# The 'Orthodont' data frame has 108 rows and 4 columns of the # change in an orthdontic measurement over time for several young # subjects. # # Format: # # This data frame contains the following columns: # # distance a numeric vector of distances from the pituitary to the # pterygomaxillary fissure (mm). These distances are measured # on x-ray images of the skull. # # age a numeric vector of ages of the subject (yr). # # Subject an ordered factor indicating the subject on which the # measurement was made. The levels are labelled 'M01' to 'M16' # for the males and 'F01' to 'F13' for the females. The # ordering is by increasing average distance within sex. # # Sex a factor with levels 'Male' and 'Female' # # Details: # # Investigators at the University of North Carolina Dental School # followed the growth of 27 children (16 males, 11 females) from age # 8 until age 14. Every two years they measured the distance # between the pituitary and the pterygomaxillary fissure, two points # that are easily identified on x-ray exposures of the side of the # head. # # Source: # # Pinheiro, J. C. and Bates, D. M. (2000), _Mixed-Effects Models in # S and S-PLUS_, Springer, New York. (Appendix A.17) # # Potthoff, R. F. and Roy, S. N. (1964), ``A generalized # multivariate analysis of variance model useful especially for # growth curve problems'', _Biometrika_, *51*, 313-326. # # Maximised Residual Log Likelihood is -115.15 # # Linear Coefficients: # Estimate Std. Error # (Intercept) 17.707 0.834 # age 0.660 0.062 # SexFemale -2.321 0.761 # # Variance Coefficients: # Estimate Std. Error # Subject 3.267 1.072 # I 2.049 0.324 # # set ple -1 set loc distance qua set loc age qua read pedigree inline M01 1 x x M 26 8 M01 2 x x M 25 10 M01 3 x x M 29 12 M01 4 x x M 31 14 M02 5 x x M 21.5 8 M02 6 x x M 22.5 10 M02 7 x x M 23 12 M02 8 x x M 26.5 14 M03 9 x x M 23 8 M03 10 x x M 22.5 10 M03 11 x x M 24 12 M03 12 x x M 27.5 14 M04 13 x x M 25.5 8 M04 14 x x M 27.5 10 M04 15 x x M 26.5 12 M04 16 x x M 27 14 M05 17 x x M 20 8 M05 18 x x M 23.5 10 M05 19 x x M 22.5 12 M05 20 x x M 26 14 M06 21 x x M 24.5 8 M06 22 x x M 25.5 10 M06 23 x x M 27 12 M06 24 x x M 28.5 14 M07 25 x x M 22 8 M07 26 x x M 22 10 M07 27 x x M 24.5 12 M07 28 x x M 26.5 14 M08 29 x x M 24 8 M08 30 x x M 21.5 10 M08 31 x x M 24.5 12 M08 32 x x M 25.5 14 M09 33 x x M 23 8 M09 34 x x M 20.5 10 M09 35 x x M 31 12 M09 36 x x M 26 14 M10 37 x x M 27.5 8 M10 38 x x M 28 10 M10 39 x x M 31 12 M10 40 x x M 31.5 14 M11 41 x x M 23 8 M11 42 x x M 23 10 M11 43 x x M 23.5 12 M11 44 x x M 25 14 M12 45 x x M 21.5 8 M12 46 x x M 23.5 10 M12 47 x x M 24 12 M12 48 x x M 28 14 M13 49 x x M 17 8 M13 50 x x M 24.5 10 M13 51 x x M 26 12 M13 52 x x M 29.5 14 M14 53 x x M 22.5 8 M14 54 x x M 25.5 10 M14 55 x x M 25.5 12 M14 56 x x M 26 14 M15 57 x x M 23 8 M15 58 x x M 24.5 10 M15 59 x x M 26 12 M15 60 x x M 30 14 M16 61 x x M 22 8 M16 62 x x M 21.5 10 M16 63 x x M 23.5 12 M16 64 x x M 25 14 F01 65 x x F 21 8 F01 66 x x F 20 10 F01 67 x x F 21.5 12 F01 68 x x F 23 14 F02 69 x x F 21 8 F02 70 x x F 21.5 10 F02 71 x x F 24 12 F02 72 x x F 25.5 14 F03 73 x x F 20.5 8 F03 74 x x F 24 10 F03 75 x x F 24.5 12 F03 76 x x F 26 14 F04 77 x x F 23.5 8 F04 78 x x F 24.5 10 F04 79 x x F 25 12 F04 80 x x F 26.5 14 F05 81 x x F 21.5 8 F05 82 x x F 23 10 F05 83 x x F 22.5 12 F05 84 x x F 23.5 14 F06 85 x x F 20 8 F06 86 x x F 21 10 F06 87 x x F 21 12 F06 88 x x F 22.5 14 F07 89 x x F 21.5 8 F07 90 x x F 22.5 10 F07 91 x x F 23 12 F07 92 x x F 25 14 F08 93 x x F 23 8 F08 94 x x F 23 10 F08 95 x x F 23.5 12 F08 96 x x F 24 14 F09 97 x x F 20 8 F09 98 x x F 21 10 F09 99 x x F 22 12 F09 100 x x F 21.5 14 F10 101 x x F 16.5 8 F10 102 x x F 19 10 F10 103 x x F 19 12 F10 104 x x F 19.5 14 F11 105 x x F 24.5 8 F11 106 x x F 25 10 F11 107 x x F 28 12 F11 108 x x F 28 14 ;;;; run set loc fem aff fem=female set iter 1000 set ple 0 fpm distance nqtl 0 c cov age + fem var distance ce cov age fem