Week 3 [16+ Sept.] Class Activities. File: week-03-15sep08.doc/.pdf Directory: \\Muserver2\USERS\B\\baileraj\Classes\sta402\handouts

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1 Week 3 [16+ Sept.] Class Activities File: week-03-15sep08.doc/.pdf Directory: \\Muserver2\USERS\B\\baileraj\Classes\sta402\handouts Week 3 Topic -- Complex table construction & output control (i.e., pretty output) * PROC TABULATE for producing nicely-formatted tables * Introduce the Output Delivery System (ODS) for customizing procedure output PROC TABULATE (producing fancier results tables in SAS) References: Haworth LE (1999) PROC TABULATE by Example. Cary, NC: SAS Institute Inc. 3.1 PROC TABULATE PROC TABULATE <option(s)>; CLASS variable(s) </ options>; * identify non-numeric vars; FREQ variable; * identify variable containing frequency of observation; TABLE <<page-expression,> row-expression,> column-expression</ table-option(s)>; VAR analysis-variable(s)</ options>; * identify analysis vars; WEIGHT variable; * identify variable name e.g. sampling wts; * FORMATTING related subcommands CLASSLEV variable(s) / STYLE=<style-element-name PARENT> <[style-attribute-specification(s)] >; KEYLABEL keyword-1='description-1' <...keyword-n='description-n'>; KEYWORD keyword(s) / STYLE=<style-element-name PARENT> <[style-attribute-specification(s)] >; [* check out results of search for Tabulate syntax on SAS doc] 1

2 Comments: * concatenation (blank) operator * crossing (*) operator * format modifiers * grouping elements (parentheses) operator * ALL class variable 3.2: Building from simple specifications: nitrofen data EXAMPLE 3.1: PROC TABULATE and the nitrofen data produces data nitrofen; infile '\\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\data\ch2- dat.txt' firstobs=16 expandtabs missover pad ; animal conc brood1 brood2 brood3 total 2.; ods rtf BODYTITLE; proc tabulate data=nitrofen; class conc; var brood1 brood2 brood3 total; table (brood1 brood2 brood3 total)*conc, n*f=3. min*f=3. q1*f=3. median*f=4.1 q3*f=3. max*f=3.; ods rtf close; brood1 N Min Q1 Median Q3 Max conc

3 brood2 brood3 Total N Min Q1 Median Q3 Max conc conc conc GOAL Variable Brood 1 Brood 2 Brood 3 Total Sample size Min Q1 Median Q3 Max data nitrofen; infile '\\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\data\ch2- dat.txt' firstobs=16 expandtabs missover pad ; animal conc brood1 brood2 brood3 total 2.; ods rtf BODYTITLE; proc tabulate data=nitrofen; var brood1 brood2 brood3 total; 3

4 table brood1 brood2 brood3 total, n min q1 median q3 max; ods rtf close; which results in brood1 brood2 brood3 total N Min Q1 Median Q3 Max proc tabulate data=nitrofen; class conc; var brood1 brood2 brood3 total; table conc, brood1 brood2 brood3 total, n min q1 median q3 max; which produces 5 tables, one for each concentration group, i.e. conc 0 N Min Q1 Median Q3 Max brood brood brood total conc 80 N Min Q1 Median Q3 Max brood brood brood total

5 and (omitting conc=160 and conc=235 tables) conc 310 Final table produced via N Min Q1 Median Q3 Max brood brood brood total proc tabulate data=nitrofen; class conc; var brood1 brood2 brood3 total; table (brood1 brood2 brood3 total)*conc, n*f=3. min*f=3. q1*f=3. median*f=4.1 q3*f=3. max*f=3.; QUESTION: how would the table change if you requested the following table table conc*(brood1 brood2 brood3 total), n*f=3. min*f=3. q1*f=3. median*f=4.1 q3*f=3. max*f=3.; 3.3: Enhancing PROC TABULATE output data country; title 'country data analysis'; infile \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\data\country.data ; input name $ area popnsize pcturban lang $ liter lifemen lifewom pcgnp; logarea = log10(area); logpopn = log10(popnsize); loggnp = log10(pcgnp); density = popnsize/area; * construct density variable; ienglish = (lang="english"); drop area popnsize pcgnp; proc univariate data=country; * calculate %tiles of density; var density; id name; data country; set country; * define density categorical var; if density <= then density_cat=1; * [min, Q1]; else if density <= then density_cat=2; * (Q1, Q3]; else density_cat=3; * (Q3, max]; 5

6 proc freq data=country; table density_cat; * check construction of density category variable; ods rtf BODYTITLE; proc tabulate data=country missing; class density_cat; var lifemen lifewom liter loggnp; table (lifemen lifewom liter loggnp)*(n mean median stddev), density_cat; ods rtf close; lifemen lifewom liter loggnp proc format; value dfmt 1= Least Dense 25%" 2= Middle 50% 3= Most Dense 25% ; ods rtf bodytitle; proc tabulate data=country missing; class density_cat; var lifemen lifewom liter loggnp; density_cat N Mean Median StdDev N Mean Median StdDev N Mean Median StdDev N Mean Median StdDev

7 format density_cat dfmt.; table (lifemen= Life Expectancy (Men) lifewom= Life Expectancy (Women) liter= % Literate loggnp= Log10 GNP )* (n= # countries *f=2. mean= Mean *f=4.1 median= Median *f=4.1 stddev= Std. Dev. *f=4.1), density_cat= Population Density ; ods rtf close; Life Expectancy (Men) Life Expectancy (Women) % Literate Log10 GNP Population Density Least Dense 25% Middle 50% Most Dense 25% # countries Mean Median Std. Dev # countries Mean Median Std. Dev # countries Mean Median Std. Dev # countries Mean Median Std. Dev Output Delivery System (ODS) ODS References Gupta, S. (2003) Quick Results with the Output Delivery System. SAS Institute Inc., Cary, NC USA. 7

8 Delwiche LD and Slaughter SJ. (2003) The Little SAS Book: A Primer, 3rd edition. SAS Institute. Cary, NC, USA. [pages ] Haworth LE (2001) Output Delivery System: The Basics. SAS Institute Inc. Cary, NC USA. ODS Basics What is ODS? * method of delivering output in a variety of formats (other than the default listing format ) * options available include HTML, Rich Text Format (RTF), PS, PDF, SAS data sets Basic ODS Terminology destinations locations to which ODS routes output (e.g. LISTING, HTML, RTF, PRINTER, PDF, OUTPUT new data set) objects output entities created by ODS to store the formatted results styles font/color/other attributes of a report Basic syntax of ODS statements * identify output objects; ODS TRACE ON </options>; * open output destination; ODS destination <FILE=filename>; * create SAS data set with output object; ODS OUTPUT output-object-name=sas-data-set-name; * [optional] select particular objects for inclusion; ODS <destination> SELECT output-object-name; PROC PROC PROC ODS <destination> CLOSE; ODS TRACE OFF; 8

9 3.4.2 Favorite Destinations RTF, HTML and even PDF data country; infile \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\data\country.data ; input name $ area popnsize pcturban lang $ liter lifemen lifewom pcgnp; loggnp = log10(pcgnp); density = popnsize/area; * construct density variable & categories; if density <= then density_cat=1; * [min, Q1]; else if density <= then density_cat=2; * (Q1, Q3]; else density_cat=3; * (Q3, max]; /* set up formats for the density categories */ proc format; value dfmt 1= Least Dense 25%" 2= Middle 50% 3= Most Dense 25% ; ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\tab.rtf ; ODS PDF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\tab.pdf ; ODS HTML file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\tab.htm l ; proc tabulate data=country missing; class density_cat; var lifemen lifewom liter loggnp; format density_cat dfmt.; table (lifemen= Life Expectancy (Men) lifewom= Life Expectancy (Women) liter= % Literate loggnp= Log10 GNP )* (n= # countries *f=2. mean= Mean *f=4.1 median= Median *f=4.1 stddev= Std. Dev. *f=4.1), density_cat= Population Density ; ODS RTF CLOSE; ODS PDF CLOSE; ODS HTML CLOSE; produced the following table in RICH TEXT FORMAT ( tab.rtf file) 9

10 Life Expectancy (Men) Life Expectancy (Women) % Literate Log10 GNP Population Density Least Dense 25% Middle 50% Most Dense 25% # countries Mean Median Std. Dev # countries Mean Median Std. Dev # countries Mean Median Std. Dev # countries Mean Median Std. Dev

11 and the following table in HTML format ( tab.html file) 11

12 data country; infile \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\data\country.data ; input name $ area popnsize pcturban lang $ liter lifemen lifewom pcgnp; loggnp = log10(pcgnp); density = popnsize/area; * construct density variable & categories; if density <= then density_cat=1; * [min, Q1]; else if density <= then density_cat=2; * (Q1, Q3]; else density_cat=3; * (Q3, max]; /* set up formats for the density categories */ proc format; value dfmt 1= Least Dense 25%" 2= Middle 50% 3= Most Dense 25% ; 12

13 ODS HTML path= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples body = country-body.html /* Output objects */ contents = country-toc.html /* Table of contents */ frame = country-frame.html /* organizes display */ newfile = NONE; /* all results to one file*/ proc univariate data=country; * calculate %tiles of density; var density; id name; proc tabulate data=country missing; class density_cat; var lifemen lifewom liter loggnp; format density_cat dfmt.; table (lifemen= Life Expectancy (Men) lifewom= Life Expectancy (Women) liter= % Literate loggnp= Log10 GNP )* (n= # countries *f=2. mean= Mean *f=4.1 median= Median *f=4.1 stddev= Std. Dev. *f=4.1), density_cat= Population Density ; proc gplot; plot lifewom*liter; label lifewom= Life Expectancy (Women) ; label liter= % literacy in population ; ods html close; 13

14 EXAMPLE 3.4: Generating a regression data set data simreg; beta0 = 3; * Simulate Y ~ N( X, sigma = 2); beta1 = 1.5; sigma = 2; do nobs = 1 to 2; do x = 1 to 10; y = beta0 + beta1*x + sigma*rannor(54321); output; end; end; ods rtf bodytitle; proc print; ods rtf close; Obs beta0 beta1 sigma nobs x y

15 Obs beta0 beta1 sigma nobs x y ods rtf bodytitle; proc reg; model y = x; ods rtf close; produces Number of Observations Read 20 Number of Observations Used 20 15

16 Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Variable DF Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept x <.0001 EXAMPLE 3.5: Listing the output objects produced. ods trace on /listing; proc reg; model y = x; ods trace off; which produces The REG Procedure Model: MODEL1 Dependent Variable: y Output Added: Name: NObs Label: Number of Observations Template: Stat.Reg.NObs Path: Reg.MODEL1.Fit.y.NObs Number of Observations Read 20 Number of Observations Used 20 16

17 Output Added: Name: ANOVA Label: Analysis of Variance Template: Stat.REG.ANOVA Path: Reg.MODEL1.Fit.y.ANOVA Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <.0001 Error Corrected Total Output Added: Name: FitStatistics Label: Fit Statistics Template: Stat.REG.FitStatistics Path: Reg.MODEL1.Fit.y.FitStatistics Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Output Added: Name: ParameterEstimates Label: Parameter Estimates Template: Stat.REG.ParameterEstimates Path: Reg.MODEL1.Fit.y.ParameterEstimates Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept x <.0001 ods select ParameterEstimates; ods rtf bodytitle; proc reg; model y = x; ods rtf close; which produces 17

18 Variable DF Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept x <.0001 As an aside, this is equivalent to using the ODS EXCLUDE statement. Here, this output object would be selected by ods exclude NObs ANOVA FitStatistics ; ods rtf bodytitle; proc reg; model y = x; ods rtf close; Another Destination that Stat Programmers should visit OUTPUT data simreg; beta0 = 3; * Simulate Y ~ N( X, sigma = 2); beta1 = 1.5; sigma = 2; do iexpt = 1 to 2000; * generate 2000 data sets; do nobs = 1 to 2; * with 2 replicates at x=1 to 10 by 1; do x = 1 to 10; y = beta0 + beta1*x + sigma*rannor(54321); output; end; end; end; ods output ParameterEstimates=ParmEst; proc sort; by iexpt; proc reg; by iexpt; model y = x; ods output close; data ParmEst2; set ParmEst; retain b0; if variable="intercept" then b0 = estimate; else do; 18

19 b1 = estimate; keep iexpt b0 b1; output; end; proc print data=parmest2; * a nice debugging check; ods rtf bodytitle; proc means; var b0 b1; proc gplot; plot b0*b1; ods rtf close; which produces Variable N Mean Std Dev Minimum Maximum b0 b and b b1 19

20 Exercises 1. One of the best ways to learn more about PROC TABULATE is to play with toy data examples. Try to predict the results of each table prior to running the procedure. options formdlim="-"; data junk; input group $ type $ var1 var2; datalines; a y a y a z a z b y b y b z b z ; proc print; proc tabulate; var var1; class group type; table group*type,var1*mean; proc tabulate; var var1; class group type; table group,type*var1*mean; proc tabulate; var var1 var2; class group; table (var1 var2)*mean, group; proc tabulate; var var1 var2; class group; table (var1 var2)*(n mean), group; 20