Analisis Data Kategorik - kusmansadik.files.wordpress.com · 10.11.2017 · Misalkan ada tiga...

Model Log-Linear(Bagian 2)

Dr. Kusman Sadik, M.Si

Program Studi Magister (S2)

Departemen Statistika IPB, 2017/2018

When fitting log-linear models to higher-way tables it is

typical to only consider models that are hierarchical in nature.

These are models that include all lower-order terms for

variables involved in higher-order terms in the model.

For a three-way contingency table, with variables X, Y, and Z,

the saturated model includes all main effects, all two-way

interactions and the three-way interaction, and is represented

symbolically as follows:

Exp( ) merupakan nilai odds antara kategori pada

baris ke-i dengan kategori baris terakhir.

Exp( ) merupakan nilai rasio odds antara odds

kategori pada baris ke-i dengan kategori baris terakhir

dengan odds kategori pada kolom ke-j dengan kategori

kolom terakhir

Lihat : Azen, hlm. 145

** Model Log-Linear untuk Data Tabel 7.4 (Azen, hlm.145) **

** relevel --> Memilih Kategori Referensi **

** Model 1 : Interaksi XY XZ YZ **

z.gen <- factor(rep(c("1Mal","2Fem"),each=3, times=3))

x.rel <- factor(rep(c("1Lib","2Mod","3Con"),each=1, times=6))

y.god <- factor(rep(c("1Y","2N","3U"),each=6,times=1))

count <- c(26,82,202,44,113,180,121,128,75,221,

204,124,24,52,74,32,49,43)

z.gen <- relevel(z.gen, ref="2Fem")

x.rel <- relevel(x.rel, ref="3Con")

y.god <- relevel(y.god, ref="3U")

data.frame(z.gen, x.rel, y.god, count)

model <- glm(count ~ x.rel + y.god + z.gen +

x.rel*y.god + x.rel*z.gen + y.god*z.gen,

family=poisson("link"=log))

summary(model)

dugaan <- round(fitted(model),2)

data.frame(z.gen, x.rel, y.god, count, dugaan)

z.gen x.rel y.god count

1 1Mal 1Lib 1Y 26

2 1Mal 2Mod 1Y 82

3 1Mal 3Con 1Y 202

4 2Fem 1Lib 1Y 44

5 2Fem 2Mod 1Y 113

6 2Fem 3Con 1Y 180

7 1Mal 1Lib 2N 121

8 1Mal 2Mod 2N 128

9 1Mal 3Con 2N 75

10 2Fem 1Lib 2N 221

11 2Fem 2Mod 2N 204

12 2Fem 3Con 2N 124

13 1Mal 1Lib 3U 24

14 1Mal 2Mod 3U 52

15 1Mal 3Con 3U 74

16 2Fem 1Lib 3U 32

17 2Fem 2Mod 3U 49

18 2Fem 3Con 3U 43

glm(formula = count ~ x.rel + y.god + z.gen + x.rel * y.god +

x.rel * z.gen + y.god * z.gen, family = poisson(link = log))

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 3.862595 0.122713 31.477 < 2e-16 ***

x.rel1Lib -0.511037 0.177163 -2.885 0.003920 **

x.rel2Mod 0.005771 0.150379 0.038 0.969388

y.god1Y 1.367890 0.134994 10.133 < 2e-16 ***

y.god2N 0.860606 0.140582 6.122 9.26e-10 ***

z.gen1Mal 0.377454 0.136017 2.775 0.005519 **

x.rel1Lib:y.god1Y -0.994715 0.209026 -4.759 1.95e-06 ***

x.rel2Mod:y.god1Y -0.548065 0.162483 -3.373 0.000743 ***

x.rel1Lib:y.god2N 1.212754 0.186792 6.493 8.44e-11 ***

x.rel2Mod:y.god2N 0.615654 0.163938 3.755 0.000173 ***

x.rel1Lib:z.gen1Mal -0.416543 0.131545 -3.167 0.001543 **

x.rel2Mod:z.gen1Mal -0.272968 0.114568 -2.383 0.017191 *

y.god1Y:z.gen1Mal -0.334342 0.146372 -2.284 0.022360 *

y.god2N:z.gen1Mal -0.640833 0.142437 -4.499 6.83e-06 ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Null deviance: 724.8365 on 17 degrees of freedom

Residual deviance: 6.5154 on 4 degrees of freedom

AIC: 146.38

z.gen x.rel y.god count dugaan

1 1Mal 1Lib 1Y 26 28.54

2 1Mal 2Mod 1Y 82 86.34

3 1Mal 3Con 1Y 202 195.12

4 2Fem 1Lib 1Y 44 41.46

5 2Fem 2Mod 1Y 113 108.66

6 2Fem 3Con 1Y 180 186.88

7 1Mal 1Lib 2N 121 115.01

8 1Mal 2Mod 2N 128 122.52

9 1Mal 3Con 2N 75 86.47

10 2Fem 1Lib 2N 221 226.99

11 2Fem 2Mod 2N 204 209.48

12 2Fem 3Con 2N 124 112.53

13 1Mal 1Lib 3U 24 27.45

14 1Mal 2Mod 3U 52 53.14

15 1Mal 3Con 3U 74 69.41

16 2Fem 1Lib 3U 32 28.55

17 2Fem 2Mod 3U 49 47.86

18 2Fem 3Con 3U 43 47.59

To determine whether the three-way interaction term in the

saturated model

H1: ≠ 0

glm(formula = count ~ x.rel + y.god + z.gen + x.rel * y.god +

x.rel * z.gen + y.god * z.gen, family = poisson(link = log))

Coefficients:

(Intercept) 3.862595 0.122713 31.477 < 2e-16 ***

x.rel1Lib -0.511037 0.177163 -2.885 0.003920 **

x.rel2Mod 0.005771 0.150379 0.038 0.969388

y.god1Y 1.367890 0.134994 10.133 < 2e-16 ***

y.god2N 0.860606 0.140582 6.122 9.26e-10 ***

z.gen1Mal 0.377454 0.136017 2.775 0.005519 **

x.rel1Lib:y.god1Y -0.994715 0.209026 -4.759 1.95e-06 ***

x.rel2Mod:y.god1Y -0.548065 0.162483 -3.373 0.000743 ***

x.rel1Lib:y.god2N 1.212754 0.186792 6.493 8.44e-11 ***

x.rel2Mod:y.god2N 0.615654 0.163938 3.755 0.000173 ***

x.rel1Lib:z.gen1Mal -0.416543 0.131545 -3.167 0.001543 **

x.rel2Mod:z.gen1Mal -0.272968 0.114568 -2.383 0.017191 *

y.god1Y:z.gen1Mal -0.334342 0.146372 -2.284 0.022360 *

y.god2N:z.gen1Mal -0.640833 0.142437 -4.499 6.83e-06 ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

AIC: 146.38

glm(formula = count ~ x.rel + y.god + z.gen + x.rel * z.gen +

y.god * z.gen, family = poisson(link = log))

Coefficients:

(Intercept) 3.75190 0.09978 37.601 < 2e-16 ***

x.rel1Lib -0.15559 0.07905 -1.968 0.049035 *

x.rel2Mod 0.05331 0.07493 0.711 0.476792

y.god1Y 0.99980 0.10503 9.519 < 2e-16 ***

y.god2N 1.48782 0.09943 14.964 < 2e-16 ***

z.gen1Mal 0.45511 0.13489 3.374 0.000741 ***

x.rel1Lib:z.gen1Mal -0.56353 0.12225 -4.610 4.04e-06 ***

x.rel2Mod:z.gen1Mal -0.34575 0.11081 -3.120 0.001808 **

y.god1Y:z.gen1Mal -0.27386 0.14465 -1.893 0.058324 .

y.god2N:z.gen1Mal -0.71771 0.14014 -5.121 3.03e-07 ***

AIC: 388.61

Nilai deviance dapat digunakan untuk menguji hipotesis

ada tidaknya hubungan suatu variabel dengan variabel

lainnya.

Misalkan ada tiga variabel X, Y, dan Z. Untuk menguji

apakah ada hubungan antara X dengan Y, dapat dilakukan

dengan membandingkan nilai deviance model (X, Y, Z, XY,

XZ, YZ) dengan nilai deviance model (X, Y, Z, XZ, YZ).

Jika dari uji hipotesis tersebut menerima model (X, Y, Z,

XY, XZ, YZ) maka dapat disimpulkan bahwa ada hubungan

yang signifikan antara X dan Y.

1. Gunakan Program R untuk menyelesaikan Problem 7.1 (Azen,

hlm.177) .

2. Gunakan Program R untuk menyelesaikan Problem 8.1 (Agresti,

hlm. 347) .

Pustaka

1. Azen, R. dan Walker, C.R. (2011). Categorical Data

Analysis for the Behavioral and Social Sciences.

Routledge, Taylor and Francis Group, New York.

2. Agresti, A. (2002). Categorical Data Analysis 2nd. New

York: Wiley.

3. Pustaka lain yang relevan.

Bisa di-download di

kusmansadik.wordpress.com

Terima Kasih

Analisis Data Kategorik - kusmansadik.files.wordpress.com · 10.11.2017 · Misalkan ada tiga...

Documents

Model Linear Terampat - kusmansadik.files.wordpress.com file08.02.2017 · The key to GLMs is to “link” the random and systematic components of the model with some mathematical

DEC Ada Developing Ada Programs on OpenVMS Systems

ADA Initiatives: ADA Transition Plan for the City of Dunbartransportation.wv.gov/conferences/2017-Planning-Conference/Docu… · ADA Initiatives: ADA Transition Plan for the City

Afghan’Development’Association’(ADA)’ Reports... · Afghan’Development’Association’(ADA) ... ADA’ ’ Afghan’Development’Association ... Technical!training!on!nutrition!of!winter!vegetables,!!

MA3231 Analisis Real - · PDF file11.2 Fungsi Monoton yang Mempunyai Turunan Bukti. (i) Misalkan xdan ybilangan sembarang di [a;b] dengan x

Ada County – ACHD...Mitra Mehta-Cooper, Ada County Carl Miller, COMPASS David Corcoran, Ada County. July 25, 2019 Ada County – ACHD Joint Workshop

Pemodelan Data Deret Waktu - kusmansadik.files.wordpress.com · Pemodelan Data Deret Waktu (ARMA dan ARIMA) Dr. Kusman Sadik, M.Si Sekolah Pascasarjana Departemen Statistika IPB Semester

ADA Quick Reference Guide - Campsites - Oregon€¦ · ADA Quick Reference Guide - Campsites ADA Accessible Sites per Campground . ADA accessible campsites per campground is based

SISTEM PENGOLAHAN DATA PERNIKAHAN PADA KANTOR …...contoh, misalkan dalam proses pendaftaran nikah diKantor Urusan Agama. Dengan sistem informasi yang baik dapat mempermudah baik

Ada Ada Look Book -Spring Summer 12

1970 April 3 Ada Evening News - Ada OK Paleofuture

CHAPTER 3: DETAILS and ADA · Design Scene – Chapter 3 (Details and ADA) Page 1 Revised 04-30-19 CHAPTER 3: DETAILS and ADA . ADA PLAN REVIEW CHECKLISTS . When there is ADA work

MTFB | MTR60B - National Public Seating · mto60b 8 60"x60" 34"x86"x54½" 29"-17" 25"-13" 27"-15" 32"-20" ada-1 ada-1 ada-2 ada-2 ada-3 ada-1 ada-2 ada-3 ada-1 ada-2 stool & bench

ADA compliant products ADA compliant products ADA-4 Discover the professional’s choice. Table of Contents Elkay ADA Water Coolers and Drinking Fountains 3

Caltrans ADA Infrastructure · PDF fileCaltrans . ADA Infrastructure Program . 2 . ... Alex Morales III . Statewide ADA Coordinator . ... ADA Infrastructure Program was created to

ADA Compliant Lectern ADA Compliant · 2020. 12. 28. · ADA Compliant A stylish and functional lectern that's ADA compliant SN3900 . ADA Compliant Lectern. Lectern • Moves smoothly

Analisis Regresi II - kusmansadik.files.wordpress.com · 10/11/2018 · To handle nonlinear relations by means of appropriate transformations applied to one or both variables. To

Analisis Data Kategorik - kusmansadik.files.wordpress.com · 09/11/2018 · 2 Using a log-linear modeling approach is advantageous to conducting inferential tests of the associations

Medical Marijuana and The ADA - Mid-Atlantic ADA Center

PACIFIC ADA CENTER ADA NATIONAL NETWORK …adapresentations.org/doc/11_12_15/transcript_11_12_15.pdf · 1 pacific ada center ada national network learning session fema promising practice: