1. April 6, 2010 Generalized Linear Models 2010 LISA Short Course Series Mark Seiss, Dept. of Statistics

2. Presentation Outline 1.Introduction to Generalized Linear Models 2.Binary Response Data - Logistic Regression Model 3.Teaching Method Example 4.Count Response Data - Poisson Regression Model 5.Mining Example 6.Open Discussion

3. Reference Material Categorical Data Analysis – Alan Agresti Contemporary Statistical Models for Plant and Soil Sciences – Oliver Schabenberger and F.J. Pierce Presentation and Data from Examples www.lisa.stat.vt.edu

4. Generalized Linear Models Generalized linear models (GLM) extend ordinary regression to non-normal response distributions. Response distribution must come from the Exponential Family of Distributions Includes Normal, Bernoulli, Binomial, Poisson, Gamma, etc. 3 Components Random – Identifies response Y and its probability distribution Systematic – Explanatory variables in a linear predictor function (Xβ) Link function – Invertible function (g(.)) that links the mean of the response (E[Y i ]=μ i ) to the systematic component.

5. Generalized Linear Models Model for i =1 to n j= 1 to p Equivalently,

6. Generalized Linear Models Why do we use GLM’s? Linear regression assumes that the response is distributed normally GLM’s allow us to analyze the linear relationship between predictor variables and the mean of the response variable when it is not reasonable to assume the data is distributed normally.

7. Generalized Linear Models Connection Between GLM’s and Multiple Linear Regression Multiple linear regression is a special case of the GLM Response is normally distributed with variance σ 2 Identity link function μ i = g(μ i ) = x i T β

8. Generalized Linear Models Predictor Variables Two Types: Continuous and Categorical Continuous Predictor Variables Examples – Time, Grade Point Average, Test Score, etc. Coded with one parameter – β i x i Categorical Predictor Variables Examples – Sex, Political Affiliation, Marital Status, etc. Actual value assigned to Category not important Ex) Sex - Male/Female, M/F, 1/2, 0/1, etc. Coded Differently than continuous variables

9. Generalized Linear Models Predictor Variables cont. Consider a categorical predictor variable with L categories One category selected as reference category Assignment of reference category is arbitrary Some suggest assign category with most observations Variable represented by L-1 dummy variables Model Identifiability

10. Generalized Linear Models Predictor Variables cont. Two types of coding Dummy Coding (Used in R) x k = 1 If predictor variable is equal to category k 0 Otherwise x k = 0 For all k if predictor variable equals category i Effect Coding (Used in JMP) x k =1 If predictor variable is equal to category k 0 Otherwise x k = -1 For all k if predictor variable equals category i

11. Generalized Linear Models Model Evaluation - -2 Log Likelihood Specified by the random component of the GLM model For independent observations, the likelihood is the product of the probability distribution functions of the observations. -2 Log likelihood is -2 times the log of the likelihood function -2 Log likelihood is used due to its distributional properties – Chi-square

12. Generalized Linear Models Saturated Model Contains a separate indicator parameter for each observation Perfect fit μ i = y i Not useful since there is no data reduction i.e. number of parameters equals number of observations Maximum achievable log likelihood (minimum -2 Log L) – baseline for comparison to other model fits

13. Generalized Linear Models Deviance Let L(β|y) = Maximum of the log likelihood for the model L(y|y) = Maximum of the log likelihood for the saturated model Deviance = D(β) = -2 [L(β|y) - L(y|y)]

14. Generalized Linear Models Deviance cont. Model Chi-Square

15. Generalized Linear Models Deviance cont. Lack of Fit test Likelihood Ratio Statistic for testing the null hypothesis that the model is a good alternative to the saturated model Has an asymptotic chi-squared distribution with N – p degrees of freedom, where p is the number of parameters in the model. Also allows for the comparison of one model to another using the likelihood ratio test.

16. Generalized Linear Models Nested Models Model 1 - Model with p predictor variables {X 1, X 2 …,X p } and vector of fitted values μ 1 Model 2 - Model with q<p predictor variables {X 1, X 2,…,X q } and vector of fitted values μ 2 Model 2 is nested within Model 1 if all predictor variables found in Model 2 are included in Model 1. i.e. the set of predictor variables in Model 2 are a subset of the set of predictor variables in Model 1

17. Generalized Linear Models Nested Models Model 2 is a special case of Model 1 - all the coefficients corresponding to X p+1, X p+2, X p+3,….,X q are equal to zero

18. Generalized Linear Models Likelihood Ratio Test Null Hypothesis for Nested Models: The predictor variables in Model 1 that are not found in Model 2 are not significant to the model fit. Alternate Hypothesis for Nested Models - The predictor variables in Model 1 that are not found in Model 2 are significant to the model fit.

19. Generalized Linear Models Likelihood Ratio Test Likelihood Ratio Statistic =-2L(y,u 2 ) - (-2L(y,u 1 )) =D(y,μ 2 ) - D(y, μ 1 ) Difference of the deviances of the two models Always D(y,μ 2 ) > D(y,μ 1 ) implies LRT > 0 LRT is distributed Chi-Squared with p-q degrees of freedom Later, the Likelihood Ratio Test will be used to test the significance of variables in Logistic and Poisson regression models.

20. Generalized Linear Models Theoretical Example of Likelihood Ratio Test 3 predictor variables – 1 Continuous (X 1 ), 1 Categorical with 4 Categories (X 2, X 3, X 4 ), 1 Categorical with 1 Category (X 5 ) Model 1 - predictor variables {X 1, X 2, X 3, X 4, X 5 } Model 2 - predictor variables {X 1, X 5 } Null Hypothesis – Variables with 4 categories is not significant to the model (β 2 = β 3 = β 4 = 0) Alternate Hypothesis - Variable with 4 categories is significant

21. Generalized Linear Models Theoretical Example of Likelihood Ratio Test Cont. Likelihood Ratio Statistic = D(y,μ 2 ) - D(y, μ 1 ) Difference of the deviance statistics from the two models Equivalently, the difference of the -2 Log L from the two models Chi-Squared Distribution with 5-2=3 degrees of freedom

22. Generalized Linear Models Model Selection 2 Goals:Complex enough to fit the data well Simple to interpret, does not overfit the data Study the effect of each predictor on the response Y Continuous Predictor – Graph Y versus X Discrete Predictor - Contingency Table of Mean of Y (μ y ) versus categories of X Unbalance Data – Few responses of one type Guideline – 10 outcomes of each type for each X terms Example – Y=1 for only 30 observations out of 1000 Model should contain no more than 3 X terms

23. Generalized Linear Models Model Selection cont. Multicollinearity Correlations among predictors resulting in an increase in variance Reduces the significance value of the variable Occurs when several predictor variables are used in the model Affects sign, size, and significance of parameter estimates Determining Model Fit Other criteria besides significance tests (i.e. Likelihood Ratio Test) can be used to select a model

24. Generalized Linear Models Model Selection cont. Determining Model Fit cont. Akaike Information Criterion (AIC) – Penalizes model for having many parameters – AIC = Deviance+2*p where p is the number of parameters in model Bayesian Information Criterion (BIC) – BIC = -2 Log L + ln(n)*p where p is the number of parameters in model and n is the number of observations – Also known as the Schwartz Information Criterion (SIC)

25. Generalized Linear Models Model Selection cont. Selection Algorithms Best subset – Tests all combinations of predictor variables to find best subset Algorithmic – Forward, Backward and Stepwise Procedures

26. Generalized Linear Models Stepwise Selection Idea: Combination of forward and backward selection Forward Step then backward step Step One:Fit each predictor variable as a single predictor variable and determine fit Step Two:Select variable that produces best fit and add to model Step Three:Add each predictor variable one at a time to the model and determine fit Step Four: Select variable that produces best fit and add to the model

27. Generalized Linear Models Stepwise Selection Cont. Step Five: Delete each variable in the model one at a time and determine fit Step Six: Remove variable that produces best fit when deleted Step Seven: Return to Step Two Loop until no variables added or deleted improve the fit.

28. Generalized Linear Models Outlier Detection Studentized Residual Plot and Deviance Residual Plots Plot against predicted values Looking for “sore thumbs”, values much larger than those for other observations

29. Generalized Linear Models Summary Setup of the Generalized Linear Model Continuous and Categorical Predictor Variables Log Likelihood Deviance and Likelihood Ratio Test Test lack of fit of the model Test the significance of a predictor variable or set of predictor variables in the model. Model Selection Outlier Detection

30. Generalized Linear Models Questions/Comments

31. Logistic Regression Consider a binary response variable. Variable with two outcomes One outcome represented by a 1 and the other represented by a 0 Examples: Does the person have a disease? Yes or No Outcome of a baseball game? Win or loss

32. Logistic Regression Teaching Method Data Set Found in Aldrich and Nelson (Sage Publications, 1984) Researcher would like to examine the effect of a new teaching method – Personalized System of Instruction (PSI) Response variable is whether the student received an A in a statistics class (1 = yes, 0 = no) Other data collected: GPA of the student Score on test entering knowledge of statistics (TUCE)

33. Logistic Regression Consider the linear probability model where y i = response for observation i x i = 1x(p+1) matrix of covariates for observation i p =number of covariates

34. Logistic Regression GLM with binomial random component and identity link g(μ) = μ Issues: π(X i ) can take on values less than 0 or greater than 1 Predicted probability for some subjects fall outside of the [0,1] range.

35. Logistic Regression Consider the logistic regression model GLM with binomial random component and logit link g(μ) = logit(μ) Range of values for π(X i ) is 0 to 1

36. Logistic Regression Interpretation of Coefficient β – Odds Ratio The odds ratio is a statistic that measures the odds of an event compared to the odds of another event. Say the probability of Event 1 is π 1 and the probability of Event 2 is π 2. Then the odds ratio of Event 1 to Event 2 is:

37. Logistic Regression Interpretation of Coefficient β – Odds Ratio Cont. Value of Odds Ratio range from 0 to Infinity Value between 0 and 1 indicate the odds of Event 2 are greater Value between 1 and infinity indicate odds of Event 1 are greater Value equal to 1 indicates events are equally likely

38. Logistic Regression Interpretation of Coefficient β – Odds Ratio Cont. Link to Logistic Regression : Thus the odds ratio between two events is Note: One should take caution when interpreting parameter estimates Multicollinearity can change the sign, size, and significance of parameters

39. Logistic Regression Interpretation of Coefficient β – Odds Ratio Cont. Consider Event 1 is Y=0 given X and Event 2 is Y=0 given X+1 From our logistic regression model Thus the ratio of the odds of Y=0 for X and X+1 is

40. Logistic Regression Interpretation for a Continuous Predictor Variable Consider the following JMP output: Parameter Estimates Term EstimateStd ErrorChiSquareProb>ChiSq Intercept11.83200254.71615526.290.0121* GPA-2.82611251.2629415.010.0252* TUCE-0.09515770.14155420.450.5014 PSI[0]1.189343790.53228214.990.0255* Interpretation of the Parameter Estimate: Exp{-2.8261125} = 0.0592 = Odds ratio between the odds at x+1 and odds at x for all x The ratio of the odds of NOT getting an A between a person with a 3.0 gpa and 2.0 gpa is equal to 0.0592 or in other words the odds of the person with the 3.0 is 0.0592 times the odds of the person with the 2.0. Equivalently, the odds of getting an A for a person with a 3.0 gpa is equal to 1/0.0592=16.8919 times the odds of getting an A for a person with a 2.0 gpa.

41. Logistic Regression Single Categorical Predictor Variable Consider the following JMP output: Parameter Estimates Term EstimateStd ErrorChiSquareProb>ChiSq Intercept11.83200254.71615526.290.0121* GPA-2.82611251.2629415.010.0252* TUCE-0.09515770.14155420.450.5014 PSI[0]1.189343790.53228214.990.0255* Interpretation of the Parameter Estimate: Exp{2*1.1893} = 10.78 = Odds ratio between the odds of NOT getting an A for a student that was not subject to the teaching method and the odds of NOT getting an A for a student that was subject to the teaching method. The odds of getting an A without the teaching method is 1/10.78=0.0927 times the odds of getting an A with the teaching method. I

42. Logistic Regression ROC Curve Receiver Operating Curve Sensitivity – Proportion of positive cases (Y=1) that were classified as a positive case by the model Specificity - Proportion of negative cases (Y=0) that were classified as a negative case by the model

43. Logistic Regression ROC Curve Cont. Cutoff Value - Selected probability where all cases in which predicted probabilities are above the cutoff are classified as positive (Y=1) and all cases in which the predicted probabilities are below the cutoff are classified as negative (Y=0) 0.5 cutoff is commonly used ROC Curve – Plot of the sensitivity versus one minus the specificity for various cutoff values False positives (1-specificity) on the x-axis and True positives (sensitivity) on the y-axis

44. Logistic Regression ROC Curve Cont. Measure the area under the ROC curve Poor fit – area under the ROC curve approximately equal to 0.5 Good fit – area under the ROC curve approximately equal to 1.0

45. Logistic Regression Teaching Method Example

46. Logistic Regression Summary Introduction to the Logistic Regression Model Interpretation of the Parameter Estimates β – Odds Ratio ROC Curves Teaching Method Example

47. Logistic Regression Questions/Comments

48. Poisson Regression Consider a count response variable. Response variable is the number of occurrences in a given time frame. Outcomes equal to 0, 1, 2, …. Examples: Number of penalties during a football game. Number of customers shop at a store on a given day. Number of car accidents at an intersection.

49. Poisson Regression Mining Data Set Found in Myers (1990) Response of interest is the number of fractures that occur in upper seam mines in the coal fields of the Appalachian region of western Virginia Want to determine if fractures is a function of the material in the land and mining area Four possible regressors Inner burden thickness Percent extraction of the lower previously mined seam Lower seam height Years the mine has been open

50. Poisson Regression Mining Data Set Cont. Coal Mine Seam

51. Poisson Regression Mining Data Set Cont. Coal Mine Upper and Lower Seams Prevalence of overburden fracturing may lead to collapse

52. Poisson Regression Consider the model where Y i = Response for observation i x i = 1x(p+1) matrix of covariates for observation i p =Number of covariates μ i = Expected number of events given x i GLM with Poisson random component and identity link g(μ) = μ Issue: Predicted values range from -∞ to +∞

53. Poisson Regression Consider the Poisson log-linear model GLM with Poisson random component and log link g(μ) = log(μ) Predicted response values fall between 0 and +∞ In the case of a single predictor, An increase of one unit of x results an increase of exp(β) in μ

54. Poisson Regression Continuous Predictor Variable Consider the JMP output Term EstimateStd ErrorL-R ChiSquareProb>ChiSqLower CLUpper CL Intercept-3.593091.025687714.1137020.0002*-5.69524-1.660388 Thickness-0.0014070.00083583.1665420.0752-0.0031620.0001349 Pct_Extraction0.06234580.012286331.951118<.0001*0.03923790.0875323 Height-0.002080.00506620.1746710.6760-0.0128740.0070806 Age-0.0308130.01626493.89443860.0484*-0.064181-0.000202 Interpretation of the parameter estimate: Exp{-0.0308} =.9697 = multiplicative effect on the expected number of fractures for an increase of 1 in the years the mine has been opened

55. Poisson Regression Overdispersion for Poisson Regression Models More variability in the response than the model allows For Y i ~Poisson(λ i ), E [Y i ] = Var [Y i ] = λ i The variance of the response is much larger than the mean. Consequences:Parameter estimates are still consistent Standard errors are inconsistent Detection:D(β)/n-p Large if overdispersion is present

56. Poisson Regression Overdispersion for Poisson Regression Models Cont. Remedies 1.Change linear predictor – X T β – Add or subtract regressors, transform regressors, add interaction terms, etc. 2.Change link function – g(X T β) 3.Change Random Component – Use Negative Binomial Distribution

57. Poisson Regression Mining Example

58. Poisson Regression Summary Introduction to the Poisson Regression Model Interpretation of β Overdispersion Mining Example

59. Poisson Regression Questions/Comments

60. Generalized Linear Models Open Discussion