Stat 302 Statistical Software and Its Applications SAS Functions

Transcription

1 1 Stat 302 Statistical Software and Its Applications SAS Functions Fritz Scholz Department of Statistics, University of Washington Winter Quarter 2015 February 14, 2015

2 2 Creating New Variables Here we create new variables using functions applied to existing variables. We will do this by example, showing code and output. This is based on Ch. 11 in Cody's book.

3 A List of Functions 3 Operator ABS(x) EXP(x) INT(x) LOG(x) LOG10(x) MOD(x,d) ROUND(num) ROUND(num,unit) SQRT(x) NORMAL(seed) UNIFORM(seed) More Math Description Absolute value of x = x Exponential of x = e x Truncate x to an integer Natural log of x, ln(x) = log e (x) Log base 10 of x = log 10 (x) Remainder when x is divided by d Round num to the nearest integer Round num to the nearest specied unit Square root of x Returns a standard normal random number Returns a uniform[0,1] random number

4 4 Example Code for Function Evaluations data functions; * data set is named functions; input x; rndx = round(x); intx = int(x); sqx = x**2; sqrtx = sqrt(x); log10x = log10(x); lnx = log(x); absx = abs(x); expx = exp(x); U = uniform(123); rndu = round(u,.02); Z = normal(123); modz = mod(z,.5); datalines; title "Function Evaluations"; proc print data=functions noobs;

5 5 SAS Output Output for Function Evaluations Page 1 of 1 Function Evaluations x rndx intx sqx sqrtx log10x lnx absx expx U rndu Z modz Here we used seed 123 in U = uniform(123) and Z = normal(123). This will always generate the same random deviates, but dierent from row to row. Using U = uniform(0) and Z = normal(0) will produce dierent results from run to run. The system provides a random seed. Seed < If seed , a missing value results, but next seeds are OK. Experiment!

6 6 Functions That Work With Missing Values libname learn "U:\learn" ; * learn is a handle for U:\learn ; data test_miss; set learn.blood; * connects to data set U:\learn\blood.sas7bdat; if Gender = then MissGender +1; if WBC =. then MissWBC + 1; if RBC =. then MissWBC + 1; if Chol < 200 and Chol ne. then Level = Low ; else if Chol ge 200 then Level = High ; title "Missing Values in Blood Data"; proc print data = test_miss noobs;

7 SAS Output Output for Function Evaluations Page 1 of 29 Missing Values in Blood Data Gender BloodType AgeGroup Subject WBC RBC Chol MissGender MissWBC Level Female AB Young Hig 7 Male AB Old Male A Young Low Male B Old Male A Young Low Male A Old Low Female A Young Hig Male O Old Low Male O Young Hig Female O Young Male B Young Low Female O Young Hig Male O Young Female O Old Low Female A Old Hig Female O Young Low Male A Old Male O Old Hig Female A Old Hig Male A Young Low Female A Old Hig Female A Young Hig Female O Young Female B Young Female AB Old Low Male O Young Low Female A Old Low

8 8 Comments on SET Statement View SET statement set learn.blood in previous SAS program as a data input statement. Rather than reading data in from a raw data le (more later) or through DATALINES, this is input from a SAS data set, ending in.sas7bdat.

9 Functions That Work With Missing Values libname learn "U:\learn" ; * comment out; libname data "U:\data"; * 2nd handle for saving data set; data data.test_miss; * not in WORK ; set learn.blood; if missing(gender) then MissGender +1; if missing(wbc) then MissWBC + 1; if missing(rbc) then MissWBC + 1; if Chol < 200 and not missing(chol) then Level = Low ; else if Chol ge 200 then Level = High ; title "Missing Values in Blood Data"; proc print data = data.test_miss noobs; 9 Same output result, but saved as permanent data set U:\data\test_miss.sas7bdat

10 10 Comment on MISSING Function The MISSING function also returns a TRUE for alternative values, such as.a,.b,.c,...,.z and. Such alternative missing values can be useful when you deal with dierent types of missing information, For example.a could stand for no answer while.b could mean not applicable and.c could mean to be determined

11 11 Descriptive Statistics Functions data psych; input ID $ Q1-Q10; if n(of Q1-Q10) ge 7 then Score = mean(of Q1-Q10); MaxScore = max(of Q1-Q10); MinScore = min(of Q1-Q10); datalines; ; title "Descriptive Stats"; proc print data = psych noobs; * var Score maxscore MinScore; * uncomment previous, specify variables and order ;

12 12 Output from Descriptive Statistics Functions SAS Output Page Descriptive Stats ID Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Score MaxScore MinScore

13 13 Output from Descriptive Statistics Functions SAS Output Page Descriptive Stats Score MaxScore MinScore

14 14 Comments on Previous Program The N function returns the number of non-missing numeric values among its arguments. A companion function NMISS returns the number of missing numeric values among its arguments. In all such functions you must precede the list of variables Var1-Varn with the key word OF, otherwise SAS assumes that you subtract Varn from Var1. The MEAN function ignores missing values. This also applies to other such functions. In R this corresponds to argument na.rm=t in such functions.

15 15 Working with Order Statistics data order_stats; set psych; * assumes that psych was created previously and is still in the WORK library; M3 = mean(largest(1,of Q1-Q10), largest(2,of Q1-Q10),largest(3,of Q1-Q10), smallest(1,of Q1-Q10),smallest(2,of Q1-Q10), smallest(3,of Q1-Q10)); * computes the average of the 3 extreme nonmissing observations from each end; title "Mean of Order Statistics"; proc print data = order_stats noobs; var Q1-Q10 M3;

16 16 Working with Order Statistics SAS Output Page Mean of Order Statistics Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 M

17 17 The SUM Function libname learn "U:\learn"; data sum; set learn.endofyear; Total = sum(0,of Pay1-Pay12, of Extra1-Extra12); Title "Total Pay"; proc print data = sum noobs; var Total; Using Total = Pay1+...+Pay12+Extra1+...+Extra12 would give missing values. And you would need to write all variables out. The SUM function ignores missing values, allows of Pay1-Pay12.

18 18 Order Statistics SAS Output Page Total Pay Total