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Logistic Regression & ClassificationLogistic Regression & Classification

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Logistic Regression ModelLogistic Regression ModelIn 1938, Ronald Fisher and Frank Yates suggested the logit link for regression with a bibinary response variable.

ln(Odds of Y 1| x)

( 1| ) ( 1| )ln ln( 0| ) 1 ( 1| )

P Y x P Y xP Y x P Y x

x

0 1ln

1x

xx

x

xx

1

lnlogit

Logistic regression model is the most popular model for binary data.

Logistic regression model is generally used to study the relationship between a binary response variable and a group of predictors (can be either continuousand a group of predictors (can be either continuous or categorical).Y = 1 (true success YES etc ) orY 1 (true, success, YES, etc.) orY = 0 ( false, failure, NO, etc.)

Logistic regression model can be extended to Logistic regression model can be extended to model a categorical response variable with more than two categories. The resulting model is sometimes referred to as the multinomial logistic regression model (in contrast to the binomial logistic regression for a binary response variable )logistic regression for a binary response variable.)

C id bi i bl Y 0 1 d i l di t Consider a binary response variable Y=0 or 1and a single predictor variable x. We want to model E(Y|x) =P(Y=1|x) as a function of x. The logistic regression model expresses the logistic transform of P(Y=1|x) as a linear function of the predictor.

( 1| )l P Y x This model can be rewritten as

0 1( | )ln

1 ( 1| )x

P Y x

)exp(11

)exp(1)exp()|1( 10

xxxxYP

E(Y|x)= P(Y=1| x) *1 + P(Y=0|x) * 0 = P(Y=1|x) is bounded between 0 and 1 for all values of x The following linear model may

)exp(1)exp(1 1010 xx

between 0 and 1 for all values of x. The following linear model may violate this condition sometimes:

YP |1 xxYP 10|1

More reasonable modelingMore reasonable modeling

Pi

LogitTransformTransform

Predictor Predictor

In the simple logistic regression, the regression coefficient has the interpretation that it is the 1log of the odds ratio of a success event (Y=1) for a unit change in x.

11010 ][)]1([)|0()|1(ln

)1|0()1|1(ln

xxYP

xYPYP

xYP11010)|0()1|0( xYPxYP

For multiple predictor variables, the logistic regression model is

kkk xx

xxxYPxxxYP

...

)|0(),...,,|1(

ln 11021

kxxxYP ),...,,|0( 21

The Logistic Regression ModelTheLogisticRegressionModel

Alternative Model EquationAlternativeModelEquation

xx xx

L gi ti R g i SAS P dLogistic Regression, SAS Procedure http://www.ats.ucla.edu/stat/sas/output/SAS_logit_output.htm Proc Logistic This page shows an example of logistic regression with

footnotes explaining the output The data were collected onfootnotes explaining the output. The data were collected on 200 high school students, with measurements on various tests, including science, math, reading and social studies. The response variable is high writing test score (honcomp),where a writing score greater than or equal to 60 is considered high and less than 60 considered low; from whichconsidered high, and less than 60 considered low; from which we explore its relationship with gender (female), reading test score (read), and science test score (science). The dataset used in this page can be downloaded from

http://www.ats.ucla.edu/stat/sas/webbooks/reg/default.htm.

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Logistic Regression SAS ProcedureLogistic Regression, SAS Procedure

data logit; set "c:\Temp\hsb2"; honcomp = (write >= 60); p ( );run; proc logistic data= logit descending; p g g g;model honcomp = female read science; run;run;

proc export data=logitproc export data logitoutfile = "c:\Temp\hsb2.xls"dbms = xls replace;dbms xls replace;run;

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Logistic Regression SAS ProcedureLogistic Regression, SAS Procedure

data logit; set "c:\Temp\hsb2"; honcomp = (write >= 60); p ( );run; proc logistic data= logit descending; p g g g;model honcomp = female read science; output out=pred p=phat lower = lcl upper = ucl;output out pred p phat lower lcl upper ucl;run;

t d t dproc export data=predoutfile = "c:\Temp\pred.xls"db l ldbms = xls replace;run;

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Logistic Regression SAS ProcedureLogistic Regression, SAS Procedure

data logit; set "c:\Temp\hsb2"; honcomp = (write >= 60); run; proc logistic data= logit descending; model honcomp = female read science /selection=stepwise; run;

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Descending option in proc logistic and proc genmod

Th d di ti i SAS th The descending option in SAS causes the levels of your response variable to be sorted from highest to lowest (by defaultsorted from highest to lowest (by default, SAS models the probability of the lower category)category).

In the binary response setting, we code the event of interest as a 1 and use theevent of interest as a 1 and use the descending option to model that probability P(Y = 1 | X = x).( | )

In our SAS example, well see what happens when this option is not used.pp p

Logistic Regression, SAS Output

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LOGISTIC and GENMOD procedure for a single continuous predictor

PROC LOGISTIC DATA= dataset ;PROC LOGISTIC DATA= dataset ;MODEL response=predictor /;OUTPUT OUT=SAS-dataset keyword=name y

;RUN;

PROC GENMOD DATA=dataset ;MAKE OBSTATS OUT=SAS-data-set;MAKE OBSTATS OUT SAS data set;MODEL response=predictors ;

RUN;

The GENMOD procedure is much more general than the logistic procedure as it can accommodate other generalized linear model, and also cope with repeated measures data. It is not required for exam.

http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_genmod_sect053.htm

Logistic RegressionLogistic Regression

Logistic Regression - Dichotomous Logistic Regression Dichotomous Response variable and numeric and/or categorical explanatory variable(s)categorical explanatory variable(s) Goal: Model the probability of a particular outcome

as a function of the predictor variable(s)as a function of the predictor variable(s) Problem: Probabilities are bounded between 0 and

1 Distribution of Responses: Binomial Link Function: Link Function:

1

ln)(g

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Logistic Regression with 1 PredictorLogistic Regression with 1 Predictor

Response - Presence/Absence of characteristic

Predictor - Numeric variable observed for each case

Model - (x) Probability of presence at predictor level x

x

xex10

1)(

xe 101)(

= 0 P(Presence) is the same at each level of x = 0 P(Presence) is the same at each level of x

> 0 P(Presence) increases as x increases

< 0 P(Presence) decreases as x increases17

Hypothesis testingHypothesis testing

Significance tests focuses on a test of H0: = 0 vs. Ha: 0.0

The Wald, Likelihood Ratio, and Score test are used (well focus on Waldtest are used (we ll focus on Wald method)

Wald CI easil obtained score and LR Wald CI easily obtained, score and LR CI numerically obtained.

For Wald, the 95% CI (on the log odds scale) is

))((961 SE)

))((96.1 11 SE

Logistic Regression with 1 Predictor

are unknown parameters and must be estimated using statistical software such as SAS, or R

Primary interest in estimating and testing y g ghypotheses regarding where S.E. stands for standard error. Large-Sample test (Wald Test): H0: = 0 HA: 0H0: 0 HA: 0

:..

2

12obsXST

:

..

22

1

obs

XRR

ES

)(:

:..22

1,

obs

obs

XPvalP

XRR

19

Example Rizatriptan for Migraine Example - Rizatriptan for Migraine

Response - Complete Pain Relief at 2 hours (Yes/No)( )

Predictor - Dose (mg): Placebo (0),2.5,5,10

Dose # Patients # Relieved % Relieved 0 67 2 3.0 0 67 3.0

2.5 75 7 9.3 5 130 29 22.3

10 145 40 27 610 145 40 27.6

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SAS DataSAS DataDose # Patients # Relieved % Relieved

0 67 2 3.0 2.5 75 7 9.3 5 130 29 22.3

10 145 40 27 610 145 40 27.6

Y Dose NumberY Dose Number0 0 651 0 21 0 20 2.5 681 2.5 70 5 1011 5 290 10 105

21

0 10 1051 10 40

SAS ProcedureSAS ProcedureData dr gData drug;Input Y Dose Number;Datalines;Datalines;0 0 651 0 2 proc logistic data= drug descending;1 0 20 2.5 681 2.5 7

proc logistic data= drug descending; model Y = Dose; f N b0 5 101

1 5 29freq Number;run;

0 10 1051 10 40;Run;

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Example - Rizatriptan for Migrainep p g

Variables in the Equation

.165 .037 19.819 1 .000 1.1802 490 285 76 456 1 000 083

DOSEConstant

Step1

a

B S.E. Wald df Sig. Exp(B)

-2.490 .285 76.456 1 .000 .083Constant1

Variable(s) entered on step 1: DOSE.a.

xe 165.0490.2^ xe

ex 165.0490.21)(

1650

0:0:2

110 HH A

843:

819.19037.0165.0:..

22

2

XRR

XST obs

000.:

84.3: 1,05.valPXRR obs

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Interpretation of a single continuousparameter

The sign () of determines whether thelog odds of y is increasing or decreasing for every 1-unit increase in x.

If > 0, there is an increase in the log odds If 0, there is an increase in the