MS-E2112 Multivariate Statistical Analysis (5cr) Lecture 5: Bivariate Correspondence Analysis

Transcription

1 MS-E2112 Multivariate Statistical (5cr) Lecture 5: Bivariate

2 Contents

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4 analysis is a PCA-type method appropriate for analyzing categorical variables. The aim in bivariate correspondence analysis is to describe dependencies (correspondences) in a two-way contingency table.

5 Example, Education and Salary In this lecture, we consider an example where we examine dependencies of categorical variables education and salary.

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7 Contingency Tables We consider a sample of size n described by two qualitative variables, x with categories A 1,...,A J and y with categories B 1,...,B K. The number of individuals having the modality (category) A j for the variable x and the modality B k for the variable y is denoted by n jk. Now the number of individuals having the modality A j for the variable x is given by n j. = K n jk, k=1 the number of individuals having the modality B k for the variable y is given by and J n.k = n jk, j=1 J K n = n jk. j=1 k=1

8 Contingency Tables The data is often displayed as a two-way contingency table. B 1 B 2 B K A 1 n 11 n 12 n 1K n 1. A 2 n 21 n 22 n 2K n A J n J1 n J2 n JK n J. n.1 n.2 n.k n Table: Contingency table

9 Example, Education and Salary We consider size 1000 sample of two categorical variables. Variable x Education is divided to categories A 1 Primary School, A 2 High School, and A 3 University, and variable y Salary is divided to categories B 1 low, B 2 average, and B 3 high.

10 Example, Education and Salary We display the Education and Salary data as a two-way contingency table. B 1 B 2 B 3 A A A Table: Contingency table In this sample of 1000 observations, there are 150 individuals that have Primary School education and low salary. In this sample of 1000 observations, there are 10 individuals that have Primary School education and high salary. In this sample of 1000 observations, there are 110 individuals that have University education and average salary....

11 Contingency Tables The value of the numbers n jk is naturally relative to the total number of observations, n. Thus it is preferable to analyze the contingency table in the form of joint relative frequencies. From the contingency table, it is straightforward to compute the associated relative frequency table (F) where the elements of the contingency table are divided by the number of individuals n leading to f jk = n jk n. The marginal relative frequencies are computed as K f j. = and f.k = k=1 f jk J f jk. j=1

12 Contingency Tables B 1 B 2 B K A 1 f 11 f 12 f 1K f 1. A 2 f 21 f 22 f 2K f A J f J1 f J2 f JK f J. f.1 f.2 f.k 1 Table: Frequency table

13 Example, Education and Salary B 1 B 2 B 3 A A A Table: Frequency table In this sample 15% of individuals have Primary School education and low salary. In this sample, 1% of individuals have Primary School education and high salary. In this sample, 11% of individuals have University education and average salary....

14 Frequencies The frequency f jk is the estimate of p jk = P(x A j, y B k ), and f j. and f.k are the estimates of p j. = P(x A j ), and respectively. p.k = P(y B k ),

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16 Tables of Conditional Frequencies The proportion of individuals that belong to category B k for the variable y among the individuals that have the modality A j for the variable x form the so called table of row profiles. The conditional frequencies for fixed j and all k are f k j = n jk n j. = n jk/n n j. /n = f jk f j.. The frequency f k j is the estimate of p k j = P(y B k x A j ).

17 B 1 B 2 B K f f 1. 1K A 1 f 11 f 1. f 12 A 2 f 21 f 2. f 22 f 2.. A J. f J1 f J... f J2 f J. Table: Row profiles f 1. 1 f 2K f f JK f J. 1

18 Example, Education and Salary B 1 B 2 B 3 A A A Table: Row profiles In this sample 75% of the individuals that have Primary School education, have low salary. In this sample, 5% of the individuals that have Primary School education, have high salary. In this sample, 55% of the individuals that have University education, have average salary....

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20 Tables of Conditional Frequencies The proportion of individuals that belong to category A j for the variable x among the individuals that have the modality B k for the variable y form the table of column profiles. The conditional frequencies for fixed k and all j are f j k = n jk n.k = n jk/n n.k /n = f jk f.k. The frequency f j k is the estimate of p j k = P(x A j y B k ).

21 B 1 B 2 B K f.2 f 1K f.k f f J2 f A 1 f 11 f.1 f 12 A 2 f 21 f.1 f 22. A J f J1 f.1 f 2K f.k f JK f.k Table: Column profiles

22 Example, Education and Salary B 1 B 2 B 3 A A A Table: Column profiles In this sample 43% of the individuals that have low salary, have Primary School education. In this sample, 7% of the individuals that have high salary, have Primary School education. In this sample, 22% of the individuals that have average salary, have University education....

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24 The variables x and y are independent if and only if for all j, k it holds that and P(x A j, y B k ) = P(x A j )P(y B k ), P(x A j y B k ) = P(x A j ), P(y B k x A j ) = P(y B k ). These equalities can be estimated by f jk f j. f.k, and respectively. f j k = f jk f.k f j., f k j = f jk f j. f.k,

25 We can now define the theoretical relative frequencies and theoretical frequencies under the assumption of independence as follows: f jk = f j.f.k and n jk = n j.n.k n = f jk n.

26 Example, Education and Salary B 1 B 2 B 3 A A A Table: Observed frequencies B 1 B 2 B 3 A A A Table: Theoretical frequencies under independence

27 Example, Education and Salary B 1 B 2 B 3 A A A Table: Observed relative frequencies B 1 B 2 B 3 A A A Table: Theoretical relative frequencies under independence

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29 The elements of the attraction repulsion matrix D are given by Note that d jk = n jk n jk = f jk f jk = f jk f j. f.k. d jk > 1 f jk > f j. f.k and f j k > f j. and f k j > f k. d jk < 1 f jk < f j. f.k f j k < f j. and f k j < f k. If d jk > 1, then the modalities (categories) A j and B k are said to be attracted to each other. If d jk < 1, then the modalities A j and B k are said to repulse each other.

30 Salary Example B 1 B 2 B 3 A A A Table: Attraction repulsion indices High salary is more frequent for people with University education. High salary is less frequent for people with a Primary School education. Low salary is less frequent for people with University education....

31 Next Week Next week we will continue discussion about correspondence analysis.

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33 I K. V. Mardia, J. T. Kent, J. M. Bibby, Multivariate, Academic Press, London, 2003 (reprint of 1979).

34 II R. V. Hogg, J. W. McKean, A. T. Craig, Introduction to Mathematical Statistics, Pearson Education, Upper Sadle River, R. A. Horn, C. R. Johnson,, Cambridge University Press, New York, R. A. Horn, C. R. Johnson, Topics in, Cambridge University Press, New York, 1991.

35 III L. Simar, An Introduction to Multivariate Data, Université Catholique de Louvain Press, 2008.