CH1. What is what CH2. A simple SPF CH3. EDA CH4. Curve fitting CH5. A first SPF CH6: Which fit is fitter CH7: Choosing the objective function CH8: Theoretical stuff Ch9: Adding variables CH11. Choosing a model equation 1 6. Which fit is fitter In this session: 1.What makes for a good fit 2.Introducing the CURE plot 3.Eliminating ‘overall bias’ 4.The bias of a fit 5.Using the CURE plot

2 What makes for a good fit? Common ‘goodness-of-fit’ measures: R 2, χ 2, AIC,... These are ‘overall’ (single-number) measures. For application SPF they are insufficient. Recall… Two perspectives on SPF E{  } and  = f(Traits, parameters) Applications centered perspective Cause and effect centered perspective

3 One judges the fit of a model by its residuals. In SPFs for applications a fit is thought good only if the residuals are closely packed around 0 everywhere. Perhaps acceptable SPF Workshop February 2014, UBCO

4 The main figure of merit for SPFs: Unbiased Everywhere Fitted is too large Fitted is too small But this one is not!

SPF Workshop February 2014, UBCO 5 Informative? The usual residual plot

6 But, when the same residuals are cumulated From spreadsheet Compute Residual → Cumulate → Plot SPF Workshop February 2014, UBCO

7 The CURE Plot Now one can see! 0-A, B-C, E-F: Observed>Fitted, not good; A-B, D-E, Fitted>Observed, bad; Where the drop is precipitous there may be outliers. Residual: Observed - Fitted SPF Workshop February 2014, UBCO

8 Benefits: 1. Chaos is replaced by clarity. 2.We can recognize a good model. 3.The cost of parameterization is clear.. (2) What should a good CURE plot look like? Should not have long up or down runs Should not have vertical drops Should meander around the horizontal axis

SPF Workshop February 2014, UBCO9 (3) The cost of parametric curve fitting is now manifest Imposing the function 1.675×(Segment Length) on the data causes bias almost everywhere! No bias Biased estimates Bad decisions Real costs

SPF Workshop February 2014, UBCO10 How much bias is there? Accumulated Accidents Fitted Accidents BiasBias/ Fitted Accident Origin to A A to B B to C... TAB=Total Accumulated Bias =303+|-688|+...

11 When the scale parameter is determined by ‘Solver’ the sum of fitted values is usually not the same as the sum of crash counts. This is a blemish. To remove this blemish, add constraint Levelling the playing field Open spreadsheet #7. OLS with constraint

12 click How to add constraints

SPF Workshop February 2014, UBCO13 With constraint Now click ‘Solve’ to get

SPF Workshop February 2014, UBCO14 When is a CURE plot good enough? Open (again): #7 OLS with constraint Open: #8 CURE computations After SOLVER with constraint was used you should now see: Copy values in columns A, B, D and E into CURE spreadsheet

SPF Workshop February 2014, UBCO15 Copied Important step: On ‘DATA’ tab choose ‘Sort’ and sort in ascending order by ‘miles’

SPF Workshop February 2014, UBCO16 Now add columns E, F, and G, Note that for the last row (n=5323) the Cumulated Residuals=0. Why? C4-D4 F3+E4

SPF Workshop February 2014, UBCO17 Below is a plot of segment length (column B) against cumulative residuals (column F) Segment Length Cumulative residuals Upward drift means that in this range ‘observed’ tends to be consistently larger than ‘fitted’. Vertical gap is possible ‘outlier’ Truncated at 3 miles The question was when a CURE plot is good enough.

SPF Workshop February 2014, UBCO18 Computing the limits which a random walk should seldom exceed. Details in text. The last ‘cumulated squared residual’ +2  ’ -2  ’

SPF Workshop February 2014, UBCO19 40% within ±0.5  ’ Stop, you are in danger of overfitting. Rule of thumb: 95% within ±2  ’. This fit does not pass muster. Guidance:

20 Which fit is better? Objective Function   ∑ squared differences ∑ absolute differences The steeper the run the larger the bias; Red increased A to B bias. Black is better

SPF Workshop February 2014, UBCO21 Summary for section 6. (Which fit is fitter?) 1.For SPFs the main figure of merit is when the fit is unbiased everywhere; 2.For applications R 2, χ 2, AIC,... ‘overall’ measures are of limited use; 3.The usual plot of residuals is not informative; the CURE plot opens one’s eyes; 4.We show how to compute bias and Total Accumulated Bias. The cost of parametric C-F was manifest; 5.It is clear what a good CURE plot should look like; 6.By adding a constraint we eliminated overall bias;

SPF Workshop February 2014, UBCO22 7.We computed ±2  ’ limits and provided guidance on when a CURE plot is acceptable and when overfitting is a danger; 8.We showed how to decide which of two CURE plots is better. 9. All fits were bad. Perhaps, partly, because minimizing SSD is not good since crash count distributions are not symmetrical. What should be optimized? Next.