sas training session 2

7/29/2019 SAS Training Session 2

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SAS Training Session 2

Basic Statistical Analysis Using SAS

Sun Li

Centre for Academic Computing

[email protected]
mailto:[email protected]:[email protected]


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Outline

Produce reports Using Output Delivery System (ODS) to produce output

Producing summary report using PROC MEANS & PROC FREQ

Perform elementary statistical procedures

Simple inference using PROC FREQ & PROC UNIVARIATE Correlation using PROC CORR

Group comparison using PROC TTEST

Introduction to regression procedures

ANOVA procedures using PROC ANOVA and PROC GLM

General Linear regression using PROC REG

Logistic models using PROC LOGISTIC


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Produce reports

Produce reports

Using Output Delivery System (ODS) to produce output

Producing HTML output

Producing RTF file Producing descriptive statistics using PROC MEANS

Computing descriptive statistics

Creating a summarized data set

Produce tabular reports using PROC FREQ Creating frequency tables

Creating cross-tabulations


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Produce reports - ODS

Using Output Delivery System (ODS)


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RTF output

icon


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** producing HTML output and RTF file;PROC PRINT data=sas2.marchflights (obs=10);RUN;ODS html body=E:\lsun\marchflights.html';PROC PRINT data=sas2.marchflights (obs=10);RUN;ODS html close;

ODS listing close;ODS rtf file=E:\lsun\insure.rtf';PROC PRINT data=sas2.insure;RUN;PROC TABULATE data=sas2.insure;var total balancedue;table min max mean, total balancedue;RUN;ODS rtf close;ODS listing;


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Produce reports PROC MEANS

PROC MEANSStatistic-keywords:

PROC MEANS ;

RUN;

CLM Two-sided confidence intervals RANGE The range

CSS Corrected sun of squares SKEWNESS Skewness

CV Coefficient of variation STDDEV Standard deviation

KURTOSIS Kurtosis STDERR Standard error of the mean

LCLM Lower confidence interval 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 of non-missing values VAR VarianceNMISS Number of missing values PROBT Probability of Students t

MEDIAN Median T Students t

Q1 25% quantile Q3 75% quantile

P1 1% quantile P5 5% quantile




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** computing statistics using proc means;DATA prdsale;set sas2.prdsale;RUN;PROC PRINT data=prdsale (obs=10); RUN;PROC MEANS data=prdsale maxdec=2 alpha=0.1 clm mean std;var actual predict;class product;RUN;PROC SORT data=prdsale;by product;RUN;PROC MEANS data=prdsale maxdec=2 alpha=0.1 clm mean std;var actual predict;by product;RUN;

B Ystatement vs. CLASSstatement:

1. Unlike CLASSprocessing, B Yprocessing

requires that your data already be sorted or indexed

in the order of the B Yvariables.

2. B Ygroup results have a layout that is different

from the layout ofCLASSgroup results.


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** creating a summarized data set using proc means;PROC MEANS data=prdsale mean clm;var actual predict;class product year;output out=prdstats mean=ave_act ave_pre uclm=uclm_actuclm_pre lclm=lclm_act lclm_pre;RUN;

OUTPUT OUT=SAS-data-set statistic=variable(s);

OUT=specifies the name of the output data set

statistic= specifies the summary statistic written out

variable(s) specifies the names of the variables to create. These variables represent

the statistics for the analysis variables that are listed in the VAR statement.


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Produce reports PROC FREQ

PROC FREQ PROC FREQ ;TABLES variables

variable-1*variable-2 ;

RUN;

**creating tables in proc freq;PROC FREQ data=Color;weight Count;

tables Eyes Hair Eyes*Hair;RUN;PROC SORT data=Color;by region;RUN;PROC FREQ data=color nlevels;weight count;tables eyes*hair /crosslist;by region;RUN;

NLEVELS: displays the number of levels for

the variables listed.

CROSSLIST: displays the cross-tabulation

results in a listing form.


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Produce reports

QUIZ 1

See the file QUIZ-SAS2.pdf.


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Elementary statistical procedures

Perform elementary statistical procedures Simple statistical inference

PROC FREQ

PROC UNIVARIATE

Correlation using PROC CORR

Group comparison using PROC TTEST

One-sample T test

Two-independent samples T test


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Elementary statistical procedures PROC FREQ

Simple statistical inference Chi-square test using PROC FREQ

More hypothesis tests in PROC UNIVARIATE

**chi-square test using proc freq;PROC FREQ data=color;weight Count;table eyes*hair /chisq cl;RUN;

CHISQ: displays Chi-square test results.

CL : displays the 95% confidence intervals of the statistics.


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PROC UNIVARIATE PLOT NORMAL ;

CLASS variable(s) ;

HISTOGRAM variable(s) /normal ;

PROBPLOT variable(s) /normal ;

QQPLOT variable(s);

VAR variable(s) ;

RUN ;

PLOT:generates a stem and leaf plot, a box plot and a normal probability plot

NORMAL:generates normality test

HISTOGRAM/normal:generates histogram with fitted distribution curve

PROBPLOT/normal:generates probability plot with specified distribution

QQPLOT:generates QQ plot

**simple hypothesis tests in proc univariate;PROC UNIVARIATE data=prdsale modes plot normal cibasic(alpha=.1);var actual;histogram /normal (color=red);qqplot;RUN;

Elementary statistical procedures PROC UNIVARIATE


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PROC UNIVARIATE vs. PROC MEANS

In general, if youre interested in a general view of your population

distribution, or if you want to do some simple hypothesis tests

(normality, etc.), then PROC UNIVARIATE is appropriate. Otherwise, if

youre looking at specific statistics, then PROC MEANS and a specificoutput may be more efficient.

If youre looking for a probability plot or a histogram, PROC UNIVARIATE

may be what you need.

Elementary statistical procedures PROC UNIVARIATE


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Correlation

Elementary statistical procedures PROC CORR

PROC CORR options;VAR variables ;

RUN ;

**correlation using proc corr;ODS html;ODS graphics on;PROC CORR data=sas2.citiday nomiss pearson spearman covplots=matrix;var snydjcm snysecm dsiuswil;RUN;ODS graphics off;ODS html close;

COV requests output of the covariance matrix (for Pearson).

PEARSON requests Pearsons product moment correlation coefficient (default).

KENDALL requests Kendalls tau-b correlation coefficient.

SPEARMAN requests Spearmans rank-order correlation coefficient.

PARTIAL produces partial correlations in the VARvariable list, controlling for the variables specified in the

PARTIALvariable list.


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Group comparison PROC TTEST ;VAR variables ;CLASS stratifier ;

RUN ;

**group comparison using proc ttest;PROC TABULATE data=prdsale;var actual;class year quarter;tables year*quarter, actual*(mean min max);RUN;PROC TTEST data=prdsale h0=500 alpha=0.1;var actual;where year=1994;RUN;PROC TTEST data=prdsale;class year;var actual;RUN;

Elementary statistical procedures PROC TTEST


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Elementary statistical procedures

Variable name Variable information

permno CRSP Permanent Number

date Numeric date

ret Holding Period Return

retx Return without dividends

mktrf Excess return on markertsmb Small-minus-big return

hml High-minus-low return

rf Risk-free return rate

umd Momentum factor

QUIZ 2

See the file QUIZ-SAS2.pdf.


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Introduction to regression procedures

Introduction to regression procedures Introduction to ANOVA procedures

Comparing groups using PROC ANOVA

Unbalanced design using PROC GLM

General linear models

The REG procedure

Logistic regression

Statistical background

The Logistic procedure


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Regression procedures PROC ANOVA

Introduction to ANOVA procedures

PROC ANOVA ;

CLASS stratifier ;

MODEL dependents = effects ;

MEANS var / options;

RUN ;

QUIT;

**comparing groups using proc anova;PROC TABULATE data=sas2.cargo99;var cargorev cargowgt;class routeid;tables routeid, cargorev*meancargowgt*mean;RUN;ODS html;ODS graphics on;PROC ANOVA data=sas2.cargo99;

class routeid;model cargorev = routeid;RUN; means routeid / Bon;RUN;ODS graphics off;ODS html close;QUIT;

CLASS specifies stratifier variables.

MODEL defines the model to be fit.

MEANS compute and compare means.


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PROC GLM ;

CLASS variables;

MODEL dependents = effects ;Means effects;

RUN ;

QUIT;

**unbalanced ANOVA for two-way design with interaction;ODS html;ODS graphics on;PROC GLM data=sas2.cargo99;class routeid;model cargorev=routeid cargowgt routeid*cargowgt / ss1 ss2 ss3 ss4;RUN; lsmeans routeid / pdiff=all adjust=bon ;RUN;ODS graphics off;ODS html close;QUIT;

Regression procedures PROC GLM


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Linear regression procedures

General linear models

XY

.XY

sy variableexplanatorofmatrix)1(theisresponses.ofvector1theis

pnn

tscoefficienregressiontheofestimatessquareleastYX1

X)X(


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PROC GLM:

It uses the method of least squares to fit general linear models

relating to one or several continuous dependent variables to one or

several independent variables.

Strengths:

direct specification of polynomial effects

ease of specifying categorical effects (PROC GLM automaticallygenerates dummy variables for class variables)

Weaknesses:

No collinearity diagnostics

No influence diagnostics

No scatter plots

Only one model at one time

Regression procedures PROC GLM


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PROC REG: Provides the most general analysis capabilities

handles multiple regression models

provides nine model-selection methods

allows interactive changes both in the model and in the data used to

fit the model

allows linear equality restrictions on parameters

tests linear hypotheses and multivariate hypotheses

produces collinearity diagnostics, influence diagnostics, and partial

regression leverage plots

saves estimates, predicted values, residuals, confidence limits, and

other diagnostic statistics in output SAS data sets

generates plots of data and of various statistics

Regression procedures PROC REG


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PROC REG ;MODEL dependent-variable = predictors /

selection=method R CLI CLM ;

PLOT r.*p. ;

RUN ;

QUIT;

*Regression using proc reg ;PROC REG data=insurance;model time = size type sizetype

/selection=none;RUN;delete sizetype;print;RUN;plot r.*p. time*p.;RUN;QUIT;

DATA insurance;input time size type @@;sizetype=size*type;datalines;17 151 0 26 92 0 21 175 0 30 31 0 22 104 00 277 0 12 210 0 19 120 0 4 290 0 16 238 028 164 1 15 272 1 11 295 1 38 68 1 31 85 121 224 1 20 166 1 13 305 1 30 124 1 14 246 1;

SELECTION:specifies model selectionmodel: forward, backward, etc.

DELETE:deletes variables from the model.

PRINT:print the analysis results.

PROT:produces diagnostic plots.



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*Polynomial regression using proc reg;PROC REG data=USPopulation;var YearSq;model Population=Year / selection=none;plot r.*p. ;RUN; add YearSq;

print;plot / cframe=ligr;RUN; plot (Population predicted. u95. l95.)*Year / overlay cframe=ligr;RUN;QUIT;ODS html;ODS graphics on;PROC REG data=USPopulation;Linear: model Population=Year;Quadratic:model Population=Year YearSq;RUN;ODS graphics off;ODS html close;

QUIT;



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Logistic regression procedures

Logistic models Binary logistic model: dichotomous response outcomes

e.g.: presence or absence of an event

PROC LOGISTIC provides the capability of model-fitting.

Ordinal logistic model: ordinal response variable with more than two

ordered categories

e.g.: a 5-point Likert scale

PROC LOGISTIC fits the proportional odds model with CLOGIT link.

Multinomial logistic model: nominal response variables with more thantwo categories

e.g.: different types of programs in school

PROC LOGISTIC fits the generalized logit model if you specify the GLOGIT link.


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Binary logistic model

Ordinal logistic model

Multinomial logistic model

)|( iii xyE

Xg )())1/(log()(logit

kiXXiYg i ,......,1,'))|(Pr(

kiXXkY

XiYii ,......,1,'

)|1Pr(

)|Pr(log

PROC LOGISTIC ;CLASS variables;

MODEL dependent-variable = predictors / options;

RUN ;

Regression procedures PROC LOGISTIC


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Binary logistic model

Variable name Variable information

age Age in years

ed Level of education

1= didnt complete high school 2= high school degree

3= college degree 4= undergraduate 5= postgraduate

employ Years with current employer

address Years in current address

income Household income in thousands

debtinc Debt to income ratio (*100)

creddebt Credit card debt in thousands

othdebt Other debts in thousands

default Previously defaulted (1=Yes; 0=No)

How to identify a person with high chance of getting defaults on the bank

loan. We have 700 records from bank database (bankloan) .



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*Binary logistic model;PROC MEANS data=sas2.bankloan;var age employ address income debtinc creddebt othdebt;class default;RUN;

PROC LOGISTIC data=sas2.bankloan;class ed(ref='1') / param=ref;model default(event='1')= ed age employ address income debtinccreddebt othdebt/ selection=stepwise slentry=0.3 slstay=0.35 detailsrsquare lackfit;output out=bankloanpred p=prob lower=lcl upper=ucl xbeta=logit;ods output parameterestimates=bankloanest;RUN;


SELECTION: specifies model selection methods.

SLENTRY=0.3: a significance level of 0.3 is required to allow a variable into the model.

SLSTAY=0.35: a significance level of 0.35 is required for a variable to stay in the model.

DETAILS:produces a detailed account of the variable selection process.

RSQUARE:produces generalized R-square measure.

LACKFIT:produces Hosmer and Lemeshow goodness-of-fit test for the final selected model.

PARAM=REF: specifies the reference cell coding.

REF:specifies reference group for categorical predictors.

EVENT:specifies reference group for dependent variable.


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Proportional Odds Model for Ordinal Logistic Model

To identify factors that influence a persons income category.

*Ordinal logistic model;DATA income;

set sas2.bankloan;if income


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Generalized Logits Model for Multinomial Logistic Model*Multinomial logistic model;DATA school;input school program style $ count;datalines;1 1 self 101 1 team 171 1 class 261 2 self 51 2 team 121 2 class 502 1 self 212 1 team 172 1 class 262 2 self 162 2 team 122 2 class 363 1 self 153 1 team 153 1 class 163 2 self 123 2 team 123 2 class 20;

To identify the difference of study types among

schools and programs.


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PROC LOGISTIC data=school;freq count;class school program / order=data;model style = school program school*program / link=glogit;output out=progstat p=prob;ods output parameterestimates=progest;RUN;PROC FREQ data=progstat;format prob 5.4;tables school*program*_level_*prob /list nopercent nocum;RUN;DATA progodd;set progest;

odds=exp(estimate);RUN;PROC PRINT data=progodd;var response estimate odds;RUN;

LINK: specifies the link function.

GLOGIT:generalized logit function.


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Resources and books

Regression methods Applied Regression Analysis, Linear Models, and Related Methods by John Fox

Regression Analysis by Example by Chatterjee, Hadi and Price

An Introduction to Generalized Linear Models, Second Edition by Annette J. Dobson

Logistic regression and categorical data analysis

Applied Logistic Regression, Second Edition by David Hosmer and Stanley Lemeshow

An Introduction to Categorical Data Analysis Alan Agresti

CAC statistical consultation support: CAC statistical WIKI page:

http://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS.aspx

Statistical consultation service: [email protected]
http://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS.aspxmailto:[email protected]:[email protected]://research2.smu.edu.sg/CAC/StatisticalComputing/Wiki/SAS.aspx


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End!

sas training session 2

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