R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(5246.24
+ ,0
+ ,5170.09
+ ,4920.10
+ ,4926.65
+ ,4716.99
+ ,5283.61
+ ,0
+ ,5246.24
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+ ,4920.10
+ ,4926.65
+ ,4979.05
+ ,0
+ ,5283.61
+ ,5246.24
+ ,5170.09
+ ,4920.10
+ ,4825.20
+ ,0
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+ ,5170.09
+ ,4695.12
+ ,0
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+ ,4979.05
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+ ,4711.54
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+ ,4695.12
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+ ,4384.96
+ ,0
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+ ,4695.12
+ ,4825.20
+ ,4378.75
+ ,0
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+ ,4472.93
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+ ,4564.07
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+ ,0
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+ ,3720.46
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+ ,3591.37
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+ ,3591.37
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+ ,4101.71
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+ ,4125.88
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+ ,4125.88
+ ,4136.22
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+ ,3761.36
+ ,4031.48
+ ,4125.88
+ ,3090.45
+ ,0
+ ,3228.47
+ ,3408.56
+ ,3761.36
+ ,4031.48
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+ ,0
+ ,3090.45
+ ,3228.47
+ ,3408.56
+ ,3761.36
+ ,2980.44
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+ ,3090.45
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+ ,3104.33
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+ ,2980.44
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+ ,2980.44
+ ,2741.14
+ ,2898.01
+ ,0
+ ,2863.86
+ ,3181.57
+ ,3104.33
+ ,2980.44
+ ,3112.33
+ ,0
+ ,2898.01
+ ,2863.86
+ ,3181.57
+ ,3104.33
+ ,3254.33
+ ,0
+ ,3112.33
+ ,2898.01
+ ,2863.86
+ ,3181.57
+ ,3513.47
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+ ,3112.33
+ ,2898.01
+ ,2863.86
+ ,3587.61
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+ ,3254.33
+ ,3112.33
+ ,2898.01
+ ,3727.45
+ ,0
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+ ,3513.47
+ ,3254.33
+ ,3112.33
+ ,3793.34
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+ ,3587.61
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+ ,3727.45
+ ,3587.61
+ ,3931.86
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+ ,3793.34
+ ,3727.45
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+ ,3845.13
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+ ,5290.85
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+ ,5850.41
+ ,5694.83
+ ,5493.88
+ ,5514.06
+ ,6175.00
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+ ,5694.83
+ ,5493.88
+ ,6513.58
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+ ,5850.41
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+ ,6383.78
+ ,0
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+ ,0
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+ ,0
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+ ,6936.61
+ ,6673.66
+ ,7584.71
+ ,0
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+ ,7300.68
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+ ,7160.79
+ ,0
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+ ,0
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+ ,0
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+ ,7160.79
+ ,7584.71
+ ,7425.75
+ ,0
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+ ,7196.19
+ ,7160.79
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+ ,7778.51
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+ ,0
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+ ,8850.41
+ ,1
+ ,9023.21
+ ,9123.29
+ ,9138.46
+ ,8802.79
+ ,8864.58
+ ,1
+ ,8850.41
+ ,9023.21
+ ,9123.29
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+ ,1
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+ ,8850.41
+ ,9023.21
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+ ,8516.66
+ ,1
+ ,9163.74
+ ,8864.58
+ ,8850.41
+ ,9023.21
+ ,8553.44
+ ,1
+ ,8516.66
+ ,9163.74
+ ,8864.58
+ ,8850.41
+ ,7555.20
+ ,1
+ ,8553.44
+ ,8516.66
+ ,9163.74
+ ,8864.58
+ ,7851.22
+ ,1
+ ,7555.20
+ ,8553.44
+ ,8516.66
+ ,9163.74
+ ,7442.00
+ ,1
+ ,7851.22
+ ,7555.20
+ ,8553.44
+ ,8516.66
+ ,7992.53
+ ,1
+ ,7442.00
+ ,7851.22
+ ,7555.20
+ ,8553.44
+ ,8264.04
+ ,1
+ ,7992.53
+ ,7442.00
+ ,7851.22
+ ,7555.20
+ ,7517.39
+ ,1
+ ,8264.04
+ ,7992.53
+ ,7442.00
+ ,7851.22
+ ,7200.40
+ ,1
+ ,7517.39
+ ,8264.04
+ ,7992.53
+ ,7442.00
+ ,7193.69
+ ,1
+ ,7200.40
+ ,7517.39
+ ,8264.04
+ ,7992.53
+ ,6193.58
+ ,1
+ ,7193.69
+ ,7200.40
+ ,7517.39
+ ,8264.04
+ ,5104.21
+ ,1
+ ,6193.58
+ ,7193.69
+ ,7200.40
+ ,7517.39
+ ,4800.46
+ ,1
+ ,5104.21
+ ,6193.58
+ ,7193.69
+ ,7200.40
+ ,4461.61
+ ,1
+ ,4800.46
+ ,5104.21
+ ,6193.58
+ ,7193.69
+ ,4398.59
+ ,1
+ ,4461.61
+ ,4800.46
+ ,5104.21
+ ,6193.58
+ ,4243.63
+ ,1
+ ,4398.59
+ ,4461.61
+ ,4800.46
+ ,5104.21
+ ,4293.82
+ ,1
+ ,4243.63
+ ,4398.59
+ ,4461.61
+ ,4800.46)
+ ,dim=c(6
+ ,104)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:104))
> y <- array(NA,dim=c(6,104),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:104))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 5246.24 0 5170.09 4920.10 4926.65 4716.99 1 0 0 0 0 0 0 0 0 0
2 5283.61 0 5246.24 5170.09 4920.10 4926.65 0 1 0 0 0 0 0 0 0 0
3 4979.05 0 5283.61 5246.24 5170.09 4920.10 0 0 1 0 0 0 0 0 0 0
4 4825.20 0 4979.05 5283.61 5246.24 5170.09 0 0 0 1 0 0 0 0 0 0
5 4695.12 0 4825.20 4979.05 5283.61 5246.24 0 0 0 0 1 0 0 0 0 0
6 4711.54 0 4695.12 4825.20 4979.05 5283.61 0 0 0 0 0 1 0 0 0 0
7 4727.22 0 4711.54 4695.12 4825.20 4979.05 0 0 0 0 0 0 1 0 0 0
8 4384.96 0 4727.22 4711.54 4695.12 4825.20 0 0 0 0 0 0 0 1 0 0
9 4378.75 0 4384.96 4727.22 4711.54 4695.12 0 0 0 0 0 0 0 0 1 0
10 4472.93 0 4378.75 4384.96 4727.22 4711.54 0 0 0 0 0 0 0 0 0 1
11 4564.07 0 4472.93 4378.75 4384.96 4727.22 0 0 0 0 0 0 0 0 0 0
12 4310.54 0 4564.07 4472.93 4378.75 4384.96 0 0 0 0 0 0 0 0 0 0
13 4171.38 0 4310.54 4564.07 4472.93 4378.75 1 0 0 0 0 0 0 0 0 0
14 4049.38 0 4171.38 4310.54 4564.07 4472.93 0 1 0 0 0 0 0 0 0 0
15 3591.37 0 4049.38 4171.38 4310.54 4564.07 0 0 1 0 0 0 0 0 0 0
16 3720.46 0 3591.37 4049.38 4171.38 4310.54 0 0 0 1 0 0 0 0 0 0
17 4107.23 0 3720.46 3591.37 4049.38 4171.38 0 0 0 0 1 0 0 0 0 0
18 4101.71 0 4107.23 3720.46 3591.37 4049.38 0 0 0 0 0 1 0 0 0 0
19 4162.34 0 4101.71 4107.23 3720.46 3591.37 0 0 0 0 0 0 1 0 0 0
20 4136.22 0 4162.34 4101.71 4107.23 3720.46 0 0 0 0 0 0 0 1 0 0
21 4125.88 0 4136.22 4162.34 4101.71 4107.23 0 0 0 0 0 0 0 0 1 0
22 4031.48 0 4125.88 4136.22 4162.34 4101.71 0 0 0 0 0 0 0 0 0 1
23 3761.36 0 4031.48 4125.88 4136.22 4162.34 0 0 0 0 0 0 0 0 0 0
24 3408.56 0 3761.36 4031.48 4125.88 4136.22 0 0 0 0 0 0 0 0 0 0
25 3228.47 0 3408.56 3761.36 4031.48 4125.88 1 0 0 0 0 0 0 0 0 0
26 3090.45 0 3228.47 3408.56 3761.36 4031.48 0 1 0 0 0 0 0 0 0 0
27 2741.14 0 3090.45 3228.47 3408.56 3761.36 0 0 1 0 0 0 0 0 0 0
28 2980.44 0 2741.14 3090.45 3228.47 3408.56 0 0 0 1 0 0 0 0 0 0
29 3104.33 0 2980.44 2741.14 3090.45 3228.47 0 0 0 0 1 0 0 0 0 0
30 3181.57 0 3104.33 2980.44 2741.14 3090.45 0 0 0 0 0 1 0 0 0 0
31 2863.86 0 3181.57 3104.33 2980.44 2741.14 0 0 0 0 0 0 1 0 0 0
32 2898.01 0 2863.86 3181.57 3104.33 2980.44 0 0 0 0 0 0 0 1 0 0
33 3112.33 0 2898.01 2863.86 3181.57 3104.33 0 0 0 0 0 0 0 0 1 0
34 3254.33 0 3112.33 2898.01 2863.86 3181.57 0 0 0 0 0 0 0 0 0 1
35 3513.47 0 3254.33 3112.33 2898.01 2863.86 0 0 0 0 0 0 0 0 0 0
36 3587.61 0 3513.47 3254.33 3112.33 2898.01 0 0 0 0 0 0 0 0 0 0
37 3727.45 0 3587.61 3513.47 3254.33 3112.33 1 0 0 0 0 0 0 0 0 0
38 3793.34 0 3727.45 3587.61 3513.47 3254.33 0 1 0 0 0 0 0 0 0 0
39 3817.58 0 3793.34 3727.45 3587.61 3513.47 0 0 1 0 0 0 0 0 0 0
40 3845.13 0 3817.58 3793.34 3727.45 3587.61 0 0 0 1 0 0 0 0 0 0
41 3931.86 0 3845.13 3817.58 3793.34 3727.45 0 0 0 0 1 0 0 0 0 0
42 4197.52 0 3931.86 3845.13 3817.58 3793.34 0 0 0 0 0 1 0 0 0 0
43 4307.13 0 4197.52 3931.86 3845.13 3817.58 0 0 0 0 0 0 1 0 0 0
44 4229.43 0 4307.13 4197.52 3931.86 3845.13 0 0 0 0 0 0 0 1 0 0
45 4362.28 0 4229.43 4307.13 4197.52 3931.86 0 0 0 0 0 0 0 0 1 0
46 4217.34 0 4362.28 4229.43 4307.13 4197.52 0 0 0 0 0 0 0 0 0 1
47 4361.28 0 4217.34 4362.28 4229.43 4307.13 0 0 0 0 0 0 0 0 0 0
48 4327.74 0 4361.28 4217.34 4362.28 4229.43 0 0 0 0 0 0 0 0 0 0
49 4417.65 0 4327.74 4361.28 4217.34 4362.28 1 0 0 0 0 0 0 0 0 0
50 4557.68 0 4417.65 4327.74 4361.28 4217.34 0 1 0 0 0 0 0 0 0 0
51 4650.35 0 4557.68 4417.65 4327.74 4361.28 0 0 1 0 0 0 0 0 0 0
52 4967.18 0 4650.35 4557.68 4417.65 4327.74 0 0 0 1 0 0 0 0 0 0
53 5123.42 0 4967.18 4650.35 4557.68 4417.65 0 0 0 0 1 0 0 0 0 0
54 5290.85 0 5123.42 4967.18 4650.35 4557.68 0 0 0 0 0 1 0 0 0 0
55 5535.66 0 5290.85 5123.42 4967.18 4650.35 0 0 0 0 0 0 1 0 0 0
56 5514.06 0 5535.66 5290.85 5123.42 4967.18 0 0 0 0 0 0 0 1 0 0
57 5493.88 0 5514.06 5535.66 5290.85 5123.42 0 0 0 0 0 0 0 0 1 0
58 5694.83 0 5493.88 5514.06 5535.66 5290.85 0 0 0 0 0 0 0 0 0 1
59 5850.41 0 5694.83 5493.88 5514.06 5535.66 0 0 0 0 0 0 0 0 0 0
60 6116.64 0 5850.41 5694.83 5493.88 5514.06 0 0 0 0 0 0 0 0 0 0
61 6175.00 0 6116.64 5850.41 5694.83 5493.88 1 0 0 0 0 0 0 0 0 0
62 6513.58 0 6175.00 6116.64 5850.41 5694.83 0 1 0 0 0 0 0 0 0 0
63 6383.78 0 6513.58 6175.00 6116.64 5850.41 0 0 1 0 0 0 0 0 0 0
64 6673.66 0 6383.78 6513.58 6175.00 6116.64 0 0 0 1 0 0 0 0 0 0
65 6936.61 0 6673.66 6383.78 6513.58 6175.00 0 0 0 0 1 0 0 0 0 0
66 7300.68 0 6936.61 6673.66 6383.78 6513.58 0 0 0 0 0 1 0 0 0 0
67 7392.93 0 7300.68 6936.61 6673.66 6383.78 0 0 0 0 0 0 1 0 0 0
68 7497.31 0 7392.93 7300.68 6936.61 6673.66 0 0 0 0 0 0 0 1 0 0
69 7584.71 0 7497.31 7392.93 7300.68 6936.61 0 0 0 0 0 0 0 0 1 0
70 7160.79 0 7584.71 7497.31 7392.93 7300.68 0 0 0 0 0 0 0 0 0 1
71 7196.19 0 7160.79 7584.71 7497.31 7392.93 0 0 0 0 0 0 0 0 0 0
72 7245.63 0 7196.19 7160.79 7584.71 7497.31 0 0 0 0 0 0 0 0 0 0
73 7347.51 0 7245.63 7196.19 7160.79 7584.71 1 0 0 0 0 0 0 0 0 0
74 7425.75 0 7347.51 7245.63 7196.19 7160.79 0 1 0 0 0 0 0 0 0 0
75 7778.51 0 7425.75 7347.51 7245.63 7196.19 0 0 1 0 0 0 0 0 0 0
76 7822.33 0 7778.51 7425.75 7347.51 7245.63 0 0 0 1 0 0 0 0 0 0
77 8181.22 0 7822.33 7778.51 7425.75 7347.51 0 0 0 0 1 0 0 0 0 0
78 8371.47 0 8181.22 7822.33 7778.51 7425.75 0 0 0 0 0 1 0 0 0 0
79 8347.71 0 8371.47 8181.22 7822.33 7778.51 0 0 0 0 0 0 1 0 0 0
80 8672.11 0 8347.71 8371.47 8181.22 7822.33 0 0 0 0 0 0 0 1 0 0
81 8802.79 0 8672.11 8347.71 8371.47 8181.22 0 0 0 0 0 0 0 0 1 0
82 9138.46 0 8802.79 8672.11 8347.71 8371.47 0 0 0 0 0 0 0 0 0 1
83 9123.29 0 9138.46 8802.79 8672.11 8347.71 0 0 0 0 0 0 0 0 0 0
84 9023.21 1 9123.29 9138.46 8802.79 8672.11 0 0 0 0 0 0 0 0 0 0
85 8850.41 1 9023.21 9123.29 9138.46 8802.79 1 0 0 0 0 0 0 0 0 0
86 8864.58 1 8850.41 9023.21 9123.29 9138.46 0 1 0 0 0 0 0 0 0 0
87 9163.74 1 8864.58 8850.41 9023.21 9123.29 0 0 1 0 0 0 0 0 0 0
88 8516.66 1 9163.74 8864.58 8850.41 9023.21 0 0 0 1 0 0 0 0 0 0
89 8553.44 1 8516.66 9163.74 8864.58 8850.41 0 0 0 0 1 0 0 0 0 0
90 7555.20 1 8553.44 8516.66 9163.74 8864.58 0 0 0 0 0 1 0 0 0 0
91 7851.22 1 7555.20 8553.44 8516.66 9163.74 0 0 0 0 0 0 1 0 0 0
92 7442.00 1 7851.22 7555.20 8553.44 8516.66 0 0 0 0 0 0 0 1 0 0
93 7992.53 1 7442.00 7851.22 7555.20 8553.44 0 0 0 0 0 0 0 0 1 0
94 8264.04 1 7992.53 7442.00 7851.22 7555.20 0 0 0 0 0 0 0 0 0 1
95 7517.39 1 8264.04 7992.53 7442.00 7851.22 0 0 0 0 0 0 0 0 0 0
96 7200.40 1 7517.39 8264.04 7992.53 7442.00 0 0 0 0 0 0 0 0 0 0
97 7193.69 1 7200.40 7517.39 8264.04 7992.53 1 0 0 0 0 0 0 0 0 0
98 6193.58 1 7193.69 7200.40 7517.39 8264.04 0 1 0 0 0 0 0 0 0 0
99 5104.21 1 6193.58 7193.69 7200.40 7517.39 0 0 1 0 0 0 0 0 0 0
100 4800.46 1 5104.21 6193.58 7193.69 7200.40 0 0 0 1 0 0 0 0 0 0
101 4461.61 1 4800.46 5104.21 6193.58 7193.69 0 0 0 0 1 0 0 0 0 0
102 4398.59 1 4461.61 4800.46 5104.21 6193.58 0 0 0 0 0 1 0 0 0 0
103 4243.63 1 4398.59 4461.61 4800.46 5104.21 0 0 0 0 0 0 1 0 0 0
104 4293.82 1 4243.63 4398.59 4461.61 4800.46 0 0 0 0 0 0 0 1 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
61 0 61
62 0 62
63 0 63
64 0 64
65 0 65
66 0 66
67 0 67
68 0 68
69 0 69
70 0 70
71 1 71
72 0 72
73 0 73
74 0 74
75 0 75
76 0 76
77 0 77
78 0 78
79 0 79
80 0 80
81 0 81
82 0 82
83 1 83
84 0 84
85 0 85
86 0 86
87 0 87
88 0 88
89 0 89
90 0 90
91 0 91
92 0 92
93 0 93
94 0 94
95 1 95
96 0 96
97 0 97
98 0 98
99 0 99
100 0 100
101 0 101
102 0 102
103 0 103
104 0 104
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
-98.98235 -368.12344 1.01131 0.16324 -0.21024 0.02255
M1 M2 M3 M4 M5 M6
99.05808 32.16264 -87.15409 89.53009 212.31613 70.63638
M7 M8 M9 M10 M11 t
87.01675 28.97323 175.44504 104.14894 -24.90582 3.29961
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-761.80 -101.53 11.68 120.83 712.70
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -98.98235 128.46007 -0.771 0.44310
X -368.12344 114.42225 -3.217 0.00183 **
Y1 1.01131 0.10684 9.466 5.54e-15 ***
Y2 0.16324 0.15089 1.082 0.28236
Y3 -0.21024 0.15095 -1.393 0.16729
Y4 0.02255 0.11015 0.205 0.83828
M1 99.05808 127.61786 0.776 0.43976
M2 32.16264 129.02156 0.249 0.80374
M3 -87.15409 128.57130 -0.678 0.49968
M4 89.53009 128.84269 0.695 0.48900
M5 212.31613 130.91175 1.622 0.10850
M6 70.63638 132.80048 0.532 0.59617
M7 87.01675 128.22664 0.679 0.49920
M8 28.97323 127.54522 0.227 0.82084
M9 175.44504 132.18703 1.327 0.18794
M10 104.14894 133.40795 0.781 0.43713
M11 -24.90582 133.70184 -0.186 0.85266
t 3.29961 1.52691 2.161 0.03348 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 261.1 on 86 degrees of freedom
Multiple R-squared: 0.9838, Adjusted R-squared: 0.9807
F-statistic: 308.1 on 17 and 86 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 2.401845e-01 4.803691e-01 0.7598155
[2,] 1.186604e-01 2.373208e-01 0.8813396
[3,] 7.913282e-02 1.582656e-01 0.9208672
[4,] 4.460354e-02 8.920709e-02 0.9553965
[5,] 2.155755e-02 4.311510e-02 0.9784425
[6,] 9.582123e-03 1.916425e-02 0.9904179
[7,] 4.111834e-03 8.223669e-03 0.9958882
[8,] 1.630297e-03 3.260593e-03 0.9983697
[9,] 1.492397e-03 2.984793e-03 0.9985076
[10,] 5.938682e-04 1.187736e-03 0.9994061
[11,] 7.202474e-03 1.440495e-02 0.9927975
[12,] 5.029358e-03 1.005872e-02 0.9949706
[13,] 2.738170e-03 5.476341e-03 0.9972618
[14,] 1.535489e-03 3.070979e-03 0.9984645
[15,] 1.935988e-03 3.871977e-03 0.9980640
[16,] 2.769089e-03 5.538179e-03 0.9972309
[17,] 2.601442e-03 5.202883e-03 0.9973986
[18,] 1.449324e-03 2.898648e-03 0.9985507
[19,] 1.970198e-03 3.940396e-03 0.9980298
[20,] 1.374582e-03 2.749165e-03 0.9986254
[21,] 7.133655e-04 1.426731e-03 0.9992866
[22,] 6.260394e-04 1.252079e-03 0.9993740
[23,] 3.524587e-04 7.049173e-04 0.9996475
[24,] 1.941799e-04 3.883598e-04 0.9998058
[25,] 9.453114e-05 1.890623e-04 0.9999055
[26,] 8.435222e-05 1.687044e-04 0.9999156
[27,] 5.880688e-05 1.176138e-04 0.9999412
[28,] 3.114122e-05 6.228244e-05 0.9999689
[29,] 1.614406e-05 3.228811e-05 0.9999839
[30,] 7.758507e-06 1.551701e-05 0.9999922
[31,] 6.267258e-06 1.253452e-05 0.9999937
[32,] 3.118954e-06 6.237907e-06 0.9999969
[33,] 1.569556e-06 3.139113e-06 0.9999984
[34,] 6.425290e-07 1.285058e-06 0.9999994
[35,] 3.605988e-07 7.211977e-07 0.9999996
[36,] 1.893056e-07 3.786111e-07 0.9999998
[37,] 1.834829e-07 3.669658e-07 0.9999998
[38,] 1.027037e-07 2.054074e-07 0.9999999
[39,] 4.353013e-08 8.706027e-08 1.0000000
[40,] 5.736813e-08 1.147363e-07 0.9999999
[41,] 3.294377e-08 6.588755e-08 1.0000000
[42,] 2.742547e-08 5.485094e-08 1.0000000
[43,] 1.445918e-08 2.891836e-08 1.0000000
[44,] 6.047247e-09 1.209449e-08 1.0000000
[45,] 2.106316e-09 4.212632e-09 1.0000000
[46,] 1.228260e-09 2.456519e-09 1.0000000
[47,] 4.971869e-10 9.943738e-10 1.0000000
[48,] 1.900638e-10 3.801276e-10 1.0000000
[49,] 1.525799e-10 3.051597e-10 1.0000000
[50,] 2.492298e-07 4.984596e-07 0.9999998
[51,] 2.177244e-07 4.354488e-07 0.9999998
[52,] 1.239934e-07 2.479869e-07 0.9999999
[53,] 2.664889e-07 5.329779e-07 0.9999997
[54,] 2.904837e-07 5.809673e-07 0.9999997
[55,] 6.770011e-07 1.354002e-06 0.9999993
[56,] 6.206230e-07 1.241246e-06 0.9999994
[57,] 2.846145e-07 5.692291e-07 0.9999997
[58,] 1.251396e-07 2.502791e-07 0.9999999
[59,] 6.775400e-08 1.355080e-07 0.9999999
[60,] 4.255703e-08 8.511406e-08 1.0000000
[61,] 3.269770e-08 6.539540e-08 1.0000000
[62,] 5.095048e-08 1.019010e-07 0.9999999
[63,] 1.438506e-08 2.877012e-08 1.0000000
> postscript(file="/var/www/html/rcomp/tmp/1a4311258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2341h1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/337751258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zcir1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e69q1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 104
Frequency = 1
1 2 3 4 5 6
140.577277 117.619404 -68.441491 -89.999463 -134.720082 111.872277
7 8 9 10 11 12
87.023065 -242.909363 -48.935527 178.317652 228.672023 -154.195764
13 14 15 16 17 18
-134.254378 6.498271 -244.755715 163.913951 346.302729 -26.592761
19 20 21 22 23 24
-5.729123 40.883411 -112.591550 -111.403145 -165.471357 -259.477934
25 26 27 28 29 30
-160.656057 -50.024343 -182.420243 222.778905 10.642462 -8.417350
31 32 33 34 35 36
-385.957804 32.280072 127.600474 46.742790 267.390675 72.363670
37 38 39 40 41 42
17.587181 44.828652 105.367262 -54.608450 -115.083113 200.359662
43 44 45 46 47 48
12.713884 -146.844801 -49.184136 -230.742054 145.038418 -8.932358
49 50 51 52 53 54
-44.424990 107.279248 149.379311 189.308232 -88.664568 23.745375
55 56 57 58 59 60
118.568149 -97.493363 -253.885946 86.687751 158.033837 202.160966
61 62 63 64 65 66
-93.771051 234.103530 -79.150379 113.010245 47.772144 202.056174
67 68 69 70 71 72
-72.613023 -17.466806 -129.844898 -580.010275 15.455188 86.111007
73 74 75 76 77 78
-61.239256 -13.504552 369.113142 -116.266519 28.789966 59.718780
79 80 81 82 83 84
-233.448669 213.131956 -98.243254 111.025863 -70.449116 150.095083
85 86 87 88 89 90
46.249243 304.347486 712.703527 -453.288162 69.842979 -659.008637
91 92 93 94 95 96
478.066962 9.499178 565.084837 499.381419 -578.669668 -88.124670
97 98 99 100 101 102
289.932031 -751.147695 -761.795414 25.151260 -164.882516 96.266482
103 104
1.376560 208.919717
> postscript(file="/var/www/html/rcomp/tmp/6lzpe1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 104
Frequency = 1
lag(myerror, k = 1) myerror
0 140.577277 NA
1 117.619404 140.577277
2 -68.441491 117.619404
3 -89.999463 -68.441491
4 -134.720082 -89.999463
5 111.872277 -134.720082
6 87.023065 111.872277
7 -242.909363 87.023065
8 -48.935527 -242.909363
9 178.317652 -48.935527
10 228.672023 178.317652
11 -154.195764 228.672023
12 -134.254378 -154.195764
13 6.498271 -134.254378
14 -244.755715 6.498271
15 163.913951 -244.755715
16 346.302729 163.913951
17 -26.592761 346.302729
18 -5.729123 -26.592761
19 40.883411 -5.729123
20 -112.591550 40.883411
21 -111.403145 -112.591550
22 -165.471357 -111.403145
23 -259.477934 -165.471357
24 -160.656057 -259.477934
25 -50.024343 -160.656057
26 -182.420243 -50.024343
27 222.778905 -182.420243
28 10.642462 222.778905
29 -8.417350 10.642462
30 -385.957804 -8.417350
31 32.280072 -385.957804
32 127.600474 32.280072
33 46.742790 127.600474
34 267.390675 46.742790
35 72.363670 267.390675
36 17.587181 72.363670
37 44.828652 17.587181
38 105.367262 44.828652
39 -54.608450 105.367262
40 -115.083113 -54.608450
41 200.359662 -115.083113
42 12.713884 200.359662
43 -146.844801 12.713884
44 -49.184136 -146.844801
45 -230.742054 -49.184136
46 145.038418 -230.742054
47 -8.932358 145.038418
48 -44.424990 -8.932358
49 107.279248 -44.424990
50 149.379311 107.279248
51 189.308232 149.379311
52 -88.664568 189.308232
53 23.745375 -88.664568
54 118.568149 23.745375
55 -97.493363 118.568149
56 -253.885946 -97.493363
57 86.687751 -253.885946
58 158.033837 86.687751
59 202.160966 158.033837
60 -93.771051 202.160966
61 234.103530 -93.771051
62 -79.150379 234.103530
63 113.010245 -79.150379
64 47.772144 113.010245
65 202.056174 47.772144
66 -72.613023 202.056174
67 -17.466806 -72.613023
68 -129.844898 -17.466806
69 -580.010275 -129.844898
70 15.455188 -580.010275
71 86.111007 15.455188
72 -61.239256 86.111007
73 -13.504552 -61.239256
74 369.113142 -13.504552
75 -116.266519 369.113142
76 28.789966 -116.266519
77 59.718780 28.789966
78 -233.448669 59.718780
79 213.131956 -233.448669
80 -98.243254 213.131956
81 111.025863 -98.243254
82 -70.449116 111.025863
83 150.095083 -70.449116
84 46.249243 150.095083
85 304.347486 46.249243
86 712.703527 304.347486
87 -453.288162 712.703527
88 69.842979 -453.288162
89 -659.008637 69.842979
90 478.066962 -659.008637
91 9.499178 478.066962
92 565.084837 9.499178
93 499.381419 565.084837
94 -578.669668 499.381419
95 -88.124670 -578.669668
96 289.932031 -88.124670
97 -751.147695 289.932031
98 -761.795414 -751.147695
99 25.151260 -761.795414
100 -164.882516 25.151260
101 96.266482 -164.882516
102 1.376560 96.266482
103 208.919717 1.376560
104 NA 208.919717
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 117.619404 140.577277
[2,] -68.441491 117.619404
[3,] -89.999463 -68.441491
[4,] -134.720082 -89.999463
[5,] 111.872277 -134.720082
[6,] 87.023065 111.872277
[7,] -242.909363 87.023065
[8,] -48.935527 -242.909363
[9,] 178.317652 -48.935527
[10,] 228.672023 178.317652
[11,] -154.195764 228.672023
[12,] -134.254378 -154.195764
[13,] 6.498271 -134.254378
[14,] -244.755715 6.498271
[15,] 163.913951 -244.755715
[16,] 346.302729 163.913951
[17,] -26.592761 346.302729
[18,] -5.729123 -26.592761
[19,] 40.883411 -5.729123
[20,] -112.591550 40.883411
[21,] -111.403145 -112.591550
[22,] -165.471357 -111.403145
[23,] -259.477934 -165.471357
[24,] -160.656057 -259.477934
[25,] -50.024343 -160.656057
[26,] -182.420243 -50.024343
[27,] 222.778905 -182.420243
[28,] 10.642462 222.778905
[29,] -8.417350 10.642462
[30,] -385.957804 -8.417350
[31,] 32.280072 -385.957804
[32,] 127.600474 32.280072
[33,] 46.742790 127.600474
[34,] 267.390675 46.742790
[35,] 72.363670 267.390675
[36,] 17.587181 72.363670
[37,] 44.828652 17.587181
[38,] 105.367262 44.828652
[39,] -54.608450 105.367262
[40,] -115.083113 -54.608450
[41,] 200.359662 -115.083113
[42,] 12.713884 200.359662
[43,] -146.844801 12.713884
[44,] -49.184136 -146.844801
[45,] -230.742054 -49.184136
[46,] 145.038418 -230.742054
[47,] -8.932358 145.038418
[48,] -44.424990 -8.932358
[49,] 107.279248 -44.424990
[50,] 149.379311 107.279248
[51,] 189.308232 149.379311
[52,] -88.664568 189.308232
[53,] 23.745375 -88.664568
[54,] 118.568149 23.745375
[55,] -97.493363 118.568149
[56,] -253.885946 -97.493363
[57,] 86.687751 -253.885946
[58,] 158.033837 86.687751
[59,] 202.160966 158.033837
[60,] -93.771051 202.160966
[61,] 234.103530 -93.771051
[62,] -79.150379 234.103530
[63,] 113.010245 -79.150379
[64,] 47.772144 113.010245
[65,] 202.056174 47.772144
[66,] -72.613023 202.056174
[67,] -17.466806 -72.613023
[68,] -129.844898 -17.466806
[69,] -580.010275 -129.844898
[70,] 15.455188 -580.010275
[71,] 86.111007 15.455188
[72,] -61.239256 86.111007
[73,] -13.504552 -61.239256
[74,] 369.113142 -13.504552
[75,] -116.266519 369.113142
[76,] 28.789966 -116.266519
[77,] 59.718780 28.789966
[78,] -233.448669 59.718780
[79,] 213.131956 -233.448669
[80,] -98.243254 213.131956
[81,] 111.025863 -98.243254
[82,] -70.449116 111.025863
[83,] 150.095083 -70.449116
[84,] 46.249243 150.095083
[85,] 304.347486 46.249243
[86,] 712.703527 304.347486
[87,] -453.288162 712.703527
[88,] 69.842979 -453.288162
[89,] -659.008637 69.842979
[90,] 478.066962 -659.008637
[91,] 9.499178 478.066962
[92,] 565.084837 9.499178
[93,] 499.381419 565.084837
[94,] -578.669668 499.381419
[95,] -88.124670 -578.669668
[96,] 289.932031 -88.124670
[97,] -751.147695 289.932031
[98,] -761.795414 -751.147695
[99,] 25.151260 -761.795414
[100,] -164.882516 25.151260
[101,] 96.266482 -164.882516
[102,] 1.376560 96.266482
[103,] 208.919717 1.376560
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 117.619404 140.577277
2 -68.441491 117.619404
3 -89.999463 -68.441491
4 -134.720082 -89.999463
5 111.872277 -134.720082
6 87.023065 111.872277
7 -242.909363 87.023065
8 -48.935527 -242.909363
9 178.317652 -48.935527
10 228.672023 178.317652
11 -154.195764 228.672023
12 -134.254378 -154.195764
13 6.498271 -134.254378
14 -244.755715 6.498271
15 163.913951 -244.755715
16 346.302729 163.913951
17 -26.592761 346.302729
18 -5.729123 -26.592761
19 40.883411 -5.729123
20 -112.591550 40.883411
21 -111.403145 -112.591550
22 -165.471357 -111.403145
23 -259.477934 -165.471357
24 -160.656057 -259.477934
25 -50.024343 -160.656057
26 -182.420243 -50.024343
27 222.778905 -182.420243
28 10.642462 222.778905
29 -8.417350 10.642462
30 -385.957804 -8.417350
31 32.280072 -385.957804
32 127.600474 32.280072
33 46.742790 127.600474
34 267.390675 46.742790
35 72.363670 267.390675
36 17.587181 72.363670
37 44.828652 17.587181
38 105.367262 44.828652
39 -54.608450 105.367262
40 -115.083113 -54.608450
41 200.359662 -115.083113
42 12.713884 200.359662
43 -146.844801 12.713884
44 -49.184136 -146.844801
45 -230.742054 -49.184136
46 145.038418 -230.742054
47 -8.932358 145.038418
48 -44.424990 -8.932358
49 107.279248 -44.424990
50 149.379311 107.279248
51 189.308232 149.379311
52 -88.664568 189.308232
53 23.745375 -88.664568
54 118.568149 23.745375
55 -97.493363 118.568149
56 -253.885946 -97.493363
57 86.687751 -253.885946
58 158.033837 86.687751
59 202.160966 158.033837
60 -93.771051 202.160966
61 234.103530 -93.771051
62 -79.150379 234.103530
63 113.010245 -79.150379
64 47.772144 113.010245
65 202.056174 47.772144
66 -72.613023 202.056174
67 -17.466806 -72.613023
68 -129.844898 -17.466806
69 -580.010275 -129.844898
70 15.455188 -580.010275
71 86.111007 15.455188
72 -61.239256 86.111007
73 -13.504552 -61.239256
74 369.113142 -13.504552
75 -116.266519 369.113142
76 28.789966 -116.266519
77 59.718780 28.789966
78 -233.448669 59.718780
79 213.131956 -233.448669
80 -98.243254 213.131956
81 111.025863 -98.243254
82 -70.449116 111.025863
83 150.095083 -70.449116
84 46.249243 150.095083
85 304.347486 46.249243
86 712.703527 304.347486
87 -453.288162 712.703527
88 69.842979 -453.288162
89 -659.008637 69.842979
90 478.066962 -659.008637
91 9.499178 478.066962
92 565.084837 9.499178
93 499.381419 565.084837
94 -578.669668 499.381419
95 -88.124670 -578.669668
96 289.932031 -88.124670
97 -751.147695 289.932031
98 -761.795414 -751.147695
99 25.151260 -761.795414
100 -164.882516 25.151260
101 96.266482 -164.882516
102 1.376560 96.266482
103 208.919717 1.376560
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7wzym1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kmnd1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9a36b1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vcul1258654105.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11due71258654105.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/124usz1258654105.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/136a781258654106.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14o86h1258654106.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15fzwp1258654106.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/167dab1258654106.tab")
+ }
>
> system("convert tmp/1a4311258654105.ps tmp/1a4311258654105.png")
> system("convert tmp/2341h1258654105.ps tmp/2341h1258654105.png")
> system("convert tmp/337751258654105.ps tmp/337751258654105.png")
> system("convert tmp/4zcir1258654105.ps tmp/4zcir1258654105.png")
> system("convert tmp/5e69q1258654105.ps tmp/5e69q1258654105.png")
> system("convert tmp/6lzpe1258654105.ps tmp/6lzpe1258654105.png")
> system("convert tmp/7wzym1258654105.ps tmp/7wzym1258654105.png")
> system("convert tmp/8kmnd1258654105.ps tmp/8kmnd1258654105.png")
> system("convert tmp/9a36b1258654105.ps tmp/9a36b1258654105.png")
> system("convert tmp/10vcul1258654105.ps tmp/10vcul1258654105.png")
>
>
> proc.time()
user system elapsed
3.167 1.586 3.562