
#Boxcox finds lam such that (y^lam1) - 1) / lam1 has smallest Residual SS

library(foreign)
library(MASS)

x <- read.spss("U:\\My Documents\\beck.sav")
x1 <- data.frame(x)
x1
attach(x1,2)
mse <- matrix(NA,60,1)
mse1 <- matrix(NA,60,1)

beck1 <- beck+1
m1 <- lm(beck1 ~ sex, data=x1)
bc <- boxcox(beck1 ~ sex)
(lambda <- bc$x[which.max(bc$y)])

> m1 <- lm(beck1 ~ sex, data=x1)
> bc <- boxcox(beck1 ~ sex)
> bc
$x
  [1] -2.00000000 -1.95959596 -1.91919192 -1.87878788 -1.83838384 -1.79797980
  [7] -1.75757576 -1.71717172 -1.67676768 -1.63636364 -1.59595960 -1.55555556
 [13] -1.51515152 -1.47474747 -1.43434343 -1.39393939 -1.35353535 -1.31313131
 [19] -1.27272727 -1.23232323 -1.19191919 -1.15151515 -1.11111111 -1.07070707
 [25] -1.03030303 -0.98989899 -0.94949495 -0.90909091 -0.86868687 -0.82828283
 [31] -0.78787879 -0.74747475 -0.70707071 -0.66666667 -0.62626263 -0.58585859
 [37] -0.54545455 -0.50505051 -0.46464646 -0.42424242 -0.38383838 -0.34343434
 [43] -0.30303030 -0.26262626 -0.22222222 -0.18181818 -0.14141414 -0.10101010
 [49] -0.06060606 -0.02020202  0.02020202  0.06060606  0.10101010  0.14141414
 [55]  0.18181818  0.22222222  0.26262626  0.30303030  0.34343434  0.38383838
 [61]  0.42424242  0.46464646  0.50505051  0.54545455  0.58585859  0.62626263
 [67]  0.66666667  0.70707071  0.74747475  0.78787879  0.82828283  0.86868687
 [73]  0.90909091  0.94949495  0.98989899  1.03030303  1.07070707  1.11111111
 [79]  1.15151515  1.19191919  1.23232323  1.27272727  1.31313131  1.35353535
 [85]  1.39393939  1.43434343  1.47474747  1.51515152  1.55555556  1.59595960
 [91]  1.63636364  1.67676768  1.71717172  1.75757576  1.79797980  1.83838384
 [97]  1.87878788  1.91919192  1.95959596  2.00000000

$y
  [1] -10050.238  -9948.230  -9846.955  -9746.442  -9646.718  -9547.810
  [7]  -9449.747  -9352.559  -9256.278  -9160.935  -9066.566  -8973.204
 [13]  -8880.886  -8789.651  -8699.536  -8610.583  -8522.833  -8436.329
 [19]  -8351.115  -8267.236  -8184.741  -8103.676  -8024.091  -7946.036
 [25]  -7869.561  -7794.719  -7721.562  -7650.144  -7580.517  -7512.735
 [31]  -7446.853  -7382.922  -7320.997  -7261.129  -7203.369  -7147.767
 [37]  -7094.373  -7043.231  -6994.388  -6947.884  -6903.760  -6862.053
 [43]  -6822.795  -6786.018  -6751.748  -6720.008  -6690.818  -6664.193
 [49]  -6640.145  -6618.682  -6599.807  -6583.522  -6569.820  -6558.696
 [55]  -6550.139  -6544.133  -6540.663  -6539.707  -6541.244  -6545.247
 [61]  -6551.688  -6560.540  -6571.769  -6585.345  -6601.232  -6619.397
 [67]  -6639.802  -6662.413  -6687.193  -6714.104  -6743.109  -6774.171
 [73]  -6807.254  -6842.320  -6879.334  -6918.259  -6959.061  -7001.703
 [79]  -7046.151  -7092.372  -7140.333  -7190.001  -7241.344  -7294.331
 [85]  -7348.931  -7405.114  -7462.851  -7522.114  -7582.874  -7645.104
 [91]  -7708.777  -7773.866  -7840.347  -7908.194  -7977.381  -8047.886
 [97]  -8119.684  -8192.753  -8267.070  -8342.611

> (lambda <- bc$x[which.max(bc$y)]
+ )
[1] 0.3030303
> 
