4-Oct-07GzLM PresentationBIOL 79321 The GzLM and SAS Or why it’s a necessary evil to learn code! Keith Lewis Department of Biology Memorial University,

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The GzLM and SAS

Or why it’s a necessary evil to learn code!

Keith Lewis

Department of Biology

Memorial University, St. John’s, Canada

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Variables, Links, and Models(Introduction to Categorical Data Analysis, A. Gresti 1996)

R.V E.V. Error Link Model

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Variables, Links, and Models(Introduction to Categorical Data Analysis, A. Gresti 1996)

R.V E.V. Error Link Model

Ratio Ratio Normal Identity Linear Reg.

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Variables, Links, and Models(Introduction to Categorical Data Analysis, A. Gresti 1996)

R.V E.V. Error Link Model

Ratio Ratio Normal Identity Linear Reg.

Ratio Categorical Normal Identity ANOVA

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Variables, Links, and Models(Introduction to Categorical Data Analysis, A. Gresti 1996)

R.V E.V. Error Link Model

Ratio Ratio Normal Identity Linear Reg.

Ratio Categorical Normal Identity ANOVA

Ratio Mixed Normal Identity ANCOVA

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Variables, Links, and Models(Introduction to Categorical Data Analysis, A. Gresti 1996)

R.V E.V. Error Link Model

Ratio Ratio Normal Identity Linear Reg.

Ratio Categorical Normal Identity ANOVA

Ratio Mixed Normal Identity ANCOVA

Poisson Mixed Poisson Log (ln) Log-linear

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Variables, Links, and Models(Introduction to Categorical Data Analysis, A. Gresti 1996)

R.V E.V. Error Link Model

Ratio Ratio Normal Identity Linear Reg.

Ratio Categorical Normal Identity ANOVA

Ratio Mixed Normal Identity ANCOVA

Poisson Mixed Poisson Log (ln) Log-linear

Poisson Ratio Poisson Identity Poisson Reg.

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Variables, Links, and Models(Introduction to Categorical Data Analysis, A. Gresti 1996)

R.V E.V. Error Link Model

Ratio Ratio Normal Identity Linear Reg.

Ratio Categorical Normal Identity ANOVA

Ratio Mixed Normal Identity ANCOVA

Poisson Mixed Poisson Log (ln) Log-linear

Poisson Ratio Poisson Identity Poisson Reg.

Binomial Mixed Binomial logit Logistic Reg.

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SAS Proc’s: the basics

• Data [dataset];

• Infile [filename];

• input [variables];

• proc [glm (or genmod)];

• model [model];

• run;

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SAS PROC GLM – Lin. Reg.

• Data nest97;

• infile ‘e:\testdata\97exp1.prn’;

• input lake treat type pred n;

• proc glm;

• model pred = lake treat type;

• run;

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SAS PROC GLM - ANOVA

• Data nest97;• infile ‘e:\testdata\97exp1.prn’;• input lake treat type pred n;

• proc glm;• class lake treat type;• model pred = lake treat type;• run;

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SAS PROC GLM - ANOVA

• Data nest97;• infile ‘e:\testdata\97exp1.prn’;• input lake $ treat $ type $ pred n;

• proc glm;• class lake treat type;• model pred = lake treat type;• run;

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SAS PROC GLM - ANCOVA

• Data nest97;• infile ‘e:\testdata\97exp1.prn’;• input lake treat type pred n;

• proc glm;• class treat type;• model pred = lake treat type;• run;

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SAS PROC GENMOD – Log-Linear

• Data nest97;• infile ‘e:\testdata\97exp1.prn’;• input lake treat type pred n;

• proc genmod;• class lake treat type;• model pred = lake treat type / dist=poisson

link=log type1 type3;• run;

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SAS PROC GENMOD – Logistic Regression

• Data nest97;• infile ‘e:\testdata\97exp1.prn’;• input lake treat type pred n;

• proc genmod;• class lake treat type;• model pred/n = lake treat type / dist=binomial

link=logit type1 type3;• run;

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A full exampledata an_01;infile 'C:\Documents and Settings\Micro-Tech Customer\My Documents\MyWork\thesis\SAS\ch4\An_2000a.csv' firstobs=2 delimiter = ',';input park $ site $ grid $ nest $ dp vt;

proc genmod;class park site grid nest;model dp = park|grid|nest / dist=bin link=logit type1 type3;/*make obstats out=keith noprint;*/title 'Schmidts model, 2000 with contrasts';lsmeans park grid nest;contrast 'bird v control' nest 1 -1 0;contrast 'contrl v large' nest 0 1 -1;estimate 'contrl v large' nest 0 1 -1;estimate 'bird v control' nest 1 1 0;estimate 'bF v bS' park 1 -1;estimate 'con v food' grid 1 -1;run;

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Deviance and G-tests

• GzLMs based on Maximum Likelihood Estimates (MLE)

• D= -2ln[likelihood of (current model) / (saturated model)]

• G=D(for model w/ variable)-D(model w/o variable)

• G is analagous to F-tests for GLM

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GENMOD output LR Statistics For Type 1 Analysis Chi- Source Deviance DF Square Pr > ChiSq Intercept 321.4338 park 319.7385 1 1.70 0.1929 grid 314.1447 1 5.59 0.0180

park*grid 313.5346 1 0.61 0.4348 nest 310.1887 2 3.35 0.1877 park*nest 310.1033 2 0.09 0.9582 grid*nest 306.9164 2 3.19 0.2032 park*grid*nest 306.3648 2 0.55 0.7590

321.4338-319.7385 = 1.70, Chisquare = 1.70, df = 1 p = 0.1929

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GENMOD output

LR Statistics For Type 3 Analysis Chi- Source DF Square Pr > ChiSq park 1 2.62 0.1052 grid 1 7.45 0.0064 park*grid 1 0.81 0.3672 nest 2 3.45 0.1783 park*nest 2 0.13 0.9391 grid*nest 2 3.37 0.1853 park*grid*nest 2 0.55 0.7590

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Why we use GzLMSame Data, Same Distribution

DATA PROC Source P-value

limpet Glm (normal error) (identity link)

Sp Se

Sp*Se

.1942

.0004

.2966

From Sokal and Rohlf 1995, Box 11.2

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Why we use GzLMSame Data, Same Distribution

DATA PROC Source P-value

limpet Glm (normal error) (identity link)

Sp Se

Sp*Se

.1942

.0004

.2966

limpet Genmod

Dist = normal Link=identity

Sp Se

Sp*Se

.1627

.0001

.2493

From Sokal and Rohlf 1995, Box 11.2

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Why we use GzLMSame Data, Different Distribution

Data Proc Source P-value Anest97 Glm

(normal errors) (identity link)

Lake Treat Type

TT*TY

.0505

.3632

.4915

.8619

(K.Lewis, M.Sc data)

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Why we use GzLMSame Data, Different Distribution

Data Proc Source P-value Anest97 Glm

(normal errors) (identity link)

Lake Treat Type

TT*TY

.0505

.3632

.4915

.8619 Anest97 Genmod

Dist=binom. link=logit

Lake Treat Type

TT*TY

.0001

.1229

.2435

.8098

(K.Lewis, M.Sc data)See Lewis 2005, Oikos

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SAS v. R

• SAS– Powerful– Widely used– Learning curve– Expensive

• R– Powerful– “limited” use– Learning curve – Free

• Resources– Peter Earle– The web!!!!

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References

• Criteria:– Readability– Examples with the software code!

• A. Agresti. 1996. Introduction to Categorical Data Analysis. Wiley & Sons, New York.

• Littel et al. 2002. SAS for Linear Models 4th ed. Cary, NC: SAS Institute Inc.