²¾ÁÛ¾õ¶·É¾²ÁË¡¤Î¤¿¤á¤Î¥Ñ¥Ã¥±¡¼¥¸DCchoice ¡Ê»ÃÄêÈÇ¡Ë
|
|
- そうりん かなり
- 4 years ago
- Views:
Transcription
1 DCchoice ( ) R DCchoice package R 2013/11/30 1 / 19
2 1 (CV) CV 2 DCchoice WTP 3 DCchoice package R 2013/11/30 2 / 19
3 (Contingent Valuation; CV) WTP CV WTP WTP DCchoice package R 2013/11/30 3 / 19
4 CV (Whitehead, 1995, modified) 1981 X X Yes No Yes X + = 2X No X = 1/2X DCchoice package R 2013/11/30 4 / 19
5 X WTP WTP X 1 st stage No X Yes 0 X DCchoice package R 2013/11/30 5 / 19
6 X X + (> X ) X (< X ) WTP 1 st stage No X Yes X - 2 nd stage X + No Yes No Yes 0 X - X X + DCchoice package R 2013/11/30 6 / 19
7 (Whitehead, 1995) R > head(ap, 10) bid1 bid2 R1 R2 income work age female married DCchoice package R 2013/11/30 7 / 19
8 I WTP (logit/probit) ln L = N [ d yy =1 ln(p yy (X, X + )) + d nn ln(p nn (X, X )) + d yn ln(p yn (X, X + )) + d ny ln(p ny (X, X + )) ] d yy d nn d yn d ny d yn = 1 X n (yes) X + (no) DCchoice package R 2013/11/30 8 / 19
9 II - - (KMT) - (SK) DCchoice package R 2013/11/30 9 / 19
10 DCchoice (DiChotomous choice) CV System requirement R ver 2153 or later Ecdat, interval, stats, MASS R-Forge R > installpackages("dcchoice", repos = c(" " " dep = TRUE, type = "source") DCchoice package R 2013/11/30 10 / 19
11 : sbchoice() sbchoice() R > sbchoice(formula, data, dist = "log-logistic") formula R1 ~ var1 + var2 bid data dist log-logistic, logistic, log-normal or normal sbchoice() S3 sbchoice summary() print() glm() DCchoice package R 2013/11/30 11 / 19
12 : dbchoice() dbchoice() R > dbchoice(formula, data, dist = "log-logistic", par = NULL) formula R1 + R2 ~ var1 + var2 bid1 + bid2 data dist log-logistic, logistic, log-normal or normal par dbchoice() S3 dbchoice summary() print() optim() DCchoice package R 2013/11/30 12 / 19
13 R > dblogit <- dbchoice(r1 + R2 ~ work + age + female + married log(bid1) + log(bid2), data = AP) R > printcoefmat(summary(dblogit)$coef) Estimate Std Error z value Pr(> z ) (Intercept) < 2e-16 *** work age < 2e-16 *** female * married log(bid) < 2e-16 *** --- Signif codes: 0 *** 0001 ** 001 * DCchoice package R 2013/11/30 13 / 19
14 WTP WTP WTP ( ) ( exp Γ 1 ˆαˆβ 1ˆβ ) ( Γ 1 + 1ˆβ ) if ˆβ > 1 ˆβ log(bid) ˆα = X ˆβ WTP Krinsky-Robb R > krci(dblogit, nsim = 1000, CI = 095) the Krinsky and Robb simulated confidence intervals Estimate LB UB Mean truncated Mean adusted truncated Mean Median bootci(dblogit, nsim = 1000, CI = 095) DCchoice package R 2013/11/30 14 / 19
15 Kriström (1990) R > kristrom(formula, data) S3 kristrom - - (KMT) R > turnbullsb(formula, data) # R > turnbulldb(formula, data) # S3 turnbull formula R1 ~ bid1 kristrom() turnbullsb() R1 + R2 ~ bid1 + bid2 turnbulldb() data S3 summary() print() plot() DCchoice package R 2013/11/30 15 / 19
16 KMT R > APsb <- turnbullsb(r1 ~ bid1, data = AP) R > summary(apsb) Survival probability: Upper Prob Inf Survival Probability WTP estimates: Mean: (Kaplan-Meier) Mean: (Spearman-Karber) Median in: [ 0, 100 ] Bid (USD) DCchoice package R 2013/11/30 16 / 19
17 R > APdb <- turnbulldb(r1 + R2 ~ bid1 + bid2, data = AP) R > summary(apdb) Survival probability: Upper Prob Inf Survival Probability WTP estimates: Mean: (Kaplan-Meier) Mean: (Spearman-Karber) Median in: [ 100, 150 ] Bid (USD) DCchoice package R 2013/11/30 17 / 19
18 CV DCchoice WTP Bioconductor DCchoice package R 2013/11/30 18 / 19
19 Kriström, B (1990): A Non-Parametric Approach to the Estimation of Welfare Measures in Discrete Response Valuation Studies, Land Economics, 66(2), Whitehead, J C (1995): Willingness to Pay for Quality Improvements: Comparative Statics and Interpretation of Contingent Valuation Results, Land Economics, 71(2), DCchoice package R 2013/11/30 19 / 19
k2 ( :35 ) ( k2) (GLM) web web 1 :
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......................
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 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 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 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 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 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 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 informationMicrosoft Word - 01マニュアル・入稿原稿p1-112.doc
4 54 55 56 ( ( 1994 1st stage 2nd stage 2012 57 / 58 365 46.6 120 365 40.4 120 13.0 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 4 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
More informationJ. JILA 64 (5), 2001
Comparison of the Value of Outdoor Recreation: A Case Study Applying Travel Cost Method and Contingent Valuation Method Yasushi SHOJI J. JILA 64 (5), 2001 J. JILA 64 (5), 2001 2) Trice, A. H. and Wood,
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 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 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 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 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 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 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 informationO1-1 O1-2 O1-3 O1-4 O1-5 O1-6
O1-1 O1-2 O1-3 O1-4 O1-5 O1-6 O1-7 O1-8 O1-9 O1-10 O1-11 O1-12 O1-13 O1-14 O1-15 O1-16 O1-17 O1-18 O1-19 O1-20 O1-21 O1-22 O1-23 O1-24 O1-25 O1-26 O1-27 O1-28 O1-29 O1-30 O1-31 O1-32 O1-33 O1-34 O1-35
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 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 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 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 informationこんにちは由美子です
Sample size power calculation Sample Size Estimation AZTPIAIDS AIDSAZT AIDSPI AIDSRNA AZTPr (S A ) = π A, PIPr (S B ) = π B AIDS (sampling)(inference) π A, π B π A - π B = 0.20 PI 20 20AZT, PI 10 6 8 HIV-RNA
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 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 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 informationPart 1 GARCH () ( ) /24, p.2/93
基盤研究 A 統計科学における数理的手法の理論と応用 ( 研究代表者 : 谷口正信 ) によるシンポジウム 計量ファイナンスと時系列解析法の新たな展開 平成 20 年 1 月 24 日 ~26 日香川大学 Realized Volatility の長期記憶性について 1 研究代表者 : 前川功一 ( 広島経済大学 ) 共同研究者 : 得津康義 ( 広島経済大学 ) 河合研一 ( 統計数理研究所リスク解析戦略研究センター
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(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 information報告書
1 2 3 4 5 6 7 or 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2.65 2.45 2.31 2.30 2.29 1.95 1.79 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 60 55 60 75 25 23 6064 65 60 1015
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 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 information( 30 ) 30 4 5 1 4 1.1............................................... 4 1.............................................. 4 1..1.................................. 4 1.......................................
More informationR Console >R ˆ 2 ˆ 2 ˆ Graphics Device 1 Rcmdr R Console R R Rcmdr Rcmdr Fox, 2007 Fox and Carvalho, 2012 R R 2
R John Fox Version 1.9-1 2012 9 4 2012 10 9 1 R R Windows R Rcmdr Mac OS X Linux R OS R R , R R Console library(rcmdr)
More informationR R 16 ( 3 )
(017 ) 9 4 7 ( ) ( 3 ) ( 010 ) 1 (P3) 1 11 (P4) 1 1 (P4) 1 (P15) 1 (P16) (P0) 3 (P18) 3 4 (P3) 4 3 4 31 1 5 3 5 4 6 5 9 51 9 5 9 6 9 61 9 6 α β 9 63 û 11 64 R 1 65 13 66 14 7 14 71 15 7 R R 16 http://wwwecoosaka-uacjp/~tazak/class/017
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 informationuntitled
18 1 2,000,000 2,000,000 2007 2 2 2008 3 31 (1) 6 JCOSSAR 2007pp.57-642007.6. LCC (1) (2) 2 10mm 1020 14 12 10 8 6 4 40,50,60 2 0 1998 27.5 1995 1960 40 1) 2) 3) LCC LCC LCC 1 1) Vol.42No.5pp.29-322004.5.
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 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 information10
z c j = N 1 N t= j1 [ ( z t z ) ( )] z t j z q 2 1 2 r j /N j=1 1/ N J Q = N(N 2) 1 N j j=1 r j 2 2 χ J B d z t = z t d (1 B) 2 z t = (z t z t 1 ) (z t 1 z t 2 ) (1 B s )z t = z t z t s _ARIMA CONSUME
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 informationuntitled
17 5 13 1 2 1.1... 2 1.2... 2 1.3... 3 2 3 2.1... 3 2.2... 5 3 6 3.1... 6 3.2... 7 3.3 t... 7 3.4 BC a... 9 3.5... 10 4 11 1 1 θ n ˆθ. ˆθ, ˆθ, ˆθ.,, ˆθ.,.,,,. 1.1 ˆθ σ 2 = E(ˆθ E ˆθ) 2 b = E(ˆθ θ). Y 1,,Y
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 information1 R Windows R 1.1 R The R project web R web Download [CRAN] CRAN Mirrors Japan Download and Install R [Windows 9
1 R 2007 8 19 1 Windows R 1.1 R The R project web http://www.r-project.org/ R web Download [CRAN] CRAN Mirrors Japan Download and Install R [Windows 95 and later ] [base] 2.5.1 R - 2.5.1 for Windows R
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 information橡森林管理に対する市民の要求の評価
kuri@for.agr.hokudai.ac.p Abstract Koichi Kuriyama: The Estimation of Public Demand for the Forest Management: Contingent Ranking Study. The purpose of this paper is to argues the measurement of the public
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 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 information1. 2 Blank and Winnick (1953) 1 Smith (1974) Shilling et al. (1987) Shilling et al. (1987) Frew and Jud (1988) James Shilling Voith (1992) (Shilling e
Estimation of the Natural Vacancy Rate and it s Instability: Evidence from the Tokyo Office Market * ** *** Sho Kuroda*, Morito Tsutsumi**, Toyokazu Imazeki*** * ** *** rent adjustment mechanismnatural
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 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 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 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 informationyy yy ;; ;; ;; ;; ;; ;; ;; ;; ;; ;; ;; ;; ;; ;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ; ; ; ;; ;; ;; ;;; ;;; ;;; ;; ;; ;; ;; ;; ; ; ; ; ; ; ;
More informationEvaluation of a SATOYAMA Forest Using a Voluntary Labor Supply Curve Version: c 2003 Taku Terawaki, Akio Muranaka URL: http
14 9 27 2003 Evaluation of a SATOYAMA Forest Using a Voluntary Labor Supply Curve 1 1 2 Version: 15 10 1 c 2003 Taku Terawaki, Akio Muranaka URL: http://www.taku-t.com/ 1 [14] 3 [10] 3 2 Andreoni[1] Duncan[7]
More informationuntitled
Data cleaning Original datan=8479 NCC/ptkgN=7958 NCC10x10E628 8139 NCC/kgPage 1-2) Covariate cleaningcovariate Page Page5-8 Kaplan-Meier method, Log-rank, Cox hazard model Overall survival Cumulative incidence
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 informationuntitled
Ÿ Ÿ ( œ ) 120,000 60,000 120,000 120,000 80,000 72,000 100,000 180,000 60,000 100,000 60,000 120,000 100,000 240,000 120,000 240,000 1,150,000 100,000 120,000 72,000 300,000 72,000 100,000 100,000 60,000
More information食道がん化学放射線療法後のsalvage手術
2006 2 17 52 Daly JM, et al. J Am Coll Surg 2000;190:562-573 Esophageal Cancer: Results of an American College of Surgeons Patient Care Evaluation Study Daly JM, et al. J Am Coll Surg 2000;190:562-573
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 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 informationN N 1,, N 2 N N N N N 1,, N 2 N N N N N 1,, N 2 N N N 8 1 6 3 5 7 4 9 2 1 12 13 8 15 6 3 10 4 9 16 5 14 7 2 11 7 11 23 5 19 3 20 9 12 21 14 22 1 18 10 16 8 15 24 2 25 4 17 6 13 8 1 6 3 5 7 4 9 2 1 12 13
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 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 informationIMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J- 9 -J-19 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN
IMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J- 9 -J-19 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN 100-8630 03 IMES Discussion Paper Series 99-J- 9 -J-19 1999 6 * * [1999] *(E-mail:
More informationSolution Report
CGE 3 GAMS * Date: 2018/07/24, Version 1.1 1 2 2 GAMSIDE 3 2.1 GAMS................................. 3 2.2 GAMSIDE................................ 3 2.3 GAMSIDE............................. 7 3 GAMS 11
More informationuntitled
146,650 168,577 116,665 122,915 22,420 23,100 7,564 22,562 140,317 166,252 133,581 158,677 186 376 204 257 5,594 6,167 750 775 6,333 2,325 298 88 5,358 756 1,273 1,657 - - 23,905 23,923 1,749 489 1,309
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 informationMicrosoft PowerPoint - CVM.ppt [互換モード]
立命館大学経済学部 寺脇 拓 2 11 1.1 調査手順 ( 環境の場合 ) 1. 評価対象となる環境に関する情報を収集する 被験者に想定する状態変化を正確に伝えるため 評価しようとしている環境やその周辺の社会経済状況が現在どのような状態にあり 将来的にどうなりうるかについて情報を集める 2. CVM 質問を含むアンケート調査票を作成する 政策的に意味のある仮想状況を設定し 現実性 (realism)
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 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> 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 informationKaplan-Meierプロットに付加情報を追加するマクロの作成
Kaplan-Meier 1, 2,3 1 2 3 A SAS macro for extended Kaplan-Meier plots Kengo Nagashima 1, Yasunori Sato 2,3 1 Department of Parmaceutical Technochemistry, Josai University 2 School of Medicine, Chiba University
More information2 H23 BioS (i) data d1; input group patno t sex censor; cards;
H BioS (i) data d1; input group patno t sex censor; cards; 0 1 0 0 0 0 1 0 1 1 0 4 4 0 1 0 5 5 1 1 0 6 5 1 1 0 7 10 1 0 0 8 15 0 1 0 9 15 0 1 0 10 4 1 0 0 11 4 1 0 1 1 5 1 0 1 1 7 0 1 1 14 8 1 0 1 15 8
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