Week 05 Class Activities

Transcription

1 Week 05 Class Activities File: week-05-24sep07.doc Directory (hp/compaq): C:\baileraj\Classes\Fall 2007\sta402\handouts Directory: \\Muserver2\USERS\B\\baileraj\Classes\sta402\handouts Better Graphics in SAS via SAS/Graph HIGH-RESOLUTION GRAPHICS AND FORMATS * Introduce concepts related to high-resolution graphs * PROC GCHART and PROC GPLOT for producing high-resolution graphs other SAS high resolution graphics procedures GCHART (bar charts, pie charts, star charts) GPLOT (line plot, scatter plot, regression plot, high-low plots, bubble plots) G3D (3-dimensional surface plots) GCONTOUR, G3GRID (interpolate/smooth data) GMAP (block, choropleth, prism, surface) GSLIDE (create text slide) GPRINT (display as a graphic SAS procedure output that has been saved in a text file) GREPLAY (combine several graphs into a single output) To list all graphics devices in the current catalog proc gdevice catalog=sashelp.devices nofs; list;

2 * SAS-supplied formats and PROC FORMAT for user-defined formats References (follow SAS/GRAPH links) or GPLOT figures /* example sas program that does simple linear regression */ /* defines library location for permanently storing a SAS data set */ libname class \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples ; options ls=75; data class.manatee; input year nboats manatees; cards; ;

3 proc print data=class.manatee; title Manatee mortality data ; /* Line printer scatterplot */ proc plot data=class.manatee; title2 PROC PLOT: manatees killed plotted vs. # boats registered ; plot manatees*nboats; /* SAS has an experimental ODS procedures for Statistical Graphics Using ODS */ ODS GRAPHICS (experimental in SAS 9) From On an experimental basis in SAS 9.1, a number of SAS/STAT procedures support an extension to the Output Delivery System (ODS) to create statistical graphics as automatically as tables. This facility is referred to as ODS Statistical Graphics (or ODS Graphics for short). With ODS Graphics, a procedure creates the graphs that are most commonly needed for a particular analysis. Using ODS Graphics eliminates the need to save numerical results in an output data set, manipulate them with a DATA step program, and display them with a graphics procedure. ODS Graph produces graphs similar in nature to what Minitab does. [Thanks to Dr. Schaefer for the links] SAS Procedures with ODS Graphics support Base SAS SAS/STAT CORR SAS/ETS ARIMA AUTOREG ENTROPY EXPAND MODEL SYSLIN TIMESERIES UCM ANOVA CORRESP GAM GENMOD GLM KDE LIFETEST LOESS LOGISTIC MI MIXED PHREG

4 VARMAX X12 SAS High-Performance Forecasting PRINCOMP PRINQUAL REG ROBUSTREG HPF (from In many ways, creating graphics with ODS is analogous to creating tables with ODS. You use procedure options and defaults to determine which graphs are created ODS destination statements (such as ODS HTML) to specify the output destination for graphics Additionally, you can use graph names in ODS SELECT and ODS EXCLUDE statements to select or exclude graphs from your output ODS styles to control the general appearance and consistency of all graphs ODS templates to control the layout and details of individual graphs. A default template is provided by SAS for each graph. In SAS 9.1, the ODS destinations that support ODS Graphics include HTML, LATEX, PRINTER, and RTF. ODS html; ODS graphics on; proc reg data=class.manatee plots(unpack); title2 Stat graphics with ODS ; model manatees=nboats; quit; ODS graphics off; ODS html close; Manatee mortality data Stat graphics with ODS The REG Procedure Model: MODEL1 Dependent Variable: manatees Number of Observations Read 14

5 Number of Observations Used 14 Analysis of Variance Source DF 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 Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > t Intercept nboats <.0001

6 Manatee mortality data Stat graphics with ODS The REG Procedure Model: MODEL1 Dependent Variable: manatees

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15 /* Now look at what ODS graphics does with GLM */ data meat; input condition $ datalines; Plastic 7.66 Plastic 6.98 Plastic 7.80 Vacuum 5.26 Vacuum 5.44 Vacuum 5.80 Mixed 7.41 Mixed 7.33 Mixed 7.04 Co Co Co ; title bacteria growth under 4 packaging conditions; ods html; ods graphics on; proc glm data=meat order=data; title2 fitting the one-way anova model via GLM;

16 class condition; model logcount = condition; quit; ods graphics off; ods html close; bacteria growth under 4 packaging conditions fitting the one-way anova model via GLM The GLM Procedure Class Level Information Class Levels Values condition 4 Plastic Vacuum Mixed Co2 Number of Observations Read 12 Number of Observations Used 12

17 bacteria growth under 4 packaging conditions fitting the one-way anova model via GLM The GLM Procedure Dependent Variable: logcount Source DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE logcount Mean Source DF Type I SS Mean Square F Value Pr > F condition <.0001 Source DF Type III SS Mean Square F Value Pr > F condition <.0001

18 /* Now how about specifying graphs in SAS/Graph */ ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05- fig1a.rtf ; proc gplot data=class.manatee; title Number of Manatees killed regressed on the number of boats registered in Florida ; plot manatees*nboats; ODS RTF CLOSE;

19 /* Let s now add bells and whistles to this plot */ ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05- fig2.rtf ; proc gplot data=class.manatee; title h=1.5 'A plot of the number of manatee deaths versus the number of boats registered in Florida';

20 title2 h=1 '[Regression line with CI for mean along with smoothing spline fit displayed]'; symbol1 interpol=rlclm95 /* r=regression, l=linear (q,c also possible), clm=conf. int. mean (cli option), 95= conf. level */ value=diamond height=3 cv=red ci=blue co=green width=2; symbol2 interpol=sm55s /* smoothing spline (SM) that first sorts the x-axis data */ ; plot manatees*nboats manatees*nboats / hminor=1 overlay regeqn; /* adds regression eqn to bottom left of plot */ ODS RTF CLOSE;

21 /* The NITROFEN data set revisited */ data class.nitrofen; infile 'M:\public.www\classes\sta402\SAS-programs\ch2-dat.txt' firstobs=16 expandtabs missover pad ; animal conc brood1 brood2 2.

22 @41 brood3 total 2.; /* creates character variable based on format */ * cbrood3 = put(brood3,totfmt.); label animal = animal ID number; label conc = Nitrofen concentration; label brood1 = number of young in first brood; label brood2 = number of young in 2nd brood; label brood3 = number of young in 3rd brood; *label total = total young produced in three broods; proc print data=class.nitrofen; * ODS RTF file='d:\baileraj\classes\fall 2003\sta402\SAS-programs\week5- fig2.rtf'; ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05- fig2.rtf ; /* SYMBOL = defines characteristics of plotted symbols */ proc gplot data=class.nitrofen; title h=1.5 'A plot of the number of C. dubia produced at different Nitrofen concentrations'; title2 h=1 '[mean +/- 2SD is plotted for each concentration]'; symbol1 interpol=std2t /* plots +/- 2 SD from the mean at each conc */ /* T= add top and bottom to each 2 SD diff */ value=dot; plot total*conc / hminor=1 /* hminor=# tick markets before x values */ haxis=0 to 350 by 50; ODS RTF CLOSE;

23 proc means data=class.nitrofen; class conc; var total; output out=nitromean mean=n_mean stddev=n_sd; proc print; Obs conc _TYPE FREQ_ n_mean n_sd

24 * ODS RTF file='d:\baileraj\classes\fall 2003\sta402\SAS-programs\week5- fig3.rtf'; ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05- fig3.rtf ; proc gplot data=nitromean; title h=1.5 'Plot of mean number of C. dubia young produced at different Nitrofen concentrations'; title2 h=1 '[bubble area proportional to std dev.]'; bubble n_mean*conc=n_sd / bsize=15; /* bsize helps resize bubble for display */ ODS RTF CLOSE;

25 GCHART figures * ODS RTF file='d:\baileraj\classes\fall 2003\sta402\SAS-programs\week5- fig4.rtf'; ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05- fig4.rtf ;

26 goptions reset=global gunit=pct border cback=white colors=(black blue green red) ftext=swiss ftitle=swissb htitle=5 htext=3.5; title1 'Average Total Young by Nitrofen concentration'; axis1 label=('total Young' j=c 'Error Bar Confidence Limits: 95%') minor=(number=1); axis2 label=('nitrofen' j=r 'Concentration'); pattern1 color=cyan; proc gchart data=class.nitrofen; hbar conc / type=mean sumvar=total /* freqlabel='number in Group' */ /* meanlabel='mean Number Young' */ errorbar=bars clm=95 midpoints=( ) raxis=axis1 maxis=axis2 noframe coutline=black; ODS RTF CLOSE;

27 ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05- fig5.rtf ; * ODS RTF file='d:\baileraj\classes\fall 2003\sta402\SAS-programs\week5- fig5.rtf'; proc gchart data=class.nitrofen; title1 'Total Young by Nitrofen concentration'; star conc / sumvar=total; proc gchart data=class.nitrofen; title1 'Total Young by Nitrofen concentration';

28 pie conc / sumvar=total; ODS RTF CLOSE;

29 GREPLAY figures /* now trying something fancy using templates and GREPLAY to get multiple figures on a page REF: */ * libname class 'D:\baileraj\Classes\Fall 2003\sta402\data ; libname class \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples ; /* libname class previously defined */

30 ftext=swissb htitle=6 htext=3; proc greplay tc=class.tempcat nofs; tdef newtemp des='five panel template' 1/llx=0 lly=10 ulx=0 uly=50 urx=50 ury=50 lrx=50 lry=10 color=blue 2/llx=0 lly=50 ulx=0 uly=90 urx=50 ury=90 lrx=50 lry=50 color=red 3/llx=50 lly=50 ulx=50 uly=90 urx=100 ury=90 lrx=100 lry=50 color=green 4/llx=50 lly=10 ulx=50 uly=50 urx=100 ury=50 lrx=100 lry=10 color=cyan; template newtemp; list template; quit; proc gplot data=class.nitrofen gout=class.graph; title c=red 'Brood 1'; plot brood1*conc; title 'Brood 2'; plot brood2*conc; title 'Brood 3'; plot brood3*conc; title 'TOTAL'; plot total*conc; goptions hsize=0in vsize=0in; proc gslide gout=class.graph; title 'PLOT of brood and total responses versus nitrofen concentration'; goptions display; ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05-fig6.rtf ; * ODS RTF file='d:\baileraj\classes\fall 2003\sta402\SAS-programs\week5-fig6.rtf'; proc greplay igout=class.graph gout=class.excat tc=class.tempcat nofs template=newtemp; treplay 3:gplot2 /* plot bottom left - brood 3 */ 1:gplot /* top left - brood 1 */ 2:gplot1 /* top right - brood 2 */ 4:gplot3 ; /* bottom right - total */ quit; ODS RTF CLOSE;

31 G3D figures libname class \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples ; * libname class 'D:\baileraj\Classes\Fall 2003\sta402\data ; data new; set class.nitrofen; retain conc; brood=1; young=brood1; output; brood=2; young=brood2; output; brood=3; young=brood3; output; keep conc brood young; data new2; set new; jconc = conc + (10-20*ranuni(0)); jbrood = brood + (1-2*ranuni(0)); ODS RTF file=

32 * ODS RTF file='d:\baileraj\classes\fall 2003\sta402\SAS-programs\week5-fig7.rtf'; proc g3d data=new2; title h=1 Scatter plot of # young by conc. and brood (jittered) ; scatter jconc*jbrood=young / xticknum=2 yticknum=2; quit; ODF RTF CLOSE; proc means data=new; class conc brood; var young; output out=new3 mean=ymean; ODS RTF file= \\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\examples\week05-fig8.rtf ; * ODS RTF file='d:\baileraj\classes\fall 2003\sta402\SAS-programs\week5-fig8.rtf';

33 title h=1 'Surface plot of mean # young by conc. and brood'; plot conc*brood=ymean / xticknum=2 yticknum=2 tilt=80; quit; ODS RTF CLOSE;

34 /* Collection of graphics used to compare distributions Stefan Stanev contributed to the code presented below as part of an independent study */ /* ================================================================= Enter a simple two group comparison data set REF: ================================================================= */ options formdlim='-'; data one; input value treat datalines; 18 Drug 40 Untreated 43 Drug 54 Untreated 28 Drug 26 Untreated 50 Drug 63 Untreated 16 Drug 21 Untreated 32 Drug 37 Untreated 13 Drug 39 Untreated 35 Drug 23 Untreated 38 Drug 48 Untreated 33 Drug 58 Untreated 6 Drug 28 Untreated 7 Drug 39 Untreated ; /* ================================================================= Construct summary statistics for the two groups ================================================================= */ proc sort data=one; by treat; proc means mean std data=one; by treat; output out=bar mean=mean std=std; options nocenter nodate nonumber; proc print data=bar; Obs treat _TYPE FREQ_ mean std 1 Drug Untreated /* ============================================================================== Dynamite/ bar graphs not necessarily best data display but often constructed ================================================================================= */ proc gchart data = bar; vbar treat/ sumvar=mean type=mean;

35 mean MEAN Drug treat Untreated goptions reset; axis1 label=none value=('drug' 'Untreated'); axis2 label=('number of Tapeworms') order=(0, 10, 20, 30, 40, 50, 60); proc gchart data = bar; vbar treat/ maxis =axis1 raxis = axis2 sumvar=mean type=mean; Number of Tapeworms Drug Untreated

36 * positioning GROUP (GCHART) MIDPOINT (GCHART) SUBGROUP (GCHART) X, Y, Z (G3D) * attributes ANGLE (pie)/ CBORDER / COLOR /LINE POSITION (placement and alignment for text strings) TEXT (text to used in label, symbol, comment) */ goptions reset; axis1 offset = (2 cm) label=none value=('drug' 'Untreated'); axis2 label=(angle =90 'Number of Tapeworms') order=(0, 10, 20, 30, 40, 50, 60); proc gchart data = bar; vbar treat/ maxis =axis1 raxis = axis2 sumvar=mean type=mean; Drug Untreated [*] So, how can we add whiskers? /* creating an ANNOTATE data set Can label points on a graph Can create custom graphs From SAS help >>>> * annotate data set each observation = command to draw graphic element = command to perform an action - annotate variables = action/position/attribute ( what / where / how ) - variable types * drawing/programming action = FUNCTION POLY/ DRAW/ MOVE/ POINT/ BAR/ LABEL (draw text)

37 data myanno; retain xsys ysys '2' ; set bar; function='move'; midpoint=treat; y=mean; output; function='draw'; y=mean+std; width=2; output; proc print data=myanno; Obs xsys ysys treat _TYPE FREQ_ mean std function midpoint y width Drug move Drug Drug draw Drug Untreated move Untreated Untreated draw Untreated goptions reset; axis1 offset = (2 cm) label=none value=('drug' 'Untreated'); axis2 label=(angle =90 'Number of Tapeworms') order=(0, 10, 20, 30, 40, 50, 60); proc gchart data = bar; vbar treat/ anno=myanno space = 2 maxis =axis1 raxis = axis2 sumvar=mean type=mean; Drug Untreated /* ================================================================= Side-by-side boxplots ================================================================= */ proc sort data=one; by treat; proc boxplot data=one; plot value*treat;

38 80 60 v a l u e Drug treat Untreated goptions reset; axis1 offset = (2 cm) label=none value=('drug' 'Untreated'); axis2 label=(angle =90 'Number of Tapeworms') order=(0, 10, 20, 30, 40, 50, 60); proc boxplot data = one; plot value*treat=' ' / haxis=axis1 vaxis=axis2 ; 80 N u m b e r o f T a p e w o r m s Drug Untreated /* ================================================================= Stacked histograms ================================================================= */ axis3 label=none; axis4 order = ( ) label=(angle =90 'Frequency'); proc capability data=one; by treat;

39 haxis = axis3 vaxis = axis4;

40 /* ================================================================= Scatterplot with superimposed mean ================================================================= */ axis5 offset = (2 cm) label = ('Points Jittered horizontally') order = ( ) value = (' ' 'Drug' ' ' 'Untreated' ' ') major=none minor=none; data jitter; set one; if treat = 'Drug' then x = ranuni(0)/3; else x =.75 + ranuni(0)/3; data anno2; retain xsys ysys '2' ; set bar; if treat = 'Drug' then do function='move'; x=0; y=mean; output; function='draw'; x=0.33; width=2; output; end; if treat = 'Untreated' then do function='move'; x=0.75; y=mean; output;

41 proc gplot data=jitter; plot value*x / haxis=axis5 vaxis=axis2 anno=anno2;

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