1. Logistic regression A quick intro

2. Why Logistic Regression? Big idea: dependent variable is a dichotomy (though can use for more than 2 categories i.e. multinomial logistic regression) Why would we use? One thing to use a t-test (or multivariate counterpart) to say groups are different, however it may be the research goal to predict group membership Clinical/Medical context Schizophrenic or not Clinical depression or not Cancer or not Social/Cognitive context Vote yes or no Preference A over B Graduate or not Things to cover Relationship to typical multiple regression Interpretation of fit Interpretation of coefficients

3. Questions Can the cases be accurately classified given a set of predictors? Can the solution generalize to predicting new cases? Comparison of equation with predictors plus intercept to a model with just the intercept What is the relative importance of each predictor? How does each variable affect the outcome? Does a predictor make the solution better or worse or have no effect? Are there interactions among predictors? Does adding interactions among predictors (continuous or categorical) significantly improve the model? Can parameters be accurately estimated? What is the strength of association between the outcome variable and a set of predictors?

4. Multiple regression approach With MR, we used a method to minimize the squared deviations from our predicted values Can’t really pull off with dichotomous variable Only two outcome values to produce residuals Can’t meet normality or homoscedasticity assumptions While it could produce what are essentially predicted probabilities of belonging to a particular group, those probabilities are not bounded by zero and 1 Logistic regression will allow us to go about the prediction/explanation process in a similar manner, but without the problems

5. Assumptions The only “real” limitation with logistic regression is that the outcome must be discrete. If the distributional assumptions are met for it then discriminant function analysis may be more powerful, although it has been shown to overestimate the association using discrete predictors. If the outcome is continuous then multiple regression is more powerful given that the assumptions are met

6. Assumptions Ratio of cases to variables: using discrete variables requires that there are enough responses in every given category to allow for reasonable estimation of parameters/predictive power Linearity in the logit – the IVs should have a linear relationship with the logit form of the DV. There is no assumption about the predictors being linearly related to each other.

7. Assumptions Absence of collinearity among predictors No outliers Independence of errors Assumes categories are mutually exclusive

8. Coefficients In interpreting coefficients we’re now thinking about a particular case’s tendency toward some outcome The problem with probabilities is that they are non-linear Going from.10 to.20 doubles the probability, but going from.80 to.90 only increases the probability somewhat With logistic regression we start to think about the odds Odds are just an alternative way of expressing the likelihood (probability) of an event. Probability is the expected number of the event divided by the total number of possible outcomes Odds are the expected number of the event divided by the expected number of non-event occurrences. Expresses the likelihood of occurrence relative to likelihood of non-occurrence

9. Odds Let's begin with probability. Let's say that the probability of success is.8, thus p =.8 Then the probability of failure is q = 1 - p =.2 The odds of success are defined as odds(success) = p/q =.8/.2 = 4, that is, the odds of success are 4 to 1. We can also define the odds of failure odds(failure) = q/p =.2/.8 =.25, that is, the odds of failure are 1 to 4.

10. Odds Ratio Next, let's compute the odds ratio by OR = odds(success)/odds(failure) = 4/.25 = 16 The interpretation of this odds ratio would be that the odds of success are 16 times greater than for failure. Now if we had formed the odds ratio the other way around with odds of failure in the numerator, we would have gotten OR = odds(failure)/odds(success) =.25/4 =.0625 Here the interpretation is that the odds of failure are one- sixteenth the odds of success.

11. Logit Natural log (e) of an odds Often called a log odds The logit scale is linear Logits are continuous and are centered on zero (kind of like z-scores) p = 0.50, odds = 1, then logit = 0 p = 0.70, odds = 2.33, then logit = 0.85 p = 0.30, odds =.43, then logit = -0.85

12. Logit So conceptually putting things in our standard regression form: Log odds = b o + b 1 X Now a one unit change in X leads to a b 1 change in the log odds In terms of odds: In terms of probability: Thus the logit, odds and probability are different ways of expressing the same thing

13. Coefficients The raw coefficients for our predictor variables in our output are the amount of increase in the log odds given a one unit increase in that predictor The coefficients are determined through an iterative process that finds the coefficients that best match the data at hand Maximum likelihood Starts with a set of coefficients (e.g. ordinary least squares estimates) and then proceeds to alter until almost no change in fit Note that with SPSS it codes the outcome variable as 0 and 1 and predicts with respect to the 0 category Might be more intuitive to switch the coefficients’ signs with your output

14. Coefficients We also receive a different type of coefficient expressed in odds Anything above 1 suggests an increase in odds of an event, less than, a decrease in the odds For example, if 1.14, moving on the independent variable 1 unit increases the odds of the event by a factor of 1.14 Essentially it is the odds ratio for one value of X vs. the next value of X More intuitively it refers to the percentage increase (or decrease) of becoming a member of group such and such with a one unit increase in the predictor variable

15. Example Example: predicting art museum visitation by education, age, income, and political views Gss93 dataset SPSS will start with “Block 0” which is testing to see whether the intercept is a worthwhile predictor by itself In other words, is just guessing one of the outcomes all the time going to be enough

16. Model fit Goodness-of-fit statistics help you to determine whether the model adequately describes the data Here statistical significance is not desired More like a badness of fit really, and problematic since one can’t accept the null due to non-significance Best used descriptively perhaps Pseudo r-squared statistics In this dichotomous situation we will have trouble with devising an r 2

17. Model fit Cox & Snell’s value would not reach 1.0 even for a perfect fit Nagelkerke is a version of C&S that would Probably preferred but may be a little optimistic (just like our regular R-square) The Hosmer and Lemeshow GOF suggests we’re ok too

18. Coefficients Would appear age is the only one that doesn’t contribute statistically significantly Note the odds ratio of 1.00 Moving one unit in age doesn’t say anything about whether you will be more or less likely to go to the museum Polview (1 extreme lib, 7 extreme cons) isn’t perhaps doing much either More conservative less likely to go to museum but only a very small change Education More education more likely to visit More interest? Income Higher income more likely to visit More leisure?

19. Classification Classification table Here we get a good sense of how well we’re able to predict the outcome. 69% overall compared to 58.7% if we just guessed no (Block 0)

20. Other measures regarding classification Measure Calculation Prevalence(a + c)/N Overall Diagnostic Power(b + d)/N Correct Classification Rate(a + d)/N Sensitivitya/(a + c) Specificityd/(b + d) False Positive Rateb/(b + d) False Negative Ratec/(a + c) Positive Predictive Powera/(a + b) Negative Predictive Powerd/(c + d) Misclassification Rate(b + c)/N Odds-ratio(ad)/(cb) Kappa (a + d) - (((a + c)(a + b) + (b + d)(c + d))/N) N - (((a + c)(a + b) + (b + d)(c + d))/N) NMI n(s) 1 - -a.ln(a)-b.ln(b)-c.ln(c)-d.ln(d)+(a+b).ln(a+b)+(c+d).ln(c+d) N.lnN - ((a+c).ln(a+c) + (b+d).ln(b+d)) Actual +Actual - Predicted +ab Predicted -cd The classification stats from DFA would apply here as well