9_boxcox.sse: Box-Cox Transformations (SSE)

In ALSM: Companion to Applied Linear Statistical Models

Description

transformation from the family of power transformations on Y. box-cox transformation according to SSE.

also plot SSE against lambda

Usage

boxcox.sse(x,y,l=seq(-2,2,.1))

Arguments

x	vector predictor variable
y	vector response variable
l	vector, Different values of lambda

References

Michael H. Kutner; Christopher J. Nachtsheim; John Neter; William Li. Applied Linear Statistical Models Fifth Edition.page 134

Examples

### table 3.9 & figure 3.17 (page 136)
boxcox.sse(Plasma[,1],Plasma[,2])

### table 18.6 page 792
boxcox.sse(factor(ServoData[,2]),ServoData[,1])

Example output

Loading required package: leaps
Loading required package: SuppDists
Loading required package: car
   lambda       SSE
1    -2.0  61.85640
2    -1.9  57.57287
3    -1.8  53.66783
4    -1.7  50.11904
5    -1.6  46.90689
6    -1.5  44.01425
7    -1.4  41.42638
8    -1.3  39.13089
9    -1.2  37.11767
10   -1.1  35.37886
11   -1.0  33.90887
12   -0.9  32.70442
13   -0.8  31.76453
14   -0.7  31.09066
15   -0.6  30.68680
16   -0.5  30.55961
17   -0.4  30.71859
18   -0.3  31.17631
19   -0.2  31.94867
20   -0.1  33.05520
41    0.0  34.51945
21    0.1  36.36939
22    0.2  38.63791
23    0.3  41.36342
24    0.4  44.59051
25    0.5  48.37072
26    0.6  52.76343
27    0.7  57.83686
28    0.8  63.66932
29    0.9  70.35050
30    1.0  77.98306
31    1.1  86.68443
32    1.2  96.58884
33    1.3 107.84974
34    1.4 120.64249
35    1.5 135.16751
36    1.6 151.65385
37    1.7 170.36340
38    1.8 191.59558
39    1.9 215.69290
40    2.0 243.04720
   lambda         SSE
1    -2.0 18730577.42
2    -1.9 11548430.59
3    -1.8  7154808.91
4    -1.7  4456709.25
5    -1.6  2792901.66
6    -1.5  1762233.21
7    -1.4  1120599.88
8    -1.3   718984.61
9    -1.2   466104.36
10   -1.1   305835.89
11   -1.0   203536.78
12   -0.9   137733.25
13   -0.8    95054.24
14   -0.7    67134.10
15   -0.6    48712.74
16   -0.5    36466.32
17   -0.4    28285.02
18   -0.3    22825.65
19   -0.2    19233.88
20   -0.1    16971.89
41    0.0    15712.16
21    0.1    15273.65
22    0.2    15585.99
23    0.3    16673.59
24    0.4    18655.80
25    0.5    21762.25
26    0.6    26365.33
27    0.7    33034.83
28    0.8    42623.69
29    0.9    56398.73
30    1.0    76238.58
31    1.1   104932.30
32    1.2   146630.20
33    1.3   207525.20
34    1.4   296884.84
35    1.5   428617.40
36    1.6   623653.39
37    1.7   913574.35
38    1.8  1346152.59
39    1.9  1993824.25
40    2.0  2966671.42

ALSM documentation built on May 2, 2019, 10:19 a.m.