k2 ( :35 ) ( k2) (GLM) web web 1 :
|
|
- たいち いくのや
- 5 years ago
- Views:
Transcription
1 k2 ( :35 ) ( k2) (GLM) kubo@ees.hokudai.ac.jp web web 1 : : : :
2 k2 ( :35 ) 2 i y i 1 i y i :!! probability distribution 1 : 1 ( ) , 2 count data R R 1 50 data R R data 2 1 R web data.rdata R load(data.rdata) data R data.rdata 2 print(data)
3 > data k2 ( :35 ) 3 [1] [26] data 3 50 length() 4 data 50 5 > length(data) # data [1] 50 summary() > summary(data) # data Min. 1st Qu. Median Mean 3rd Qu. Max summary(data) Min. Max. data 1st Qu., Median, 3rd Qu. data 25%, 50%, 75% 6 Median Mean sample mean R table() > table(data) histogram [1] [26] R length() () () argument data length(data) R length() length() 5 R # 6 R seq(-0.5, 9.5, 1) { 0.5, 0.5, 1.5, 2.5,, 8.5, 9.5} hist() breaks
4 k2 ( :35 ) 4 Histogram of data Frequency 10 data 2 50 R hist() > hist(data, breaks = seq(-0.5, 9.5, 1)) R 2 variability sample variance > var(data) [1] sample standard deviation R > sd(data) [1] > sqrt(var(data)) [1] probability distribution
5 k2 ( :35 ) 5 Poisson distribution 10 random variable i y i y i y i = 2 y i = 2 1 y 1 = 2 parameter R 3.56 y R > y <- 0:9 > prob <- dpois(y, lambda = 3.56) R dpois(y, lambda = 3.56) prob y 3 > plot(y, prob, type = "b", lty = 2) plot() 4 y prob
6 k2 ( :35 ) 6 y prob y {0, 1, 2,, 9} R y p(y λ) prob cbind(y, prob) prob y 4 λ = 3.56 y prob 3 R plot() type = "b" lty = 2 Histogram of data Frequency 10 data 5 2 y
7 k2 ( :35 ) 7 prob lambda y 6 λ λ {3.5, 7.7, 15.1} 11 3 p(y λ) = λy exp( λ) y! p(y λ) λ y 12 y! y 4! λ 3 4 y {0, 1, 2,, } y 1 λ λ 0 y=0 p(y λ) = hist() lines(y, 50 * prob) 12 p(y λ)
8 k2 ( :35 ) 8 : λ = = λ 6 1 y i {0, 1, 2, } 2 y i 3 13 error measurement error 50 λ λ 1 λ 2 2 λ 2 3 λ 4 50 λ
9 k2 ( :35 ) lambda = 2.0 log L = lambda = 2.4 log L = lambda = 2.8 log L = lambda = 3.2 log L = lambda = 3.6 log L = lambda = 4.0 log L = lambda = 4.4 log L = lambda = 4.8 log L = lambda = 5.2 log L = λ lambda logl 2 4 maximum likelihood estimation i y i {y 1, y 2, y 3,, y 49, y 50 } = {2, 2, 4,, 2, 3} 50 {y i } Y = {y i } p(y i λ) λ y i 3 λ = 3.56 p(y 1 = 2 λ = 3.56) λ λ 17 18
10 k2 ( :35 ) 10 log likelihood * ˆλ λ λ = 3.56 ˆλ 7 λ i p(y i λ) 3 {y 1, y 2, y 3 } = {2, 2, 4} = L(λ) L(λ) = (y 1 2 ) (y 2 2 ) (y 50 3 ) = p(y 1 λ) p(y 2 λ) p(y 3 λ) p(y 50 λ) = p(y i λ) = λ yi exp( λ), i i y i! 19 y 1 2 y L(λ) log likelihood function log L(λ) = i ( y i log λ λ y i k ) log k λ 7 7 log L(λ) λ 8 R i {1, 2,, 50}?? i
11 k2 ( :35 ) 11 > logl <- function(m) sum(dpois(data, m, log = TRUE)) > lambda <- seq(2, 5, 0.1) > plot(lambda, sapply(lambda, logl), type = "l") λ log L L λ λ ˆλ 21 8 λ log L(λ) λ ˆλ = 1 50 log L(λ) λ i = i y i = y i { yi λ 1 } = 1 λ i y i 50 = = 3.56 ˆλ 3.56 ˆλ maximum likelihood estimator {y 1, y 2, y 3,, y 49, y 50 } = {2, 2, 4,, 2, 3} y i ˆλ = 3.56 maximum likelihood estimate θ y i p(y θ) L(θ Y) = i p(y i θ), log L(θ Y) = i log p(y i θ), ˆθ p(y θ) 22 5 : 21 ˆλ 22
12 k2 ( :35 ) 12 λ = 3.5 y ˆλ = 3.56 y y 9 λ = 3.5 ˆλ = 3.56 y : y 10 9 validation > data [1] [26] random number 23
13 k2 ( :35 ) 13 sampling λ estimation fitting 9 ˆλ = prediction 24 prediction interval : 5 missing data 25 goodness of prediction R R
14 k2 ( :35 ) 14 6 : Poisson distribution : binomial distribution : {0, 1, 2,, N} normal distribution : [, + ] gamma distribution : [0, + ] :
15 k2 ( :35 ) 15 i f i C: T: y i x i 11 i x i f i y i i y i body size x i 30 x i 50 i {1, 2,, 50} C 50 i {51, 52,, 100}, T x i x i f i 8 R CSV 31 web data3a.csv R > d <- read.csv("data3a.csv") d (table) CSV comma-separated value CSV
16 k2 ( :35 ) 16 R d print(d) > d y x f C C C T T d y x f d x y > d$x [1] [9] [97] > d$y [1] [17] [97] f > d$f [1] C C C C C C C C C C C C C C C C C C C C C C C C C [26] C C C C C C C C C C C C C C C C C C C C C C C C C [51] T T T T T T T T T T T T T T T T T T T T T T T T T [76] T T T T T T T T T T T T T T T T T T T T T T T T T Levels: C T f factor R read.csv() CSV C T factor f C T 2 level R Levels f C 1 T 2 read.csv() 33 R class() 32 print() head(d) 6 head(d, 10) 10 33
17 k2 ( :35 ) 17 > class(d) # d data.frame [1] "data.frame" > class(d$y) # y integer [1] "integer" > class(d$x) # x numeric [1] "numeric" > class(d$f) # f factor [1] "factor" R summary() d > summary(d) y x f Min. : 2.00 Min. : C:50 1st Qu.: st Qu.: T:50 Median : 8.00 Median : Mean : 7.83 Mean : rd Qu.: rd Qu.: Max. :15.00 Max. : summary() summary (numeric) y x f C 50 T 50 9 summary() 34 plot() > plot(d$x, d$y, pch = c(21, 19)[d$f]) > legend("topleft", legend = c("c", "T"), pch = c(21, 19)) 12 x y scatter plot plot(d$f, d$y) 13 box-whisker plot R 13 ±
18 k2 ( :35 ) 18 d$y C T d$x 12 y i x i f i C T C T 13 f i plot(d$f, d$y) 75%, 50%, 25% 95% 95%
19 k2 ( :35 ) x y f 10 λ λ i x f i x i 36 x i y i f i i y i p(y i λ i ) p(y i λ i ) = λyi i exp( λ i ) y i! 10.1 λ i x i i λ i λ i = exp(β 1 + β 2 x i ) β 1 β 2 parameter β 1 intercept β 2 slope 37 λ i = exp(β 1 + β 2 x i ) linear predictor link function GLM λ i log λ i = β 1 + β 2 x i 36 x i y i x i 37 coefficient x i covariate 38 y i x i log x i exp(β 1 + β 2 log x i ) log( ) = β 1 + β 2 log x i x i 0
20 k2 ( :35 ) 20 i λi {β 1, β 2} = { 2, 0.8} {β 1, β 2} = { 1, 0.4} i x i 14 i λ i x i λ i = exp(β 1 + β 2x i) x i x i 7 13 β 1 + β 2 x i β 1 + β 2 x i + β 3 x 2 i {β 1, β 2, β 3 } log λ i = λ i = log link function GLM GLM canonical link function R glm() family GLM λ i = exp( ) 0 14 R fitting log L ˆβ 1 ˆβ 2 Y log L(β 1, β 2 ) = i log λyi i exp( λ i ) y i! log λ i = β 1 + β 2 x i λ i β 1 β 2
21 k2 ( :35 ) 21 結果を格納するオブジェクト 関数名 確率分布の指定 fit <- glm( y ~ x, モデル式 family = poisson(link = "log"), data = d ) data.frame の指定 リンク関数の指定 ( 省略可 ) 15 glm() {β 1, β 2 } R GLM > fit <- glm(y ~ x, data = d, family = poisson) 39 β 1 β 2 glm() 15 family = poisson 40 fit 41 fit 42 > fit # print(fit) Call: glm(formula = y ~ x, family = poisson, data = d) Coefficients: (Intercept) x summary(fit) 39 argument data = d d data y ~ x formula = y ~ x help(glm) 40 family = poisson(link = "log") poisson family default link function "log" 41 fit names(fit) str(fit) 42 glm() deviance
22 k2 ( :35 ) 22 Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) x (Intercept) β 1 x β 2 Estimate ˆβ 1 = 1.29 ˆβ 2 = Std. Error standard error, SE ˆβ 1 ˆβ 2 SE 44 SE z value SE Wald 16 z Wald Wald statistics Pr(> z ) glm() 45 z 1 z ˆβ 1 ˆβ 2 16 ˆβ 2 2 ˆβ 2 z Pr(> z ) Pr(> z ) P statistical test 46 confidence interval 47 Wald 43 R.Rprofile options(show.signif.stars = FALSE) 44 Wald 45 glm() family glm() family = gaussian t α% α%
23 k2 ( :35 ) 23 β 2 β R glm() β 2 2 Pr(> z ) Pr(> z = ) β 1 Pr(> z ) Pr(> z = ) d$y d$x 17 λ 12 λ maximum log likelihood goodness of fit log L(β 1, β 2 ) { ˆβ 1, ˆβ 2 } R > loglik(fit) log Lik (df=2) (df=2) degrees of freedom 2 2 β 1 β 2
24 k2 ( :35 ) x λ prediction x λ { ˆβ 1, ˆβ 2 } λ = exp( x) R > plot(d$x, d$y, pch = c(21, 19)[d$f]) > xx <- seq(min(d$x), max(d$x), length = 100) > lines(xx, exp( * xx), lwd = 2) 17 λ predict() > yy <- predict(fit, newdata = data.frame(x = xx), type = "response") > lines(xx, yy, lwd = 2) 11 f i (p.16 ) R factor C T C 1 T 2 48 GLM R dummy variable 49 glm() f x i f i λ i = exp(β 1 + β 3 d i ) β 1 β 3 f i d i : R contrasts
25 k2 ( :35 ) 25 d i = { 0 (f i = C ) 1 (f i = T ) i f i = C f i = T R λ i = exp(β 1 ) λ i = exp(β 1 + β 3 ) glm() glm() > fit.f <- glm(y ~ f, data = d, family = poisson) fit.f Call: glm(formula = y ~ f, family = poisson, data = d) Coefficients: (Intercept) ft Coefficients f i ft f i T f i C T 2 R f i C T i f i C T λ i = exp( ) = exp(2.05) = 7.77 λ i = exp( ) = exp(2.0628) = 7.87 > loglik(fit.f) log Lik (df=2) (p.23 ) x i
26 k2 ( :35 ) 26 3 A B f i f i {C, TA, TB} 3 R 2 λ i = exp(β 1 + β 3 d i,a + β 4 d i,b ) β 3 A β 4 B { 0 (f i TA ) d i,a = 1 (f i TA ) { 0 (f i TB ) d i,b = 1 (f i TB ) 3 R glm(y ~ f,...) β 3 β 4 fta ftb 12 + x i f i 50 GLM log λ i = β 1 + β 2 x i + β 3 d i β 1 β 2 x i β 3 (2 f i d i ) 51 R glm() x + f > fit.all <- glm(y ~ x + f, data = d, family = poisson) fit.all 50 multiple regression 51
27 k2 ( :35 ) 27 Call: glm(formula = y ~ x + f, family = poisson, data = d) Coefficients: (Intercept) x ft Degrees of Freedom: 99 Total (i.e. Null); Null Deviance: Residual Deviance: AIC: Residual ft > loglik(fit.all) log Lik (df=3) p.23 x i (-235.4) 12.1 : 10 + glm() glm(y ~ x + f,...) x i i f i C λ i = exp( x i ) T λ i = exp( x i 0.032) λ i = exp(1.26) exp(0.08x i ) exp( 0.032) = x i x i 1 λ i exp(0.08 1) = 1.08
28 k2 ( :35 ) 28 (A) (B) λi x i x i 18 (A) (B) 3 3 exp( 0.032) = λ i 18 (A) 52 x i R identity link function 53 λ i λ i = x i 0.205d i 18 (B) GLM 12.1 generalized linear model, LM glm() family = poisson(link = "identity") λ 54 (A)
29 k2 ( :35 ) 29 (A) y x (B) y x 19 GLM x x {0.5, 1.1, 1.7} y (A) y β 1 + β 2x (B) y exp(β 1 + β 2x) general lienar model LM linear regression 19 (x i, y i ) 19 (A) GLM 55 : {x 1, x 2,, x n } {y 1, y 2,, y n } X = {x i } Y = {y i } Y µ i σ i µ i = β 1 + β 2 x i LM GLM 56 55?? 56 LM multiple regression x i ANOVA
30 k2 ( :35 ) y 0, 1, 2 x y y 19 A : (B) GLM y?? y log y y 59 y GLM GLM ANCOVA 57 R glm() family = gaussian family = gaussian(link = "log") 58 R 2 P 59 log 0 =
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
More informationkubostat2017c p (c) Poisson regression, a generalized linear model (GLM) : :
kubostat2017c p.1 2017 (c), a generalized linear model (GLM) : kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 kubostat2017c (http://goo.gl/76c4i) 2017 (c) 2017 11 14 1 / 47 agenda
More information講義のーと : データ解析のための統計モデリング. 第2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
More information一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM
.. ( ) (2) GLMM kubo@ees.hokudai.ac.jp I http://goo.gl/rrhzey 2013 08 27 : 2013 08 27 08:29 kubostat2013ou2 (http://goo.gl/rrhzey) ( ) (2) 2013 08 27 1 / 74 I.1 N k.2 binomial distribution logit link function.3.4!
More informationkubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or
kubostat207e p. I 207 (e) GLM kubo@ees.hokudai.ac.jp https://goo.gl/z9ycjy 207 4 207 6:02 N y 2 binomial distribution logit link function 3 4! offset kubostat207e (https://goo.gl/z9ycjy) 207 (e) 207 4
More informationkubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib
kubostat2015e p.1 I 2015 (e) GLM kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2015 07 22 2015 07 21 16:26 kubostat2015e (http://goo.gl/76c4i) 2015 (e) 2015 07 22 1 / 42 1 N k 2 binomial distribution logit
More information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
More informationk3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k
2012 11 01 k3 (2012-10-24 14:07 ) 1 6 3 (2012 11 01 k3) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 3 2 : 4 3 AIC 6 4 7 5 8 6 : 9 7 11 8 12 8.1 (1)........ 13 8.2 (2) χ 2....................
More informationkubo2017sep16a p.1 ( 1 ) : : :55 kubo ( ( 1 ) / 10
kubo2017sep16a p.1 ( 1 ) kubo@ees.hokudai.ac.jp 2017 09 16 : http://goo.gl/8je5wh : 2017 09 13 16:55 kubo (http://goo.gl/ufq2) ( 1 ) 2017 09 16 1 / 106 kubo (http://goo.gl/ufq2) ( 1 ) 2017 09 16 2 / 106
More informationkubostat2017b p.1 agenda I 2017 (b) probability distribution and maximum likelihood estimation :
kubostat2017b p.1 agenda I 2017 (b) probabilit distribution and maimum likelihood estimation kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 1 : 2 3? 4 kubostat2017b (http://goo.gl/76c4i)
More informationkubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i
kubostat2018d p.1 I 2018 (d) model selection and kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2018 06 25 : 2018 06 21 17:45 1 2 3 4 :? AIC : deviance model selection misunderstanding kubostat2018d (http://goo.gl/76c4i)
More information1 15 R Part : website:
1 15 R Part 4 2017 7 24 4 : website: email: http://www3.u-toyama.ac.jp/kkarato/ kkarato@eco.u-toyama.ac.jp 1 2 2 3 2.1............................... 3 2.2 2................................. 4 2.3................................
More information(lm) lm AIC 2 / 1
W707 s-taiji@is.titech.ac.jp 1 / 1 (lm) lm AIC 2 / 1 : y = β 1 x 1 + β 2 x 2 + + β d x d + β d+1 + ϵ (ϵ N(0, σ 2 )) y R: x R d : β i (i = 1,..., d):, β d+1 : ( ) (d = 1) y = β 1 x 1 + β 2 + ϵ (d > 1) y
More information(2/24) : 1. R R R
R? http://hosho.ees.hokudai.ac.jp/ kubo/ce/2004/ : kubo@ees.hokudai.ac.jp (2/24) : 1. R 2. 3. R R (3/24)? 1. ( ) 2. ( I ) : (p ) : cf. (power) p? (4/24) p ( ) I p ( ) I? ( ) (5/24)? 0 2 4 6 8 A B A B (control)
More informationUse R
Use R! 2008/05/23( ) Index Introduction (GLM) ( ) R. Introduction R,, PLS,,, etc. 2. Correlation coefficient (Pearson s product moment correlation) r = Sxy Sxx Syy :, Sxy, Sxx= X, Syy Y 1.96 95% R cor(x,
More information60 (W30)? 1. ( ) 2. ( ) web site URL ( :41 ) 1/ 77
60 (W30)? 1. ( ) kubo@ees.hokudai.ac.jp 2. ( ) web site URL http://goo.gl/e1cja!! 2013 03 07 (2013 03 07 17 :41 ) 1/ 77 ! : :? 2013 03 07 (2013 03 07 17 :41 ) 2/ 77 2013 03 07 (2013 03 07 17 :41 ) 3/ 77!!
More informationRによる計量分析:データ解析と可視化 - 第3回 Rの基礎とデータ操作・管理
R 3 R 2017 Email: gito@eco.u-toyama.ac.jp October 23, 2017 (Toyama/NIHU) R ( 3 ) October 23, 2017 1 / 34 Agenda 1 2 3 4 R 5 RStudio (Toyama/NIHU) R ( 3 ) October 23, 2017 2 / 34 10/30 (Mon.) 12/11 (Mon.)
More information/ 60 : 1. GLM? 2. A: (pwer functin) x y?
2009-03-17 1/ 60 (2009-03-17) GLM 1. GLM :, link,, deviance (20 ) 2. GLM : (60 ) 3. GLM ( ): ffset (40 ) http://hsh.ees.hkudai.ac.jp/ kub/ce/ecsj2009.html 2009-03-17 2/ 60 : 1. GLM? 2. A: (pwer functin)
More informationDAA09
> summary(dat.lm1) Call: lm(formula = sales ~ price, data = dat) Residuals: Min 1Q Median 3Q Max -55.719-19.270 4.212 16.143 73.454 Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) 237.1326
More information最小2乗法
2 2012 4 ( ) 2 2012 4 1 / 42 X Y Y = f (X ; Z) linear regression model X Y slope X 1 Y (X, Y ) 1 (X, Y ) ( ) 2 2012 4 2 / 42 1 β = β = β (4.2) = β 0 + β (4.3) ( ) 2 2012 4 3 / 42 = β 0 + β + (4.4) ( )
More informationkubostat7f p GLM! logistic regression as usual? N? GLM GLM doesn t work! GLM!! probabilit distribution binomial distribution : : β + β x i link functi
kubostat7f p statistaical models appeared in the class 7 (f) kubo@eeshokudaiacjp https://googl/z9cjy 7 : 7 : The development of linear models Hierarchical Baesian Model Be more flexible Generalized Linear
More information/ 55 2 : : (GLM) 1. 1/23 ( )? GLM? (GLM ) 2.! 1/25 ( ) ffset (GLM )
2012 01 25 1/ 55 ( II) : (2012 1 ) 2 2 (GLM) 2012 01 25! kub@ees.hkudai.ac.jp http://g.gl/76c4i 2012 01 25 2/ 55 2 : : (GLM) 1. 1/23 ( )? GLM? (GLM ) 2.! 1/25 ( ) ffset (GLM ) 2012 01 25 3/ 55 1. : 2.
More information1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3.
1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3. 2 4, 2. 1 2 2 Depress Conservative. 3., 3,. SES66 Alien67 Alien71,
More information80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = i=1 i=1 n λ x i e λ i=1 x i! = λ n i=1 x i e nλ n i=1 x
80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = n λ x i e λ x i! = λ n x i e nλ n x i! n n log l(λ) = log(λ) x i nλ log( x i!) log l(λ) λ = 1 λ n x i n =
More informationStata11 whitepapers mwp-037 regress - regress regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F(
mwp-037 regress - regress 1. 1.1 1.2 1.3 2. 3. 4. 5. 1. regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F( 2, 71) = 69.75 Model 1619.2877 2 809.643849 Prob > F = 0.0000 Residual
More informationkubostat2017j p.2 CSV CSV (!) d2.csv d2.csv,, 286,0,A 85,0,B 378,1,A 148,1,B ( :27 ) 10/ 51 kubostat2017j (http://goo.gl/76c4i
kubostat2017j p.1 2017 (j) Categorical Data Analsis kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 15 : 2017 11 08 17:11 kubostat2017j (http://goo.gl/76c4i) 2017 (j) 2017 11 15 1 / 63 A B C D E F G
More informationこんにちは由美子です
1 2 . sum Variable Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- var1 13.4923077.3545926.05 1.1 3 3 3 0.71 3 x 3 C 3 = 0.3579 2 1 0.71 2 x 0.29 x 3 C 2 = 0.4386
More information²¾ÁÛ¾õ¶·É¾²ÁË¡¤Î¤¿¤á¤Î¥Ñ¥Ã¥±¡¼¥¸DCchoice ¡Ê»ÃÄêÈÇ¡Ë
DCchoice ( ) R 2013 2013 11 30 DCchoice package R 2013/11/30 1 / 19 1 (CV) CV 2 DCchoice WTP 3 DCchoice package R 2013/11/30 2 / 19 (Contingent Valuation; CV) WTP CV WTP WTP 1 1989 2 DCchoice package R
More informationuntitled
2011/6/22 M2 1*1+2*2 79 2F Y YY 0.0 0.2 0.4 0.6 0.8 0.000 0.002 0.004 0.006 0.008 0.010 0.012 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Y 0 50 100 150 200 250 YY A (Y = X + e A ) B (YY = X + e B ) X 0.00 0.05 0.10
More informationこんにちは由美子です
Analysis of Variance 2 two sample t test analysis of variance (ANOVA) CO 3 3 1 EFV1 µ 1 µ 2 µ 3 H 0 H 0 : µ 1 = µ 2 = µ 3 H A : Group 1 Group 2.. Group k population mean µ 1 µ µ κ SD σ 1 σ σ κ sample mean
More information分布
(normal distribution) 30 2 Skewed graph 1 2 (variance) s 2 = 1/(n-1) (xi x) 2 x = mean, s = variance (variance) (standard deviation) SD = SQR (var) or 8 8 0.3 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 8 0 1 8 (probability
More information,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i
Armitage.? SAS.2 µ, µ 2, µ 3 a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 µ, µ 2, µ 3 log a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 * 2 2. y t y y y Poisson y * ,, Poisson 3 3. t t y,, y n Nµ,
More information第11回:線形回帰モデルのOLS推定
11 OLS 2018 7 13 1 / 45 1. 2. 3. 2 / 45 n 2 ((y 1, x 1 ), (y 2, x 2 ),, (y n, x n )) linear regression model y i = β 0 + β 1 x i + u i, E(u i x i ) = 0, E(u i u j x i ) = 0 (i j), V(u i x i ) = σ 2, i
More information12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71
2010-12-02 (2010 12 02 10 :51 ) 1/ 71 GCOE 2010-12-02 WinBUGS kubo@ees.hokudai.ac.jp http://goo.gl/bukrb 12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? 2010-12-02 (2010 12
More informationAR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t
87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
More information(pdf) (cdf) Matlab χ ( ) F t
(, ) (univariate) (bivariate) (multi-variate) Matlab Octave Matlab Matlab/Octave --...............3. (pdf) (cdf)...3.4....4.5....4.6....7.7. Matlab...8.7.....9.7.. χ ( )...0.7.3.....7.4. F....7.5. t-...3.8....4.8.....4.8.....5.8.3....6.8.4....8.8.5....8.8.6....8.9....9.9.....9.9.....0.9.3....0.9.4.....9.5.....0....3
More information201711grade2.pdf
2017 11 26 1 2 28 3 90 4 5 A 1 2 3 4 Web Web 6 B 10 3 10 3 7 34 8 23 9 10 1 2 3 1 (A) 3 32.14 0.65 2.82 0.93 7.48 (B) 4 6 61.30 54.68 34.86 5.25 19.07 (C) 7 13 5.89 42.18 56.51 35.80 50.28 (D) 14 20 0.35
More informationI L01( Wed) : Time-stamp: Wed 07:38 JST hig e, ( ) L01 I(2017) 1 / 19
I L01(2017-09-20 Wed) : Time-stamp: 2017-09-20 Wed 07:38 JST hig e, http://hig3.net ( ) L01 I(2017) 1 / 19 ? 1? 2? ( ) L01 I(2017) 2 / 19 ?,,.,., 1..,. 1,2,.,.,. ( ) L01 I(2017) 3 / 19 ? I. M (3 ) II,
More informationtokei01.dvi
2. :,,,. :.... Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN 4 3. (probability),, 1. : : n, α A, A a/n. :, p, p Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN
More informationECCS. ECCS,. ( 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file e
1 1 2015 4 6 1. ECCS. ECCS,. (https://ras.ecc.u-tokyo.ac.jp/guacamole/) 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file editor, Do View Do-file Editor Execute(do). 3. Mac System
More informationy i OLS [0, 1] OLS x i = (1, x 1,i,, x k,i ) β = (β 0, β 1,, β k ) G ( x i β) 1 G i 1 π i π i P {y i = 1 x i } = G (
7 2 2008 7 10 1 2 2 1.1 2............................................. 2 1.2 2.......................................... 2 1.3 2........................................ 3 1.4................................................
More information1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.
1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press. 2 3 2 Conservative Depress. 3.1 2. SEM. 1. x SEM. Depress.
More information2 と入力すると以下のようになる > x1<-c(1.52,2,3.01,9,2,6.3,5,11.2) > y1<-c(4,0.21,-1.5,8,2,6,9.915,5.2) > cor(x1,y1) [1] > cor.test(x1,y1) Pearson's produ
1 統計 データ解析セミナーの予習 2010.11.24 粕谷英一 ( 理 生物 生態 ) GCOE アジア保全生態学 本日のメニュー R 一般化線形モデル (Generalized Linear Models 略して GLM) R で GLM を使う R でグラフを描く 説明しないこと :R でできること全般 たくさんあるので時間的に無理 R でするプログラミング-データ解析なら使いやすい R 起動と終了
More information¥¤¥ó¥¿¡¼¥Í¥Ã¥È·×¬¤È¥Ç¡¼¥¿²òÀÏ Âè2²ó
2 2015 4 20 1 (4/13) : ruby 2 / 49 2 ( ) : gnuplot 3 / 49 1 1 2014 6 IIJ / 4 / 49 1 ( ) / 5 / 49 ( ) 6 / 49 (summary statistics) : (mean) (median) (mode) : (range) (variance) (standard deviation) 7 / 49
More information第13回:交差項を含む回帰・弾力性の推定
13 2018 7 27 1 / 31 1. 2. 2 / 31 y i = β 0 + β X x i + β Z z i + β XZ x i z i + u i, E(u i x i, z i ) = 0, E(u i u j x i, z i ) = 0 (i j), V(u i x i, z i ) = σ 2, i = 1, 2,, n x i z i 1 3 / 31 y i = β
More information% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti One-sample test of pr
1 1. 2014 6 2014 6 10 10% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti 1029 0.35 0.40 One-sample test of proportion x: Number of obs = 1029 Variable Mean Std.
More informationBMIdata.txt DT DT <- read.table("bmidata.txt") DT head(dt) names(dt) str(dt)
?read.table read.table(file, header = FALSE, sep = "", quote = "\" ", dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"), row.names, col.names, as.is =!stringsasfactors, na.strings = "NA", colclasses
More information1 Tokyo Daily Rainfall (mm) Days (mm)
( ) r-taka@maritime.kobe-u.ac.jp 1 Tokyo Daily Rainfall (mm) 0 100 200 300 0 10000 20000 30000 40000 50000 Days (mm) 1876 1 1 2013 12 31 Tokyo, 1876 Daily Rainfall (mm) 0 50 100 150 0 100 200 300 Tokyo,
More informationJMP V4 による生存時間分析
V4 1 SAS 2000.11.18 4 ( ) (Survival Time) 1 (Event) Start of Study Start of Observation Died Died Died Lost End Time Censor Died Died Censor Died Time Start of Study End Start of Observation Censor
More informationStata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 s
BR003 Stata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 sampsi 47 mwp-044 sdtest 54 mwp-043 signrank/signtest
More informationDAA12
Observed Data (Total variance) Predicted Data (prediction variance) Errors in Prediction (error variance) Shoesize 23 24 25 26 27 male female male mean female mean overall mean Shoesize 23 24 25 26 27
More informationyamadaiR(cEFA).pdf
R 2012/10/05 Kosugi,E.Koji (Yamadai.R) Categorical Factor Analysis by using R 2012/10/05 1 / 9 Why we use... 3 5 Kosugi,E.Koji (Yamadai.R) Categorical Factor Analysis by using R 2012/10/05 2 / 9 FA vs
More information1 kawaguchi p.1/81
1 kawaguchi atsushi@kurume-u.ac.jp 2005 7 2 p.1/81 2.1 2.2 2.2.3 2.3 AUC 4.4 p.2/81 X Z X = α + βz + e α : Z = 0 X ( ) β : Z X ( ) e : 0 σ 2 p.3/81 2.1 Z X 1 0.045 2 0.114 4 0.215 6 0.346 7 0.41 8 0.52
More informationH22 BioS t (i) treat1 treat2 data d1; input patno treat1 treat2; cards; ; run; 1 (i) treat = 1 treat =
H BioS t (i) treat treat data d; input patno treat treat; cards; 3 8 7 4 8 8 5 5 6 3 ; run; (i) treat treat data d; input group patno period treat y; label group patno period ; cards; 3 8 3 7 4 8 4 8 5
More informationGLM PROC GLM y = Xβ + ε y X β ε ε σ 2 E[ε] = 0 var[ε] = σ 2 I σ 2 0 σ 2 =... 0 σ 2 σ 2 I ε σ 2 y E[y] =Xβ var[y] =σ 2 I PROC GLM
PROC MIXED ( ) An Introdunction to PROC MIXED Junji Kishimoto SAS Institute Japan / Keio Univ. SFC / Univ. of Tokyo e-mail address: jpnjak@jpn.sas.com PROC MIXED PROC GLM PROC MIXED,,,, 1 1.1 PROC MIXED
More informationR John Fox R R R Console library(rcmdr) Rcmdr R GUI Windows R R SDI *1 R Console R 1 2 Windows XP Windows * 2 R R Console R ˆ R
R John Fox 2006 8 26 2008 8 28 1 R R R Console library(rcmdr) Rcmdr R GUI Windows R R SDI *1 R Console R 1 2 Windows XP Windows * 2 R R Console R ˆ R GUI R R R Console > ˆ 2 ˆ Fox(2005) jfox@mcmaster.ca
More informationMicrosoft Word - 計量研修テキスト_第5版).doc
Q10-2 テキスト P191 1. 記述統計量 ( 変数 :YY95) 表示変数として 平均 中央値 最大値 最小値 標準偏差 観測値 を選択 A. 都道府県別 Descriptive Statistics for YY95 Categorized by values of PREFNUM Date: 05/11/06 Time: 14:36 Sample: 1990 2002 Included
More information28
y i = Z i δ i +ε i ε i δ X y i = X Z i δ i + X ε i [ ] 1 δ ˆ i = Z i X( X X) 1 X Z i [ ] 1 σ ˆ 2 Z i X( X X) 1 X Z i Z i X( X X) 1 X y i σ ˆ 2 ˆ σ 2 = [ ] y i Z ˆ [ i δ i ] 1 y N p i Z i δ ˆ i i RSTAT
More informationuntitled
2 : n =1, 2,, 10000 0.5125 0.51 0.5075 0.505 0.5025 0.5 0.4975 0.495 0 2000 4000 6000 8000 10000 2 weak law of large numbers 1. X 1,X 2,,X n 2. µ = E(X i ),i=1, 2,,n 3. σi 2 = V (X i ) σ 2,i=1, 2,,n ɛ>0
More information10:30 12:00 P.G. vs vs vs 2
1 10:30 12:00 P.G. vs vs vs 2 LOGIT PROBIT TOBIT mean median mode CV 3 4 5 0.5 1000 6 45 7 P(A B) = P(A) + P(B) - P(A B) P(B A)=P(A B)/P(A) P(A B)=P(B A) P(A) P(A B) P(A) P(B A) P(B) P(A B) P(A) P(B) P(B
More information数理統計学Iノート
I ver. 0/Apr/208 * (inferential statistics) *2 A, B *3 5.9 *4 *5 [6] [],.., 7 2004. [2].., 973. [3]. R (Wonderful R )., 9 206. [4]. ( )., 7 99. [5]. ( )., 8 992. [6],.., 989. [7]. - 30., 0 996. [4] [5]
More informationMicrosoft Word - 計量研修テキスト_第5版).doc
Q9-1 テキスト P166 2)VAR の推定 注 ) 各変数について ADF 検定を行った結果 和文の次数はすべて 1 である 作業手順 4 情報量基準 (AIC) によるラグ次数の選択 VAR Lag Order Selection Criteria Endogenous variables: D(IG9S) D(IP9S) D(CP9S) Exogenous variables: C Date:
More information卒業論文
Y = ax 1 b1 X 2 b2...x k bk e u InY = Ina + b 1 InX 1 + b 2 InX 2 +...+ b k InX k + u X 1 Y b = ab 1 X 1 1 b 1 X 2 2...X bk k e u = b 1 (ax b1 1 X b2 2...X bk k e u ) / X 1 = b 1 Y / X 1 X 1 X 1 q YX1
More informationX X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I
(missing data analysis) - - 1/16/2011 (missing data, missing value) (list-wise deletion) (pair-wise deletion) (full information maximum likelihood method, FIML) (multiple imputation method) 1 missing completely
More information¥¤¥ó¥¿¡¼¥Í¥Ã¥È·×¬¤È¥Ç¡¼¥¿²òÀÏ Âè2²ó
2 212 4 13 1 (4/6) : ruby 2 / 35 ( ) : gnuplot 3 / 35 ( ) 4 / 35 (summary statistics) : (mean) (median) (mode) : (range) (variance) (standard deviation) 5 / 35 (mean): x = 1 n (median): { xr+1 m, m = 2r
More information2009 5 1...1 2...3 2.1...3 2.2...3 3...10 3.1...10 3.1.1...10 3.1.2... 11 3.2...14 3.2.1...14 3.2.2...16 3.3...18 3.4...19 3.4.1...19 3.4.2...20 3.4.3...21 4...24 4.1...24 4.2...24 4.3 WinBUGS...25 4.4...28
More informationH22 BioS (i) I treat1 II treat2 data d1; input group patno treat1 treat2; cards; ; run; I
H BioS (i) I treat II treat data d; input group patno treat treat; cards; 8 7 4 8 8 5 5 6 ; run; I II sum data d; set d; sum treat + treat; run; sum proc gplot data d; plot sum * group ; symbol c black
More informationσ t σ t σt nikkei HP nikkei4csv H R nikkei4<-readcsv("h:=y=ynikkei4csv",header=t) (1) nikkei header=t nikkei4csv 4 4 nikkei nikkei4<-dataframe(n
R 1 R R R tseries fseries 1 tseries fseries R Japan(Tokyo) R library(tseries) library(fseries) 2 t r t t 1 Ω t 1 E[r t Ω t 1 ] ɛ t r t = E[r t Ω t 1 ] + ɛ t ɛ t 2 iid (independently, identically distributed)
More informationJune 2016 i (statistics) F Excel Numbers, OpenOffice/LibreOffice Calc ii *1 VAR STDEV 1 SPSS SAS R *2 R R R R *1 Excel, Numbers, Microsoft Office, Apple iwork, *2 R GNU GNU R iii URL http://ruby.kyoto-wu.ac.jp/statistics/training/
More informationp.1/22
p.1/22 & & & & Excel / p.2/22 & & & & Excel / p.2/22 ( ) ( ) p.3/22 ( ) ( ) Baldi Web p.3/22 ( ) ( ) Baldi Web ( ) ( ) ( p.3/22 ) Text Mining for Clementine True Teller Text Mining Studio Text Miner Trustia
More information201711grade1ouyou.pdf
2017 11 26 1 2 52 3 12 13 22 23 32 33 42 3 5 3 4 90 5 6 A 1 2 Web Web 3 4 1 2... 5 6 7 7 44 8 9 1 2 3 1 p p >2 2 A 1 2 0.6 0.4 0.52... (a) 0.6 0.4...... B 1 2 0.8-0.2 0.52..... (b) 0.6 0.52.... 1 A B 2
More information: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99
218 6 219 6.11: (EQS) /EQUATIONS V1 = 30*V999 + 1F1 + E1; V2 = 25*V999 +.54*F1 + E2; V3 = 16*V999 + 1.46*F1 + E3; V4 = 10*V999 + 1F2 + E4; V5 = 19*V999 + 1.29*F2 + E5; V6 = 17*V999 + 2.22*F2 + E6; CALIS.
More informationkubostat2018a p.1 統計モデリング入門 2018 (a) The main language of this class is 生物多様性学特論 Japanese Sorry An overview: Statistical Modeling 観測されたパターンを説明する統計モデル
p.1 統計モデリング入門 2018 (a) The main language of this class is 生物多様性学特論 Japanese Sorry An overview: Statistical Modeling 観測されたパターンを説明する統計モデル 久保拓弥 (北海道大 環境科学) Why in Japanese? because even in Japanese, statistics
More information統計モデリング入門 2018 (a) 生物多様性学特論 An overview: Statistical Modeling 観測されたパターンを説明する統計モデル 久保拓弥 (北海道大 環境科学) 統計モデリング入門 2018a 1
統計モデリング入門 2018 (a) 生物多様性学特論 An overview: Statistical Modeling 観測されたパターンを説明する統計モデル 久保拓弥 (北海道大 環境科学) kubo@ees.hokudai.ac.jp 1/56 The main language of this class is Japanese Sorry Why in Japanese? because
More informationuntitled
WinLD R (16) WinLD https://www.biostat.wisc.edu/content/lan-demets-method-statistical-programs-clinical-trials WinLD.zip 2 2 1 α = 5% Type I error rate 1 5.0 % 2 9.8 % 3 14.3 % 5 22.6 % 10 40.1 % 3 Type
More informationインターネットを活用した経済分析 - フリーソフト Rを使おう
R 1 1 1 2017 2 15 2017 2 15 1/64 2 R 3 R R RESAS 2017 2 15 2/64 2 R 3 R R RESAS 2017 2 15 3/64 2-4 ( ) ( (80%) (20%) 2017 2 15 4/64 PC LAN R 2017 2 15 5/64 R R 2017 2 15 6/64 3-4 R 15 + 2017 2 15 7/64
More information/22 R MCMC R R MCMC? 3. Gibbs sampler : kubo/
2006-12-09 1/22 R MCMC R 1. 2. R MCMC? 3. Gibbs sampler : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/ 2006-12-09 2/22 : ( ) : : ( ) : (?) community ( ) 2006-12-09 3/22 :? 1. ( ) 2. ( )
More informationPackageSoft/R-033U.tex (2018/March) R:
................................................................................ R: 2018 3 29................................................................................ R AI R https://cran.r-project.org/doc/contrib/manuals-jp/r-intro-170.jp.pdf
More information( 28 ) ( ) ( ) 0 This note is c 2016, 2017 by Setsuo Taniguchi. It may be used for personal or classroom purposes, but not for commercial purp
( 28) ( ) ( 28 9 22 ) 0 This ote is c 2016, 2017 by Setsuo Taiguchi. It may be used for persoal or classroom purposes, but ot for commercial purposes. i (http://www.stat.go.jp/teacher/c2epi1.htm ) = statistics
More information自由集会時系列part2web.key
spurious correlation spurious regression xt=xt-1+n(0,σ^2) yt=yt-1+n(0,σ^2) n=20 type1error(5%)=0.4703 no trend 0 1000 2000 3000 4000 p for r xt=xt-1+n(0,σ^2) random walk random walk variable -5 0 5 variable
More informationuntitled
IT (1, horiike@ml.me.titech.ac.jp) (1, jun-jun@ms.kagu.tus.ac.jp) 1. 1-1 19802000 2000ITIT IT IT TOPIX (%) 1TOPIX 2 1-2. 80 80 ( ) 2004/11/26 S-PLUS 2 1-3. IT IT IT IT 2. 2-1. a. b. (Size) c. B/M(Book
More informationStata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestim
TS001 Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestimation 49 mwp-055 corrgram/ac/pac 56 mwp-009 dfgls
More informationdvi
2017 65 2 185 200 2017 1 2 2016 12 28 2017 5 17 5 24 PITCHf/x PITCHf/x PITCHf/x MLB 2014 PITCHf/x 1. 1 223 8522 3 14 1 2 223 8522 3 14 1 186 65 2 2017 PITCHf/x 1.1 PITCHf/x PITCHf/x SPORTVISION MLB 30
More information( 30 ) 30 4 5 1 4 1.1............................................... 4 1.............................................. 4 1..1.................................. 4 1.......................................
More information151021slide.dvi
: Mac I 1 ( 5 Windows (Mac Excel : Excel 2007 9 10 1 4 http://asakura.co.jp/ books/isbn/978-4-254-12172-8/ (1 1 9 1/29 (,,... (,,,... (,,, (3 3/29 (, (F7, Ctrl + i, (Shift +, Shift + Ctrl (, a i (, Enter,
More information> usdata01 と打ち込んでエンター キーを押すと V1 V2 V : : : : のように表示され 読み込まれていることがわかる ここで V1, V2, V3 は R が列のデータに自 動的につけた変数名である ( variable
R による回帰分析 ( 最小二乗法 ) この資料では 1. データを読み込む 2. 最小二乗法によってパラメーターを推定する 3. データをプロットし 回帰直線を書き込む 4. いろいろなデータの読み込み方について簡単に説明する 1. データを読み込む 以下では read.table( ) 関数を使ってテキストファイル ( 拡張子が.txt のファイル ) のデー タの読み込み方を説明する 1.1
More information2 1,2, , 2 ( ) (1) (2) (3) (4) Cameron and Trivedi(1998) , (1987) (1982) Agresti(2003)
3 1 1 1 2 1 2 1,2,3 1 0 50 3000, 2 ( ) 1 3 1 0 4 3 (1) (2) (3) (4) 1 1 1 2 3 Cameron and Trivedi(1998) 4 1974, (1987) (1982) Agresti(2003) 3 (1)-(4) AAA, AA+,A (1) (2) (3) (4) (5) (1)-(5) 1 2 5 3 5 (DI)
More informationwaseda2010a-jukaiki1-main.dvi
November, 2 Contents 6 2 8 3 3 3 32 32 33 5 34 34 6 35 35 7 4 R 2 7 4 4 9 42 42 2 43 44 2 5 : 2 5 5 23 52 52 23 53 53 23 54 24 6 24 6 6 26 62 62 26 63 t 27 7 27 7 7 28 72 72 28 73 36) 29 8 29 8 29 82 3
More information2 / 39
W707 s-taiji@is.titech.ac.jp 1 / 39 2 / 39 1 2 3 3 / 39 q f (x; α) = α j B j (x). j=1 min α R n+2 n ( d (Y i f (X i ; α)) 2 2 ) 2 f (x; α) + λ dx 2 dx. i=1 f B j 4 / 39 : q f (x) = α j B j (x). j=1 : x
More information!!! 2!
2016/5/17 (Tue) SPSS (mugiyama@l.u-tokyo.ac.jp)! !!! 2! 3! 4! !!! 5! (Population)! (Sample) 6! case, observation, individual! variable!!! 1 1 4 2 5 2 1 5 3 4 3 2 3 3 1 4 2 1 4 8 7! (1) (2) (3) (4) categorical
More information1 環境統計学ぷらす 第 5 回 一般 ( 化 ) 線形混合モデル 高木俊 2013/11/21
1 環境統計学ぷらす 第 5 回 一般 ( 化 ) 線形混合モデル 高木俊 shun.takagi@sci.toho-u.ac.jp 2013/11/21 2 予定 第 1 回 : Rの基礎と仮説検定 第 2 回 : 分散分析と回帰 第 3 回 : 一般線形モデル 交互作用 第 4.1 回 : 一般化線形モデル 第 4.2 回 : モデル選択 (11/29?) 第 5 回 : 一般化線形混合モデル
More information4 OLS 4 OLS 4.1 nurseries dual c dual i = c + βnurseries i + ε i (1) 1. OLS Workfile Quick - Estimate Equation OK Equation specification dual c nurser
1 EViews 2 2007/5/17 2007/5/21 4 OLS 2 4.1.............................................. 2 4.2................................................ 9 4.3.............................................. 11 4.4
More information土木学会論文集 D3( 土木計画学 ), Vol. 71, No. 2, 31-43,
1 2 1 305 8506 16 2 E-mail: murakami.daisuke@nies.go.jp 2 305 8573 1 1 1 E-mail: tsutsumi@sk.tsukuba.ac.jp Key Words: sampling design, geostatistics, officially assessed land price, prefectural land price
More information03.Œk’ì
HRS KG NG-HRS NG-KG AIC Fama 1965 Mandelbrot Blattberg Gonedes t t Kariya, et. al. Nagahara ARCH EngleGARCH Bollerslev EGARCH Nelson GARCH Heynen, et. al. r n r n =σ n w n logσ n =α +βlogσ n 1 + v n w
More informationMicrosoft Word - 研究デザインと統計学.doc
Study design and the statistical basics Originality Accuracy Objectivity Verifiability Readability perfect Interdisciplinary Sciences Health Science 2014.12.25 2 1. 7 2. 7 3. Bias8 4. random sampling8
More informationR による共和分分析 1. 共和分分析を行う 1.1 パッケージ urca インスツールする 共和分分析をするために R のパッケージ urca をインスツールする パッケージとは通常の R には含まれていない 追加的な R のコマンドの集まりのようなものである R には追加的に 600 以上のパッ
R による共和分分析 1. 共和分分析を行う 1.1 パッケージ urca インスツールする 共和分分析をするために R のパッケージ urca をインスツールする パッケージとは通常の R には含まれていない 追加的な R のコマンドの集まりのようなものである R には追加的に 600 以上のパッケージが用意されており それぞれ分析の目的に応じて標準の R にパッケージを追加していくことになる インターネットに接続してあるパソコンで
More informationuntitled
3 3. (stochastic differential equations) { dx(t) =f(t, X)dt + G(t, X)dW (t), t [,T], (3.) X( )=X X(t) : [,T] R d (d ) f(t, X) : [,T] R d R d (drift term) G(t, X) : [,T] R d R d m (diffusion term) W (t)
More informationuntitled
1 Hitomi s English Tests 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 1 0 1 1 0 1 0 0 0 1 0 0 1 0 2 0 0 1 1 0 0 0 0 0 1 1 1 1 0 3 1 1 0 0 0 0 1 0 1 0 1 0 1 1 4 1 1 0 1 0 1 1 1 1 0 0 0 1 1 5 1 1 0 1 1 1 1 0 0 1 0
More informationuntitled
R kiyo@affrc.go.jp 1 Excel 1, 2.6, 2/3, 105.2, 0.0043 1, 3, 0, 245 A, B, C... ; 0 1 0.2, 3/4, 0.99 MS Excel»» R Macintosh MS Excel Excel Excel MS Excel MS Access Excel R R R R.Data win Mac ctrl + R Win,
More information