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Generalized Linear Models 2010 LISA Short Course Series

Generalized Linear Models 2010 LISA Short Course Series. Mark Seiss , Dept. of Statistics. Presentation Outline. 1. Introduction to Generalized Linear Models 2. Binary Response Data - Logistic Regression Model 3. Teaching Method Example

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Generalized Linear Models 2010 LISA Short Course Series

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  1. Generalized Linear Models2010 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 (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[Yi]=μi) to the systematic component. Generalized Linear Models

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

  6. 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. Generalized Linear Models

  7. Connection Between GLM’s and Multiple Linear Regression Multiple linear regression is a special case of the GLM Response is normally distributed with variance σ2 Identity link function μi= g(μi) = xiTβ Generalized Linear Models

  8. Predictor Variables Two Types: Continuous and Categorical Continuous Predictor Variables Examples – Time, Grade Point Average, Test Score, etc. Coded with one parameter – βixi 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 Generalized Linear Models

  9. 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 Generalized Linear Models

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

  11. 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 Generalized Linear Models

  12. Saturated Model Contains a separate indicator parameter for each observation Perfect fit μi = yi 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 Generalized Linear Models

  13. 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)] Generalized Linear Models

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

  15. 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. Generalized Linear Models

  16. Nested Models Model 1 - Model with p predictor variables {X1, X2…,Xp} and vector of fitted values μ1 Model 2 - Model with q<p predictor variables {X1, X2,…,Xq} and vector of fitted values μ2 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 Generalized Linear Models

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

  18. 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. Generalized Linear Models

  19. Likelihood Ratio Test Likelihood Ratio Statistic = -2L(y,u2) - (-2L(y,u1)) = D(y,μ2) - D(y, μ1) Difference of the deviances of the two models Always D(y,μ2) > D(y,μ1) 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. Generalized Linear Models

  20. Theoretical Example of Likelihood Ratio Test 3 predictor variables – 1 Continuous (X1), 1 Categorical with 4 Categories (X2, X3, X4), 1 Categorical with 1 Category (X5) Model 1 - predictor variables {X1, X2, X3, X4, X5} Model 2 - predictor variables {X1, X5} Null Hypothesis – Variables with 4 categories is not significant to the model (β2 = β3 = β4= 0) Alternate Hypothesis - Variable with 4 categories is significant Generalized Linear Models

  21. Theoretical Example of Likelihood Ratio Test Cont. Likelihood Ratio Statistic = D(y,μ2) - D(y, μ1) 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 Generalized Linear Models

  22. 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 (μy) 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 Generalized Linear Models

  23. 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 Generalized Linear Models

  24. 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) Generalized Linear Models

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

  26. 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 Generalized Linear Models

  27. 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. Generalized Linear Models

  28. 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 Generalized Linear Models

  29. 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 Generalized Linear Models

  30. Questions/Comments Generalized Linear Models

  31. 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 Logistic Regression

  32. 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) Logistic Regression

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

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

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

  36. 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 π1and the probability of Event 2 is π2. Then the odds ratio of Event 1 to Event 2 is: Logistic Regression

  37. 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 Logistic Regression

  38. 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 Logistic Regression

  39. 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 Logistic Regression

  40. Interpretation for a Continuous Predictor Variable Consider the following JMP output: Parameter Estimates Term Estimate Std Error ChiSquareProb>ChiSq Intercept 11.8320025 4.7161552 6.29 0.0121* GPA -2.8261125 1.262941 5.01 0.0252* TUCE -0.0951577 0.1415542 0.45 0.5014 PSI[0] 1.18934379 0.5322821 4.99 0.0255* 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. Logistic Regression

  41. Single Categorical Predictor Variable Consider the following JMP output: Parameter Estimates Term Estimate Std Error ChiSquareProb>ChiSq Intercept 11.8320025 4.7161552 6.29 0.0121* GPA -2.8261125 1.262941 5.01 0.0252* TUCE -0.0951577 0.1415542 0.45 0.5014 PSI[0] 1.18934379 0.5322821 4.99 0.0255* 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 Logistic Regression

  42. 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 Logistic Regression

  43. 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 Logistic Regression

  44. 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 Logistic Regression

  45. Teaching Method Example Logistic Regression

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

  47. Questions/Comments Logistic Regression

  48. 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. Poisson Regression

  49. 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 Poisson Regression

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

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