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Sas tutorial glm1

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WenSheng Chang
WenSheng Chang

This document provides an example of using PROC GLM in SAS to perform a balanced ANOVA on stem length data from a randomized complete block experiment with seven plant types. The PROC GLM is used to fit a model with main effects for block and type. Contrast statements are included to test specific pairwise comparisons between types. Means for each type are also obtained.

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SAS tutorial: GLM (1)
Example 1 ? xx効yy議PS頁焚N
Generalized Linear Model
Example 1 yy=xx yy=xx*xx
Code of example 1 D PROC GLM ods graphics on; proc glm data=ex1; proc sgscatter data=ex1; model yy=xx; plot yy*xx; run; proc glm data=ex1; ods graphics off; model yy=xx*xx; run;
PROC GLM┨δ棕 ? Simple / multiple regression ? ANOVA / MANOVA ? ANOVA for repeated measures ? ANCOVA ? Weighted regression ? Partial correlation ? Polynomial regression
PROC GLM (syntax) PROC GLM <options> ; CLASS variables </ option> ; MODEL dependents=independents </ options> ; RUN; Nominal or ordinal scale ? CONTRAST、TEST、ESTIMATE、RANDOM ? 凪麿 statement 効 PROC ANOVA 餓音謹
Some statements ? CONTRAST祭Ρ鳩嵶 C CONTRAST ¨label¨ effect values <...effect values> </ options> ; C E.g. CONTRAST ｀A1B1 vs A2B2¨ A*B 1 0 0 -1; ? TEST砦原丼惚c餓恬Fz協 C TEST <H=effects> E=effect </ options> ; C E.g. TEST h=A*B e=S*A*B; ? CONTRAST駅[壓MODEL岻瘁TEST、 MANOVA、REPEATED、RANDOM岻念
Some statements ? ESTIMATE差績亀直來M栽 C ESTIMATE ¨label¨ effect values <...effect values > < / options > ; C Z隈効CONTRAST匯咤孔嬬匆餓音謹 ? RANDOM砦原陳乂頁 random effect C RANDOM effects < / options > ; C E.g. RANDOM A B;
Example: unbalanced ANOVA data exp; input A $ B $ Y @@; datalines; A1 B1 12 A1 B1 14 A1 B2 11 A1 B2 9 A2 B1 20 A2 B1 18 A2 B2 17 ; proc glm data=exp; class A B; model Y=A B A*B; run;
Result
勣議|廉 ? N-way ANOVA (with interaction) ? Unbalanced ANOVA ? Polynomial regression
Example 39.1 title ¨Balanced Data from Randomized Complete Block¨; data plants; input Type $ @; do Block = 1 to 3; input StemLength @; output; end; datalines; Clarion 32.7 32.3 31.5 Clinton 32.1 29.7 29.1 Knox 35.7 35.9 33.1 O¨Neill 36.0 34.2 31.2 Compost 31.8 28.0 29.2 Wabash 38.2 37.8 31.9 Webster 32.5 31.1 29.7 ;
Example 39.1 proc glm; class Block Type; model StemLength = Block Type; run; proc glm order=data; class Block Type; model StemLength = Block Type / solution; /*----------------------------------clrn-cltn-knox-onel-cpst-wbsh-wstr */ contrast 'Compost vs. others' Type -1 -1 -1 -1 6 -1 -1; contrast 'River soils vs. non' Type -1 -1 -1 -1 0 5 -1, Type -1 4 -1 -1 0 0 -1; contrast 'Glacial vs. drift' Type -1 0 1 1 0 0 -1; contrast 'Clarion vs. Webster' Type -1 0 0 0 0 0 1; contrast 'Knox vs. O¨Neill' Type 0 0 1 -1 0 0 0; run; means Type / waller regwq; run;
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Sas tutorial glm1

  • 5. Code of example 1 D PROC GLM ods graphics on; proc glm data=ex1; proc sgscatter data=ex1; model yy=xx; plot yy*xx; run; proc glm data=ex1; ods graphics off; model yy=xx*xx; run;
  • 6. PROC GLM┨δ棕 ? Simple / multiple regression ? ANOVA / MANOVA ? ANOVA for repeated measures ? ANCOVA ? Weighted regression ? Partial correlation ? Polynomial regression
  • 7. PROC GLM (syntax) PROC GLM <options> ; CLASS variables </ option> ; MODEL dependents=independents </ options> ; RUN; Nominal or ordinal scale ? CONTRAST、TEST、ESTIMATE、RANDOM ? 凪麿 statement 効 PROC ANOVA 餓音謹
  • 8. Some statements ? CONTRAST祭Ρ鳩嵶 C CONTRAST ¨label¨ effect values <...effect values> </ options> ; C E.g. CONTRAST ｀A1B1 vs A2B2¨ A*B 1 0 0 -1; ? TEST砦原丼惚c餓恬Fz協 C TEST <H=effects> E=effect </ options> ; C E.g. TEST h=A*B e=S*A*B; ? CONTRAST駅[壓MODEL岻瘁TEST、 MANOVA、REPEATED、RANDOM岻念
  • 9. Some statements ? ESTIMATE差績亀直來M栽 C ESTIMATE ¨label¨ effect values <...effect values > < / options > ; C Z隈効CONTRAST匯咤孔嬬匆餓音謹 ? RANDOM砦原陳乂頁 random effect C RANDOM effects < / options > ; C E.g. RANDOM A B;
  • 10. Example: unbalanced ANOVA data exp; input A $ B $ Y @@; datalines; A1 B1 12 A1 B1 14 A1 B2 11 A1 B2 9 A2 B1 20 A2 B1 18 A2 B2 17 ; proc glm data=exp; class A B; model Y=A B A*B; run;
  • 12. 勣議|廉 ? N-way ANOVA (with interaction) ? Unbalanced ANOVA ? Polynomial regression
  • 13. Example 39.1 title ¨Balanced Data from Randomized Complete Block¨; data plants; input Type $ @; do Block = 1 to 3; input StemLength @; output; end; datalines; Clarion 32.7 32.3 31.5 Clinton 32.1 29.7 29.1 Knox 35.7 35.9 33.1 O¨Neill 36.0 34.2 31.2 Compost 31.8 28.0 29.2 Wabash 38.2 37.8 31.9 Webster 32.5 31.1 29.7 ;
  • 14. Example 39.1 proc glm; class Block Type; model StemLength = Block Type; run; proc glm order=data; class Block Type; model StemLength = Block Type / solution; /*----------------------------------clrn-cltn-knox-onel-cpst-wbsh-wstr */ contrast 'Compost vs. others' Type -1 -1 -1 -1 6 -1 -1; contrast 'River soils vs. non' Type -1 -1 -1 -1 0 5 -1, Type -1 4 -1 -1 0 0 -1; contrast 'Glacial vs. drift' Type -1 0 1 1 0 0 -1; contrast 'Clarion vs. Webster' Type -1 0 0 0 0 0 1; contrast 'Knox vs. O¨Neill' Type 0 0 1 -1 0 0 0; run; means Type / waller regwq; run;