sas short course presentation 11-4-09

7/27/2019 SAS Short Course Presentation 11-4-09

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November 4, 2009

Introduction to SAS

LISA Short Course Series

Mark Seiss, Dept. of Statistics


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Reference Material

The Little SAS BookDelwiche and Slaughter

SAS Programming I: Essentials

SAS Programming II: Manipulating Data with theDATA Step

Presentation and Data

http://www.lisa.stat.vt.edu/?q=node/167


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Presentation Outline

1. Introduction to the SAS Environment

2. Working With SAS Data Sets

3. Summary Procedures

4. Basic Statistical Analysis Procedures


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Presentation Outline

Questions/Comments


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Introduction to theSAS Environment

1. SAS Programs

2. SAS Data Sets and Data Libraries

2. Creating SAS Data Sets


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SAS Programs

File extension - .sas

Editor window has four uses:

Access and edit existing SAS programs

Write new SAS programs

Submitting SAS programs for execution Saving SAS programs

SAS programsequence of steps that the user submits forexecution

Submitting SAS programs Entire program

Selection of the program


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SAS Programs

Syntax Rules for SAS statements Free-formatcan use upper or lower case

Usually begin with an identifying keyword

Can span multiple lines

Always end with a semicolon

Multiple statements can be on the same line

Errors

Misspelled key words

Missing or invalid punctuation (missing semi-colon common)

Invalid options

Indicated in the Log window


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SAS Programs

2 Basic steps in SAS programs: Data Steps

Typically used to create SAS datasets and manipulate data,

Begins with DATA statement

Proc Steps

Typically used to process SAS data sets

Begins with PROC statement

The end of the data or proc steps are indicated by:

RUN statementmost steps QUIT statementsome steps

Beginning of another step (DATA or PROC statement)


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SAS Programs

Output generated from SAS program2 Windows SAS log

Information about the processing of the SAS program

Includes any warnings or error messages

Accumulated in the order the data and procedure steps are

submitted

SAS output

Reports generated by the SAS procedures

Accumulates output in the order it is generated


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SAS Data Sets and Data Libraries

SAS Data Set Specifically structured file that contains data values.

File extension - .sas7bdat

Rows and Columns formatsimilar to Excel

Columnsvariables in the table corresponding to fields of data

Rowssingle record or observation

Two types of variables

Charactercontain any value (letters, numbers, symbols, etc.)

Numericfloating point numbers

Located in SAS Data Libraries


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SAS Data Libraries Contain SAS data sets

Identified by assigning a library reference namelibref

Temporary

Work library

SAS data files are deleted when session ends

Library reference name not necessary

Permanent

SAS data sets are saved after session ends SASUSER library

You can create and access your own libraries


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SAS Data Libraries cont. Assigning library references

Syntax

LIBNAME libref SAS-data-library;

Rules for Library References

8 characters or less

Must begin with letter or underscore

Other characters are letters, numbers, or under scores


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SAS Data Libraries cont. Identifying SAS data sets within SAS Data Libraries

libref.filename

Accessing SAS data sets within SAS Data Libraries

Example: DATA new_data_set;

set libref.filename;

run;

Creating SAS data sets within SAS Data LibrariesExample: DATA libref.filename;

set old_data_set;

run;


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Creating SAS Data Sets

Creating a SAS data sets from raw data 4 methods

1. Importing existing raw data in SAS program

2. Manually entering raw data in SAS program

3. Importing existing data sets using Import menu option

4. Manually entering raw data using Table Editor


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Importing existing raw data in SAS program1. Start Data step and name the SAS data set to be created

(include SAS Data library to be stored in)

DATA libref.SAS-data-set;

2. Identify the file that contains the raw data file (.dat file)

INFILE raw-data-filename;

3. Provide instruction on how to read data from raw data file

INPUT input-specifications;


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Input Specifications Specifies the names of the SAS variables in the new data set

Specifies whether the SAS variables are character or numeric

Identifies the locations of the variables in the raw data file

List Input

Column Input

Formatted Input

Mixed Input


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List Input Used when raw data is separated by spaces

All data in a row must be read in

All missing data must be indicated by period

Simple character datano embedded spaces, no lengths greater

than 8

INPUT statement

Simply list variables after the INPUT keyword in the order theyappear on file.

If variables are character format, place a $ after the variable name

Example) INPUT Name $ City $ Age Height Weight Sex $;


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Column Input Used when raw data file does not have delimiters between values

(large data sets)

Each variables values are found in the same columns in each row

Numeric data must be standardnumbers, decimals, signs, and

scientific notation only Advantages

No spaces required

Missing values left blank

Character data can have embedded spaces

Ability to skip unwanted variables


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Column Input cont. INPUT Statement

Numeric variableslist variable name then list column or rangeof columns where the variable is found on the raw data file

Character variableslist variable name, dollar sign, and then

column or range of columns Example) INPUT Name $ 1-10 Age 26-28 Sex $ 35;


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Formatted Input Appropriate for reading:

Data in fixed columns

Standard and nonstandard character and numeric data

Calendar values to be converted to SAS date value

Read data in using SAS informats

Instruction that SAS uses to read in data values

General forms

Character - $informatw.

Numericinformatw.d

Dateinformatw.


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Formatted Input cont. Character Informats

$w.character string with a width of w, trims leading blanks

$charw.character string with a width of w, does not trim leadingor trailing blanks

Numeric Informats

w.dstandard numeric data with width w and d numbers afterthe decimal

Raw Data Value = 1234567informat = 8.2SAS Data Value = 12345.67

COMMAw.dnumeric data with embedded commas Raw Data Value =1,000,001 informat=COMMA10.

SAS Data Value=1000001


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Formatted Input cont. SAS date values

Stored as special numeric number data

Number of days between January 1, 1960 and the specified data

Informats are used to read and convert the dates

Raw Data Value Informat

11/04/2009 MMDDYY10.

11/04/09 MMDDYY8.

04NOV2009 Date9.

04/11/2009 DDMMYY10.


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Formatted Input cont. Columns read are determined by the starting point and width of the

informat

Example:

INPUT Name $10. Age 3. Height 5.1 BirthDate MMDDYY10.;

- Name Character of length 10, columns 1-10

- Age Numeric with length 3, columns 11-13

- Height Numeric with length 5 (including decimal) and one

decimal place (120.9 for instance), columns 14-18

- Birthdate Date format MMDDYY (11-04-2009 for instance),columns 19 - 28


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Formatted Input cont. Pointer controls

+n moves pointer n positions

@n moves pointer to column n

Example:INPUT Flight 3. +4 Date mmddyy8. @20 Destination $3.;

- Flight - Number of length 3, columns 1 through 3

- DateDate format mmddyy (11/04/09) of length 8, columns 8 through 15

- DestinationCharacter of length 3, columns 20 through 22


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Mixed Formatted Input Styles Mix and match the previous 3 input styles

Example:

Raw Data: Great Smoky Mountains NC/TN 1926 520,269

INPUT ParkName $ 1-22 State $ Year @40 Acreage COMMA9.;

- Parkname - Character of length 22, columns 1 through 22

- State - Character, separated by spaces

- Year - Numeric, separated by spaces

- Acreage - Numeric with informat COMMA9., starts column 40


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Manually Entering Raw Data Files in SAS program1. Start Data step and name the SAS data set to be created

DATA library.SAS-data-set;

2. Provide instructions on how to read data from raw data fileINPUT input-specifications;

3. Manually enter raw data

DATALINES;


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Manually Entering Raw Data Files in SAS programExample:

Data uspresidents;

INPUT President $ Party $ Number;

DATALINES;

Adams F 2

Lincoln R 16

Grant R 18

Kennedy D 35

;

Run;


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Using the import data menu option

1. FileImport Data

2. Standard data sourceselect the file format

3. Specify file location or Browse to select file4. Create name for the new SAS data set and specify location


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Compatible file formats Microsoft Excel Spreadsheets

Microsoft Access Databases

Comma Separate Files (.csv)

Tab Delimited Files (.txt)

dBASE Files (.dbf) JMP data sets

SPSS Files

Lotus Spreadsheets

Stata Files

Paradox Files


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Enter raw data directly into a SAS data set1. ToolsTable Editor

2. Enter data manually into table

- Observations in each row

- Variables in each column

3. Left Click ColumnColumn Attributes

- Variable Name, Variable Label, TypeCharacter/Numeric,

Format, Informat

Note: Informats determine how raw data is read. Formats

determine how variable is displayed.4. Close window Save ChangesYes

Specify File name and directory


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Introduction to theSAS Environment

Questions/Comments


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Working With SAS Data Sets

1. Data Set Manipulation

2. Data Set Processing

3. Combining Data Sets

A. Concatenating/Appending

B. Merging


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Data Set Manipulation

Create a new SAS data set using an existing SAS data set asinput

Specify name of the new SAS data set after the DATA statement

Use SET statement to identify SAS data set being read

Syntax:

DATA output_data_set;

SET input_data_set;

;

RUN;

By default the SET statement reads all observations and variablesfrom the input data set into the output data set.


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Assignment Statements Evaluate an expression

Assign resulting value to a variable

General Form: variable = expression;

Example: miles_per_hour = distance/time;

SAS Functions

Perform arithmetic functions, compute simple statistics, manipulatedates, etc.

General Form: variable=function_name(argument1, argument2,); Example: Time_worked = sum(Day1,Day2, Day3, Day4, Day5);


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Selecting Variables Use DROP and KEEP to determine which variables are written to

new SAS data set.

2 Ways

DROP and KEEP as statements

Form: DROP = Variable1 Variable2;KEEP = Variable3 Variable4 Variable5;

DROP and KEEP options in SET statement

Form: SET input_data_set (KEEP=Var1);


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Conditional Processing Uses IF-THEN-ELSE logic

General Form: IF THEN ;

ELSE IF THEN ;

ELSE ;

is a true/false statement, such as:

Day1=Day2, Day1 > Day2, Day1 < Day2

Day1+Day2=10

Sum(day1,day2)=10

Day1=5 and Day2=5


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Conditional ProcessingSymbolic Mnemonic Example

= EQ IF region=Spain;

~= or ^= NE IF region ne Spain;

> GT IF rainfall > 20;

< LT IF rainfall lt 20;>= GE IF rainfall ge 20;


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Conditional Processing cont. If is true, is processed

ELSE IF and ELSE are only processed if is false

Only one statement specified using this form

Use DO and END statements to execute group of statements

General Form: IF THEN DO;

;

END;

ELSE DO;

;

END;


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Subsetting Rows (Observations) We will look at two ways

Using IF statement

Using WHERE option in SET statement

IF statement

Only writes observations to the new data set in which anexpression is true;

General Form: IF ;

Example: IF career = Teacher;

IF sex ne M;

In the second example, only observations where sex is not equalto M will be written to the output data set


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Subsetting Rows (Observations) cont. Where Option in SET statement

Use option to only read rows from the input data set in which theexpression is true

General Form: SET input_data_set (where=());

Example: SET vacation (where=(destination=Bermuda)); Only observations where the destination equals Bermuda will be

read from the input data set

Comparison

Resulting output data set is equivalent

IF statementall rows read from the input data set

Where optiononly rows where expression is true are read frominput data set

Difference in processing time when working with big data sets


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PROC SORT sorts data according to specified variables General Form: PROC SORT DATA=input_data_set ;

BY Variable1 Variable2;

RUN;

Sorts data according to Variable1 and then Variable2;

By default, SAS sorts data in ascending order

Number low to high

A to Z

Use DESCENDING statement for numbers high to low and letters Z to A BY City DESCENDING Population;

SAS sorts data first by city A to Z and then Population high to low


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Some Options NODUPKEY

Eliminates observations that have the same values for the BYvariables

OUT=output_data_set By default, PROC SORT replaces the input data set with the

sorted data set

Using this option, PROC SORT creates a newly sorted data setand the input data set remains unchanged


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Data Set Processing

Data Set Processing DATA steps read in data from existing data sets or raw data files one

row at a time, like a loop

DATA step reads data from the input data set in the following way:

1. Read in current row from input data set to Program Data

Vector (PDV)2. Process SAS statements

3. PDV to output data set

4. Set current row to the next row in the input data set

5. Iterate to Step 1

One row at a time is processed

Thus we cannot simply add the value of a variable in one row to thevalue in another row


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Data Set Processing

Data Set ProcessingExample Let the following be the input data set dfwlax:

Flight Date Dest FirstClass Economy

439 14955 LAX 20 137

921 14955 DFW 15 131

114 14956 LAX 15 85

982 14956 DFW 5 196

439 14957 LAX 14 116

982 14957 DFW 20 166


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Data Set Processing

Data Set ProcessingExample Consider the following submitted code:

DATA onboard;

SET dfwlax;

Total=FirstClass+Economy;IF FirstClass=20 then FirstClassFull=1;

ELSE FirstClassFull=0;

RUN;


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Data Set Processing

Data Set ProcessingExample Execution of the Data Step

DATA onboard;Current SET dfwlax;

Total=FirstClass+Economy;IF FirstClass=20 then FirstClassFull=1;ELSE FirstClassFull=0;

RUN;

PDV

Onboard

Flight Date Dest FirstClass Economy Total FirstClassFull

439 14955 LAX 20 137 . .



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Data Set Processing


DATA onboard;SET dfwlax;

Current Total=FirstClass+Economy;IF FirstClass=20 then FirstClassFull=1;ELSE FirstClassFull=0;

RUN;

PDV

Onboard


439 14955 LAX 20 137 157 .



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Data Set Processing


DATA onboard;SET dfwlax;Total=FirstClass+Economy;

Current IF FirstClass=20 then FirstClassFull=1;ELSE FirstClassFull=0;

RUN;

PDV

Onboard


439 14955 LAX 20 137 157 1



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Data Set Processing


DATA onboard;SET dfwlax;Total=FirstClass+Economy;IF FirstClass=20 then FirstClassFull=1;ELSE FirstClassFull=0;

Current RUN;

PDV

Onboard


439 14955 LAX 20 137 157 1


439 14955 LAX 20 137 157 1


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Data Set Processing


Current DATA onboard;SET dfwlax;Total=FirstClass+Economy;IF FirstClass=20 then FirstClassFull=1;ELSE FirstClassFull=0;

RUN;

PDV

Onboard


439 14955 LAX 20 137 . .


439 14955 LAX 20 137 157 1


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Data Set Processing


DATA onboard;Current SET dfwlax;


RUN;

PDV

Onboard


921 14955 DFW 15 131 . .


439 14955 LAX 20 137 157 1


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Data Set Processing



Current Total=FirstClass+Economy;IF FirstClass=20 then FirstClassFull=1;ELSE FirstClassFull=0;

RUN;

PDV

Onboard


921 14955 DFW 15 131 146 .


439 14955 LAX 20 137 157 1


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Data Set Processing



Total=FirstClass+Economy;IF FirstClass=20 then FirstClassFull=1;

Current ELSE FirstClassFull=0;RUN;

PDV

Onboard


921 14955 DFW 15 131 146 0


439 14955 LAX 20 137 157 1


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Data Set Processing




Current RUN;

PDV

Onboard


921 14955 DFW 15 131 146 0


439 14955 LAX 20 137 157 1

921 14955 DFW 15 131 146 0


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Combining Data Sets

Concatenating (or Appending) Stacks each data set upon the other

If one data set does not have a variable that the other datasetsdo, the variable in the new data set is set to missing for theobservations from that data set.

General Form: DATA output_data_set;

SET data1 data2;

run;

PROC APPEND may also be used


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Combining Data Sets

Merging Data Sets One-to-One Match Merge

A single record in a data set corresponds to a single record in allother data sets

Example: Patient and Billing Information

One-to-Many Match Merge

Matching one observation from one data set to multipleobservations in other data sets

Example: County and State Information

Note: Data must be sorted before merging can be done

(PROC SORT)


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Combining Data Sets

One-to-One Match Merge Usually need at least one common variable between data sets

matching purposes

For the example, a patient ID would be needed

Do not need common variable if all data sets are in exactly the sameorder


MERGE input_data_set1 input_data_set2;

By variable1 variable2;

RUN;


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Combining Data Sets

One-to-One Match Merge Example:

Performance Goals

Code:

DATA compare;

MERGE performance goals;

BY month;

difference=sales-goal;

RUN;

Month Sales

1 8223

2 6034

3 4220

Month Goal

1 9000

2 6000

3 5000


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Combining Data Sets

One-to-One Match Merge Example cont.:

Compare

Month Sales Goal Difference

1 8223 9000 -777

2 6034 6000 34

3 4220 5000 -780


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Combining Data Sets

One-to-Many Match Merge Requires at least one common variable in the data sets for matching

purposes

For the example, State information is in both the state and countyfiles

If two data sets have variables with the same name, the variables inthe second data set will overwrite the variable in the first.


MERGE Data1 Data2 Data3;


RUN:


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Combining Data Sets

One-to-Many Match Merge Example:

Videos Adjustment

Code:

DATA prices;

MERGE videos adjustment

BY category;

NewPrice=(1-adjustment)*sales;

RUN;

Category Sales

Aerobics 12.99

Aerobics 13.99

Aerobics 13.99

Step 12.99

Step 12.99

Weights 15.99

Category Adjustment

Aerobics .20

Step .30

Weights .25


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Combining Data Sets

One-to-One Many Merge Example cont.:

VideosCategory Sales Adjustment NewPrice

Aerobics 12.99 .20 10.39

Aerobics 13.99 .20 11.19

Aerobics 13.99 .20 11.19

Step 12.99 .30 9.09

Step 12.99 .30 9.09

Weights 15.99 .25 11.99


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Working With SAS Data Sets

Questions/Comments


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Summary Procedures

1. Print Procedure

2. Plot Procedure

3. Univariate Procedure

4. Means Procedure5. Freq Procedure


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Print Procedure

PROC PRINT is used to print data to the output window By default, prints all observations and variables in the SAS data set

General Form: PROC PRINT DATA=input_data_set

;

RUN;

Some Options

input_data_set (obs=n) - Specifies the number of observations tobe printed in the output

NOOBS - Suppresses printing observation number

LABEL - Prints the labels instead of variablenames


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Print Procedure

Optional SAS statements BY variable1 variable2 variable3;

Starts a new section of output for every new value of the BYvariables

ID variable1 variable2 variable3; Prints ID variables on the left hand side of the page and

suppresses the printing of the observation numbers

SUM variable1 variable2 variable3;

Prints sum of listed variables at the bottom of the output

VAR variable1 variable2 variable3;

Prints only listed variables in the output


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Plot Procedure

Used to create basic scatter plots of the data Use PROC GPLOT or PROC SGPLOT for more sophisticated plots

General Form: PROC PLOT DATA=input_data_set;

PLOT vertical_variable *horizontal_variable/;

RUN;

By default, SAS uses letters to mark points on plots

A for a single observation, B for two observations at the same point,etc.

To specify a different character to represent a point

PLOT vertical_variable * horizontal variable = *;


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Plot Procedure

To specify a third variable to use to mark points PLOT vertical_variable * horizontal_variable = third_variable;

To plot more than one variable on the vertical axis

PLOT vertical_variable1 * horizontal_variable=2

vertical_variable2 * horizontal_variable=1/OVERLAY;


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Univariate Procedure

PROC UNIVARIATE is used to examine the distribution of data Produces summary statistics for a single variable

Includes mean, median, mode, standard deviation, skewness,kurtosis, quantiles, etc.

General Form: PROC UNIVARIATE DATA=input_data_set ;VAR variable1 variable2 variable3;

RUN ;

If the variable statement is not used, summary statistics will be produced

for all numeric variables in the input data set.


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Univariate Procedure

Options include: PLOTproduces Stem-and-leaf plot, Box plot, and Normal

probability plot;

NORMALproduces tests of Normality


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Means Procedure

Similar to the Univariate procedure General Form: PROC MEANS DATA=input_data_set options;

;

RUN;

With no options or optional SAS statements, the Means procedure willprint out the number of non-missing values, mean, standard deviation,minimum, and maximum for all numeric variables in the input data set


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Means Procedure Options

Statistics Available

Note: The default alpha level for confidence limits is 95%. Use ALPHA= option tospecify different alpha level.

CLM Two-Sided Confidence Limits RANGE Range

CSS Corrected Sum of Squares SKEWNESS Skewness

CV Coefficient of Variation STDDEV Standard Deviation

KURTOSIS Kurtosis STDERR Standard Error of Mean

LCLM Lower Confidence Limit SUM Sum

MAX Maximum Value SUMWGT Sum of Weight Variables

MEAN Mean UCLM Upper Confidence Limit

MIN Minimum Value USS Uncorrected Sum of Squares

N Number Non-missing Values VAR Variance

NMISS Number Missing Values PROBT Probability for Students t

MEDIAN (or P50) Median T Students t

Q1 (P25) 25% Quantile Q3 (P75) 75% Quantile

P1 1% Quantile P5 5% Quantile




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Means Procedure

Optional SAS Statements VAR Variable1 Variable2;

Specifies which numeric variables statistics will be produced for


Calculates statistics for each combination of the BY variables

Output out=output_data_set;

Creates data set with the default statistics


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FREQ Procedure PROC FREQ is used to generate frequency tables

Most common usage is create table showing the distribution of categoricalvariables

General Form: PROC FREQ DATA=input_data_set;

TABLE variable1*variable2*variable3/;

RUN;

Options

LISTprints cross tabulations in list format rather than grid

MISSINGspecifies that missing values should be included in the tabulations

OUT=output_data_setcreates a data set containing frequencies, list format

NOPRINTsuppress printing in the output window

Use BY statement to get percentages within each category of a variable


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Summary Procedures

Questions/Comments


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Statistical Analysis Procedures

1. CorrelationPROC CORR

2. RegressionPROC REG

3. Analysis of VariancePROC ANOVA

4. Chi-square Test of AssociationPROC FREQ5. General Linear ModelsPROC GENMOD


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CORR Procedure PROC CORR is used to calculate the correlations between variables

Correlation coefficient measures the linear relationship between two variables

Values Range from -1 to 1

Negative correlation - as one variable increases the other decreases

Positive correlationas one variable increases the other increases

0no linear relationship between the two variables 1perfect positive linear relationship

-1perfect negative linear relationship

General Form: PROC CORR DATA=input_data_set

VAR Variable1 Variable2;With Variable3;

RUN;

CO


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CORR Procedure

If the VAR and WITH statements are not used, correlation is computedfor all pairs of numeric variables

Options include

SPEARMANcomputes Spearmans rank correlations

KENDALLcomputes Kendalls Tau coefficients

HOEFFDINGcomputes HoeffdingsD statistic

REG P d


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REG Procedure PROC REG is used to fit linear regression models by least squares estimation

One of many SAS procedures that can perform regression analysis

Only continuous independent variables (Use GENMOD for categorical variables)

General Form:

PROC REG DATA=input_data_set

MODEL dependent=independent1 independent2/;;

RUN;

PROC REG statement options include

PCOMIT=m - performs principle component estimation with m principlecomponents

CORRdisplays correlation matrix for independent variables in the model

REG P d


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REG Procedure

MODEL statement options include SELECTION=

Specifies a model selection procedure be conductedFORWARD, BACKWARD, and STEPWISE

ADJRSQ - Computes the Adjusted R-Square MSEComputes the Mean Square Error

COLLINperforms collinearity analysis

CLBcomputes confidence limits for parameter estimates

ALPHA=

Sets significance value for confidence and prediction intervalsand tests

REG P d


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REG Procedure

Optional statements include PLOT Dependent*Independent1generates plot of data

ANOVA P d


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ANOVA Procedure

PROC ANOVA performs analysis of variance Designed for balanced data (PROC GLM used for unbalance data)

Can handle nested and crossed effects and repeated measures

General Form: PROC ANOVA DATA=input_data_set ;

CLASS independent1 independent2;

MODEL dependent=independent1 independent2;;

Run;

Class statement must come before model statement, used to defineclassification variables

ANOVA P d


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ANOVA Procedure

Useful PROC ANOVA statement optionOUTSTAT=output_data_set Generates output data set that contains sums of squares,

degrees of freedom, statistics, and p-values for each effect in themodel

Useful optional statementMEANS independent1/

Used to perform multiple comparisons analysis

Set to:

TUKEYTukeysstudentized range test

BONBonferroni t test

Tpairwise t tests DuncanDuncans multiple-range test

ScheffeScheffesmultiple comparison procedure

FREQ P d


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FREQ Procedure

PROC FREQ can also be used to perform analysis with categorical data General Form: PROC FREQ DATA=input_data_set;

TABLE variable1 variable2/;

RUN;

TABLE statement options include: AGREE Tests and measures of classification agreement including McNemarstest,

Bowkerstest, Cochrans Q test, and Kappa statistics

CHISQ - Chi-square test of homogeneity and measures of association

MEASURE - Measures of association include Pearson and Spearman correlation,gamma, Kendalls Tau, Stuarts tau, SomersD, lambda, odds ratios, riskratios, and confidence intervals

GENMOD P d


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GENMOD Procedure

PROC GENMOD is used to estimate linear models in which the responseis not necessarily normal

Logistic and Poisson regression are examples of generalized linearmodels

General Form:

PROC GENMOD DATA=input_data_set;

CLASS independent1;

MODEL dependent = independent1 independent2/

dist=

link=;

run;

GENMOD P d


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GENMOD Procedure DIST = - specifies the distribution of the response variable

LINK= - specifies the link function from the linear predictor to the mean ofthe response

ExampleLogistic Regression

DIST = binomial LINK = logit

ExamplePoisson Regression

DIST = poisson

LINK = log

St ti ti l A l i P d


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Statistical Analysis Procedures

Questions/Comments

sas short course presentation 11-4-09

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