R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(6340.5
+ ,0
+ ,7901.5
+ ,0
+ ,8191.1
+ ,0
+ ,7181.7
+ ,0
+ ,7594.4
+ ,0
+ ,7384.7
+ ,0
+ ,7876.7
+ ,0
+ ,8463.4
+ ,0
+ ,8317.2
+ ,0
+ ,7778.7
+ ,0
+ ,8532.8
+ ,0
+ ,7272.2
+ ,0
+ ,6680.1
+ ,0
+ ,8427.6
+ ,0
+ ,8752.8
+ ,0
+ ,7952.7
+ ,0
+ ,8694.3
+ ,0
+ ,7787
+ ,0
+ ,8474.2
+ ,0
+ ,9154.7
+ ,0
+ ,8557.2
+ ,0
+ ,7951.1
+ ,0
+ ,9156.7
+ ,0
+ ,7865.7
+ ,0
+ ,7337.4
+ ,0
+ ,9131.7
+ ,0
+ ,8814.6
+ ,0
+ ,8598.8
+ ,0
+ ,8439.6
+ ,0
+ ,7451.8
+ ,0
+ ,8016.2
+ ,0
+ ,9544.1
+ ,0
+ ,8270.7
+ ,0
+ ,8102.2
+ ,0
+ ,9369
+ ,0
+ ,7657.7
+ ,0
+ ,7816.6
+ ,0
+ ,9391.3
+ ,0
+ ,9445.4
+ ,0
+ ,9533.1
+ ,0
+ ,10068.7
+ ,0
+ ,8955.5
+ ,0
+ ,10423.9
+ ,0
+ ,11617.2
+ ,0
+ ,9391.1
+ ,0
+ ,10872
+ ,0
+ ,10230.4
+ ,0
+ ,9221
+ ,0
+ ,9428.6
+ ,0
+ ,10934.5
+ ,0
+ ,10986
+ ,0
+ ,11724.6
+ ,0
+ ,11180.9
+ ,0
+ ,11163.2
+ ,0
+ ,11240.9
+ ,0
+ ,12107.1
+ ,0
+ ,10762.3
+ ,0
+ ,11340.4
+ ,0
+ ,11266.8
+ ,0
+ ,9542.7
+ ,0
+ ,9227.7
+ ,0
+ ,10571.9
+ ,0
+ ,10774.4
+ ,0
+ ,10392.8
+ ,0
+ ,9920.2
+ ,0
+ ,9884.9
+ ,1
+ ,10174.5
+ ,1
+ ,11395.4
+ ,1
+ ,10760.2
+ ,1
+ ,10570.1
+ ,1
+ ,10536
+ ,1
+ ,9902.6
+ ,1
+ ,8889
+ ,1
+ ,10837.3
+ ,1
+ ,11624.1
+ ,1
+ ,10509
+ ,1
+ ,10984.9
+ ,1
+ ,10649.1
+ ,1
+ ,10855.7
+ ,1
+ ,11677.4
+ ,1
+ ,10760.2
+ ,1
+ ,10046.2
+ ,1
+ ,10772.8
+ ,1
+ ,9987.7
+ ,1
+ ,8638.7
+ ,1
+ ,11063.7
+ ,1
+ ,11855.7
+ ,1
+ ,10684.5
+ ,1
+ ,11337.4
+ ,1
+ ,10478
+ ,1
+ ,11123.9
+ ,1
+ ,12909.3
+ ,1
+ ,11339.9
+ ,1
+ ,10462.2
+ ,1
+ ,12733.5
+ ,1
+ ,10519.2
+ ,1
+ ,10414.9
+ ,1
+ ,12476.8
+ ,1
+ ,12384.6
+ ,1
+ ,12266.7
+ ,1
+ ,12919.9
+ ,1
+ ,11497.3
+ ,1
+ ,12142
+ ,1
+ ,13919.4
+ ,1
+ ,12656.8
+ ,1
+ ,12034.1
+ ,1
+ ,13199.7
+ ,1
+ ,10881.3
+ ,1
+ ,11301.2
+ ,1
+ ,13643.9
+ ,1
+ ,12517
+ ,1
+ ,13981.1
+ ,1
+ ,14275.7
+ ,1
+ ,13435
+ ,1
+ ,13565.7
+ ,1
+ ,16216.3
+ ,1
+ ,12970
+ ,1
+ ,14079.9
+ ,1
+ ,14235
+ ,1
+ ,12213.4
+ ,1
+ ,12581
+ ,1)
+ ,dim=c(2
+ ,121)
+ ,dimnames=list(c('y'
+ ,'x')
+ ,1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('y','x'),1:121))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6340.5 0 1 0 0 0 0 0 0 0 0 0 0 1
2 7901.5 0 0 1 0 0 0 0 0 0 0 0 0 2
3 8191.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 7181.7 0 0 0 0 1 0 0 0 0 0 0 0 4
5 7594.4 0 0 0 0 0 1 0 0 0 0 0 0 5
6 7384.7 0 0 0 0 0 0 1 0 0 0 0 0 6
7 7876.7 0 0 0 0 0 0 0 1 0 0 0 0 7
8 8463.4 0 0 0 0 0 0 0 0 1 0 0 0 8
9 8317.2 0 0 0 0 0 0 0 0 0 1 0 0 9
10 7778.7 0 0 0 0 0 0 0 0 0 0 1 0 10
11 8532.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 7272.2 0 0 0 0 0 0 0 0 0 0 0 0 12
13 6680.1 0 1 0 0 0 0 0 0 0 0 0 0 13
14 8427.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 8752.8 0 0 0 1 0 0 0 0 0 0 0 0 15
16 7952.7 0 0 0 0 1 0 0 0 0 0 0 0 16
17 8694.3 0 0 0 0 0 1 0 0 0 0 0 0 17
18 7787.0 0 0 0 0 0 0 1 0 0 0 0 0 18
19 8474.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 9154.7 0 0 0 0 0 0 0 0 1 0 0 0 20
21 8557.2 0 0 0 0 0 0 0 0 0 1 0 0 21
22 7951.1 0 0 0 0 0 0 0 0 0 0 1 0 22
23 9156.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 7865.7 0 0 0 0 0 0 0 0 0 0 0 0 24
25 7337.4 0 1 0 0 0 0 0 0 0 0 0 0 25
26 9131.7 0 0 1 0 0 0 0 0 0 0 0 0 26
27 8814.6 0 0 0 1 0 0 0 0 0 0 0 0 27
28 8598.8 0 0 0 0 1 0 0 0 0 0 0 0 28
29 8439.6 0 0 0 0 0 1 0 0 0 0 0 0 29
30 7451.8 0 0 0 0 0 0 1 0 0 0 0 0 30
31 8016.2 0 0 0 0 0 0 0 1 0 0 0 0 31
32 9544.1 0 0 0 0 0 0 0 0 1 0 0 0 32
33 8270.7 0 0 0 0 0 0 0 0 0 1 0 0 33
34 8102.2 0 0 0 0 0 0 0 0 0 0 1 0 34
35 9369.0 0 0 0 0 0 0 0 0 0 0 0 1 35
36 7657.7 0 0 0 0 0 0 0 0 0 0 0 0 36
37 7816.6 0 1 0 0 0 0 0 0 0 0 0 0 37
38 9391.3 0 0 1 0 0 0 0 0 0 0 0 0 38
39 9445.4 0 0 0 1 0 0 0 0 0 0 0 0 39
40 9533.1 0 0 0 0 1 0 0 0 0 0 0 0 40
41 10068.7 0 0 0 0 0 1 0 0 0 0 0 0 41
42 8955.5 0 0 0 0 0 0 1 0 0 0 0 0 42
43 10423.9 0 0 0 0 0 0 0 1 0 0 0 0 43
44 11617.2 0 0 0 0 0 0 0 0 1 0 0 0 44
45 9391.1 0 0 0 0 0 0 0 0 0 1 0 0 45
46 10872.0 0 0 0 0 0 0 0 0 0 0 1 0 46
47 10230.4 0 0 0 0 0 0 0 0 0 0 0 1 47
48 9221.0 0 0 0 0 0 0 0 0 0 0 0 0 48
49 9428.6 0 1 0 0 0 0 0 0 0 0 0 0 49
50 10934.5 0 0 1 0 0 0 0 0 0 0 0 0 50
51 10986.0 0 0 0 1 0 0 0 0 0 0 0 0 51
52 11724.6 0 0 0 0 1 0 0 0 0 0 0 0 52
53 11180.9 0 0 0 0 0 1 0 0 0 0 0 0 53
54 11163.2 0 0 0 0 0 0 1 0 0 0 0 0 54
55 11240.9 0 0 0 0 0 0 0 1 0 0 0 0 55
56 12107.1 0 0 0 0 0 0 0 0 1 0 0 0 56
57 10762.3 0 0 0 0 0 0 0 0 0 1 0 0 57
58 11340.4 0 0 0 0 0 0 0 0 0 0 1 0 58
59 11266.8 0 0 0 0 0 0 0 0 0 0 0 1 59
60 9542.7 0 0 0 0 0 0 0 0 0 0 0 0 60
61 9227.7 0 1 0 0 0 0 0 0 0 0 0 0 61
62 10571.9 0 0 1 0 0 0 0 0 0 0 0 0 62
63 10774.4 0 0 0 1 0 0 0 0 0 0 0 0 63
64 10392.8 0 0 0 0 1 0 0 0 0 0 0 0 64
65 9920.2 0 0 0 0 0 1 0 0 0 0 0 0 65
66 9884.9 1 0 0 0 0 0 1 0 0 0 0 0 66
67 10174.5 1 0 0 0 0 0 0 1 0 0 0 0 67
68 11395.4 1 0 0 0 0 0 0 0 1 0 0 0 68
69 10760.2 1 0 0 0 0 0 0 0 0 1 0 0 69
70 10570.1 1 0 0 0 0 0 0 0 0 0 1 0 70
71 10536.0 1 0 0 0 0 0 0 0 0 0 0 1 71
72 9902.6 1 0 0 0 0 0 0 0 0 0 0 0 72
73 8889.0 1 1 0 0 0 0 0 0 0 0 0 0 73
74 10837.3 1 0 1 0 0 0 0 0 0 0 0 0 74
75 11624.1 1 0 0 1 0 0 0 0 0 0 0 0 75
76 10509.0 1 0 0 0 1 0 0 0 0 0 0 0 76
77 10984.9 1 0 0 0 0 1 0 0 0 0 0 0 77
78 10649.1 1 0 0 0 0 0 1 0 0 0 0 0 78
79 10855.7 1 0 0 0 0 0 0 1 0 0 0 0 79
80 11677.4 1 0 0 0 0 0 0 0 1 0 0 0 80
81 10760.2 1 0 0 0 0 0 0 0 0 1 0 0 81
82 10046.2 1 0 0 0 0 0 0 0 0 0 1 0 82
83 10772.8 1 0 0 0 0 0 0 0 0 0 0 1 83
84 9987.7 1 0 0 0 0 0 0 0 0 0 0 0 84
85 8638.7 1 1 0 0 0 0 0 0 0 0 0 0 85
86 11063.7 1 0 1 0 0 0 0 0 0 0 0 0 86
87 11855.7 1 0 0 1 0 0 0 0 0 0 0 0 87
88 10684.5 1 0 0 0 1 0 0 0 0 0 0 0 88
89 11337.4 1 0 0 0 0 1 0 0 0 0 0 0 89
90 10478.0 1 0 0 0 0 0 1 0 0 0 0 0 90
91 11123.9 1 0 0 0 0 0 0 1 0 0 0 0 91
92 12909.3 1 0 0 0 0 0 0 0 1 0 0 0 92
93 11339.9 1 0 0 0 0 0 0 0 0 1 0 0 93
94 10462.2 1 0 0 0 0 0 0 0 0 0 1 0 94
95 12733.5 1 0 0 0 0 0 0 0 0 0 0 1 95
96 10519.2 1 0 0 0 0 0 0 0 0 0 0 0 96
97 10414.9 1 1 0 0 0 0 0 0 0 0 0 0 97
98 12476.8 1 0 1 0 0 0 0 0 0 0 0 0 98
99 12384.6 1 0 0 1 0 0 0 0 0 0 0 0 99
100 12266.7 1 0 0 0 1 0 0 0 0 0 0 0 100
101 12919.9 1 0 0 0 0 1 0 0 0 0 0 0 101
102 11497.3 1 0 0 0 0 0 1 0 0 0 0 0 102
103 12142.0 1 0 0 0 0 0 0 1 0 0 0 0 103
104 13919.4 1 0 0 0 0 0 0 0 1 0 0 0 104
105 12656.8 1 0 0 0 0 0 0 0 0 1 0 0 105
106 12034.1 1 0 0 0 0 0 0 0 0 0 1 0 106
107 13199.7 1 0 0 0 0 0 0 0 0 0 0 1 107
108 10881.3 1 0 0 0 0 0 0 0 0 0 0 0 108
109 11301.2 1 1 0 0 0 0 0 0 0 0 0 0 109
110 13643.9 1 0 1 0 0 0 0 0 0 0 0 0 110
111 12517.0 1 0 0 1 0 0 0 0 0 0 0 0 111
112 13981.1 1 0 0 0 1 0 0 0 0 0 0 0 112
113 14275.7 1 0 0 0 0 1 0 0 0 0 0 0 113
114 13435.0 1 0 0 0 0 0 1 0 0 0 0 0 114
115 13565.7 1 0 0 0 0 0 0 1 0 0 0 0 115
116 16216.3 1 0 0 0 0 0 0 0 1 0 0 0 116
117 12970.0 1 0 0 0 0 0 0 0 0 1 0 0 117
118 14079.9 1 0 0 0 0 0 0 0 0 0 1 0 118
119 14235.0 1 0 0 0 0 0 0 0 0 0 0 1 119
120 12213.4 1 0 0 0 0 0 0 0 0 0 0 0 120
121 12581.0 1 1 0 0 0 0 0 0 0 0 0 0 121
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
5906.22 -1277.30 -274.59 1446.18 1478.51 1162.21
M5 M6 M7 M8 M9 M10
1357.09 747.64 1204.14 2450.98 1064.88 945.79
M11 t
1561.14 64.22
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1517.665 -348.343 9.971 370.902 1686.429
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5906.222 224.516 26.306 < 2e-16 ***
x -1277.304 216.050 -5.912 4.09e-08 ***
M1 -274.590 259.807 -1.057 0.292939
M2 1446.179 266.279 5.431 3.54e-07 ***
M3 1478.505 266.156 5.555 2.05e-07 ***
M4 1162.211 266.069 4.368 2.91e-05 ***
M5 1357.087 266.017 5.102 1.47e-06 ***
M6 747.644 266.421 2.806 0.005956 **
M7 1204.140 266.225 4.523 1.58e-05 ***
M8 2450.976 266.064 9.212 3.10e-15 ***
M9 1064.882 265.939 4.004 0.000115 ***
M10 945.788 265.850 3.558 0.000559 ***
M11 1561.144 265.796 5.873 4.88e-08 ***
t 64.224 3.083 20.835 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 594.3 on 107 degrees of freedom
Multiple R-squared: 0.9157, Adjusted R-squared: 0.9054
F-statistic: 89.38 on 13 and 107 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,] 7.220724e-02 1.444145e-01 0.9277928
[2,] 2.968931e-02 5.937862e-02 0.9703107
[3,] 9.097970e-03 1.819594e-02 0.9909020
[4,] 2.656654e-03 5.313307e-03 0.9973433
[5,] 1.803323e-03 3.606647e-03 0.9981967
[6,] 1.273638e-03 2.547276e-03 0.9987264
[7,] 4.103731e-04 8.207462e-04 0.9995896
[8,] 1.299169e-04 2.598337e-04 0.9998701
[9,] 3.723305e-05 7.446610e-05 0.9999628
[10,] 1.213697e-05 2.427394e-05 0.9999879
[11,] 2.496240e-05 4.992479e-05 0.9999750
[12,] 1.157674e-05 2.315348e-05 0.9999884
[13,] 1.955786e-05 3.911572e-05 0.9999804
[14,] 2.631757e-04 5.263515e-04 0.9997368
[15,] 9.792892e-04 1.958578e-03 0.9990207
[16,] 5.824541e-04 1.164908e-03 0.9994175
[17,] 1.248729e-03 2.497458e-03 0.9987513
[18,] 9.440721e-04 1.888144e-03 0.9990559
[19,] 4.697381e-04 9.394761e-04 0.9995303
[20,] 3.705778e-04 7.411557e-04 0.9996294
[21,] 2.526683e-04 5.053365e-04 0.9997473
[22,] 1.384033e-04 2.768065e-04 0.9998616
[23,] 7.137003e-05 1.427401e-04 0.9999286
[24,] 2.460022e-04 4.920043e-04 0.9997540
[25,] 9.519929e-04 1.903986e-03 0.9990480
[26,] 9.408564e-04 1.881713e-03 0.9990591
[27,] 7.156611e-03 1.431322e-02 0.9928434
[28,] 3.585347e-02 7.170694e-02 0.9641465
[29,] 2.689678e-02 5.379355e-02 0.9731032
[30,] 1.392767e-01 2.785533e-01 0.8607233
[31,] 1.089326e-01 2.178652e-01 0.8910674
[32,] 9.037702e-02 1.807540e-01 0.9096230
[33,] 1.086520e-01 2.173040e-01 0.8913480
[34,] 1.022763e-01 2.045526e-01 0.8977237
[35,] 9.182705e-02 1.836541e-01 0.9081730
[36,] 2.872796e-01 5.745592e-01 0.7127204
[37,] 2.908812e-01 5.817624e-01 0.7091188
[38,] 4.508175e-01 9.016350e-01 0.5491825
[39,] 4.757491e-01 9.514983e-01 0.5242509
[40,] 4.361673e-01 8.723347e-01 0.5638327
[41,] 3.943838e-01 7.887676e-01 0.6056162
[42,] 5.009267e-01 9.981466e-01 0.4990733
[43,] 4.739253e-01 9.478506e-01 0.5260747
[44,] 4.609799e-01 9.219597e-01 0.5390201
[45,] 4.850744e-01 9.701489e-01 0.5149256
[46,] 4.877674e-01 9.755348e-01 0.5122326
[47,] 4.841288e-01 9.682575e-01 0.5158712
[48,] 5.053946e-01 9.892108e-01 0.4946054
[49,] 5.735370e-01 8.529260e-01 0.4264630
[50,] 5.368241e-01 9.263518e-01 0.4631759
[51,] 4.971057e-01 9.942114e-01 0.5028943
[52,] 4.381049e-01 8.762099e-01 0.5618951
[53,] 4.788304e-01 9.576607e-01 0.5211696
[54,] 5.445989e-01 9.108022e-01 0.4554011
[55,] 4.980805e-01 9.961610e-01 0.5019195
[56,] 6.279354e-01 7.441292e-01 0.3720646
[57,] 6.255216e-01 7.489568e-01 0.3744784
[58,] 5.881222e-01 8.237555e-01 0.4118778
[59,] 7.442517e-01 5.114965e-01 0.2557483
[60,] 7.068838e-01 5.862323e-01 0.2931162
[61,] 6.596706e-01 6.806589e-01 0.3403294
[62,] 7.172547e-01 5.654907e-01 0.2827453
[63,] 7.404629e-01 5.190742e-01 0.2595371
[64,] 7.089623e-01 5.820754e-01 0.2910377
[65,] 7.172478e-01 5.655044e-01 0.2827522
[66,] 7.094096e-01 5.811809e-01 0.2905904
[67,] 6.785502e-01 6.428995e-01 0.3214498
[68,] 7.824824e-01 4.350352e-01 0.2175176
[69,] 7.919592e-01 4.160816e-01 0.2080408
[70,] 7.460459e-01 5.079081e-01 0.2539541
[71,] 8.513979e-01 2.972042e-01 0.1486021
[72,] 8.472345e-01 3.055310e-01 0.1527655
[73,] 8.199860e-01 3.600280e-01 0.1800140
[74,] 7.766644e-01 4.466712e-01 0.2233356
[75,] 7.174793e-01 5.650415e-01 0.2825207
[76,] 6.733345e-01 6.533310e-01 0.3266655
[77,] 6.077469e-01 7.845062e-01 0.3922531
[78,] 6.845931e-01 6.308138e-01 0.3154069
[79,] 7.086799e-01 5.826403e-01 0.2913201
[80,] 7.427573e-01 5.144854e-01 0.2572427
[81,] 6.935597e-01 6.128807e-01 0.3064403
[82,] 6.075232e-01 7.849535e-01 0.3924768
[83,] 7.839183e-01 4.321634e-01 0.2160817
[84,] 7.029277e-01 5.941445e-01 0.2970723
[85,] 6.024273e-01 7.951453e-01 0.3975727
[86,] 5.270876e-01 9.458249e-01 0.4729124
[87,] 3.825451e-01 7.650902e-01 0.6174549
[88,] 4.668709e-01 9.337417e-01 0.5331291
> postscript(file="/var/www/html/rcomp/tmp/1ihxa1229176166.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2snzw1229176166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ca5i1229176166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41z8e1229176166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mzdf1229176166.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 = 121
Frequency = 1
1 2 3 4 5 6
644.643649 420.651139 613.701139 -143.628861 9.971139 345.490738
7 8 9 10 11 12
316.770738 -407.589262 768.080738 284.450738 358.970738 595.290738
13 14 15 16 17 18
213.556373 176.063863 404.713863 -143.316137 339.183863 -22.896538
19 20 21 22 23 24
143.583462 -486.976538 237.393462 -313.836538 212.183462 418.103462
25 26 27 28 29 30
100.169097 109.476586 -304.173414 -267.903414 -686.203414 -1128.783815
31 32 33 34 35 36
-1085.103815 -868.263815 -819.793815 -933.423815 -346.203815 -560.583815
37 38 39 40 41 42
-191.318180 -401.610690 -444.060690 -104.290690 172.209310 -395.771091
43 44 45 46 47 48
551.908909 434.148909 -470.081091 1065.688909 -255.491091 232.028909
49 50 51 52 53 54
649.994544 370.902034 325.852034 1316.522034 513.722034 1041.241633
55 56 57 58 59 60
598.221633 153.361633 130.431633 763.401633 10.221633 -216.958367
61 62 63 64 65 66
-321.592732 -762.385242 -656.435242 -785.965242 -1517.665242 269.558367
67 68 69 70 71 72
38.438367 -51.721633 634.948367 499.718367 -213.961633 649.558367
73 74 75 76 77 78
-153.675998 9.631492 699.881492 -163.148508 53.651492 263.071091
79 80 81 82 83 84
-51.048909 -540.408909 -135.738909 -794.868909 -747.848909 -36.028909
85 86 87 88 89 90
-1174.663274 -534.655784 160.794216 -758.335784 -364.535784 -678.716185
91 92 93 94 95 96
-553.536185 -79.196185 -326.726185 -1149.556185 442.163815 -275.216185
97 98 99 100 101 102
-169.150550 107.756939 -80.993061 53.176939 447.276939 -430.103462
103 104 105 106 107 108
-306.123462 160.216538 219.486538 -348.343462 137.676538 -683.803462
109 110 111 112 113 114
-53.537827 504.169663 -719.280337 996.889663 1032.389663 736.909262
115 116 117 118 119 120
346.889262 1686.429262 -238.000738 926.769262 402.289262 -122.390738
121
455.574897
> postscript(file="/var/www/html/rcomp/tmp/6wxmd1229176166.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 644.643649 NA
1 420.651139 644.643649
2 613.701139 420.651139
3 -143.628861 613.701139
4 9.971139 -143.628861
5 345.490738 9.971139
6 316.770738 345.490738
7 -407.589262 316.770738
8 768.080738 -407.589262
9 284.450738 768.080738
10 358.970738 284.450738
11 595.290738 358.970738
12 213.556373 595.290738
13 176.063863 213.556373
14 404.713863 176.063863
15 -143.316137 404.713863
16 339.183863 -143.316137
17 -22.896538 339.183863
18 143.583462 -22.896538
19 -486.976538 143.583462
20 237.393462 -486.976538
21 -313.836538 237.393462
22 212.183462 -313.836538
23 418.103462 212.183462
24 100.169097 418.103462
25 109.476586 100.169097
26 -304.173414 109.476586
27 -267.903414 -304.173414
28 -686.203414 -267.903414
29 -1128.783815 -686.203414
30 -1085.103815 -1128.783815
31 -868.263815 -1085.103815
32 -819.793815 -868.263815
33 -933.423815 -819.793815
34 -346.203815 -933.423815
35 -560.583815 -346.203815
36 -191.318180 -560.583815
37 -401.610690 -191.318180
38 -444.060690 -401.610690
39 -104.290690 -444.060690
40 172.209310 -104.290690
41 -395.771091 172.209310
42 551.908909 -395.771091
43 434.148909 551.908909
44 -470.081091 434.148909
45 1065.688909 -470.081091
46 -255.491091 1065.688909
47 232.028909 -255.491091
48 649.994544 232.028909
49 370.902034 649.994544
50 325.852034 370.902034
51 1316.522034 325.852034
52 513.722034 1316.522034
53 1041.241633 513.722034
54 598.221633 1041.241633
55 153.361633 598.221633
56 130.431633 153.361633
57 763.401633 130.431633
58 10.221633 763.401633
59 -216.958367 10.221633
60 -321.592732 -216.958367
61 -762.385242 -321.592732
62 -656.435242 -762.385242
63 -785.965242 -656.435242
64 -1517.665242 -785.965242
65 269.558367 -1517.665242
66 38.438367 269.558367
67 -51.721633 38.438367
68 634.948367 -51.721633
69 499.718367 634.948367
70 -213.961633 499.718367
71 649.558367 -213.961633
72 -153.675998 649.558367
73 9.631492 -153.675998
74 699.881492 9.631492
75 -163.148508 699.881492
76 53.651492 -163.148508
77 263.071091 53.651492
78 -51.048909 263.071091
79 -540.408909 -51.048909
80 -135.738909 -540.408909
81 -794.868909 -135.738909
82 -747.848909 -794.868909
83 -36.028909 -747.848909
84 -1174.663274 -36.028909
85 -534.655784 -1174.663274
86 160.794216 -534.655784
87 -758.335784 160.794216
88 -364.535784 -758.335784
89 -678.716185 -364.535784
90 -553.536185 -678.716185
91 -79.196185 -553.536185
92 -326.726185 -79.196185
93 -1149.556185 -326.726185
94 442.163815 -1149.556185
95 -275.216185 442.163815
96 -169.150550 -275.216185
97 107.756939 -169.150550
98 -80.993061 107.756939
99 53.176939 -80.993061
100 447.276939 53.176939
101 -430.103462 447.276939
102 -306.123462 -430.103462
103 160.216538 -306.123462
104 219.486538 160.216538
105 -348.343462 219.486538
106 137.676538 -348.343462
107 -683.803462 137.676538
108 -53.537827 -683.803462
109 504.169663 -53.537827
110 -719.280337 504.169663
111 996.889663 -719.280337
112 1032.389663 996.889663
113 736.909262 1032.389663
114 346.889262 736.909262
115 1686.429262 346.889262
116 -238.000738 1686.429262
117 926.769262 -238.000738
118 402.289262 926.769262
119 -122.390738 402.289262
120 455.574897 -122.390738
121 NA 455.574897
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 420.651139 644.643649
[2,] 613.701139 420.651139
[3,] -143.628861 613.701139
[4,] 9.971139 -143.628861
[5,] 345.490738 9.971139
[6,] 316.770738 345.490738
[7,] -407.589262 316.770738
[8,] 768.080738 -407.589262
[9,] 284.450738 768.080738
[10,] 358.970738 284.450738
[11,] 595.290738 358.970738
[12,] 213.556373 595.290738
[13,] 176.063863 213.556373
[14,] 404.713863 176.063863
[15,] -143.316137 404.713863
[16,] 339.183863 -143.316137
[17,] -22.896538 339.183863
[18,] 143.583462 -22.896538
[19,] -486.976538 143.583462
[20,] 237.393462 -486.976538
[21,] -313.836538 237.393462
[22,] 212.183462 -313.836538
[23,] 418.103462 212.183462
[24,] 100.169097 418.103462
[25,] 109.476586 100.169097
[26,] -304.173414 109.476586
[27,] -267.903414 -304.173414
[28,] -686.203414 -267.903414
[29,] -1128.783815 -686.203414
[30,] -1085.103815 -1128.783815
[31,] -868.263815 -1085.103815
[32,] -819.793815 -868.263815
[33,] -933.423815 -819.793815
[34,] -346.203815 -933.423815
[35,] -560.583815 -346.203815
[36,] -191.318180 -560.583815
[37,] -401.610690 -191.318180
[38,] -444.060690 -401.610690
[39,] -104.290690 -444.060690
[40,] 172.209310 -104.290690
[41,] -395.771091 172.209310
[42,] 551.908909 -395.771091
[43,] 434.148909 551.908909
[44,] -470.081091 434.148909
[45,] 1065.688909 -470.081091
[46,] -255.491091 1065.688909
[47,] 232.028909 -255.491091
[48,] 649.994544 232.028909
[49,] 370.902034 649.994544
[50,] 325.852034 370.902034
[51,] 1316.522034 325.852034
[52,] 513.722034 1316.522034
[53,] 1041.241633 513.722034
[54,] 598.221633 1041.241633
[55,] 153.361633 598.221633
[56,] 130.431633 153.361633
[57,] 763.401633 130.431633
[58,] 10.221633 763.401633
[59,] -216.958367 10.221633
[60,] -321.592732 -216.958367
[61,] -762.385242 -321.592732
[62,] -656.435242 -762.385242
[63,] -785.965242 -656.435242
[64,] -1517.665242 -785.965242
[65,] 269.558367 -1517.665242
[66,] 38.438367 269.558367
[67,] -51.721633 38.438367
[68,] 634.948367 -51.721633
[69,] 499.718367 634.948367
[70,] -213.961633 499.718367
[71,] 649.558367 -213.961633
[72,] -153.675998 649.558367
[73,] 9.631492 -153.675998
[74,] 699.881492 9.631492
[75,] -163.148508 699.881492
[76,] 53.651492 -163.148508
[77,] 263.071091 53.651492
[78,] -51.048909 263.071091
[79,] -540.408909 -51.048909
[80,] -135.738909 -540.408909
[81,] -794.868909 -135.738909
[82,] -747.848909 -794.868909
[83,] -36.028909 -747.848909
[84,] -1174.663274 -36.028909
[85,] -534.655784 -1174.663274
[86,] 160.794216 -534.655784
[87,] -758.335784 160.794216
[88,] -364.535784 -758.335784
[89,] -678.716185 -364.535784
[90,] -553.536185 -678.716185
[91,] -79.196185 -553.536185
[92,] -326.726185 -79.196185
[93,] -1149.556185 -326.726185
[94,] 442.163815 -1149.556185
[95,] -275.216185 442.163815
[96,] -169.150550 -275.216185
[97,] 107.756939 -169.150550
[98,] -80.993061 107.756939
[99,] 53.176939 -80.993061
[100,] 447.276939 53.176939
[101,] -430.103462 447.276939
[102,] -306.123462 -430.103462
[103,] 160.216538 -306.123462
[104,] 219.486538 160.216538
[105,] -348.343462 219.486538
[106,] 137.676538 -348.343462
[107,] -683.803462 137.676538
[108,] -53.537827 -683.803462
[109,] 504.169663 -53.537827
[110,] -719.280337 504.169663
[111,] 996.889663 -719.280337
[112,] 1032.389663 996.889663
[113,] 736.909262 1032.389663
[114,] 346.889262 736.909262
[115,] 1686.429262 346.889262
[116,] -238.000738 1686.429262
[117,] 926.769262 -238.000738
[118,] 402.289262 926.769262
[119,] -122.390738 402.289262
[120,] 455.574897 -122.390738
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 420.651139 644.643649
2 613.701139 420.651139
3 -143.628861 613.701139
4 9.971139 -143.628861
5 345.490738 9.971139
6 316.770738 345.490738
7 -407.589262 316.770738
8 768.080738 -407.589262
9 284.450738 768.080738
10 358.970738 284.450738
11 595.290738 358.970738
12 213.556373 595.290738
13 176.063863 213.556373
14 404.713863 176.063863
15 -143.316137 404.713863
16 339.183863 -143.316137
17 -22.896538 339.183863
18 143.583462 -22.896538
19 -486.976538 143.583462
20 237.393462 -486.976538
21 -313.836538 237.393462
22 212.183462 -313.836538
23 418.103462 212.183462
24 100.169097 418.103462
25 109.476586 100.169097
26 -304.173414 109.476586
27 -267.903414 -304.173414
28 -686.203414 -267.903414
29 -1128.783815 -686.203414
30 -1085.103815 -1128.783815
31 -868.263815 -1085.103815
32 -819.793815 -868.263815
33 -933.423815 -819.793815
34 -346.203815 -933.423815
35 -560.583815 -346.203815
36 -191.318180 -560.583815
37 -401.610690 -191.318180
38 -444.060690 -401.610690
39 -104.290690 -444.060690
40 172.209310 -104.290690
41 -395.771091 172.209310
42 551.908909 -395.771091
43 434.148909 551.908909
44 -470.081091 434.148909
45 1065.688909 -470.081091
46 -255.491091 1065.688909
47 232.028909 -255.491091
48 649.994544 232.028909
49 370.902034 649.994544
50 325.852034 370.902034
51 1316.522034 325.852034
52 513.722034 1316.522034
53 1041.241633 513.722034
54 598.221633 1041.241633
55 153.361633 598.221633
56 130.431633 153.361633
57 763.401633 130.431633
58 10.221633 763.401633
59 -216.958367 10.221633
60 -321.592732 -216.958367
61 -762.385242 -321.592732
62 -656.435242 -762.385242
63 -785.965242 -656.435242
64 -1517.665242 -785.965242
65 269.558367 -1517.665242
66 38.438367 269.558367
67 -51.721633 38.438367
68 634.948367 -51.721633
69 499.718367 634.948367
70 -213.961633 499.718367
71 649.558367 -213.961633
72 -153.675998 649.558367
73 9.631492 -153.675998
74 699.881492 9.631492
75 -163.148508 699.881492
76 53.651492 -163.148508
77 263.071091 53.651492
78 -51.048909 263.071091
79 -540.408909 -51.048909
80 -135.738909 -540.408909
81 -794.868909 -135.738909
82 -747.848909 -794.868909
83 -36.028909 -747.848909
84 -1174.663274 -36.028909
85 -534.655784 -1174.663274
86 160.794216 -534.655784
87 -758.335784 160.794216
88 -364.535784 -758.335784
89 -678.716185 -364.535784
90 -553.536185 -678.716185
91 -79.196185 -553.536185
92 -326.726185 -79.196185
93 -1149.556185 -326.726185
94 442.163815 -1149.556185
95 -275.216185 442.163815
96 -169.150550 -275.216185
97 107.756939 -169.150550
98 -80.993061 107.756939
99 53.176939 -80.993061
100 447.276939 53.176939
101 -430.103462 447.276939
102 -306.123462 -430.103462
103 160.216538 -306.123462
104 219.486538 160.216538
105 -348.343462 219.486538
106 137.676538 -348.343462
107 -683.803462 137.676538
108 -53.537827 -683.803462
109 504.169663 -53.537827
110 -719.280337 504.169663
111 996.889663 -719.280337
112 1032.389663 996.889663
113 736.909262 1032.389663
114 346.889262 736.909262
115 1686.429262 346.889262
116 -238.000738 1686.429262
117 926.769262 -238.000738
118 402.289262 926.769262
119 -122.390738 402.289262
120 455.574897 -122.390738
> 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/712kr1229176166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88z9x1229176166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91iiq1229176166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10910i1229176166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11x68v1229176166.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/12sv071229176166.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/13s7eg1229176167.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/14jpx81229176167.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/15ovhg1229176167.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/160lpj1229176167.tab")
+ }
>
> system("convert tmp/1ihxa1229176166.ps tmp/1ihxa1229176166.png")
> system("convert tmp/2snzw1229176166.ps tmp/2snzw1229176166.png")
> system("convert tmp/3ca5i1229176166.ps tmp/3ca5i1229176166.png")
> system("convert tmp/41z8e1229176166.ps tmp/41z8e1229176166.png")
> system("convert tmp/5mzdf1229176166.ps tmp/5mzdf1229176166.png")
> system("convert tmp/6wxmd1229176166.ps tmp/6wxmd1229176166.png")
> system("convert tmp/712kr1229176166.ps tmp/712kr1229176166.png")
> system("convert tmp/88z9x1229176166.ps tmp/88z9x1229176166.png")
> system("convert tmp/91iiq1229176166.ps tmp/91iiq1229176166.png")
> system("convert tmp/10910i1229176166.ps tmp/10910i1229176166.png")
>
>
> proc.time()
user system elapsed
3.392 1.722 3.970