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1 SAS 9: (reprise) SAS Institute Japan Copyright 2004, SAS Institute Inc. All rights reserved.
2 Greetings, SAS 9 SAS Copyright 2004, SAS Institute Inc. All rights reserved. 2
3 Informations of SAS 9 SAS 9.1 WEB Copyright 2004, SAS Institute Inc. All rights reserved. 3
4 Informations of SAS 9 (continued) WEB Copyright 2004, SAS Institute Inc. All rights reserved. 4
5 Informations of SAS 9 (continued) PDF Copyright 2004, SAS Institute Inc. All rights reserved. 5
6 Informations of SAS 9 (continued) What s New Copyright 2004, SAS Institute Inc. All rights reserved. 6
7 Shortcut to the SAS 9 Copyright 2004, SAS Institute Inc. All rights reserved. 7
8 Power Analysis, Sample Size Determination and PSS application SAS9.1 POWER GLMPOWER Production GUI Web PSS Application Copyright 2004, SAS Institute Inc. All rights reserved. 8
9 PROC POWER Supports: POWER MULTREG TYPE III F ONEWAYANOVA 1 ONECORR Pearson ONESAMPLEFREQ 2 Copyright 2004, SAS Institute Inc. All rights reserved. 9
10 PROC POWER Supports: (continued) PAIREDFREQ McNemar TWOSAMPLEFREQ 2 2 Pearson 2 Fisher ONESAMPLEMEANS 1 t PAIREDMEANS t TWOSAMPLEMEANS 2 t TWOSAMPLESURVIVAL Gehan Wilcoxon Log- Rank Tarone-Ware Copyright 2004, SAS Institute Inc. All rights reserved. 10
11 PROC POWER: Syntax /*1 t */ proc power; onesamplemeans mean = 8 to 16 by 2 ntotal = 10 to 100 by 10 stddev = 20 power =. ; plot x=n max=100 min=5 ; run; Copyright 2004, SAS Institute Inc. All rights reserved. 11
12 PROC POWER: Graph Output Copyright 2004, SAS Institute Inc. All rights reserved. 12
13 PROC POWER: Syntax (2) /*1 t */ proc power; onesamplemeans mean = 8 to 16 by 2 ntotal =. stddev = 20 power = 0.6 to 0.9 by.05 ; plot ; run; Copyright 2004, SAS Institute Inc. All rights reserved. 13
14 PROC POWER: Output (2) Copyright 2004, SAS Institute Inc. All rights reserved. 14
15 PROC GLMPOWER Supports: GLMPOWER Copyright 2004, SAS Institute Inc. All rights reserved. 15
16 PROC GLMPOWER: Syntax /*2-way ANOVA */ proc glmpower data=sample; class x1 x2; model y = x1 x2; power ntotal = 50 stddev = 10 power =. ; run; Copyright 2004, SAS Institute Inc. All rights reserved. 16
17 Power and Sample Size (PSS) Application PSS Application Web Web Server Stand-alone Tomcat Copyright 2004, SAS Institute Inc. All rights reserved. 17
18 PSS Application: Interface Copyright 2004, SAS Institute Inc. All rights reserved. 18
19 ODS Graphics (experimental) Base SAS, SAS/STAT, SAS/ETS CORR GLM, LIFETEST, LOGISTIC, MIXED, REG, ARIMA, AUTOREG, VARMAX, X12, Copyright 2004, SAS Institute Inc. All rights reserved. 19
20 ODS Graphics (experimental) (cont d) HTML PDF, PS, PCL RTF LaTeX Copyright 2004, SAS Institute Inc. All rights reserved. 20
21 ODS Graphics: How to Specify? ODS HTML; ODS GRAPHICS ON; /* PROC */ ODS GRAPHICS OFF; ODS HTML CLOSE; Copyright 2004, SAS Institute Inc. All rights reserved. 21
22 ODS Graphics Template Language ODS Graphics TEMPLATE proc template; define statgraph StatGraph.PredActual; layout Gridded; layout Overlay / yaxisopts=( label="predicted and Actual Values" ); Band ylimitlower=eval (PREDICTED-TVAL*SQRT((1+LEVERAGE)*MSE)) ylimitupper=eval (PREDICTED+TVAL*SQRT((1+LEVERAGE)*MSE)) x=id / fill=true lines=false fillcolor=statgraphconfidence:foreground legendlabel="95% Confidence Limits" name="conf"; SeriesPlot y=predicted x=id / linecolor= StatGraphPredictionLines:contrastcolor linepattern= StatGraphPredictionLines:linestyle legendlabel="predicted" name ="Predicted"; Scatter y=actual x=id / markersize=graphdatadefault:markersize markersymbol=graphdatadefault:markersymbol markercolor= GraphDataDefault:contrastcolor legendlabel="actual" name= "Actual"; EndLayout; DiscreteLegend "Actual" "Predicted" "Conf" / border=true; EndLayout; Copyright 2004, SAS Institute Inc. All rights reserved. 22
23 ODS Graphics in future SAS9.1 experimental??? Graphics GUI ) SAS/GRAPH Copyright 2004, SAS Institute Inc. All rights reserved. 23
24 Generalized Linear Mixed Models (experimental) GLIMMIX SAS Institute Inc. WEB EXE PDF Copyright 2004, SAS Institute Inc. All rights reserved. 24
25 Multiple Imputation monotone REGPMM CLASS exeperimental CLASS exeperimental Copyright 2004, SAS Institute Inc. All rights reserved. 25
26 Survival Analysis LIFETEST STRATA Tarone-Ware Peto Modified Peto Fleming-Harrington (confidence band) Copyright 2004, SAS Institute Inc. All rights reserved. 26
27 Survival Analysis (continued) TPHREG (experimental) PHREG WEIGHT Proportional rate/mean (cumulative residual) (experimental) etc Copyright 2004, SAS Institute Inc. All rights reserved. 27
28 Logistic Regression STRATA SCORE EXACT OFFSET= OUTDESIGN= Copyright 2004, SAS Institute Inc. All rights reserved. 28
29 Other Enhancements DISTANCE MIXED (experimental) BOXPLOT What s New ROBUSTREG Copyright 2004, SAS Institute Inc. All rights reserved. 29
30 Other Enhancements (continued) FREQ ZEROS 0 2 SURVEYLOGISTIC, SURVEYFREQ SURVEYREG, SURVEYMEANS, SURVEYSELECT REG, GLM CPU Copyright 2004, SAS Institute Inc. All rights reserved. 30
31 Random Number Generator Mersenne Twister (1998) Mersenne Twister RAND SAS9.1 production RANUNI, RANNOR (623 ) Copyright 2004, SAS Institute Inc. All rights reserved. 31
32 Function compiler FCMP Procedure Base SAS FCMP VB SAS/STAT SAS/ETS SAS/OR DATA Copyright 2004, SAS Institute Inc. All rights reserved. 32
33 Copyright 2004, SAS Institute Inc. All rights reserved. 33
34 Copyright 2004, SAS Institute Inc. All rights reserved. 34
Slide 1
ODS 統 計 グラフ 機 能 を 用 いたグラフの 作 成 SAS Institute Japan 株 式 会 社 プロフェッショナルサービス 本 部 テクニカルサポート 部 深 澤 武 志 Copyright 2010 SAS Institute Inc. All rights reserved. 目 次 ODSとは ODS 統 計 グラフのご 紹 介 ODSテンプレートに 関 して ODS 統
Kaplan-Meierプロットに付加情報を追加するマクロの作成
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ODS GRAPHICS ON; ODS GRAPHICS ON; PROC TTEST DATA=SASHELP.CLASS SIDE=2 DIST=NORMAL H0=58 PLOTS(ONLY SHOWH0)=(SUMMARY); VAR HEIGHT;
Summer 2009 1 8 12 14 16 16 16 ODS GRAPHICS ON; ODS GRAPHICS ON; PROC TTEST DATA=SASHELP.CLASS SIDE=2 DIST=NORMAL H0=58 PLOTS(ONLY SHOWH0)=(SUMMARY); VAR HEIGHT; PROC SGPLOT DATA=SASHELP.PRDSALE; HBAR
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SG (Statistical Graphics) Procedures による Kaplan-Meier プロットの作成 魚住龍史 1, * 浜田知久馬 2 1 日本化薬株式会社医薬データセンター 2 東京理科大学工学部経営工学科 Kaplan-Meier plots using Statistical Graphics Procedures Ryuji Uozumi 1, * and Chikuma
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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
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3180, 3599, 3280, 2980, 3500, 3099, 3200, 2980, 3380, 3780, 3199, 2979, 3680, 2780, 2950, 3180, 3200, 3100, 3780, 3200 DATA Sample1 /**/ INPUT Price @@ /* @@1 */ DATALINES 3180 3599 3280 2980 3500 3099
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SAS Enterprise Miner PFD SAS Rapid Predictive Modeler & SAS SEMMA 5 SEMMA SAS Rapid Predictive Modeler SAS Rapid Predictive Modeler SAS Enterprise Gui
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SAS による生存時間解析の実務 張方紅グラクソ スミスクライン ( 株 バイオメディカルデータサイエンス部 Practice of Survival Analysis sing SAS Fanghong Zhang Biomedical Data Science Department, GlaxoSmithKline K.K. 要旨 : SASによる生存時間解析の実務経験を共有する. データの要約
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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
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Release Notes for JMP book
8.0.2 JMP, A Business Unit of SAS SAS Campus Drive Cary, NC 27513 SAS Institute Inc. 2009.JMP 8.0.2, Cary, NC: SAS Institute Inc. JMP 8.0.2 Copyright 2009, SAS Institute Inc., Cary, NC, USA All rights
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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
Kaplan-Meierプロットに付加情報を追加するマクロの作成
1 / 47 Kaplan-Meier A SAS macro for extended Kaplan-Meier plots 1, 2,3 1 2 3 Kengo Nagashima 1, Yasunori Sato 2,3 1 Department of Parmaceutical Technochemistry, Josai University 2 School of Medicine, Chiba
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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) = 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 =
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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
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PHREG プロシジャにおける 共変量調整解析に関連したオプション機能 魚住龍史 1 * 矢田真城 2 浜田知久馬 3 1 京都大学大学院医学研究科医学統計生物情報学 2 エイツーヘルスケア株式会社 3 東京理科大学 Investigating fascinating aspects associa
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kubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i
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PROC SORT DATA=SortData OUT=OutData NOEQUALS; BY DESCENDING group; /* group*/ /* */ DATA MeansData1; INPUT x y; DATALINES; 2 5 4 6 1 9 3 12 ; /* MEANS */ PROC MEANS DATA=MeansData1 MEAN MEDIAN SUM; VAR
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A Comparison of SAS Functions Designed for Creating Excel Output in a Stand-alone Environment and a BI Environment. Koichi Satoh Takumi Information Technology Co., Ltd. ODS EXCELXP ODS HTML ODS CSVALL
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
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,
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