Next, compute the equations for each group in logit terms. (The range we set here will determine the range on the x-axis of the final plot, by the way.) X1_range <- seq(from=min(data$X1), to=max(data$X1), by=.01) That’s the only variable we’ll enter as a whole range. Note: If you were working in SPSS (or for some other reason you have run a model but can’t generate a plot for it), you can enter in your coefficients here, like this: b0 <-0.5872841 # interceptįirst, decide what variable you want on your x-axis. # X1:groupb X1:groupc X2:groupb X2:groupc # Number of Fisher Scoring iterations: 7 model$coef # (Intercept) X1 X2 groupb groupc X1:X2 # Residual deviance: 205.46 on 290 degrees of freedom # Null deviance: 412.88 on 299 degrees of freedom # (Dispersion parameter for binomial family taken to be 1) # note this use of exponent in a formula will give us all 2-way interactions For example contrasts='eatment' will make it traditional dummy coding if it isn't already. # If you want to change what contrasts will run, you can add an argument to the glm() call. And we can see that a is the reference group. # d 0 0 1 # Great, that's what we wanted. Get the coefficients from your logistic regression modelįirst, whenever you’re using a categorical predictor in a model in R (or anywhere else, for that matter), make sure you know how it’s being coded!! For this example, we want it dummy coded (so we can easily plug in 0’s and 1’s to get equations for the different groups).

set.seed(24601) # setting this so the random results will be repeatable We’ll run a nice, complicated logistic regresison and then make a plot that highlights a continuous by categorical interaction. In the space below, refit a logistic regression using just Lag1 and Lag2, which seemed to have the highest predictive power in the original logistic regression model.Plotting the results of your logistic regression Part 1: Continuous by categorical interaction Relationship with the response tends to cause a deterioration in the testĮrror rate (since such predictors cause an increase in variance without aĬorresponding decrease in bias), and so removing such predictors may in

Variables that appear not to be helpful in predicting Direction, we can Though not very small, corresponded to Lag1. We recall that the logistic regression model had very underwhelming pvaluesĪssociated with all of the predictors, and that the smallest p-value, (After all, if it were possible to do so, then the authors of this book would probablyīe out striking it rich rather than teaching statistics.) Is not all that surprising, given that one would not generally expect to beĪble to use previous days’ returns to predict future market performance. Rate is 52%, which is worse than random guessing! Of course this result The results are rather disappointing: the test error The $!=$ notation means not equal to, and so the last command computes To create a held out data set of observations from 2005. To the observations from 2001 through 2004. Like we did with KNN, we will first create a vector corresponding We used to fit the model, but rather on days in the future for which the We will be interested in our model’s performance not on the data that This will yield a more realistic error rate, in the sense that in practice Using part of the data, and then examine how well it predicts the held outĭata. Of the logistic regression model in this setting, we can fit the model Tends to underestimate the test error rate. Have seen previously, the training error rate is often overly optimistic - it In other words, 100− 52.2 = 47.8% is the training error rate. But remember, this result is misleadingīecause we trained and tested the model on the same set of 1,250 observations. In this case, logistic regressionĬorrectly predicted the movement of the market 52.2% of the time.Īt first glance, it appears that the logistic regression model is workingĪ little better than random guessing. The mean() function can be used to compute the fraction ofĭays for which the prediction was correct. It would go down on 145 days, for a total of 507 + 145 = 652 correct Hence our modelĬorrectly predicted that the market would go up on 507 days and that

While the off-diagonals represent incorrect predictions. The diagonal elements of the confusion matrix indicate correct predictions,

Logistic regression in r studio