R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(1
+ ,8
+ ,1
+ ,14
+ ,4
+ ,2
+ ,8
+ ,3
+ ,82
+ ,1
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+ ,10
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+ ,5
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+ ,3
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+ ,4
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+ ,13
+ ,13
+ ,14
+ ,8
+ ,56
+ ,12
+ ,14
+ ,14
+ ,7
+ ,31
+ ,15)
+ ,dim=c(5
+ ,102)
+ ,dimnames=list(c('Postition'
+ ,'starters'
+ ,'last'
+ ,'since'
+ ,'number')
+ ,1:102))
> y <- array(NA,dim=c(5,102),dimnames=list(c('Postition','starters','last','since','number'),1:102))
> 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 = 'Do not include Seasonal 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
> 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
Postition starters last since number t
1 1 8 1 14 4 1
2 2 8 3 82 1 2
3 3 8 2 14 3 3
4 4 8 1 16 5 4
5 5 8 5 140 7 5
6 6 8 8 173 2 6
7 7 8 3 9 8 7
8 8 8 8 13 6 8
9 1 12 12 17 4 9
10 2 12 3 16 9 10
11 3 12 8 21 7 11
12 4 12 3 14 2 12
13 5 12 3 15 12 13
14 6 12 3 10 8 14
15 7 12 3 14 1 15
16 8 12 1 16 6 16
17 9 12 2 14 10 17
18 10 12 20 17 3 18
19 11 12 2 10 5 19
20 12 12 1 23 11 20
21 1 9 1 21 2 21
22 2 9 6 14 4 22
23 3 9 8 14 7 23
24 4 9 5 14 11 24
25 5 9 1 16 5 25
26 6 9 7 14 1 26
27 7 9 7 14 9 27
28 8 9 5 7 3 28
29 9 9 8 17 10 29
30 1 14 2 14 3 30
31 2 14 5 21 4 31
32 3 14 2 24 7 32
33 4 14 5 7 6 33
34 5 14 1 30 13 34
35 6 14 2 93 16 35
36 7 14 6 14 9 36
37 8 14 3 14 1 37
38 9 14 6 107 10 38
39 10 14 6 231 5 39
40 11 14 1 385 2 40
41 12 14 2 14 11 41
42 13 14 10 29 14 42
43 14 14 1 16 15 43
44 1 13 2 7 10 44
45 2 13 1 21 3 45
46 3 13 1 14 2 46
47 4 13 1 17 13 47
48 5 13 6 14 4 48
49 6 13 4 21 1 49
50 7 13 9 15 9 50
51 8 13 10 10 5 51
52 9 13 6 15 8 52
53 10 13 1 7 7 53
54 11 13 6 12 12 54
55 12 13 18 84 6 55
56 13 13 3 17 11 56
57 1 19 4 14 4 57
58 2 19 1 10 9 58
59 3 19 3 17 15 59
60 4 19 5 91 14 60
61 5 19 4 21 17 61
62 6 19 4 21 3 62
63 7 19 1 16 7 63
64 8 19 17 35 1 64
65 9 19 2 17 16 65
66 10 19 1 15 13 66
67 11 19 6 14 5 67
68 12 19 10 28 18 68
69 13 19 9 14 6 69
70 14 19 5 14 10 70
71 15 19 1 20 12 71
72 16 19 13 35 20 72
73 17 19 11 28 8 73
74 18 19 9 17 11 74
75 19 19 4 14 19 75
76 1 13 4 10 4 76
77 2 13 5 10 1 77
78 3 13 2 14 3 78
79 4 13 1 7 9 79
80 5 13 2 14 11 80
81 6 13 4 14 12 81
82 7 13 12 10 2 82
83 8 13 14 10 7 83
84 9 13 2 21 6 84
85 10 13 7 10 5 85
86 11 13 4 17 8 86
87 12 13 1 17 10 87
88 13 13 6 24 13 88
89 1 14 2 16 2 89
90 2 14 1 63 9 90
91 3 14 4 17 4 91
92 4 14 6 21 1 92
93 5 14 7 7 14 93
94 6 14 9 49 7 94
95 7 14 1 7 10 95
96 8 14 3 14 6 96
97 9 14 6 210 11 97
98 10 14 8 35 5 98
99 11 14 8 14 3 99
100 12 14 4 28 13 100
101 13 14 8 56 12 101
102 14 14 7 31 15 102
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) starters last since number t
-0.60370 0.16268 0.31046 0.01037 0.35292 0.02100
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.1840 -2.7183 0.1351 2.3977 7.1066
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.603704 1.615450 -0.374 0.709447
starters 0.162679 0.140570 1.157 0.250030
last 0.310458 0.091308 3.400 0.000983 ***
since 0.010373 0.006851 1.514 0.133291
number 0.352921 0.083241 4.240 5.15e-05 ***
t 0.020995 0.014521 1.446 0.151469
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.566 on 96 degrees of freedom
Multiple R-squared: 0.3553, Adjusted R-squared: 0.3217
F-statistic: 10.58 on 5 and 96 DF, p-value: 4.122e-08
> 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,] 1.571807e-47 3.143615e-47 1.000000e+00
[2,] 3.938704e-63 7.877407e-63 1.000000e+00
[3,] 1.143805e-78 2.287609e-78 1.000000e+00
[4,] 3.984841e-95 7.969681e-95 1.000000e+00
[5,] 1.884319e-107 3.768637e-107 1.000000e+00
[6,] 6.894248e-121 1.378850e-120 1.000000e+00
[7,] 1.497617e-138 2.995235e-138 1.000000e+00
[8,] 2.436095e-155 4.872190e-155 1.000000e+00
[9,] 2.763597e-164 5.527194e-164 1.000000e+00
[10,] 6.088112e-183 1.217622e-182 1.000000e+00
[11,] 4.384234e-201 8.768469e-201 1.000000e+00
[12,] 9.870022e-217 1.974004e-216 1.000000e+00
[13,] 1.748691e-01 3.497382e-01 8.251309e-01
[14,] 2.879958e-01 5.759916e-01 7.120042e-01
[15,] 3.065391e-01 6.130782e-01 6.934609e-01
[16,] 2.806677e-01 5.613354e-01 7.193323e-01
[17,] 2.217282e-01 4.434564e-01 7.782718e-01
[18,] 1.779943e-01 3.559887e-01 8.220057e-01
[19,] 1.345851e-01 2.691702e-01 8.654149e-01
[20,] 1.227033e-01 2.454065e-01 8.772967e-01
[21,] 9.651805e-02 1.930361e-01 9.034820e-01
[22,] 1.572618e-01 3.145236e-01 8.427382e-01
[23,] 1.681878e-01 3.363756e-01 8.318122e-01
[24,] 1.521585e-01 3.043169e-01 8.478415e-01
[25,] 1.232477e-01 2.464955e-01 8.767523e-01
[26,] 1.011218e-01 2.022435e-01 8.988782e-01
[27,] 8.407769e-02 1.681554e-01 9.159223e-01
[28,] 6.296069e-02 1.259214e-01 9.370393e-01
[29,] 6.949828e-02 1.389966e-01 9.305017e-01
[30,] 5.552946e-02 1.110589e-01 9.444705e-01
[31,] 4.570176e-02 9.140353e-02 9.542982e-01
[32,] 4.935933e-02 9.871866e-02 9.506407e-01
[33,] 6.917937e-02 1.383587e-01 9.308206e-01
[34,] 6.470120e-02 1.294024e-01 9.352988e-01
[35,] 9.895686e-02 1.979137e-01 9.010431e-01
[36,] 1.773302e-01 3.546603e-01 8.226698e-01
[37,] 1.638298e-01 3.276597e-01 8.361702e-01
[38,] 1.345108e-01 2.690217e-01 8.654892e-01
[39,] 1.332694e-01 2.665388e-01 8.667306e-01
[40,] 1.043688e-01 2.087376e-01 8.956312e-01
[41,] 8.762094e-02 1.752419e-01 9.123791e-01
[42,] 6.873673e-02 1.374735e-01 9.312633e-01
[43,] 5.173470e-02 1.034694e-01 9.482653e-01
[44,] 4.096529e-02 8.193058e-02 9.590347e-01
[45,] 5.547250e-02 1.109450e-01 9.445275e-01
[46,] 5.074585e-02 1.014917e-01 9.492541e-01
[47,] 4.730959e-02 9.461919e-02 9.526904e-01
[48,] 1.914620e-01 3.829239e-01 8.085380e-01
[49,] 2.290520e-01 4.581041e-01 7.709480e-01
[50,] 2.575642e-01 5.151284e-01 7.424358e-01
[51,] 3.639454e-01 7.278907e-01 6.360546e-01
[52,] 4.210591e-01 8.421181e-01 5.789409e-01
[53,] 5.505994e-01 8.988011e-01 4.494006e-01
[54,] 5.064109e-01 9.871782e-01 4.935891e-01
[55,] 4.667109e-01 9.334218e-01 5.332891e-01
[56,] 4.511024e-01 9.022048e-01 5.488976e-01
[57,] 4.555680e-01 9.111360e-01 5.444320e-01
[58,] 4.369551e-01 8.739102e-01 5.630449e-01
[59,] 4.196356e-01 8.392713e-01 5.803644e-01
[60,] 4.827147e-01 9.654295e-01 5.172853e-01
[61,] 4.699485e-01 9.398971e-01 5.300515e-01
[62,] 4.624432e-01 9.248864e-01 5.375568e-01
[63,] 4.808545e-01 9.617089e-01 5.191455e-01
[64,] 5.375018e-01 9.249963e-01 4.624982e-01
[65,] 5.417755e-01 9.164490e-01 4.582245e-01
[66,] 5.708882e-01 8.582237e-01 4.291118e-01
[67,] 1.000000e+00 1.220270e-301 6.101351e-302
[68,] 1.000000e+00 3.210082e-280 1.605041e-280
[69,] 1.000000e+00 8.017815e-268 4.008907e-268
[70,] 1.000000e+00 5.713010e-273 2.856505e-273
[71,] 1.000000e+00 2.834131e-259 1.417066e-259
[72,] 1.000000e+00 2.986524e-232 1.493262e-232
[73,] 1.000000e+00 3.229084e-217 1.614542e-217
[74,] 1.000000e+00 8.060746e-207 4.030373e-207
[75,] 1.000000e+00 4.409414e-186 2.204707e-186
[76,] 1.000000e+00 1.875497e-177 9.377485e-178
[77,] 1.000000e+00 3.066997e-155 1.533498e-155
[78,] 1.000000e+00 1.629516e-145 8.147578e-146
[79,] 1.000000e+00 4.444104e-139 2.222052e-139
[80,] 1.000000e+00 1.259651e-117 6.298254e-118
[81,] 1.000000e+00 3.948377e-105 1.974189e-105
[82,] 1.000000e+00 1.293666e-87 6.468330e-88
[83,] 1.000000e+00 5.331253e-73 2.665627e-73
[84,] 1.000000e+00 3.071422e-61 1.535711e-61
[85,] 1.000000e+00 1.797525e-46 8.987625e-47
> postscript(file="/var/www/rcomp/tmp/1f6mo1322129659.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/20cgx1322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/334le1322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4nuf21322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5qubp1322129659.ps",horizontal=F,onefile=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 = 102
Frequency = 1
1 2 3 4 5 6
-1.58608660 -0.87459789 0.41438652 0.97726059 -1.27766208 0.19226658
7 8 9 10 11 12
2.30720594 2.39827145 -5.85091974 -3.83202664 -3.75133413 0.61717897
13 14 15 16 17 18
-1.94340326 0.49915227 3.90711498 3.72168285 2.99929006 0.82938123
19 20 21 22 23 24
6.76339886 5.80048424 -1.53543615 -2.74195322 -3.44262864 -2.94393531
25 26 27 28 29 30
1.37368412 1.92237245 0.07800605 3.86806646 1.34151733 -3.12855417
31 32 33 34 35 36
-3.50645583 -2.68596011 -2.10906685 -2.59725883 -3.64097552 -0.61388554
37 38 39 40 41 42
4.11986469 0.02651334 1.48387266 3.47648917 4.81712810 2.09810943
43 44 45 46 47 48
5.65316422 -5.65764617 -2.04295547 -0.63841816 -3.57266781 -0.93854140
49 50 51 52 53 54
1.64753272 -1.68688580 0.44521169 1.55541943 4.52261990 2.13286255
55 56 57 58 59 60
0.75704316 5.32330276 -5.48265364 -5.29538970 -7.12744038 -7.18403252
61 62 63 64 65 66
-6.22722351 -0.30731892 0.24323942 -1.82464283 -1.29587456 1.07309858
67 68 69 70 71 72
3.33355757 -1.66247007 4.00727180 4.81642316 6.26917930 0.54372146
73 74 75 76 77 78
6.45131033 7.10657008 6.84561285 -4.86399703 -3.13668596 -1.97364184
79 80 81 82 83 84
-2.72909636 -2.83900338 -2.83383598 -0.76778925 -2.17430749 2.76901210
85 86 87 88 89 90
2.66275128 3.44175498 4.64629112 2.94163057 -4.03509159 -5.70360977
91 92 93 94 95 96
-3.41421373 -2.03885272 -5.81306213 -3.42018966 -0.58061858 1.11654483
97 98 99 100 101 102
-2.63354038 1.65735272 3.56003351 2.10643434 1.90608436 2.39610815
> postscript(file="/var/www/rcomp/tmp/6rxnk1322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 102
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.58608660 NA
1 -0.87459789 -1.58608660
2 0.41438652 -0.87459789
3 0.97726059 0.41438652
4 -1.27766208 0.97726059
5 0.19226658 -1.27766208
6 2.30720594 0.19226658
7 2.39827145 2.30720594
8 -5.85091974 2.39827145
9 -3.83202664 -5.85091974
10 -3.75133413 -3.83202664
11 0.61717897 -3.75133413
12 -1.94340326 0.61717897
13 0.49915227 -1.94340326
14 3.90711498 0.49915227
15 3.72168285 3.90711498
16 2.99929006 3.72168285
17 0.82938123 2.99929006
18 6.76339886 0.82938123
19 5.80048424 6.76339886
20 -1.53543615 5.80048424
21 -2.74195322 -1.53543615
22 -3.44262864 -2.74195322
23 -2.94393531 -3.44262864
24 1.37368412 -2.94393531
25 1.92237245 1.37368412
26 0.07800605 1.92237245
27 3.86806646 0.07800605
28 1.34151733 3.86806646
29 -3.12855417 1.34151733
30 -3.50645583 -3.12855417
31 -2.68596011 -3.50645583
32 -2.10906685 -2.68596011
33 -2.59725883 -2.10906685
34 -3.64097552 -2.59725883
35 -0.61388554 -3.64097552
36 4.11986469 -0.61388554
37 0.02651334 4.11986469
38 1.48387266 0.02651334
39 3.47648917 1.48387266
40 4.81712810 3.47648917
41 2.09810943 4.81712810
42 5.65316422 2.09810943
43 -5.65764617 5.65316422
44 -2.04295547 -5.65764617
45 -0.63841816 -2.04295547
46 -3.57266781 -0.63841816
47 -0.93854140 -3.57266781
48 1.64753272 -0.93854140
49 -1.68688580 1.64753272
50 0.44521169 -1.68688580
51 1.55541943 0.44521169
52 4.52261990 1.55541943
53 2.13286255 4.52261990
54 0.75704316 2.13286255
55 5.32330276 0.75704316
56 -5.48265364 5.32330276
57 -5.29538970 -5.48265364
58 -7.12744038 -5.29538970
59 -7.18403252 -7.12744038
60 -6.22722351 -7.18403252
61 -0.30731892 -6.22722351
62 0.24323942 -0.30731892
63 -1.82464283 0.24323942
64 -1.29587456 -1.82464283
65 1.07309858 -1.29587456
66 3.33355757 1.07309858
67 -1.66247007 3.33355757
68 4.00727180 -1.66247007
69 4.81642316 4.00727180
70 6.26917930 4.81642316
71 0.54372146 6.26917930
72 6.45131033 0.54372146
73 7.10657008 6.45131033
74 6.84561285 7.10657008
75 -4.86399703 6.84561285
76 -3.13668596 -4.86399703
77 -1.97364184 -3.13668596
78 -2.72909636 -1.97364184
79 -2.83900338 -2.72909636
80 -2.83383598 -2.83900338
81 -0.76778925 -2.83383598
82 -2.17430749 -0.76778925
83 2.76901210 -2.17430749
84 2.66275128 2.76901210
85 3.44175498 2.66275128
86 4.64629112 3.44175498
87 2.94163057 4.64629112
88 -4.03509159 2.94163057
89 -5.70360977 -4.03509159
90 -3.41421373 -5.70360977
91 -2.03885272 -3.41421373
92 -5.81306213 -2.03885272
93 -3.42018966 -5.81306213
94 -0.58061858 -3.42018966
95 1.11654483 -0.58061858
96 -2.63354038 1.11654483
97 1.65735272 -2.63354038
98 3.56003351 1.65735272
99 2.10643434 3.56003351
100 1.90608436 2.10643434
101 2.39610815 1.90608436
102 NA 2.39610815
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.87459789 -1.58608660
[2,] 0.41438652 -0.87459789
[3,] 0.97726059 0.41438652
[4,] -1.27766208 0.97726059
[5,] 0.19226658 -1.27766208
[6,] 2.30720594 0.19226658
[7,] 2.39827145 2.30720594
[8,] -5.85091974 2.39827145
[9,] -3.83202664 -5.85091974
[10,] -3.75133413 -3.83202664
[11,] 0.61717897 -3.75133413
[12,] -1.94340326 0.61717897
[13,] 0.49915227 -1.94340326
[14,] 3.90711498 0.49915227
[15,] 3.72168285 3.90711498
[16,] 2.99929006 3.72168285
[17,] 0.82938123 2.99929006
[18,] 6.76339886 0.82938123
[19,] 5.80048424 6.76339886
[20,] -1.53543615 5.80048424
[21,] -2.74195322 -1.53543615
[22,] -3.44262864 -2.74195322
[23,] -2.94393531 -3.44262864
[24,] 1.37368412 -2.94393531
[25,] 1.92237245 1.37368412
[26,] 0.07800605 1.92237245
[27,] 3.86806646 0.07800605
[28,] 1.34151733 3.86806646
[29,] -3.12855417 1.34151733
[30,] -3.50645583 -3.12855417
[31,] -2.68596011 -3.50645583
[32,] -2.10906685 -2.68596011
[33,] -2.59725883 -2.10906685
[34,] -3.64097552 -2.59725883
[35,] -0.61388554 -3.64097552
[36,] 4.11986469 -0.61388554
[37,] 0.02651334 4.11986469
[38,] 1.48387266 0.02651334
[39,] 3.47648917 1.48387266
[40,] 4.81712810 3.47648917
[41,] 2.09810943 4.81712810
[42,] 5.65316422 2.09810943
[43,] -5.65764617 5.65316422
[44,] -2.04295547 -5.65764617
[45,] -0.63841816 -2.04295547
[46,] -3.57266781 -0.63841816
[47,] -0.93854140 -3.57266781
[48,] 1.64753272 -0.93854140
[49,] -1.68688580 1.64753272
[50,] 0.44521169 -1.68688580
[51,] 1.55541943 0.44521169
[52,] 4.52261990 1.55541943
[53,] 2.13286255 4.52261990
[54,] 0.75704316 2.13286255
[55,] 5.32330276 0.75704316
[56,] -5.48265364 5.32330276
[57,] -5.29538970 -5.48265364
[58,] -7.12744038 -5.29538970
[59,] -7.18403252 -7.12744038
[60,] -6.22722351 -7.18403252
[61,] -0.30731892 -6.22722351
[62,] 0.24323942 -0.30731892
[63,] -1.82464283 0.24323942
[64,] -1.29587456 -1.82464283
[65,] 1.07309858 -1.29587456
[66,] 3.33355757 1.07309858
[67,] -1.66247007 3.33355757
[68,] 4.00727180 -1.66247007
[69,] 4.81642316 4.00727180
[70,] 6.26917930 4.81642316
[71,] 0.54372146 6.26917930
[72,] 6.45131033 0.54372146
[73,] 7.10657008 6.45131033
[74,] 6.84561285 7.10657008
[75,] -4.86399703 6.84561285
[76,] -3.13668596 -4.86399703
[77,] -1.97364184 -3.13668596
[78,] -2.72909636 -1.97364184
[79,] -2.83900338 -2.72909636
[80,] -2.83383598 -2.83900338
[81,] -0.76778925 -2.83383598
[82,] -2.17430749 -0.76778925
[83,] 2.76901210 -2.17430749
[84,] 2.66275128 2.76901210
[85,] 3.44175498 2.66275128
[86,] 4.64629112 3.44175498
[87,] 2.94163057 4.64629112
[88,] -4.03509159 2.94163057
[89,] -5.70360977 -4.03509159
[90,] -3.41421373 -5.70360977
[91,] -2.03885272 -3.41421373
[92,] -5.81306213 -2.03885272
[93,] -3.42018966 -5.81306213
[94,] -0.58061858 -3.42018966
[95,] 1.11654483 -0.58061858
[96,] -2.63354038 1.11654483
[97,] 1.65735272 -2.63354038
[98,] 3.56003351 1.65735272
[99,] 2.10643434 3.56003351
[100,] 1.90608436 2.10643434
[101,] 2.39610815 1.90608436
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.87459789 -1.58608660
2 0.41438652 -0.87459789
3 0.97726059 0.41438652
4 -1.27766208 0.97726059
5 0.19226658 -1.27766208
6 2.30720594 0.19226658
7 2.39827145 2.30720594
8 -5.85091974 2.39827145
9 -3.83202664 -5.85091974
10 -3.75133413 -3.83202664
11 0.61717897 -3.75133413
12 -1.94340326 0.61717897
13 0.49915227 -1.94340326
14 3.90711498 0.49915227
15 3.72168285 3.90711498
16 2.99929006 3.72168285
17 0.82938123 2.99929006
18 6.76339886 0.82938123
19 5.80048424 6.76339886
20 -1.53543615 5.80048424
21 -2.74195322 -1.53543615
22 -3.44262864 -2.74195322
23 -2.94393531 -3.44262864
24 1.37368412 -2.94393531
25 1.92237245 1.37368412
26 0.07800605 1.92237245
27 3.86806646 0.07800605
28 1.34151733 3.86806646
29 -3.12855417 1.34151733
30 -3.50645583 -3.12855417
31 -2.68596011 -3.50645583
32 -2.10906685 -2.68596011
33 -2.59725883 -2.10906685
34 -3.64097552 -2.59725883
35 -0.61388554 -3.64097552
36 4.11986469 -0.61388554
37 0.02651334 4.11986469
38 1.48387266 0.02651334
39 3.47648917 1.48387266
40 4.81712810 3.47648917
41 2.09810943 4.81712810
42 5.65316422 2.09810943
43 -5.65764617 5.65316422
44 -2.04295547 -5.65764617
45 -0.63841816 -2.04295547
46 -3.57266781 -0.63841816
47 -0.93854140 -3.57266781
48 1.64753272 -0.93854140
49 -1.68688580 1.64753272
50 0.44521169 -1.68688580
51 1.55541943 0.44521169
52 4.52261990 1.55541943
53 2.13286255 4.52261990
54 0.75704316 2.13286255
55 5.32330276 0.75704316
56 -5.48265364 5.32330276
57 -5.29538970 -5.48265364
58 -7.12744038 -5.29538970
59 -7.18403252 -7.12744038
60 -6.22722351 -7.18403252
61 -0.30731892 -6.22722351
62 0.24323942 -0.30731892
63 -1.82464283 0.24323942
64 -1.29587456 -1.82464283
65 1.07309858 -1.29587456
66 3.33355757 1.07309858
67 -1.66247007 3.33355757
68 4.00727180 -1.66247007
69 4.81642316 4.00727180
70 6.26917930 4.81642316
71 0.54372146 6.26917930
72 6.45131033 0.54372146
73 7.10657008 6.45131033
74 6.84561285 7.10657008
75 -4.86399703 6.84561285
76 -3.13668596 -4.86399703
77 -1.97364184 -3.13668596
78 -2.72909636 -1.97364184
79 -2.83900338 -2.72909636
80 -2.83383598 -2.83900338
81 -0.76778925 -2.83383598
82 -2.17430749 -0.76778925
83 2.76901210 -2.17430749
84 2.66275128 2.76901210
85 3.44175498 2.66275128
86 4.64629112 3.44175498
87 2.94163057 4.64629112
88 -4.03509159 2.94163057
89 -5.70360977 -4.03509159
90 -3.41421373 -5.70360977
91 -2.03885272 -3.41421373
92 -5.81306213 -2.03885272
93 -3.42018966 -5.81306213
94 -0.58061858 -3.42018966
95 1.11654483 -0.58061858
96 -2.63354038 1.11654483
97 1.65735272 -2.63354038
98 3.56003351 1.65735272
99 2.10643434 3.56003351
100 1.90608436 2.10643434
101 2.39610815 1.90608436
> 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/rcomp/tmp/7uiat1322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8t1h81322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/973x71322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10fe5i1322129659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11irx61322129659.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/rcomp/tmp/12lc9c1322129660.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/rcomp/tmp/137k1z1322129660.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/rcomp/tmp/14ipjc1322129660.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/rcomp/tmp/157t611322129660.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/rcomp/tmp/16vna31322129660.tab")
+ }
>
> try(system("convert tmp/1f6mo1322129659.ps tmp/1f6mo1322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/20cgx1322129659.ps tmp/20cgx1322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/334le1322129659.ps tmp/334le1322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nuf21322129659.ps tmp/4nuf21322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qubp1322129659.ps tmp/5qubp1322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rxnk1322129659.ps tmp/6rxnk1322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uiat1322129659.ps tmp/7uiat1322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t1h81322129659.ps tmp/8t1h81322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/973x71322129659.ps tmp/973x71322129659.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fe5i1322129659.ps tmp/10fe5i1322129659.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.976 0.744 5.742