Successfully reported this slideshow.
Your SlideShare is downloading. ×

Mixed Effects Models - Autocorrelation

More Related Content

Related Books

Free with a 30 day trial from Scribd

See all

Related Audiobooks

Free with a 30 day trial from Scribd

See all

Mixed Effects Models - Autocorrelation

  1. 1. Course Business ! Lab materials on Canvas ! Package to install for today’s analyses: languageR
  2. 2. Week 11.2: Autocorrelation ! Autocorrelation ! Introduction ! Testing for Autocorrelation ! Visualizing Autocorrelation ! Direction & Lag ! Why Does Autocorrelation Matter? ! Lab
  3. 3. Longitudinal Designs • Two big questions mixed-effects models can help us answer about longitudinal data: 1) What is the overall trajectory of change across time? " Monday: Growth curve analysis 2) How does an observation at one time point relate to the next time point? " Today: Autocorrelation
  4. 4. Autocorrelation • Previously, we looked at general trajectory across time • e.g., kids who are 28 months old have larger vocabs than kids who are 20 months old • But, there may be relations among specific observations • Does reading to your kids a lot one month increase their vocab growth the next month? • Does being in a positive mood one day carry over to the next day? • Does purchasing a particular brand once make you more likely to purchase it again?
  5. 5. relationship.csv • One member of a dating couple rates their warmth towards the partner • For each of 10 consecutive days • First, let’s see if WarmthToday consistently increases or decreases across the 10 Days of the study • model.time <- lmer(WarmthToday ~ 1 + Day + (1 + Day|Couple), data=relationship)
  6. 6. relationship.csv • No linear increase or decrease in warmth over the 10 days • Makes sense … this is a relatively short timescale
  7. 7. Autocorrelation • We found no overall increase/decrease • Nevertheless, succeeding days might be more similar, even if there is no overall trend 1 2 3 4 5 L I N E A R T R E N D
  8. 8. Autocorrelation • We found no overall increase/decrease • Nevertheless, succeeding days might be more similar, even if there is no overall trend • If you have warm feelings towards your partner one day, maybe warmer the next, too + 2 3 4 5 L I N E A R T R E N D
  9. 9. Autocorrelation • We found no overall increase/decrease • Nevertheless, succeeding days might be more similar, even if there is no overall trend • If you have warm feelings towards your partner one day, maybe warmer the next, too + + 3 4 5 L I N E A R T R E N D
  10. 10. Autocorrelation • We found no overall increase/decrease • Nevertheless, succeeding days might be more similar, even if there is no overall trend • If you have warm feelings towards your partner one day, maybe warmer the next, too • If you have negative feelings, maybe less warm the next day + + 3 - 5 L I N E A R T R E N D
  11. 11. Autocorrelation • We found no overall increase/decrease • Nevertheless, succeeding days might be more similar, even if there is no overall trend • If you have warm feelings towards your partner one day, maybe warmer the next, too • If you have negative feelings, maybe less warm the next day + + 3 - - L I N E A R T R E N D
  12. 12. Autocorrelation • We found no overall increase/decrease • Nevertheless, succeeding days might be more similar, even if there is no overall trend • If you have warm feelings towards your partner one day, maybe warmer the next, too • If you have negative feelings, maybe less warm the next day + + 3 - -
  13. 13. Autocorrelation • relationship %>% head(n=10) Couple 01
  14. 14. Autocorrelation • These are examples of autocorrelation Couple 01 Couple 02
  15. 15. Autocorrelation "Maybe in order to understand mankind, we have to look at that word itself: MANKIND. Basically, it's made up of two separate words, 'mank' and 'ind.' What do these words mean? It's a mystery, and that's why so is mankind.” -- Jack Handey
  16. 16. Autocorrelation To understand autocorrelation, we have to look at the two separate words that make it up auto (self) + correlation • Autocorrelation refers to a variable correlating with itself, over time • Examples: • Positive mood in the morning # Positive mood in the afternoon • RT on the previous trial # RT on this trial due to waxing & waning of attention
  17. 17. Autocorrelation • These are examples of autocorrelation Couple 01 Couple 02
  18. 18. Week 11.2: Autocorrelation ! Autocorrelation ! Introduction ! Testing for Autocorrelation ! Visualizing Autocorrelation ! Direction & Lag ! Why Does Autocorrelation Matter? ! Lab
  19. 19. Testing for Autocorrelation • Is there autocorrelation in relationship warmth? • We want to test whether yesterday’s warmth predicts today’s warmth • So, we need to add WarmthYesterday as a variable • With package languageR… • relationship %>% mutate(WarmthYesterday = lags.fnc(relationship, time='Day', depvar='WarmthToday', group='Couple’, lag=1)) -> relationship • For each observation, get the value of WarmthToday from 1 day previous and store in WarmthYesterday Variable that identifies the time point Dependent variable Variable with the level-2 grouping factor (e.g., subjects or schools) Dataframe name Baayen & Milin, 2010
  20. 20. Testing for Autocorrelation • Is there autocorrelation in relationship warmth? • We want to test whether yesterday’s warmth predicts today’s warmth • So, we need to add WarmthYesterday as a variable • relationship %>% head(n=13) Day 1 for each couple gets the couple’s mean Baayen & Milin, 2010
  21. 21. Testing for Autocorrelation • Is there autocorrelation in relationship warmth? • Now, include WarmthYesterday in our model • model.auto <- lmer(WarmthToday ~ 1 + Day + WarmthYesterday + (1 + Day + WarmthYesterday|Couple), data=relationship)
  22. 22. Testing for Autocorrelation • Non-significant linear effect: No consistent increase/decrease in warmth over these 10 days • But, significant autocorrelation: Adjacent days are more similar in warmth towards partner • Random walk 1 2 3 4 5 L I N E A R T R E N D X
  23. 23. Autocorrelation • These are examples of autocorrelation Couple 01 Couple 02
  24. 24. Week 11.2: Autocorrelation ! Autocorrelation ! Introduction ! Testing for Autocorrelation ! Visualizing Autocorrelation ! Direction & Lag ! Why Does Autocorrelation Matter? ! Lab
  25. 25. Visualization of Autocorrelation • Visualization of the autocorrelation • With package languageR: • acf.fnc(xxx , time='xxx ', x=' ', group=' ') Variable that identifies the time point Dependent variable Variable with the level-2 grouping factor (e.g., subjects or schools). Needs to be a FACTOR variable Dataframe name Baayen & Milin, 2010
  26. 26. Visualization of Autocorrelation • Visualization of the autocorrelation • With package languageR: • acf.fnc(relationship, time='Day', x='WarmthToday', group='Couple') Variable that identifies the time point Dependent variable Variable with the level-2 grouping factor (e.g., subjects or schools). Needs to be a FACTOR variable Dataframe name Baayen & Milin, 2010
  27. 27. Lag Acf -0.5 0.0 0.5 1.0 0 2 4 6 8 Couple01 Couple02 0 2 4 6 8 Couple03 Couple04 0 2 4 6 8 Couple05 Couple06 0 2 4 6 8 Couple07 Couple08 Couple09 Couple10 Couple11 Couple12 Couple13 -0.5 0.0 0.5 1.0 Couple14 -0.5 0.0 0.5 1.0 Couple15 Couple16 Couple17 Couple18 Couple19 Couple20 Couple21 Couple22 Couple23 Couple24 Couple25 Couple26 Couple27 -0.5 0.0 0.5 1.0 Couple28 -0.5 0.0 0.5 1.0 Couple29 Couple30 Couple31 Couple32 Couple33 Couple34 Couple35 Couple36 0 2 4 6 8 Couple37 Couple38 0 2 4 6 8 Couple39 -0.5 0.0 0.5 1.0 Couple40 One box per couple Height of the second line indicates strength of autocorrelation for that couple
  28. 28. Week 11.2: Autocorrelation ! Autocorrelation ! Introduction ! Testing for Autocorrelation ! Visualizing Autocorrelation ! Direction & Lag ! Why Does Autocorrelation Matter? ! Lab
  29. 29. Direction of Autocorrelation • Positive autocorrelation—Having more of something at time t means you have more of it at time t+1 • More warmth on day 3 # More warmth on day 4 • Longer RT on one trial # Longer RT on the next trial • Negative autocorrelation—Having more at time t means you have less at t+1 • e.g., homeostatic processes • Very hungry at time t # Get something to eat # Not hungry at time t+1 • Stock prices: Price increases # People sell the stock # Price decreases • Both analyzed the same way—just look at the sign to understand the effect
  30. 30. Positive autocorrelation. This bar would be < 0 if it were a negative autocorrelation. Lag Acf -0.5 0.0 0.5 1.0 0 2 4 6 8 Couple01 Couple02 0 2 4 6 8 Couple03 Couple04 0 2 4 6 8 Couple05 Couple06 0 2 4 6 8 Couple07 Couple08 Couple09 Couple10 Couple11 Couple12 Couple13 -0.5 0.0 0.5 1.0 Couple14 -0.5 0.0 0.5 1.0 Couple15 Couple16 Couple17 Couple18 Couple19 Couple20 Couple21 Couple22 Couple23 Couple24 Couple25 Couple26 Couple27 -0.5 0.0 0.5 1.0 Couple28 -0.5 0.0 0.5 1.0 Couple29 Couple30 Couple31 Couple32 Couple33 Couple34 Couple35 Couple36 0 2 4 6 8 Couple37 Couple38 0 2 4 6 8 Couple39 -0.5 0.0 0.5 1.0 Couple40
  31. 31. Autocorrelation: Lag • Autocorrelation can happen at different time scales or lags • Most common is lag 1: an observation correlates with the next one 1 2 3 4 5 Autocorrelation: Lag • Autocorrelation can happen at different time scales or lags • Most common is lag 1: an observation correlates with the next one 1 2 3 4 5
  32. 32. Autocorrelation: Lag • Autocorrelation can happen at different time scales or lags • Most common is lag 1: an observation correlates with the next one • Lag 2: an observation correlates not with the next observation, but the one two time points later • Like the (false) idea that twins “skip a generation” • Effects that recur, but not immediately (e.g., earthquakes) 1 2 3 4 5
  33. 33. Autocorrelation: Lag • Autocorrelation can happen at different time scales or lags • Most common is lag 1: an observation correlates with the next one • Lag 2: an observation correlates not with the next observation, but the one two time points later • Like the (false) idea that twins “skip a generation” • Lag 3, Lag 4, etc… • Mood might have a lag 7 autocorrelation – weekly change (sad Monday, happy Friday) • But, be careful with autocorrelations >1—is there a theoretically plausible reason to expect them?
  34. 34. Lag 1 autocorrelation Lag 2 autocorrelation Lag 3 autocorrelation Leftmost line is lag 0—i.e., the correlation of an observation with itself This will always be 1 It’s shown for purposes of comparison
  35. 35. Lag Acf -0.5 0.0 0.5 1.0 0 2 4 6 8 Couple01 Couple02 0 2 4 6 8 Couple03 Couple04 0 2 4 6 8 Couple05 Couple06 0 2 4 6 8 Couple07 Couple08 Couple09 Couple10 Couple11 Couple12 Couple13 -0.5 0.0 0.5 1.0 Couple14 -0.5 0.0 0.5 1.0 Couple15 Couple16 Couple17 Couple18 Couple19 Couple20 Couple21 Couple22 Couple23 Couple24 Couple25 Couple26 Couple27 -0.5 0.0 0.5 1.0 Couple28 -0.5 0.0 0.5 1.0 Couple29 Couple30 Couple31 Couple32 Couple33 Couple34 Couple35 Couple36 0 2 4 6 8 Couple37 Couple38 0 2 4 6 8 Couple39 -0.5 0.0 0.5 1.0 Couple40
  36. 36. Testing Autocorrelation, continued • Does our lag 1 autocorrelation account for the sequential dependency? • Examine the autocorrelation of the model residuals • relationship %>% mutate(resids = resid(model.auto)) -> relationship • Now run acf.fnc() on resids rather than WarmthToday Baayen & Milin, 2010
  37. 37. Lag Acf -0.5 0.0 0.5 1.0 0 2 4 6 8 Couple01 Couple02 0 2 4 6 8 Couple03 Couple04 0 2 4 6 8 Couple05 Couple06 0 2 4 6 8 Couple07 Couple08 Couple09 Couple10 Couple11 Couple12 Couple13 -0.5 0.0 0.5 1.0 Couple14 -0.5 0.0 0.5 1.0 Couple15 Couple16 Couple17 Couple18 Couple19 Couple20 Couple21 Couple22 Couple23 Couple24 Couple25 Couple26 Couple27 -0.5 0.0 0.5 1.0 Couple28 -0.5 0.0 0.5 1.0 Couple29 Couple30 Couple31 Couple32 Couple33 Couple34 Couple35 Couple36 0 2 4 6 8 Couple37 Couple38 0 2 4 6 8 Couple39 -0.5 0.0 0.5 1.0 Couple40 No consistent autocorrelation remaining in the data
  38. 38. Week 11.2: Autocorrelation ! Autocorrelation ! Introduction ! Testing for Autocorrelation ! Visualizing Autocorrelation ! Direction & Lag ! Why Does Autocorrelation Matter? ! Lab
  39. 39. Why Does Autocorrelation Matter? • Autocorrelation can be an interesting research question in its own right • Important for model assumptions • A simple growth-curve model: • Assumes that time matters only insofar as there’s a general increase/decrease with time • Otherwise, all observations from a couple equally similar or dissimilar … error terms independent = Warmth Yi(j) γ00 Overall baseline Time γ10x1(j) + U0j Random intercept for couple + Error term— independent, identically distributed Ei(j) +
  40. 40. Why Does Autocorrelation Matter? • Autocorrelation can be an interesting research question in its own right • Important for model assumptions • Assumption all observations from a couple equally similar or dissimilar … error terms independent • Not true if there’s autocorrelation … observations similar in time dependent on one another • Underestimates standard error # inflates Type I error 1 2 3 4 5 L I N E A R T R E N D
  41. 41. Week 11.2: Autocorrelation ! Autocorrelation ! Introduction ! Testing for Autocorrelation ! Visualizing Autocorrelation ! Direction & Lag ! Why Does Autocorrelation Matter? ! Lab

×