Logistic regression. Recall the simple linear regression model: y = 0 + 1 x + where we are trying...

Logistic regression

Recall the simple linear regression model:

y = 0 + 1x +

where we are trying to predict a continuous dependent variable y from a continuous independent variable x.

This model can be extended to Multiple linear regression model:

y = 0 + 1x1 + 2x2 + … + + pxp + Here we are trying to predict a continuous dependent variable y from a several continuous dependent variables x1 , x2 , … , xp .

Now suppose the dependent variable y is binary.

It takes on two values “Success” (1) or “Failure” (0)

This is the situation in which Logistic Regression is used

We are interested in predicting a y from a continuous dependent variable x.

Example

We are interested how the success (y) of a new antibiotic cream is curing “acne problems” and how it depends on the amount (x) that is applied daily.

The values of y are 1 (Success) or 0 (Failure).

The values of x range over a continuum

The logisitic Regression ModelLet p denote P[y = 1] = P[Success].

This quantity will increase with the value of x.

The ratio: 1

p

pis called the odds ratio

This quantity will also increase with the value of x, ranging from zero to infinity.

The quantity: ln1

p

p

is called the log odds ratio

Example: odds ratio, log odds ratio

Suppose a die is rolled:Success = “roll a six”, p = 1/6

1 16 6

516 6

1

1 1 5

p

p

The odds ratio

1ln ln ln 0.2 1.69044

1 5

p

p

The log odds ratio

The logisitic Regression Model

i. e. :

0 1

1xp

ep

In terms of the odds ratio

0 1ln1

px

p

Assumes the log odds ratio is linearly related to x.

The logisitic Regression Model

0 1

1xp

ep

or

Solving for p in terms x.

0 1 1xp e p

0 1 0 1x xp pe e

0 1

0 11

x

x

ep

e

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10

Interpretation of the parameter 0

(determines the intercept)

p

0

01

e

e

x

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10


(determines when p is 0.50 (along with 0))

p 0 1

0 1

1 1

1 1 1 2

x

x

ep

e

x

00 1

1

0 or x x

when

Also0 1

0 11

x

x

dp d e

dx dx e

0

1

x

when

0 1 0 1 0 1 0 1

0 1

1 1

2

1

1

x x x x

x

e e e e

e

0 1

0 1

1 12 41

x

x

e

e

1

4

is the rate of increase in p with respect to x when p = 0.50

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10


(determines slope when p is 0.50 )

p

x

1slope 4

The data

The data will for each case consist of

1. a value for x, the continuous independent variable

2. a value for y (1 or 0) (Success or Failure)

Total of n = 250 cases

case x y

1 0.8 02 2.3 13 2.5 04 2.8 15 3.5 16 4.4 17 0.5 08 4.5 19 4.4 110 0.9 011 3.3 112 1.1 013 2.5 114 0.3 115 4.5 116 1.8 017 2.4 118 1.6 019 1.9 120 4.6 1

case x y

230 4.7 1231 0.3 0232 1.4 0233 4.5 1234 1.4 1235 4.5 1236 3.9 0237 0.0 0238 4.3 1239 1.0 0240 3.9 1241 1.1 0242 3.4 1243 0.6 0244 1.6 0245 3.9 0246 0.2 0247 2.5 0248 4.1 1249 4.2 1250 4.9 1

Estimation of the parameters

The parameters are estimated by Maximum Likelihood estimation and require a statistical package such as SPSS

Using SPSS to perform Logistic regression

Open the data file:

Choose from the menu:

Analyze -> Regression -> Binary Logistic

The following dialogue box appears

Select the dependent variable (y) and the independent variable (x) (covariate).

Press OK.

Here is the output

The Estimates and their S.E.

The parameter Estimates

SE

X 1.0309 0.1334Constant -2.0475 0.332

1 1.03090 -2.0475


(determines the intercept)

0

0

-2.0475

-2.0475intercept 0.1143

1 1

e e

e e


(determines when p is 0.50 (along with 0))

0

1

2.04751.986

1.0309x

Another interpretation of the parameter 1

1

4

is the rate of increase in p with respect to x when p = 0.50

1 1.03090.258

4 4

The Multiple Logistic Regression model

Here we attempt to predict the outcome of a binary response variable Y from several independent variables X1, X2 , … etc

0 1 1ln1 p p

pX X

p

0 1 1

0 1 1 or

1

p p

p p

X X

X X

ep

e

Multiple Logistic Regression an example

In this example we are interested in determining the risk of infants (who were born prematurely) of developing BPD (bronchopulmonary dysplasia)

More specifically we are interested in developing a predictive model which will determine the probability of developing BPD from

X1 = gestational Age and X2 = Birthweight

For n = 223 infants in prenatal ward the following measurements were determined

1. X1 = gestational Age (weeks),

2. X2 = Birth weight (grams) and3. Y = presence of BPD

The datacase Gestational Age Birthweight presence of BMD

1 28.6 1119 12 31.5 1222 03 30.3 1311 14 28.9 1082 05 30.3 1269 06 30.5 1289 07 28.5 1147 08 27.9 1136 19 30 972 0

10 31 1252 011 27.4 818 012 29.4 1275 013 30.8 1231 014 30.4 1112 015 31.1 1353 116 26.7 1067 117 27.4 846 118 28 1013 019 29.3 1055 020 30.4 1226 021 30.2 1237 022 30.2 1287 023 30.1 1215 024 27 929 125 30.3 1159 026 27.4 1046 1

The resultsVariables in the Equation

-.003 .001 4.885 1 .027 .998

-.505 .133 14.458 1 .000 .604

16.858 3.642 21.422 1 .000 2.1E+07

Birthweight

GestationalAge

Constant

Step1

a

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

Variable(s) entered on step 1: Birthweight, GestationalAge.a.

ln 16.858 .003 .5051

pBW GA

p

16.858 .003 .505

1BW GAp

ep

16.858 .003 .505

16.858 .003 .5051

BW GA

BW GA

ep

e

Graph: Showing Risk of BPD vs GA and BrthWt

0

0.2

0.4

0.6

0.8

1

700 900 1100 1300 1500 1700

GA = 27

GA = 28

GA = 29

GA = 30

GA = 31

GA = 32

Discrete Multivariate Analysis

Analysis of Multivariate Categorical Data

Example 1

Data Set #1 - A two-way frequency table Serum Systolic Blood pressure

Cholesterol <127 127-146 147-166 167+ Total <200 117 121 47 22 307 200-219 85 98 43 20 246 220-259 119 209 68 43 439 260+ 67 99 46 33 245 Total 388 527 204 118 1237

In this study we examine n = 1237 individuals measuring X, Systolic Blood Pressure and Y, Serum Cholesterol

Example 2

The following data was taken from a study of parole success involving 5587 parolees in Ohio between 1965 and 1972 (a ten percent sample of all parolees during this period).

The study involved a dichotomous response Y– Success (no major parole violation) or – Failure (returned to prison either as technical

violators or with a new conviction)

based on a one-year follow-up.

The predictors of parole success included are: 1. type of committed offence (Person offense or

Other offense), 2. Age (25 or Older or Under 25), 3. Prior Record (No prior sentence or Prior

Sentence), and 4. Drug or Alcohol Dependency (No drug or

Alcohol dependency or Drug and/or Alcohol dependency).

• The data were randomly split into two parts. The counts for each part are displayed in the table, with those for the second part in parentheses.

• The second part of the data was set aside for a validation study of the model to be fitted in the first part.

Table

No drug or alcohol dependency Drug and/or alcohol dependency 25 or older Under 25 25 or Older Under 25 Person

offense Other

offense Person offense

Other offense

Person offense

Other offense

Person offense

Other offense

No prior Sentence of Any Kind Success 48 34 37 49 48 28 35 57 (44) (34) (29) (58) (47) (38) (37) (53) Failure 1 5 7 11 3 8 5 18 (1) (7) (7) (5) (1) (2) (4) (24) Prior Sentence Success 117 259 131 319 197 435 107 291 (111) (253) (131) (320) (202) (392) (103) (294) Failure 23 61 20 89 38 194 27 101 (27) (55) (25) (93) (46) (215) (34) (102)

Analysis of a Two-way Frequency Table:

Frequency Distribution (Serum Cholesterol and Systolic Blood Pressure)

Serum Systolic Blood pressure Cholesterol <127 127-146 147-166 167+ Total

<200 117 121 47 22 307 200-219 85 98 43 20 246 220-259 119 209 68 43 439

260+ 67 99 46 33 245 Total 388 527 204 118 1237

Joint and Marginal Distributions (Serum Cholesterol and Systolic Blood Pressure)

Serum Systolic Blood pressure Marginal distn Cholesterol <127 127-146 147-166 167+ (Serum Chol.)

<200 9.46 9.78 3.80 1.78 24.82 200-219 6.87 7.92 3.48 1.62 19.89 220-259 9.62 16.90 5.50 3.48 35.49

260+ 5.42 8.00 3.72 2.67 19.81 Marginal distn (BP)

31.37 42.60 16.49 9.54 100.00

The Marginal distributions allow you to look at the effect of one variable, ignoring the other. The joint distribution allows you to look at the two variables simultaneously.

Conditional Distributions ( Systolic Blood Pressure given Serum Cholesterol )

The conditional distribution allows you to look at the effect of one variable, when the other variable is held fixed or known.


<200 38.11 39.41 15.31 7.17 100.00 200-219 34.55 39.84 17.48 8.13 100.00 220-259 27.11 47.61 15.49 9.79 100.00

260+ 27.35 40.41 18.78 13.47 100.00 Marginal distn (BP)

31.37 42.60 16.49 9.54 100.00

Conditional Distributions

(Serum Cholesterol given Systolic Blood Pressure)

Serum Systolic Blood pressure Marginal distn Cholesterol <127 127-146 147-166 167+ (Serum Chol.)

<200 30.15 22.96 23.04 18.64 24.82 200-219 21.91 18.60 21.08 16.95 19.89 220-259 30.67 39.66 33.33 36.44 35.49

260+ 17.27 18.79 22.55 27.97 19.81 Total 100.00 100.00 100.00 100.00 100.00

GRAPH: Conditional distributions of Systolic Blood Pressure given Serum Cholesterol

127-146 147-166<127 167+

SYSTOLIC BLOOD PRESSURE

<200

200-219

260+

220-259

Marginal Distribution

SERUM CHOLESTEROL

40%

50%

30%

20%

10%

Notation:

Let xij denote the frequency (no. of cases) where X (row variable) is i and Y (row variable) is j.

1

c

i i ijj

x R x

1

r

j j iji

x C x

1 1 1 1

r c r c

ij i ji j i j

x N x x x

Different Models

,ij P X i Y j

11 1211 12 11 12

11

, , , rcxx xrc rc

rc

Nf x x x

x x

The Multinomial Model:

Here the total number of cases N is fixed and xij follows a multinomial distribution with parameters ij

11 1211 12

11

!

! !rcxx x

rcrc

N

x x

ij ij ijE x N

11 1211 12 1| 2| |

1 1

, , , ic

ri xx x

rc i i c ii i ic

Rf x x x

x x

The Product Multinomial Model:

Here the row (or column) totals Ri are fixed and for a given row i, xij follows a multinomial distribution with parameters j|i

|ij ij i j iE x R

11 121 1

, , ,!

ij

ij

xr cij

rci j ij

f x x x ex

The Poisson Model:

In this case we observe over a fixed period of time and all counts in the table (including Row, Column and overall totals) follow a Poisson distribution. Let ij denote the mean of xij.

ij ijE x

!

ij

ij

xij

ij ijij

f x ex

Independence

Multinomial Model

,ij P X i Y j P X i P Y j

i j

ij ij i jN N

if independent

and

The estimated expected frequency in cell (i,j) in the case of independence is:

ˆ ˆ ˆ jiij ij i j

xxm N N

N N

i j i jx x R C

N N

The same can be shown for the other two models – the Product Multinomial model and the Poisson model

namely

The estimated expected frequency in cell (i,j) in the case of independence is:

ˆ i j i jij ij

R C x xm

N x

Standardized residuals are defined for each cell:

ij ijij

ij

x mr

m

The Chi-Square Statistic

2

2 2

1 1 1 1

r c r cij ij

iji j i j ij

x mr

m

The Chi-Square test for independence

Reject H0: independence if

2

2 2/ 2

1 1

1 1r c

ij ij

i j ij

x mdf r c

m

TableExpected frequencies, Observed frequencies,

Standardized Residuals


<200 96.29 130.79 50.63 29.29 307 (117) (121) (47) (22) 2.11 -0.86 -0.51 -1.35

200-219 77.16 104.80 40.47 23.47 246 (85) (98) (43) (20) 0.86 -0.66 0.38 -0.72

220-259 137.70 187.03 72.40 41.88 439 (119) (209) (68) (43) -1.59 1.61 -0.52 0.17

260+ 76.85 104.38 40.04 23.37 245 (67) (99) (46) (33) -1.12 -0.53 0.88 1.99

Total 388 527 204 118 1237 2 = 20.85 (p = 0.0133)

Example

In the example N = 57,407 cases in which individuals were victimized twice by crimes were studied.

The crime of the first victimization (X) and the crime of the second victimization (Y) were noted.

The data were tabulated on the following slide

Table 1: Frequencies

Second Victimization in Pair Ra A Ro PP/PS PL B HL MV Total Ra 26 50 11 6 82 39 48 11 273 A 65 2997 238 85 2553 1083 1349 216 8586

First Ro 12 279 197 36 459 197 221 47 1448 Victimization PP/PS 3 102 40 61 243 115 101 38 703

in pair PL 75 2628 413 229 12137 2658 3689 687 22516 B 52 1117 191 102 2649 3210 1973 301 9595 HL 42 1251 206 117 3757 1962 4646 391 12372 MV 3 221 51 24 678 301 367 269 1914 Total 278 8645 1347 660 22558 9565 12394 1960

Table 2: Standardized residuals

Second Victimization in Pair Ra A Ro PP/PS PL B HL MV Ra 21.5 1.4 1.8 1.6 -2.4 -1.0 -1.9 0.6 A 3.6 47.4 2.6 -1.4 -14.1 -9.2 -11.7 -4.5

First Ro 1.9 4.1 28.0 4.7 -4.6 -2.8 -5.2 -0.3 Victimization PP/PS -0.2 -0.4 5.8 18.6 -2.0 -0.2 -4.1 2.9

in pair PL -3.3 -13.1 -5.0 -1.9 35.0 -17.9 -16.8 -2.9 B 0.8 -8.6 -2.3 -0.8 -18.3 40.3 -2.2 -1.5 HL -2.3 -14.2 -4.9 -2.1 -15.8 -2.2 38.2 -1.5 MV -2.1 -4.0 0.9 0.4 -2.7 -1.0 -2.3 25.2

11,430 (highly significant)

Table 3: Conditional distribution of second victimization given the first victimization (%)

Second Victimization in Pair Ra A Ro PP/PS PL B HL MV Ra 9.5 18.3 4.0 2.2 30.0 14.3 17.6 4.0 100.0 A 0.8 34.9 2.8 1.0 29.7 12.6 15.7 2.5 100.0

First Ro 0.8 19.3 13.6 2.5 31.7 13.6 15.3 3.2 100.0 Victimization PP/PS 0.4 14.5 5.7 8.7 34.6 16.4 14.4 5.4 100.0

in pair PL 0.3 11.7 1.8 1.0 53.9 11.8 16.4 3.1 100.0 B 0.5 11.6 2.0 1.1 27.6 33.5 20.6 3.1 100.0 HL 0.3 10.1 1.7 0.9 30.4 15.9 37.6 3.2 100.0 MV 0.2 11.5 2.7 1.3 35.4 15.7 19.2 14.1 100.0

Marginal 0.5 15.1 2.3 1.1 39.3 16.7 21.6 3.4 100.0

Log Linear Model

Recall, if the two variables, rows (X) and columns (Y) are independent then

ij ij i jN N

and

ln ln ln lnij i jN

In general let

1( ) 2( ) 12( , )ln ij i j i ju u u u

1ln ij

i j

urc

1( )

1lni ij

j

u uc

2( )

1lnj ij

i

u ur

12( , ) 1( ) 2( )lni j ij i ju u u u

then

where1( ) 2( ) 12( , ) 12( , ) 0i j i j i j

i j i j

u u u u

(1)

Equation (1) is called the log-linear model for the frequencies xij.

Note: X and Y are independent if

1( ) 2( )ln ij i ju u u

In this case the log-linear model becomes

12( , ) 0 for all ,i ju i j

Three-way Frequency Tables

ExampleData from the Framingham Longitudinal Study of Coronary Heart Disease (Cornfield [1962])

Variables

1. Systolic Blood Pressure (X)– < 127, 127-146, 147-166, 167+

2. Serum Cholesterol– <200, 200-219, 220-259, 260+

3. Heart Disease– Present, Absent

The data is tabulated on the next slide

Three-way Frequency Table

Coronary Heart

Serum Cholesterol

Systolic Blood pressure (mm Hg)

Disease (mm/100 cc) <127 127-146 147-166 167+ <200 2 3 3 4

Present 200-219 3 2 0 3 220-259 8 11 6 6 260+ 7 12 11 11 <200 117 121 47 22

Absent 200-219 85 98 43 20 220-259 119 209 68 43 260+ 67 99 46 33

Log-Linear model for three-way tables

Let ijk denote the expected frequency in cell (i,j,k) of the table then in general

1( ) 2( ) 3( ) 12( , )ln ij i j k i ju u u u u

1( ) 2( ) 3( ) 12( , ) 12( , )0 i j k i j i ji j k i j

u u u u u

13( , ) 23( , ) 123( , , )i k j k i j ku u u where

13( , ) 13( , ) 23( , ) 23( , )i k i k j k j ki k j k

u u u u 123( , , ) 123( , , ) 123( , , )i j k i j k i j k

i j k

u u u

Hierarchical Log-linear models for categorical Data

For three way tables

The hierarchical principle:

If an interaction is in the model, also keep lower order interactions and main effects associated with that interaction

1.Model: (All Main effects model)

ln ijk = u + u1(i) + u2(j) + u3(k)

i.e. u12(i,j) = u13(i,k) = u23(j,k) = u123(i,j,k) = 0.

Notation:

[1][2][3]

Description:

Mutual independence between all three variables.

2.Model:

ln ijk = u + u1(i) + u2(j) + u3(k) + u12(i,j)

i.e. u13(i,k) = u23(j,k) = u123(i,j,k) = 0.

Notation:

[12][3]

Description:

Independence of Variable 3 with variables 1 and 2.

3.Model:

ln ijk = u + u1(i) + u2(j) + u3(k) + u13(i,k)

i.e. u12(i,j) = u23(j,k) = u123(i,j,k) = 0.

Notation:

[13][2]

Description:


4.Model:

ln ijk = u + u1(i) + u2(j) + u3(k) + u23(j,k)

i.e. u12(i,j) = u13(i,k) = u123(i,j,k) = 0.

Notation:

[23][1]

Description:


5.Model:

ln ijk = u + u1(i) + u2(j) + u3(k) + u12(i,j) + u13(i,k)

i.e. u23(j,k) = u123(i,j,k) = 0.

Notation:

[12][13]

Description:

Conditional independence between variables 2 and 3 given variable 1.

6.Model:

ln ijk = u + u1(i) + u2(j) + u3(k) + u12(i,j) + u23(j,k)

i.e. u13(i,k) = u123(i,j,k) = 0.

Notation:

[12][23]

Description:


7.Model:

ln ijk = u + u1(i) + u2(j) + u3(k) + u13(i,k) + u23(j,k)

i.e. u12(i,j) = u123(i,j,k) = 0.

Notation:

[13][23]

Description:


8.Model:

ln ijk = u + u1(i) + u2(j) + u3(k) + u12(i,j) + u13(i,k) + u23(j,k)

i.e. u123(i,j,k) = 0.Notation:

[12][13][23] Description:Pairwise relations among all three variables, with each two variable interaction unaffected by the value of the third variable.

9.Model: (the saturated model)

ln ijk = u + u1(i) + u2(j) + u3(k) + u12(i,j) + u13(i,k) + u23(j,k) + u123(i,j,k)

Notation:

[123]

Description:

No simplifying dependence structure.

Hierarchical Log-linear models for 3 way table

Model Description

[1][2][3] Mutual independence between all three variables.

[1][23] Independence of Variable 1 with variables 2 and 3.



[12][13] Conditional independence between variables 2 and 3 given variable 1.



[12][13] [23] Pairwise relations among all three variables, with each two variable interaction unaffected by the value of the third variable.

[123] The saturated model

Maximum Likelihood Estimation

Log-Linear Model

For any Model it is possible to determine the maximum Likelihood Estimators of the parameters

Example

Two-way table – independence – multinomial model

11 1211 12 11 12

11

, , , rcxx xrc rc

rc

Nf x x x

x x

11 12

11 12

11

!

! !

rcxx x

rc

rc

N

x x N N N

ij ij ijE x N orij

ij N

Log-likelihood

11 12, , ln ! ln !rc iji j

l N x

ln lnij ij iji j i j

N x x lnij ij

i j

K x where ln ! ln ! lnij

i j

K N x N N

1 2ln ij i ju u u

With the model of independence

and

1 1 1 2 1 2, , , , , ,c rl u u u u u K

1 2ij i ji j

x u u u

with 1 2 0i ji j

u u

1 2i ji ji j

K Nu x u x u

1 2 1 2i j i ju u u u uuij

i j i j i j

e e e e N

also

Let

1 2 21 1 1 2 1 2, , , , , , , , ,c rg u u u u u

1 2

1 11 2i ju uu

i ji j i j

u u e e e N

1 2i ji ji j

K Nu x u x u

Now

1 2 1 0i ju uu

i j

gN e e e N

u

1

1 2

1

1

i ju uui

ji

gx e e e

u

1

11 0

i

i

u

i u

i

ex N

e

1

1

1i

i

u

i iu

i

x xe

N Ne

1 111 and 0

ii i

i

xx

rN N N

Since

Now 1

1iu

ie x K

or 11 ln lniiu x K

11 ln ln 0iii i

u x r K

Hence

1

1ln lni ii

i

u x xr

1

1ln ln i

i

K xr

and

2

1ln lnj jj

i

u x xc Similarly

1 2 1 2i j i ju u u u uuij

i j i j i j

e e e e N

Finally

Hence

2

1

1

ju j

c c

jj

xe

x

Now

1 2i j

uu u

i j

Ne

e e

and

1

1

1

iu i

r r

ii

xe

x

11

1 1

r c cru

i ji ji j

i j

Ne x x

x x

11

1 1

1 r c cr

i ji j

x xN

Hence

Note

1 1ln ln lni j

i j

u x x Nr c

1 2ln ij i ju u u 1 1

ln ln lni ji j

x x Nr c

1 1ln ln ln lni i j j

i i

x x x xr c

ln ln lni jN x x

or i jij

x x

N

Comments

• Maximum Likelihood estimates can be computed for any hierarchical log linear model (i.e. more than 2 variables)

• In certain situations the equations need to be solved numerically

• For the saturated model (all interactions and main effects), the estimate of ijk… is xijk… .

Goodness of Fit Statistics

These statistics can be used to check if a log-linear model will fit the

observed frequency table

Goodness of Fit StatisticsThe Chi-squared statistic

2

2 Observed Expected

Expected

The Likelihood Ratio statistic:

2 2 ln 2 lnˆ

ijkijk

ijk

xObservedG Observed x

Expected

d.f. = # cells - # parameters fitted

2ˆ

ˆijk ijk

ijk

x

We reject the model if 2 or G2 is greater than2

/ 2

Example: Variables

Coronary Heart

Serum Cholesterol

Systolic Blood pressure (mm Hg)

Disease (mm/100 cc) <127 127-146 147-166 167+ <200 2 3 3 4

Present 200-219 3 2 0 3 220-259 8 11 6 6 260+ 7 12 11 11 <200 117 121 47 22

Absent 200-219 85 98 43 20 220-259 119 209 68 43 260+ 67 99 46 33

1. Systolic Blood Pressure (B)Serum Cholesterol (C)Coronary Heart Disease (H)

MODEL DF LIKELIHOOD- PROB. PEARSON PROB. RATIO CHISQ CHISQ ----- -- ----------- ------- ------- ------- B,C,H. 24 83.15 0.0000 102.00 0.0000 B,CH. 21 51.23 0.0002 56.89 0.0000 C,BH. 21 59.59 0.0000 60.43 0.0000 H,BC. 15 58.73 0.0000 64.78 0.0000 BC,BH. 12 35.16 0.0004 33.76 0.0007 BH,CH. 18 27.67 0.0673 26.58 0.0872 n.s. CH,BC. 12 26.80 0.0082 33.18 0.0009 BC,BH,CH. 9 8.08 0.5265 6.56 0.6824 n.s.

Goodness of fit testing of Models

Possible Models:1. [BH][CH] – B and C independent given H.2. [BC][BH][CH] – all two factor interaction model

Model 1: [BH][CH] Log-linear parameters

Heart disease -Blood Pressure Interaction

Bp Hd <127 127-146 147-166 167+ Pres -0.256 -0.241 0.066 0.431 Abs 0.256 0.241 -0.066 -0.431

,HB i ju

Bp Hd <127 127-146 147-166 167+ Pres -2.607 -2.733 0.660 4.461 Abs 2.607 2.733 -0.660 -4.461

,

,

HB i j

HB i j

u

uz

Multiplicative effect

,

, ,exp HB i ju

HB i j HB i ju e

Bp Hd <127 127-146 147-166 167+ Pres 0.774 0.786 1.068 1.538 Abs 1.291 1.272 0.936 0.65

, ,ln ijk H i B j C k HB i j HC i ku u u u u u

, ,H i B j C k HB i j HC i ku u u u uuijk e e e e e e

Log-Linear Model

, ,H i B j C k HB i j HC i k

Heart Disease - Cholesterol Interaction

Chol Hd <200 200-219 220-259 260+ Pres -0.233 -0.325 0.063 0.494 Abs 0.233 0.325 -0.063 -0.494

,HC i ku

,

,

HC i k

HC i k

u

uz

Chol Hd <200 200-219 220-259 260+ Pres -1.889 -2.268 0.677 5.558 Abs 1.889 2.268 -0.677 -5.558


,

, ,exp HB i ku

HC i k HB i ku e

Chol Hd <200 200-219 220-259 260+ Pres 0.792 0.723 1.065 1.640 Abs 1.262 1.384 0.939 0.610

Model 2: [BC][BH][CH] Log-linear parameters

Blood pressure-Cholesterol interaction: ,BC j ku

Bp Chol <200 200-219 220-259 260+ <200 0.222 -0.019 -0.034 -0.169 200-219 0.114 -0.041 0.013 -0.086 220-259 -0.114 0.154 -0.058 0.018 260+ -0.221 -0.094 0.079 0.237

,

,

BC j k

BC j k

u

uz

Bp Chol <200 200-219 220-259 260+ <200 2.68 -0.236 -0.326 -1.291 200-219 1.27 -0.472 0.117 -0.626 220-259 -1.502 2.253 -0.636 0.167 260+ -2.487 -1.175 0.785 2.051

Bp Chol <200 200-219 220-259 260+ <200 1.248 0.981 0.967 0.844 200-219 1.120 0.960 1.013 0.918 220-259 0.892 1.166 0.944 1.018 260+ 0.802 0.910 1.082 1.267

Multiplicative effect ,

, ,exp HB j ku

BC j k BC j ku e

Heart disease -Blood Pressure Interaction

Bp Hd <127 127-146 147-166 167+ Pres -0.211 -0.232 0.055 0.389 Abs 0.211 0.232 -0.055 -0.389

,HB i ju

Bp Hd <127 127-146 147-166 167+ Pres -2.125 -2.604 0.542 3.938

Abs 2.125 2.604 -0.542 -3.938

,

,

HB i j

HB i j

u

uz


,

, ,exp HB i ju

HB i j HB i ju e

Bp Hd <127 127-146 147-166 167+ Pres 0.809 0.793 1.056 1.475

Abs 1.235 1.261 0.947 0.678

Heart Disease - Cholesterol Interaction

Chol Hd <200 200-219 220-259 260+ Pres -0.212 -0.316 0.069 0.460

Abs 0.212 0.316 -0.069 -0.460

,HC i ku

,

,

HC i k

HC i k

u

uz

Chol Hd <200 200-219 220-259 260+ Pres -1.712 -2.199 0.732 5.095

Abs 1.712 2.199 -0.732 -5.095


,

, ,exp HB i ku

HC i k HB i ku e

Chol Hd <200 200-219 220-259 260+ Pres 0.809 0.729 1.071 1.584

Abs 1.237 1.372 0.933 0.631

Logistic regression. Recall the simple linear regression model: y = 0 + 1 x + where we are trying...

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