... ; , 3/4, 0.99
5 MS Excel»» R Macintosh
6 MS Excel Excel Excel
7 MS Excel MS Access Excel R
8 R R R.Data win Mac ctrl + R Win, command + Mac
9 R > > 1 1 > 2 * 2 > 4 / 2 > 3^2 > sqrt(9) > 9^(1/2) ½
10 R Tips tips/r.html Rjp Wiki
11 > a < 3.14 > b < c( 1, 2, 3, 4, 5, 6) > x < matrix(nrow = 2, ncol = 3) > data < read.csv( HandSize.csv, header = T) b[3] x[2, 3] data$hl
12 CSV CSV *.csv R read.csv(.csv, header = T) R write.csv(.csv
13 [,] > d[d$hl== F,] # d d$hl== F TRUE > d[d$hl== F,]$HL # HL subest() > d.f < subset(d, HL == F ) # d HL== H d.f > d.f$hl
14 plot(), boxplot(), histogram() plot(hl, data = d) HL boxplot(hl, data = d) plot(hl ~ Sex, data = d) HL Sex plot(hl ~ BL, data = d) histogram(d$hl, breaks = ) breaks
15
16 a X 1, X 2,...X n a a X 1 a, X 2 a,...x n a n Σ X i a) # Σ X i a # Σ X i a) 2 # 2
17 a a f(a) a f(a) = (X 1 a) 2 + (X 2 a) (X n a) 2 = X 12 + X X n2 2a(X 1 + X X n ) + na 2 a f(a) a f(a) a f (a) = 0 2(X 1 + X X n ) + 2a = 0 a = (X 1 + X X n ) /n
18 N μ, σ 2 μ σ 2
19 lm( ) lm()
20 m0 null Null model μ μ ( x ) 2
21 m0 P P t = 0 t
22 HL HL t p α α = 0.05 = 1/20 p α HL
23 α p p = /20... Type I
24 m0 P P t = 0 t
25 ( x / > t.cal < (mean(d$hl) 0) / sqrt(var(d$hl)/n) n 1 t n 25
26 m1 ANOVA lm(hl ~ 1, data = d) lm(hl ~ Sex, data = d)
27 m1 ANOVA p p t
28 ... lm(hl ~ Sex, data = d) t > var.test(bl.f, BL.m) > t.test(bl.f, BL.m,,var.equal = TRUE) BL.f BL.m t p lm()
29 ...
30 m2 Y Y p p Y
31 m3 ANCOVA p 0.05
32 m4 ANCOVA
33 HL ~ 1 HL ~ Sex + HL ~ BL + BL HL ~ BL + Sex + BL + ) + BL HL ~ BL + Sex + BL:Sex + BL + ) + BL summary a Intercept b BL a m SexM b m BL:SexM
34 ... p
35
36 Model 0: HL ~ 1 Model 1: HL ~ Sex Model 2: HL ~BL HL male female HL HL BL BL BL Model 3: HL ~ BL + Sex Model 4: HW ~ BL + Sex + BL:Sex HL HL BL BL
37 AIC AIC 2 e.g.
38 AIC AIC Model 3 Model 4
39 AIC. BL*Sex BL +Sex BL Sex 1 AIC BL*Sex e e BL+Sex e e BL N/A 7.017e Sex 4.994e
40 Excel R Excel R Excel
41 R MCMC ( ) Crawley: :R
42 2
43 anova(h0, h1) 2 h 0 h p.value = 1 pchisq(2*(mll.h1$value MLL.h0$value), h1 h0 ) F 2
... 3... 3... 3... 3... 4... 7... 10... 10... 11... 12... 12... 13... 14... 15... 18... 19... 20... 22... 22... 23 2
1 ... 3... 3... 3... 3... 4... 7... 10... 10... 11... 12... 12... 13... 14... 15... 18... 19... 20... 22... 22... 23 2 3 4 5 6 7 8 9 Excel2007 10 Excel2007 11 12 13 - 14 15 16 17 18 19 20 21 22 Excel2007
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2012 11 01 k2 (2012-10-26 16:35 ) 1 6 2 (2012 11 01 k2) (GLM) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 : 2 2 4 3 7 4 9 5 : 11 5.1................... 13 6 14 6.1......................
(interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2
7 2 1 (interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2 (confidence interval) 5 X a σ sqrt N µ X a σ sqrt N - 6 P ( X
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講義のーと : データ解析のための統計モデリング. 第３回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
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講義のーと : データ解析のための統計モデリング. 第５回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
n=360 28.6% 34.4% 36.9% n=360 2.5% 17.8% 19.2% n=64 0.8% 0.3% n=69 1.7% 3.6% 0.6% 1.4% 1.9% < > n=218 1.4% 5.6% 3.1% 60.6% 0.6% 6.9% 10.8% 6.4% 10.3% 33.1% 1.4% 3.6% 1.1% 0.0% 3.1% n=360 0% 50%
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