1. Discrete Multivariate Analysis Analysis of Multivariate Categorical Data

2. Example 1 In this study we examine n = 1237 individuals measuring X, Systolic Blood Pressure and Y, Serum Cholesterol

3. 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).

4. 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).

5. 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.

6. Table

7. Analysis of a Two-way Frequency Table:

8. Frequency Distribution (Serum Cholesterol and Systolic Blood Pressure)

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

10. 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.

11. Conditional Distributions (Serum Cholesterol given Systolic Blood Pressure)

12. GRAPH: Conditional distributions of Systolic Blood Pressure given Serum Cholesterol

13. Notation: Let x ij denote the frequency (no. of cases) where X (row variable) is i and Y (row variable) is j.

14. Different Models The Multinomial Model: Here the total number of cases N is fixed and x ij follows a multinomial distribution with parameters  ij

15. The Product Multinomial Model: Here the row (or column) totals R i are fixed and for a given row i, x ij follows a multinomial distribution with parameters  j|i

16. 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 x ij.

17. Independence

18. Multinomial Model if independent and The estimated expected frequency in cell (i,j) in the case of independence is:

19. 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: Standardized residuals are defined for each cell:

20. The Chi-Square Statistic The Chi-Square test for independence Reject H 0 : independence if

21. Table Expected frequencies, Observed frequencies, Standardized Residuals  2 = 20.85 (p = 0.0133)

22. 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

23. Table 1: Frequencies

24. Table 2: Standardized residuals

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

26. Log Linear Model

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

28. In general let then where (1) Equation (1) is called the log-linear model for the frequencies x ij.

29. Note: X and Y are independent if In this case the log-linear model becomes

30. Another formulation

31. Three-way Frequency Tables

32. With two variables the dependence structure is simple: the variables are either dependent or independent. When there are three or more variables the dependence structure is much more complicated.

33. Marginal distributions Distributions of two variables ignoring the third. 1. X 1, X 2 ignoring X 3 2. X 1, X 3 ignoring X 2 3. X 2, X 3 ignoring X 1 Distributions of one variable ignoring the other two. 1. X 1 ignoring X 2, X 3 2. X 2 ignoring X 1, X 3 3. X 3 ignoring X 1, X 2

34. Conditional distributions Distributions of two variables given the third. 1. X 1, X 2 given X 3 2. X 1, X 3 given X 2 3. X 2, X 3 given X 1 Distributions of one variable given the other two. 1. X 1 given X 2, X 3 2. X 2 given X 1, X 3 3. X 3 given X 1, X 2

35. Distributions of one variable given either of the other two. 1. X 1 given X 2 2. X 1 given X 3 3. X 2 given X 1 4. X 2 given X 3 5. X 3 given X 1 6. X 3 given X 2

36. Example Data 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

37. Three-way Frequency Table

38. Log-Linear model for three-way tables Let  ijk denote the expected frequency in cell (i,j,k) of the table then in general where

39. 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

40. 1.Model: (All Main effects model) ln  ijk = u + u 1(i) + u 2(j) + u 3(k) i.e. u 12(i,j) = u 13(i,k) = u 23(j,k) = u 123(i,j,k) = 0. Notation: [1][2][3] Description: Mutual independence between all three variables.

41. 2.Model: ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 12(i,j) i.e. u 13(i,k) = u 23(j,k) = u 123(i,j,k) = 0. Notation: [12][3] Description: Independence of Variable 3 with variables 1 and 2.

42. 3.Model: ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 13(i,k) i.e. u 12(i,j) = u 23(j,k) = u 123(i,j,k) = 0. Notation: [13][2] Description: Independence of Variable 2 with variables 1 and 3.

43. 4.Model: ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 23(j,k) i.e. u 12(i,j) = u 13(i,k) = u 123(i,j,k) = 0. Notation: [23][1] Description: Independence of Variable 3 with variables 1 and 2.

44. 5.Model: ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 12(i,j) + u 13(i,k) i.e. u 23(j,k) = u 123(i,j,k) = 0. Notation: [12][13] Description: Conditional independence between variables 2 and 3 given variable 1.

45. 6.Model: ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 12(i,j) + u 23(j,k) i.e. u 13(i,k) = u 123(i,j,k) = 0. Notation: [12][23] Description: Conditional independence between variables 1 and 3 given variable 2.

46. 7.Model: ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 13(i,k) + u 23(j,k) i.e. u 12(i,j) = u 123(i,j,k) = 0. Notation: [13][23] Description: Conditional independence between variables 1 and 2 given variable 3.

47. 8.Model: ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 12(i,j) + u 13(i,k) + u 23(j,k) i.e. u 123(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.

48. 9.Model: (the saturated model) ln  ijk = u + u 1(i) + u 2(j) + u 3(k) + u 12(i,j) + u 13(i,k) + u 23(j,k) + u 123(i,j,k) Notation: [123] Description: No simplifying dependence structure.

49. Hierarchical Log-linear models for 3 way table ModelDescription [1][2][3] Mutual independence between all three variables. [1][23] Independence of Variable 1 with variables 2 and 3. [2][13] Independence of Variable 2 with variables 1 and 3. [3][12] Independence of Variable 3 with variables 1 and 2. [12][13] Conditional independence between variables 2 and 3 given variable 1. [12][23] Conditional independence between variables 1 and 3 given variable 2. [13][23] Conditional independence between variables 1 and 2 given variable 3. [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

50. Maximum Likelihood Estimation Log-Linear Model

51. For any Model it is possible to determine the maximum Likelihood Estimators of the parameters Example Two-way table – independence – multinomial model or

52. Log-likelihood where With the model of independence

53. and with also

54. Let Now

55. Since

56. Now or

57. Hence and Similarly Finally

58. Hence Now and

59. Hence Note or

60. 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 x ijk….

61. Goodness of Fit Statistics These statistics can be used to check if a log-linear model will fit the observed frequency table

62. Goodness of Fit Statistics The Chi-squared statistic The Likelihood Ratio statistic: d.f. = # cells - # parameters fitted We reject the model if  2 or G 2 is greater than

63. Example: Variables 1.Systolic Blood Pressure (B) Serum Cholesterol (C) Coronary Heart Disease (H)

64. 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

65. Model 1: [BH][CH] Log-linear parameters Heart disease -Blood Pressure Interaction

66. Multiplicative effect Log-Linear Model

67. Heart Disease - Cholesterol Interaction

68. Multiplicative effect

69. Model 2: [BC][BH][CH] Log-linear parameters Blood pressure-Cholesterol interaction:

70. Multiplicative effect

71. Heart disease -Blood Pressure Interaction

72. Multiplicative effect

73. Heart Disease - Cholesterol Interaction

74. Multiplicative effect

75. Next topic: Discrete Multivariate Analysis IIDiscrete Multivariate Analysis II