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生物統計中心 諮詢師及種子教師訓練課程

生物統計中心 諮詢師及種子教師訓練課程. 96年5月8日(二)下午1:30~3:00 訓練講者：李郁芬. 訓練內容. 1. 邏輯斯迴歸 2. SAS PROC LOGISTIC 3. SAS MACRO 語法練習. 邏輯斯迴歸. Logistic Regression Binary Outcome Y Consider Prob(Y=1)= p One covariate X. The logistic function. 模式. Model. SAS Procedures.

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生物統計中心 諮詢師及種子教師訓練課程

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  1. 生物統計中心諮詢師及種子教師訓練課程 96年5月8日(二)下午1:30~3:00 訓練講者：李郁芬

  2. 訓練內容 1. 邏輯斯迴歸 2. SAS PROC LOGISTIC 3. SAS MACRO 語法練習

  3. 邏輯斯迴歸 • Logistic Regression • Binary Outcome Y • Consider • Prob(Y=1)=p • One covariate X

  4. The logistic function

  5. 模式 • Model

  6. SAS Procedures • You have many choices of performing logistic regression in the SAS System: • CATMOD, • GENMOD, • LOGISTIC, and • PROBIT

  7. The LOGISTIC Procedure PROC LOGISTIC < options >; BY variables ; CLASS variable <(v-options)> <variable <(v-options)>... >                           < / v-options >; MODEL events/trials = < effects > < / options >; MODEL variable < (variable_options) > = < effects > < / options >; STRATA effects < / options >; UNITS independent1 = list1 < ... independentk = listk > < /option > ; WEIGHT variable </ option >; FREQ variable ;

  8. 資料範例 In an experiment comparing the effects of five different drugs. The outcome of each experiment is the presence or absence of a positive response in a subject. The following artificial data represent the number of responses r in the n subjects for the five different drugs, labeled A through E. The response is measured for different levels of a continuous covariate x for each drug.

  9. DATA I data drug; input drug$ x r n @@; datalines; A .1 1 10 A .23 2 12 A .67 1 9 B .2 3 13 B .3 4 15 B .45 5 16 B .78 5 13 C .04 0 10 C .15 0 11 C .56 1 12 C .7 2 12 D .34 5 10 D .6 5 9 D .7 8 10 E .2 12 20 E .34 15 20 E .56 13 15 E .8 17 20 ; run;

  10. SAS CODE I PROC LOGISTIC DATA=drug; CLASS drug(CODING=REF REF=‘A'); MODEL r/n = x drug; RUN;

  11. DATA II

  12. SAS CODE II PROC LOGISTIC DATA=new; CLASS drug(CODING=REF REF='A'); MODEL y(EVENT="1")=drug x; RUN; PROC LOGISTIC DATA=newDESCENDING; CLASS drug(CODING=REF REF='A'); MODEL y=drug x; RUN;

  13. HERE COMES THE PROBLEM

  14. HERE COMES THE PROBLEM

  15. HERE COMES THE PROBLEM • The original SAS code PROC LOGISTIC; CLASS z (CODING=REF REF=‘0'); MODEL y= x z; RUN; • 10 outcomes (Y’s) and 4 exposure matrixes (Z’s)  40 models

  16. SAS MACRO LANGUAGE • The SAS MACRO LANGUAGE %MACRO name <(parameter-list)>; ……… ……… %mend; (SAS code)

  17. Macro Facility Reserved Words

  18. SAS MACRO LANGUAGE • PROC LOGISTIC; • CLASS z (CODING=REF REF=“0”); • MODEL y= x z; • RUN; %MACRO xyz (depvar, indepvar); PROC LOGISTIC; CLASS &indepvar(CODING=REF REF=“0”); MODEL &depvar = x &indepvar ; RUN; %mend;

  19. SAS MACRO LANGUAGE • Call the self-defined MACRO %xyz(y1, z1); %xyz(y2, z2); …… …… %xyz(y4, z1); %xyz(y4, z2); • we can make it more efficient

  20. USEFUL LINK • http://www.ats.ucla.edu/STAT/sas/topics/programming.htm • Array • Macro

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