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Presentation on theme: "STATISTICAL COMPUTING FOR RESEARCH JODY CIOLINO JANUARY 10,2011 SAS: Review and Helpful Hints."— Presentation transcript:


2 When do I use SAS? “Quick” analysis of collaborative projects Usually use R to confirm findings (neither is perfect) Output is generally easier to handle Graphics are inferior to R

3 Motivation: Collaborative Work with Clinicians Oftentimes clinicians will take their best shot at analyzing their own data, until they get stuck… In my experience they either use Excel, Jmp, or SPSS Once they determine they need a statistician’s help, they come to you…what do you do? First, you need to find out a way to get the data into a format that is easy for you to work with…

4 Getting the Data Into SAS Format Import Wizard  Easy, fast  Can use PROC IMPORT to do so as well, but I have always used Import Wizard A quick demonstration…  Distal Esophageal Spasm data (Daniel Pohl)  Uses SPSS  Converted file to Excel Spreadsheet  Use import wizard to create SAS dataset

5 Outline Basics Libraries Formats and Labels More on getting data IN and OUT The DATA step  @, @@  Operators  Functions and Random # generation  Arrays Intro to ODS  Rft, pdf, html  Capturing output info as datasets  Graphics Examples and Handouts

6 The Basics Data step:  Create new datasets  Import existing datasets  Modify existing datasets (new variables, delete variables, delete observations, formatting and labeling of variables)  Combine datasets (merge or stack-set)  Random sampling from known distributions Procs: SORT, MEANS, UNIVARIATE, GPLOT, REG, CORR, FREQ, TTEST, ANOVA, NPAR1WAY, MIXED, GLM, GENMOD, LOGISTIC, GLIMMIX

7 Libraries Libraries may hold several permanent datasets and must be assigned before any calls or modifications to datasets Default: WORK  temporary datasets, all datasets will be temporarily stored in this library unless defined as permanent DATA MYLIB.NEW; INPUT MYNUM MYCHAR$; DATALINES; ……… ; RUN;

8 Assigning Libraries Assignment should be at the beginning of SAS program, outside DATA and PROC steps Syntax: LIBNAME mylib "C:\..........."; Be careful with permanent datasets that have predefined formats:  Use OPTIONS FMTSEARCH=(mylib); outside of PROC and DATA steps  Creating permanent formats must be done with PROC FORMAT

9 PROC FORMAT Formatting data values Examples  YES/NO is oftentimes coded as 1/0 in databases  1,2,3 may correspond to ‘mild,’ ‘moderate,’ and ‘severe’ Syntax: PROC FORMAT LIBRARY=mylib; *creates permanent formats; VALUE fmtname 0=‘NO’ 1=‘YES’; RUN; Calling formats in the DATA step…(see example code)

10 Other Tips: Labels, Formats, Drop/Keep Dates (see p.124 of Cody and Smith):  MMDDYY8., MMDDYY10.  Note that dates are tricky (read as numeric values, # days from 01/01/1960) Labels can be used to make variable names more meaningful DROP or KEEP statements can be used at the end of the DATA step to narrow down the number of variables in a dataset Note that the default character length is 8, but this can be overridden with INFORMAT myvar $20.; (or some other length, or myvar: $20. in the input statement)

11 Converting SAS datasets SAS datasets and easily be imported and/or exported as different data files Use Import/Export Wizard in File drop-down menu The Import/Export Wizard has an option that will allow you to save the code SAS uses to import/export datasets PROC IMPORT or INFILE statement in DATA step DATA NEW; INFILE ‘…..’ DLM=“,”; INPUT ID GENDER $ AGE ; RUN;  See Cody and Smith (Applied Statistics and the SAS Programming Language)

12 @ vs. @@ Placing @@ at the end of the INPUT statement allows for multiple observations per line Placing @ after a variable allows for a logic statement for that variable  Example: DATA WEIGHT; INFILE ‘…’; INPUT GENDER$ @; IF GENDER = ‘M’ THEN DELETE; INPUT WEIGHT AGE; RUN;

13 Conditional Operators IF …. THEN …. ELSE IF … THEN … ELSE …. LT, GT, =, NE, LE, GE Be careful with missing values: IF AGE NE. THEN DO; IF AGE LT 18 THEN DELETE; END;

14 Useful Functions LOG: base e LOG10: base 10 SIN, COS, TAN, ARSIN, ARCOS, ARTAN INT: drops fractional part of number SQRT: square root ROUND(X,.1), ROUND(X,1), ROUND(X,100) MEAN(A,B,C); MEAN_X=MEAN(OF X1-X5)  Careful with missing values  MIN, MAX, SUM, STD, STDERR, N, NMISS

15 Date and Time Functions MDY(month, day, year): converts to a SAS date YRDIF(early date, later date, ‘ACTUAL’): Computes # of years from early to later date  ‘ACTUAL’ tells SAS to factor in leap years and days of months  NOTE that a SAS date constant is represented by ‘ddMMMyyyy’D (‘15MAY2004’D) YEAR, MONTH, DAY (from 1 to 31 returned), WEEKDAY (1 to 7), HOUR, MINUTE, SECOND INTCK(‘interval’, start, end)  Returns number of intervals  Interval may be DAY, WEEK, MONTH, QTR, YEAR, HOUR, MINUTE, SECOND INTNX(‘interval’,start,# intervals)  Returns a date

16 Converting Numeric  Character PUT() converts numeric variables to character variables  Newvar = PUT(oldvar, format)  Formats: $length. INPUT() converts character variables to numeric variables  Newvar = INPUT(oldvar, format)  Formats: length.length Note: COMPRESS(var, delim) can take away things like dashes in Social Security Numbers: COMPRESS(SS, ‘-’)

17 Generating Random Numbers x = ranuni(seed)/* uniform between 0 & 1 */ x = a+(b-a)*ranuni(seed);/* uniform between a & b */ x = ranbin(seed,n,p);/* binomial size n prob p */ x = rancau(seed);/* cauchy with loc 0 & scale 1 */ x = a+b*rancau(seed);/* cauchy with loc a & scale b */ x = ranexp(seed);/* exponential with scale 1 */ x = ranexp(seed) / a;/* exponential with scale a */ x = a-b*log(ranexp(seed));/* extreme value loc a & scale b */ x = rangam(seed,a);/* gamma with shape a */ x = b*rangam(seed,a);/* gamma with shape a & scale b */ x = 2*rangam(seed,a);/* chi-square with d.f. = 2*a */ x = rannor(seed);/* normal with mean 0 & SD 1 */ x = a+b*rannor(seed);/* normal with mean a & SD b */ x = ranpoi(seed,a);/* poisson with mean a */ x = rantri(seed,a);/* triangular with peak at a */ x = rantbl(seed,p1,p2,p3);/* random from (1,2,3) with probs */ /* p1,p2,p3 */

18 Example Example of sample from Uniform DATA UNIFORM; DO i = 1 TO 100; uni=RANUNI(0); OUTPUT; END; Do loops are often useful:  DO var = … TO …;  DO var = …….;  DO WHILE (); evaluated before loop  DO UNTIL (); evaluated after loop  Must finish with END,OUTPUT ensures that new value created after each loop run  Seed should be 0 (uses clock to generate sequence) or positive integer

19 Arrays Declaring arrays in the DATA step: DATA NEW; SET OLD; ARRAY x[5] x1-x5; * Can be ARRAY x[5] A B C D E or ARRAY x[5]; DO i = 1 to 5 IF x[i] = 999 THEN x[i] =.; End; DROP i; RUN: If we use _NUMERIC_ (and x[*]) after array declaration, all numeric variables will have the new conversion _CHARACTER_ can also be used, but $ must be placed after array name

20 Single Obs/Subject  Multiple Obs/Subject Suppose we have the following data: ObsIDSCORE1SCORE2SCORE3 11224 2313. 35011 461.. 5723. …And we want to convert this dataset to one with multiple observations per ID

21 Use the Following Code… *CONVERT TO MULTIPLE OBSERVATIONS PER SUBJECT; DATA MULTIPLE; SET SINGLE; ARRAY SCORE_ARRAY[3] SCORE1-SCORE3; *Score1 is stored in Score_Array[1], etc; DO TIME = 1 TO 3; SCORE=SCORE_ARRAY[TIME]; *Score1-Score3 are each stored in the SCORE variable in order; *IF SCORE NE. THEN OUTPUT; OUTPUT; *After each time value, output score; END; KEEP ID TIME SCORE; RUN;

22 Resulting Dataset… ObsIDTIMESCORE 1112 2122 3134 4311 5323 633. 7510 8521 9531 10611 1162. 1263. 13712 14723 1573.

23 Multiple Obs/Subject  Single Obs/Subject Now, if we want to convert this dataset back, use the following code… PROC SORT DATA=MULTIPLE OUT=MULTIPLE2; BY ID TIME; RUN; DATA SINGLE2; ARRAY S[3]; *creates variables s1, s2, and s3; RETAIN S1-S3; *must retain each variable because in each data step iteration, SAS sets variables to missing; SET MULTIPLE2; BY ID; IF FIRST.ID THEN DO I=1 TO 3; S[I]=.; *initializes to missing because while the RETAIN statement is required here, it also will result in previously stored value placed in truly missing spot; END; S[TIME]=SCORE; *places proper score variable into s1, s2, or s3; IF LAST.ID THEN OUTPUT; *only outputs the last entry for each subject; KEEP ID S1-S3; RUN;

24 Some Important Points RETAIN is required here! If it is not used, S1 and S2 will automatically be set to missing and S3 will be correct Must “reset” all score values (S1-S3) for each individual. Otherwise, a missing score value for one individual will be replaced with the previously stored value “FIRST.” and “LAST.” are useful built-in statements that can be used for longitudinal data  Must sort dataset by the appropriate variable  Must use “SET dataset; BY sortedvar;” before using FIRST. or LAST.

25 Note PROC TRANSPOSE can also be used to convert datasets to/from multiple records per subject  single record per subject Personally, I have always used arrays to do this Refer to handout(s) for more info on  PROC TRANSPOSE and ARRAYS  The RETAIN statement

26 Output Delivery System While there is another lecture on ODS, I thought I would briefly show you how I use ODS Can make.rft,.pdf, html files that are much easier to read than the output window This is very simple to do… ODS PDF FILE=“…..pdf”; ------Whatever you want in the file------- ODS PDF CLOSE;

27 List of Styles Default Journal Statistical Analysis Astronomy Banker BarrettsBlue Beige BlockPrint Brick Brown Curve D3d Education Electronics FancyPrinter Gears Magnify Minimal Money NoFontDefault Printer RSVP RTF SansPrinter SASDocPrinter SASWeb Science SerifPrinter Sketch StatDoc Theme Torn Watercolor

28 Other Cool Features ODS GRAPHICS  Makes pretty diagnostic plots  Proc Reg ODS TRACE ON/LISTING;  Able to store SAS created objects as your own datasets ods listing close; ** turns off output display; proc means; var x; ods output summary=sum1; run; ods listing; ** turns it back on; yles.htm yles.htm

29 Thank you! Good Luck!


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