# Multinomial and Ordinal Logistic Regression

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1 Multinomial and Ordinal Logistic Regression ME104: Linear Regression Analysis Kenneth Benoit August 22, 2012

2 Regression with categorical dependent variables When the dependent variable is categorical, with > 2 categories Example: Which party did you vote for? Data from the European Social Survey (2002/2003), British sample Question: For which party did you vote in 2001? (Here we only consider the Conservatives, Labour, and the Liberal Democrats) party Valid Missing Total Conservative Labour Liberal Democrat Total other party/no answer Cumulative Frequency Percent Valid Percent Percent

3 The multinomial logistic regression model We have data for n sets of observations (i = 1, 2,... n) Y is a categorical (polytomous) response variable with C categories, taking on values 0, 1,..., C 1 We have k explanatory variables X 1, X 2,..., X k The multinomial logistic regression model is defined by the following assumptions: Observations Yi are statistically independent of each other Observations Yi are a random sample from a population where Y i has a multinomial distribution with probability parameters: π (0) i, π (1) i,..., π (C 1) i As with binomial logistic regression, we have to set aside one category for a base category (hence the C 1 parameters π)

4 The multinomial logistic regression model The logit for each non-reference category j = 1,..., C 1 against the reference category 0 depends on the values of the explanatory variables through: ( ) π (j) i log π (0) = α (j) + β (j) 1 X 1i + + β (j) k X ki i for each j = 1,..., C 1 where α (j) and β (j) 1,..., β(j) k unknown population parameters are

5 Multinomial distribution Pr(Y 1 = y 1,..., Y k = y k ) = { n! y j! y k! π(0) 1 π (C 1) k when k j=1 y j = n 0 otherwise E(y ij ) = nπ j Var(y ij ) = nπ j (1 π j )

6 Example: vote choice Response variable: Party voted for in 2001 Labour is the reference category, j = 0 Conservatives are the j = 1 category Liberal Democrats will be j = 2 (note that this coding is arbitrary) Explanatory variables: Age: continuous in years X 1 Educational level (categorical) lower secondary or less (omitted reference category) upper secondary (X 2 = 1) post-secondary (X 3 = 1)

7 If we were to fit binary logistic models One model for the log odds of voting Convervative v. Labour: ( ) π (1) i log π (0) = α (1) + β (1) 1 X 1i + β (1) 2 X 2i + β (1) 3 X 3i i A second model for the log odds of voting Lib Dem v. Labour: ( ) π (2) i log π (0) = α (2) + β (2) 1 X 1i + β (2) 2 X 2i + β (2) 3 X 3i i ( ) π (LD) i log π (Lab) = α (LD) +β (LD) 1 age i +β (LD) 2 upper sec i +β (LD) 3 post sec i i

8 Estimates of this model party a Conservative Liberal Democrat Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] a. The reference category is: Labour. b. This parameter is set to zero because it is redundant. Parameter Estimates 95% Confidence Interval for Exp(B) Lower Bound Upper Bound B Std. Error Wald df Sig. Exp(B) b b

9 Interpreting ˆβ: continuous X party Conservative Liberal Democrat Parameter Estimates Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] B Exp(B) Holding education level constant, a one-year increase in age multiplies the odds of voting Conservative rather than Labour by 1.021, i.e. increases them by 2.1% A five-year increase in age (controlling for education level) multiplies the odds of voting Conservative rather than Labour by exp( ) = = 1.11, i.e. increases them by 11% Holding education level constant, a one-year increase in age multiplies the odds of voting Lib Dem rather than Labour by 1.005, i.e. increases them by 0.5%

10 Interpreting ˆβ: categorical X party Conservative Liberal Democrat Parameter Estimates Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] B Exp(B) Holding age constant, the odds for someone with post-secondary education of voting Conservative rather than Labour are times (89.2% higher than) the odds for someone with lower secondary or less education Holding age constant, the odds for someone with upper secondary education of voting Conservative rather than Labour are times (60.6% higher than) the odds for someone with lower secondary or less education

11 Interpreting ˆβ: categorical X party Conservative Liberal Democrat Parameter Estimates Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] B Exp(B) Holding age constant, the odds for someone with post-secondary education of voting Lib Dem rather than Labour are times (179.1% higher than) the odds for someone with lower secondary or less education Holding age constant, the odds for someone with upper secondary education of voting Lib Dem rather than Labour are times (110.8% higher than) the odds for someone with lower secondary or less education

12 Interpreting ˆβ between non-reference categories of X party Conservative Liberal Democrat Parameter Estimates Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] B Exp(B) Holding age constant, the odds for someone with post-secondary education of voting Conservative rather than Labour are times (17.8% higher than) the odds for someone with upper secondary education Calculation: exp( ) = exp(0.164) or 1.892/1.606 Holding age constant, the odds for someone with upper secondary education of voting Lib Dem rather than Labour are times (24.4% lower than) the odds for someone with post-secondary education Calculation: exp( ) = exp( 0.28) or 2.108/2.791

13 Interpreting ˆβ between non-reference categories of Y ( ) π (j) i log π (1) i party Conservative Liberal Democrat Parameter Estimates Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] Intercept age [educ=post_sec] [educ=upper_sec] [educ=lower_sec] = (α (1) α (j) ) + (β (j) 1 β(1) 1 )X 1i + + (β (j) for each j = 2,..., C 1 B Exp(B) k β (1) k Holding age constant, the odds for someone with post-secondary education of voting Lib Dem rather than Conservative are 1.47 times (47.4% higher than) the odds for someone with lower secondary education Calculation: exp( ) = exp(0.388) or 2.791/1.892 )X ki

14 Computing fitted probabilities We fit a logit model for each non-reference category j Let L(j) = log(π i (j)/ pi i (0)) the log odds of a response in category j rather than the reference category 0 Probability of response in category j can be calculated as π (j) = P(Y = j) = exp(l (j) ) 1 + exp(l (1) ) + + exp(l (C 1) ) Probability of response in category 0 can be calculated as π (0) = P(Y = 0) = exp(l (1) ) + + exp(l (C 1) )

15 Fitted probabilities from the example (with voting Labour as the reference category) Logit for voting Conservative rather than Labour: L (Cons) = log(π (Cons) i /π (Lab) i ) = age upper sec post se Logit for voting Liberal Democrat rather than Labour: L (Lib) = log(π (Lib) i /π (Lab) i ) = age upper sec post sec Estimated logits for (for example), a 55-year old with upper secondary education: L(Cons) = (55) (1) (0) = L(Lib) = (55) (1) (0) = 0.788

16 More fitted probabilities from the example Probability of 55 year old with upper secondary education voting Conservative: ˆπ (Cons) = exp( 0.232) 1 + exp( 0.232) + exp( 0.788) = = 0.35 Probability of 55 year old with upper secondary education voting Liberal Democrat: ˆπ (Lib) = exp( 0.788) 1 + exp( 0.232) + exp( 0.788) = = 0.20 Probability of 55 year old with upper secondary education voting Labour: ˆπ (Lab) = exp( 0.232) + exp( 0.788) = = 0.44

17 for a categorical explanatory variable Fitted probabilities of party choice given education, with age fixed at 55 years: Lower secondary Upper secondary Postsecondary Conservative Lib Dem Labour

18 for a continuous explanatory variable Fitted probabilities of party choice given age, with education fixed at lower secondary or less:

19 for all three response categories

20 Confidence intervals for ˆβ These are calculated just as for binomial logits, using ±1.96ˆσ ˆβ So the 95% confidence interval for an estimated coefficient is: ˆβ (j) ± 1.96 ŝe( ˆβ (j) ) and the 95% confidence interval for an odds ratio is: ( ) exp[ ˆβ (j) 1.96 ŝe( ˆβ (j) )]; exp[ ˆβ (j) ŝe( ˆβ (j) )]

21 Wald tests for ˆβ Wald tests are provided in the SPSS output by default Here, testing H 0 : β (j) = 0 i.e. null hypothesis that a given X has no effect on odds of Y = j versus Y = 0 But we often want to test the null hypothesis that a given X has no effect on odds of any category of the response variable, e.g. H 0 : β (1) = β (2) = = β (C 1) = 0 We can use likelihood ratio comparison test, in the usual way, to test several coefficients at once

22 Likelihood ratio comparison tests Reminder: we compare two models: Model 1 is the simpler, restricted, model, with likelihood L1 Model 2 is the more complex, full, model, with likelihood L2 Must be nested: so Model 2 is Model 1 with some extra parameters H 0 : more complex model is no better than simpler one; then L 1 and L 2 will be similar, i.e. difference between them will be small Likelihood ratio test statistic: D = 2(logL 2 logl 1 ) = ( 2logL 1 ) ( 2logL 2 ) Obtain p-value for tests statistic from χ 2 distribution with degrees of freedom equal to the difference in the degrees of freedom in the two models (i.e. the number of extra parameters in the larger model)

23 Likelihood ratio comparison tests: Example We can test a more restricted model excluding age. H 0 : β (Cons) age = β (Lib) age = 0-2 log likelihood of Model 1, without age = log likelihood of Model 2, including age = Difference in -2 log likelihoods = Difference in degrees of freedom = 2 p-value for on χ 2 distribution with 2 d.f. < Reject H 0 ; keep age in the model

24 Likelihood ratio comparison tests: Example We can test a more restricted model excluding education. H 0 : β (Cons) educ2 = β(lib) educ2 = β(cons) educ3 = β(lib) educ3 = 0-2 log likelihood of Model 1, without education = log likelihood of Model 2, including education = Difference in -2 log likelihoods = Difference in degrees of freedom = 4 p-value for on χ 2 distribution with 4 d.f. < Reject H 0 ; keep education in the model

25 Ordinal response variables: An example Data from the U.S. General Social Survey in 1977 and 1989 Response variable Y is the answer to the following item: A working mother can establish just as warm and secure a relationship with her children as a mother who does not work. 1=Strongly disagree (SD), 2=Disagree (D), 3=Agree (A) and 4=Strongly agree (SA) In this and many other examples, the categories of the response have a natural ordering A multinomial logistic model can be used here too, but it has the disadvantage of ignoring the ordering An ordinal logistic model (proportional odds model) does take the ordering into account This gives a model with fewer parameters to interpret

26 Cumulative probabilities Suppose response variable Y has C ordered categories j = 1, 2,..., C, with probabilities P(Y = j) = π (j) for j = 1,..., C In multinomial logistic model, we considered the C 1 ratios P(Y = j)/p(y = 1) = π (j) /π (1) for j = 2,..., C and wrote down a model for each of them Now we will consider the C 1 cumulative probabilities γ (j) = P(Y j) = π (1) + + π (j) for j = 1,..., C 1 and write down a model for each of them Note that γ (C) = P(Y C) = 1 always, so it need not be modelled

27 The ordinal logistic model Data: (Y i, X 1i,..., X ki ) for observations i = 1,..., n, where Y is a response variable with C ordered categories j = 1,..., C, and probabilities π (j) = P(Y = j) X1,..., X k are k explanatory variables Observations Y i are statistically independent of each other The following holds for γ (j) i = P(Y i j) for each unit i and each category j = 1,..., C 1: ( ) γ (j) ( ) i P(Yi j) log 1 γ (j) = log = α (j) (β 1 X 1i + +β k X ki ) P(Y i > j) i

28 The ordinal logistic model In other words, the ordinal logistic model considers a set of dichotomies, one for each possible cut-off of the response categories into two sets, of high and low responses This is meaningful only if the categories of Y do have an ordering In our example, these cut-offs are Strongly disagree vs. (Disagree, Agree, or Strongly agree), i.e. SD vs. (D, A, or SA) (SD or D) vs. (A or SA) (SD, D, or SA) vs. SA A binary logistic model is then defined for the log-odds of each of these cuts

29 Parameters of the model The model for the cumulative probabilities is γ (j) = P(Y j) = exp[α(j) (β 1 X β k X k )] 1 + exp[α (j) (β 1 X β k X k )] The intercept terms must be α (1) < α (2) < < α (C 1), to guarantee that γ (1) < γ (2) < < γ (C 1) β 1, β 2,..., β k are the same for each value of j There is thus only one set of regression coefficients, not C 1 as in a multinomial logistic model The curves for γ (1),..., γ (C 1) are parallel as seen below This is the assumption of proportional odds. The ordinal logistic model is also known as the proportional odds model

30 Probabilities from the model The probabilities of individual categories are P(Y = 1) = γ (1) = exp[α(1) (β 1 X β k X k )] 1 + exp[α (1) (β 1 X β k X k )] P(Y = j) = γ (j) γ (j 1) = exp[α(j) (β 1 X β k X k )] 1 + exp[α (j) (β 1 X β k X k )] for j = 2,..., C 1, and exp[α(j 1) (β 1 X β k X k )] 1 + exp[α (j 1) (β 1 X β k X k )] P(Y = C) = 1 γ (C 1) = 1 exp[α(c 1) (β 1 X β k X k )] 1 + exp[α (C 1) (β 1 X β k X k ) Illustrated below with plots for a case with C = 4 categories.

31 Cumulative probabilities: An example Pr(y<=m) x Pr(y<=1 x) Pr(y<=2 x) Pr(y<=3 x)

32 Probabilities of individual categories: An example Pr(y=m) x Pr(y=1 x) Pr(y=2 x) Pr(y=3 x) Pr(y=4 x)

33 Estimation and inference Everything here is unchanged from binary logistic models: Parameters are estimated using maximum likelihood estimation Hypotheses of interest are typically of the form β j = 0, for one or more coefficients β j Wald tests, likelihood ratio tests and confidence intervals are defined and used as before

34 Back to the example Response variable Y (variable warm): A working mother can establish just as warm and secure a relationship with her children as a mother who does not work., with levels SD, D, A, and SA Explanatory variables: yr89: a dummy variable for survey year 1989 (1=1989, 0=1977) white: a dummy variable for ethnic group white (1=white, 0=non-white) age in years ed: years of education male: a dummy variable for men (1=Male, 2=Female)

35 Fitted model: An example. ologit warm yr89 white age ed male Ordered logistic regression Number of obs = 22 LR chi2(5) = 298. Prob > chi2 = 0.00 Log likelihood = Pseudo R2 = warm Coef. Std. Err. z P> z [95% Conf. Interva yr white age ed male /cut /cut /cut

36 Interpretation of the coefficients Exponentiated coefficients are interpreted as partial odds ratios for being in the higher rather than the lower half of the dichotomy here (SA, A, or D) vs. SD, (SA or A) vs. (D or SD), and SA vs. (A, D, or SD) odds ratio is the same for each of these e.g. exp( ˆβ male ) = 0.48: Controlling for the other explanatory variables, men have 52% lower odds than women of giving a response that indicates higher levels of agreement with the statement e.g. exp( ˆβ ed ) = 1.088: Controlling for the other explanatory variables, 1 additional year of education is associated with a 8.8% increase in odds of giving a response that indicates higher levels of agreement with the statement

37 Example with an interaction Consider adding an interaction between sex and education. Just to show something new, include it in the form of two variables: gen male_ed=male*ed gen fem_ed=(1-male)*ed instead of using ed and male*ed as previously Both versions give the same model In this version, the coefficients of male ed and fem ed are the coefficients of education for men and women respectively

38 Example with an interaction This version of interaction can be tested using a likelihood ratio test as before: ologit warm yr89 white age male ed estimates store mod1 ologit warm yr89 white age male male_ed fem_ed lrtest mod1. Likelihood-ratio test LR chi2(1) = 4.48 (Assumption: mod1 nested in.) Prob > chi2 = or with a Wald test of the hypothesis that β male ed = β fem ed : test male_ed=fem_ed ( 1) [warm]male_ed - [warm]fem_ed = 0 chi2( 1) = 4.47 Prob > chi2 =

39 Example: Fitted probabilities Predicted Cumulative Probabilities Age SD SD, D or A SD or D (Other variables fixed at: year 1989, white, male, 12 years of education)

40 Example: Fitted probabilities Predicted Probabilities Age SD A D SA (Other variables fixed at: year 1989, white, male, 12 years of education)

41 Example: Fitted probabilities Predicted Probabilities of A or SA Education Men Women (year 1989, white, male, 45 years old)

42 Assessing the proportional odds assumption The ordinal logistic model log[γ (j) /(1 γ (j) )] = α (j) (β 1 X β k X k ) assumes the same coefficients β 1,..., β k for each cut-off j This is good for the parsimony of the model, because it means that the effect of an explanatory variable on the ordinal response is described by one parameter However, it is also a restriction on the flexibility of the model, which may or may not be adequate for the data There are a number of ways of checking the assumption Here we consider briefly only one, comparing (informally) estimates and fitted probabilities between ordinal and multinomial logistic model

43 Multinomial logistic model in the example. mlogit warm yr89 white age male male_ed fem_ed, base(1) warm Coef. Std. Err. z P> z [95% Conf. Interval] D yr white age male male_ed fem_ed _cons A yr white age male male_ed fem_ed _cons SA yr white age male male_ed fem_ed _cons (warm==1sd is the base outcome)

44 Ordinal vs. multinomial models in the example In the multinomial model, (more or less) all the coefficients at least imply the same ordering of the categories e.g. for age: 0 (SD) > (D) > (A) > (SA) this is not always the case: e.g. an example in the computer class For fitted probabilities in our example: for most of the variables, the agreement in the strengths of association is reasonable if not perfect see plot for age below The biggest difference is for period (1977 vs. 1989): the ordinal model forces the change (in cumulative odds) to be the same for all cut-off points according the multinomial model, some categories have shifted more than others between the two periods in particular, the shift away from Strongly disagree has been a bit stronger than the ordinal model allows

45 Ordinal vs. multinomial: Fitted probabilities Predicted Probabilities Age SD D A SA SD (multinomial) D (multinomial) A (multinomial) SA (multinomial)

46 Ordinal vs. multinomial: Fitted probabilities Model Year SD D A SA Ordinal Multinomial (white man, aged 45, 12 years of education)

47 .. Latent-variable motivation of the model The ordinal logit model can also be derived from a model for a hypothetical unobserved (latent) continuous response variable Y Suppose Y = β 1 x β k x k + ɛ, where ɛ is a random error term which has standard logistic distribution: a bell-shaped distribution quite similar to the normal, with mean 0 and variance π 2 /3 Suppose that we do not observe Y but a grouped version Y : α1 α2 α3 In other words, we record Y = j if Y is in the interval α j 1 Y < α j (where α 0 = and α C = )

48 Latent-variable motivation of the model Then the model for Y (rather than Y ) is an ordinal logit (proportional odds model) This derivation in terms of a hypothetical underlying Y is sometimes useful for motivating the model and deriving some of its properties and sometimes Y even makes substantive sense Other models also have analogous latent-variable derivations if C = 2 (only one cut-off point), we get the binary logit model if ɛ is assumed to have a standard normal (rather than logistic) distribution, we get the (ordinal or binary) probit model the latent-variable motivation of the multinomial logistic model is completely different

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