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Sep 22nd, 2014
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  1. library(dummies)
  2.  
  3. #------ parameters ------
  4. n <- 1000
  5. beta0 <- 0.07
  6. betaB <- 0.1
  7. betaC <- -0.15
  8. betaD <- -0.03
  9. betaE <- 0.9
  10. #------------------------
  11.  
  12. #------ initialisation ------
  13. beta0Hat <- rep(NA, 1000)
  14. betaBHat <- rep(NA, 1000)
  15. betaCHat <- rep(NA, 1000)
  16. betaDHat <- rep(NA, 1000)
  17. betaEHat <- rep(NA, 1000)
  18. #----------------------------
  19.  
  20. #------ simulations ------
  21. for(i in 1:1000)
  22. {
  23. #data generation
  24. x <- sample(x=c("pict1","pict2", "pict3", "pict4", "pict5"),
  25. size=n, replace=TRUE, prob=rep(1/5, 5)) #(a)
  26. linpred <- cbind(1, dummy(x)[, -1]) %*% c(beta0, betaB, betaC, betaD, betaE) #(b)
  27. pi <- exp(linpred) / (1 + exp(linpred)) #(c)
  28. y <- rbinom(n=n, size=1, prob=pi) #(d)
  29. data <- data.frame(picture=x, choice=y)
  30.  
  31. #fit the logistic model
  32. mod <- glm(choice ~ picture, family="binomial", data=data)
  33.  
  34. #save the estimates
  35. beta0Hat[i] <- mod$coef[1]
  36. betaBHat[i] <- mod$coef[2]
  37. betaCHat[i] <- mod$coef[3]
  38. betaDHat[i] <- mod$coef[4]
  39. betaEHat[i] <- mod$coef[5]
  40. }
  41.  
  42. > summary(data)
  43. picture choice
  44. pict1:200 Min. :0.000
  45. pict2:207 1st Qu.:0.000
  46. pict3:217 Median :1.000
  47. pict4:163 Mean :0.559
  48. pict5:213 3rd Qu.:1.000
  49. Max. :1.000
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