R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(395.3,0,395.1,0,403.5,0,403.3,0,405.7,0,406.7,0,407.2,0,412.4,0,415.9,0,414.0,0,411.8,0,409.9,0,412.4,0,415.9,0,416.3,0,417.2,0,421.8,0,421.4,0,415.1,0,412.4,0,411.8,0,408.8,0,404.5,0,402.5,0,409.4,0,410.7,0,413.4,0,415.2,0,417.7,0,417.8,0,417.9,0,418.4,0,418.2,0,416.6,0,418.9,0,421.0,0,423.5,0,432.3,0,432.3,0,428.6,0,426.7,0,427.3,0,428.5,0,437.0,0,442.0,0,444.9,0,441.4,0,440.3,0,447.1,0,455.3,0,478.6,0,486.5,0,487.8,0,485.9,0,483.8,0,488.4,0,494.0,0,493.6,0,487.3,0,482.1,0,484.2,0,496.8,0,501.1,0,499.8,0,495.5,0,498.1,0,503.8,0,516.2,0,526.1,0,527.1,0,525.1,0,528.9,0,540.1,0,549.0,0,556.0,0,568.9,0,589.1,0,590.3,0,603.3,0,638.8,0,643.0,0,656.7,0,656.1,0,654.1,0,659.9,0,662.1,0,669.2,0,673.1,0,678.3,0,677.4,0,678.5,0,672.4,0,665.3,0,667.9,0,672.1,0,662.5,0,682.3,0,692.1,0,702.7,0,721.4,0,733.2,0,747.7,0,737.6,0,729.3,0,706.1,0,674.3,0,659.0,0,645.7,0,646.1,0,633.0,1,622.3,1,628.2,1,637.3,1,639.6,1,638.5,1,650.5,1,655.4,1),dim=c(2,117),dimnames=list(c('Y','X'),1:117))
> y <- array(NA,dim=c(2,117),dimnames=list(c('Y','X'),1:117))
> 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
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 t
1 395.3 0 1
2 395.1 0 2
3 403.5 0 3
4 403.3 0 4
5 405.7 0 5
6 406.7 0 6
7 407.2 0 7
8 412.4 0 8
9 415.9 0 9
10 414.0 0 10
11 411.8 0 11
12 409.9 0 12
13 412.4 0 13
14 415.9 0 14
15 416.3 0 15
16 417.2 0 16
17 421.8 0 17
18 421.4 0 18
19 415.1 0 19
20 412.4 0 20
21 411.8 0 21
22 408.8 0 22
23 404.5 0 23
24 402.5 0 24
25 409.4 0 25
26 410.7 0 26
27 413.4 0 27
28 415.2 0 28
29 417.7 0 29
30 417.8 0 30
31 417.9 0 31
32 418.4 0 32
33 418.2 0 33
34 416.6 0 34
35 418.9 0 35
36 421.0 0 36
37 423.5 0 37
38 432.3 0 38
39 432.3 0 39
40 428.6 0 40
41 426.7 0 41
42 427.3 0 42
43 428.5 0 43
44 437.0 0 44
45 442.0 0 45
46 444.9 0 46
47 441.4 0 47
48 440.3 0 48
49 447.1 0 49
50 455.3 0 50
51 478.6 0 51
52 486.5 0 52
53 487.8 0 53
54 485.9 0 54
55 483.8 0 55
56 488.4 0 56
57 494.0 0 57
58 493.6 0 58
59 487.3 0 59
60 482.1 0 60
61 484.2 0 61
62 496.8 0 62
63 501.1 0 63
64 499.8 0 64
65 495.5 0 65
66 498.1 0 66
67 503.8 0 67
68 516.2 0 68
69 526.1 0 69
70 527.1 0 70
71 525.1 0 71
72 528.9 0 72
73 540.1 0 73
74 549.0 0 74
75 556.0 0 75
76 568.9 0 76
77 589.1 0 77
78 590.3 0 78
79 603.3 0 79
80 638.8 0 80
81 643.0 0 81
82 656.7 0 82
83 656.1 0 83
84 654.1 0 84
85 659.9 0 85
86 662.1 0 86
87 669.2 0 87
88 673.1 0 88
89 678.3 0 89
90 677.4 0 90
91 678.5 0 91
92 672.4 0 92
93 665.3 0 93
94 667.9 0 94
95 672.1 0 95
96 662.5 0 96
97 682.3 0 97
98 692.1 0 98
99 702.7 0 99
100 721.4 0 100
101 733.2 0 101
102 747.7 0 102
103 737.6 0 103
104 729.3 0 104
105 706.1 0 105
106 674.3 0 106
107 659.0 0 107
108 645.7 0 108
109 646.1 0 109
110 633.0 1 110
111 622.3 1 111
112 628.2 1 112
113 637.3 1 113
114 639.6 1 114
115 638.5 1 115
116 650.5 1 116
117 655.4 1 117
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X t
334.952 -71.068 3.297
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-54.458 -32.646 -6.284 35.384 76.448
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 334.9516 7.3390 45.640 < 2e-16 ***
X -71.0679 15.4982 -4.586 1.17e-05 ***
t 3.2971 0.1158 28.468 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 38.05 on 114 degrees of freedom
Multiple R-squared: 0.8861, Adjusted R-squared: 0.8841
F-statistic: 443.4 on 2 and 114 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,] 4.664964e-04 9.329928e-04 9.995335e-01
[2,] 4.672256e-05 9.344512e-05 9.999533e-01
[3,] 3.267757e-06 6.535514e-06 9.999967e-01
[4,] 2.650111e-07 5.300222e-07 9.999997e-01
[5,] 3.344390e-08 6.688780e-08 1.000000e+00
[6,] 2.469043e-08 4.938086e-08 1.000000e+00
[7,] 2.706360e-08 5.412719e-08 1.000000e+00
[8,] 6.446260e-09 1.289252e-08 1.000000e+00
[9,] 8.594733e-10 1.718947e-09 1.000000e+00
[10,] 1.254204e-10 2.508408e-10 1.000000e+00
[11,] 1.869946e-11 3.739892e-11 1.000000e+00
[12,] 2.775819e-12 5.551637e-12 1.000000e+00
[13,] 3.825499e-13 7.650998e-13 1.000000e+00
[14,] 5.944142e-13 1.188828e-12 1.000000e+00
[15,] 2.022262e-12 4.044525e-12 1.000000e+00
[16,] 3.574767e-12 7.149535e-12 1.000000e+00
[17,] 1.029498e-11 2.058995e-11 1.000000e+00
[18,] 5.342400e-11 1.068480e-10 1.000000e+00
[19,] 1.595243e-10 3.190486e-10 1.000000e+00
[20,] 6.633048e-11 1.326610e-10 1.000000e+00
[21,] 2.272213e-11 4.544426e-11 1.000000e+00
[22,] 6.538253e-12 1.307651e-11 1.000000e+00
[23,] 1.821904e-12 3.643808e-12 1.000000e+00
[24,] 5.377100e-13 1.075420e-12 1.000000e+00
[25,] 1.511337e-13 3.022674e-13 1.000000e+00
[26,] 4.053722e-14 8.107444e-14 1.000000e+00
[27,] 1.053749e-14 2.107498e-14 1.000000e+00
[28,] 2.585535e-15 5.171069e-15 1.000000e+00
[29,] 6.165099e-16 1.233020e-15 1.000000e+00
[30,] 1.388053e-16 2.776105e-16 1.000000e+00
[31,] 3.236646e-17 6.473292e-17 1.000000e+00
[32,] 8.777948e-18 1.755590e-17 1.000000e+00
[33,] 1.570744e-17 3.141488e-17 1.000000e+00
[34,] 1.484963e-17 2.969927e-17 1.000000e+00
[35,] 4.533825e-18 9.067650e-18 1.000000e+00
[36,] 9.971679e-19 1.994336e-18 1.000000e+00
[37,] 2.097456e-19 4.194913e-19 1.000000e+00
[38,] 4.410846e-20 8.821691e-20 1.000000e+00
[39,] 3.598267e-20 7.196534e-20 1.000000e+00
[40,] 7.966249e-20 1.593250e-19 1.000000e+00
[41,] 2.063108e-19 4.126215e-19 1.000000e+00
[42,] 1.123577e-19 2.247154e-19 1.000000e+00
[43,] 3.925910e-20 7.851819e-20 1.000000e+00
[44,] 4.047729e-20 8.095459e-20 1.000000e+00
[45,] 2.446625e-19 4.893249e-19 1.000000e+00
[46,] 2.608284e-15 5.216569e-15 1.000000e+00
[47,] 2.653410e-12 5.306820e-12 1.000000e+00
[48,] 1.391292e-10 2.782585e-10 1.000000e+00
[49,] 9.978910e-10 1.995782e-09 1.000000e+00
[50,] 2.388043e-09 4.776085e-09 1.000000e+00
[51,] 5.836239e-09 1.167248e-08 1.000000e+00
[52,] 1.590995e-08 3.181990e-08 1.000000e+00
[53,] 2.567010e-08 5.134020e-08 1.000000e+00
[54,] 1.991424e-08 3.982849e-08 1.000000e+00
[55,] 1.150282e-08 2.300564e-08 1.000000e+00
[56,] 6.904851e-09 1.380970e-08 1.000000e+00
[57,] 6.395803e-09 1.279161e-08 1.000000e+00
[58,] 6.497576e-09 1.299515e-08 1.000000e+00
[59,] 5.585441e-09 1.117088e-08 1.000000e+00
[60,] 4.472325e-09 8.944650e-09 1.000000e+00
[61,] 4.234377e-09 8.468754e-09 1.000000e+00
[62,] 5.056968e-09 1.011394e-08 1.000000e+00
[63,] 9.262544e-09 1.852509e-08 1.000000e+00
[64,] 2.526515e-08 5.053030e-08 1.000000e+00
[65,] 6.693192e-08 1.338638e-07 9.999999e-01
[66,] 1.942435e-07 3.884870e-07 9.999998e-01
[67,] 7.973435e-07 1.594687e-06 9.999992e-01
[68,] 4.769194e-06 9.538388e-06 9.999952e-01
[69,] 3.729083e-05 7.458167e-05 9.999627e-01
[70,] 3.405526e-04 6.811051e-04 9.996594e-01
[71,] 3.109085e-03 6.218169e-03 9.968909e-01
[72,] 2.210365e-02 4.420730e-02 9.778963e-01
[73,] 9.597198e-02 1.919440e-01 9.040280e-01
[74,] 2.741367e-01 5.482735e-01 7.258633e-01
[75,] 5.383210e-01 9.233581e-01 4.616790e-01
[76,] 7.213270e-01 5.573459e-01 2.786730e-01
[77,] 8.396213e-01 3.207575e-01 1.603787e-01
[78,] 8.937913e-01 2.124174e-01 1.062087e-01
[79,] 9.201624e-01 1.596753e-01 7.983764e-02
[80,] 9.354493e-01 1.291013e-01 6.455065e-02
[81,] 9.433786e-01 1.132427e-01 5.662137e-02
[82,] 9.473052e-01 1.053896e-01 5.269480e-02
[83,] 9.476657e-01 1.046685e-01 5.233427e-02
[84,] 9.454715e-01 1.090571e-01 5.452854e-02
[85,] 9.392255e-01 1.215489e-01 6.077446e-02
[86,] 9.294470e-01 1.411060e-01 7.055300e-02
[87,] 9.166385e-01 1.667229e-01 8.336147e-02
[88,] 9.086926e-01 1.826148e-01 9.130741e-02
[89,] 9.041228e-01 1.917543e-01 9.587716e-02
[90,] 9.027798e-01 1.944404e-01 9.722020e-02
[91,] 9.360210e-01 1.279579e-01 6.397896e-02
[92,] 9.476600e-01 1.046799e-01 5.233997e-02
[93,] 9.543834e-01 9.123324e-02 4.561662e-02
[94,] 9.535221e-01 9.295589e-02 4.647794e-02
[95,] 9.375867e-01 1.248265e-01 6.241326e-02
[96,] 9.180063e-01 1.639874e-01 8.199372e-02
[97,] 9.369994e-01 1.260013e-01 6.300064e-02
[98,] 9.650152e-01 6.996967e-02 3.498483e-02
[99,] 9.945543e-01 1.089147e-02 5.445737e-03
[100,] 9.998562e-01 2.875259e-04 1.437629e-04
[101,] 9.999864e-01 2.710773e-05 1.355386e-05
[102,] 9.999954e-01 9.226633e-06 4.613317e-06
[103,] 9.999681e-01 6.370496e-05 3.185248e-05
[104,] 9.997674e-01 4.651253e-04 2.325626e-04
[105,] 9.998772e-01 2.455365e-04 1.227683e-04
[106,] 9.985998e-01 2.800438e-03 1.400219e-03
> postscript(file="/var/www/html/rcomp/tmp/1xvbo1258470133.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d29s1258470133.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31wia1258470133.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4iw7f1258470133.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5h2zd1258470133.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 = 117
Frequency = 1
1 2 3 4 5 6
57.0513212 53.5542614 58.6572016 55.1601418 54.2630820 51.9660222
7 8 9 10 11 12
49.1689624 51.0719025 51.2748427 46.0777829 40.5807231 35.3836633
13 14 15 16 17 18
34.5866035 34.7895437 31.8924839 29.4954241 30.7983643 27.1013045
19 20 21 22 23 24
17.5042447 11.5071849 7.6101251 1.3130653 -6.2839945 -11.5810543
25 26 27 28 29 30
-7.9781141 -9.9751739 -10.5722337 -12.0692935 -12.8663533 -16.0634131
31 32 33 34 35 36
-19.2604730 -22.0575328 -25.5545926 -30.4516524 -31.4487122 -32.6457720
37 38 39 40 41 42
-33.4428318 -27.9398916 -31.2369514 -38.2340112 -43.4310710 -46.1281308
43 44 45 46 47 48
-48.2251906 -43.0222504 -41.3193102 -41.7163700 -48.5134298 -52.9104896
49 50 51 52 53 54
-49.4075494 -44.5046092 -24.5016690 -19.8987288 -21.8957886 -27.0928485
55 56 57 58 59 60
-32.4899083 -31.1869681 -28.8840279 -32.5810877 -42.1781475 -50.6752073
61 62 63 64 65 66
-51.8722671 -42.5693269 -41.5663867 -46.1634465 -53.7605063 -54.4575661
67 68 69 70 71 72
-52.0546259 -42.9516857 -36.3487455 -38.6458053 -43.9428651 -43.4399249
73 74 75 76 77 78
-35.5369847 -29.9340445 -26.2311043 -16.6281641 0.2747760 -1.8222838
79 80 81 82 83 84
7.8806564 40.0835966 40.9865368 51.3894770 47.4924172 42.1953574
85 86 87 88 89 90
44.6982976 43.6012378 47.4041780 48.0071182 49.9100584 45.7129986
91 92 93 94 95 96
43.5159388 34.1188790 23.7218192 23.0247594 23.9276996 11.0306398
97 98 99 100 101 102
27.5335800 34.0365202 41.3394604 56.7424005 65.2453407 76.4482809
103 104 105 106 107 108
63.0512211 51.4541613 24.9571015 -10.1399583 -28.7370181 -45.3340779
109 110 111 112 113 114
-48.2311377 6.4397093 -7.5573505 -4.9544103 0.8485299 -0.1485299
115 116 117
-4.5455897 4.1573505 5.7602907
> postscript(file="/var/www/html/rcomp/tmp/6yeof1258470133.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 = 117
Frequency = 1
lag(myerror, k = 1) myerror
0 57.0513212 NA
1 53.5542614 57.0513212
2 58.6572016 53.5542614
3 55.1601418 58.6572016
4 54.2630820 55.1601418
5 51.9660222 54.2630820
6 49.1689624 51.9660222
7 51.0719025 49.1689624
8 51.2748427 51.0719025
9 46.0777829 51.2748427
10 40.5807231 46.0777829
11 35.3836633 40.5807231
12 34.5866035 35.3836633
13 34.7895437 34.5866035
14 31.8924839 34.7895437
15 29.4954241 31.8924839
16 30.7983643 29.4954241
17 27.1013045 30.7983643
18 17.5042447 27.1013045
19 11.5071849 17.5042447
20 7.6101251 11.5071849
21 1.3130653 7.6101251
22 -6.2839945 1.3130653
23 -11.5810543 -6.2839945
24 -7.9781141 -11.5810543
25 -9.9751739 -7.9781141
26 -10.5722337 -9.9751739
27 -12.0692935 -10.5722337
28 -12.8663533 -12.0692935
29 -16.0634131 -12.8663533
30 -19.2604730 -16.0634131
31 -22.0575328 -19.2604730
32 -25.5545926 -22.0575328
33 -30.4516524 -25.5545926
34 -31.4487122 -30.4516524
35 -32.6457720 -31.4487122
36 -33.4428318 -32.6457720
37 -27.9398916 -33.4428318
38 -31.2369514 -27.9398916
39 -38.2340112 -31.2369514
40 -43.4310710 -38.2340112
41 -46.1281308 -43.4310710
42 -48.2251906 -46.1281308
43 -43.0222504 -48.2251906
44 -41.3193102 -43.0222504
45 -41.7163700 -41.3193102
46 -48.5134298 -41.7163700
47 -52.9104896 -48.5134298
48 -49.4075494 -52.9104896
49 -44.5046092 -49.4075494
50 -24.5016690 -44.5046092
51 -19.8987288 -24.5016690
52 -21.8957886 -19.8987288
53 -27.0928485 -21.8957886
54 -32.4899083 -27.0928485
55 -31.1869681 -32.4899083
56 -28.8840279 -31.1869681
57 -32.5810877 -28.8840279
58 -42.1781475 -32.5810877
59 -50.6752073 -42.1781475
60 -51.8722671 -50.6752073
61 -42.5693269 -51.8722671
62 -41.5663867 -42.5693269
63 -46.1634465 -41.5663867
64 -53.7605063 -46.1634465
65 -54.4575661 -53.7605063
66 -52.0546259 -54.4575661
67 -42.9516857 -52.0546259
68 -36.3487455 -42.9516857
69 -38.6458053 -36.3487455
70 -43.9428651 -38.6458053
71 -43.4399249 -43.9428651
72 -35.5369847 -43.4399249
73 -29.9340445 -35.5369847
74 -26.2311043 -29.9340445
75 -16.6281641 -26.2311043
76 0.2747760 -16.6281641
77 -1.8222838 0.2747760
78 7.8806564 -1.8222838
79 40.0835966 7.8806564
80 40.9865368 40.0835966
81 51.3894770 40.9865368
82 47.4924172 51.3894770
83 42.1953574 47.4924172
84 44.6982976 42.1953574
85 43.6012378 44.6982976
86 47.4041780 43.6012378
87 48.0071182 47.4041780
88 49.9100584 48.0071182
89 45.7129986 49.9100584
90 43.5159388 45.7129986
91 34.1188790 43.5159388
92 23.7218192 34.1188790
93 23.0247594 23.7218192
94 23.9276996 23.0247594
95 11.0306398 23.9276996
96 27.5335800 11.0306398
97 34.0365202 27.5335800
98 41.3394604 34.0365202
99 56.7424005 41.3394604
100 65.2453407 56.7424005
101 76.4482809 65.2453407
102 63.0512211 76.4482809
103 51.4541613 63.0512211
104 24.9571015 51.4541613
105 -10.1399583 24.9571015
106 -28.7370181 -10.1399583
107 -45.3340779 -28.7370181
108 -48.2311377 -45.3340779
109 6.4397093 -48.2311377
110 -7.5573505 6.4397093
111 -4.9544103 -7.5573505
112 0.8485299 -4.9544103
113 -0.1485299 0.8485299
114 -4.5455897 -0.1485299
115 4.1573505 -4.5455897
116 5.7602907 4.1573505
117 NA 5.7602907
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 53.5542614 57.0513212
[2,] 58.6572016 53.5542614
[3,] 55.1601418 58.6572016
[4,] 54.2630820 55.1601418
[5,] 51.9660222 54.2630820
[6,] 49.1689624 51.9660222
[7,] 51.0719025 49.1689624
[8,] 51.2748427 51.0719025
[9,] 46.0777829 51.2748427
[10,] 40.5807231 46.0777829
[11,] 35.3836633 40.5807231
[12,] 34.5866035 35.3836633
[13,] 34.7895437 34.5866035
[14,] 31.8924839 34.7895437
[15,] 29.4954241 31.8924839
[16,] 30.7983643 29.4954241
[17,] 27.1013045 30.7983643
[18,] 17.5042447 27.1013045
[19,] 11.5071849 17.5042447
[20,] 7.6101251 11.5071849
[21,] 1.3130653 7.6101251
[22,] -6.2839945 1.3130653
[23,] -11.5810543 -6.2839945
[24,] -7.9781141 -11.5810543
[25,] -9.9751739 -7.9781141
[26,] -10.5722337 -9.9751739
[27,] -12.0692935 -10.5722337
[28,] -12.8663533 -12.0692935
[29,] -16.0634131 -12.8663533
[30,] -19.2604730 -16.0634131
[31,] -22.0575328 -19.2604730
[32,] -25.5545926 -22.0575328
[33,] -30.4516524 -25.5545926
[34,] -31.4487122 -30.4516524
[35,] -32.6457720 -31.4487122
[36,] -33.4428318 -32.6457720
[37,] -27.9398916 -33.4428318
[38,] -31.2369514 -27.9398916
[39,] -38.2340112 -31.2369514
[40,] -43.4310710 -38.2340112
[41,] -46.1281308 -43.4310710
[42,] -48.2251906 -46.1281308
[43,] -43.0222504 -48.2251906
[44,] -41.3193102 -43.0222504
[45,] -41.7163700 -41.3193102
[46,] -48.5134298 -41.7163700
[47,] -52.9104896 -48.5134298
[48,] -49.4075494 -52.9104896
[49,] -44.5046092 -49.4075494
[50,] -24.5016690 -44.5046092
[51,] -19.8987288 -24.5016690
[52,] -21.8957886 -19.8987288
[53,] -27.0928485 -21.8957886
[54,] -32.4899083 -27.0928485
[55,] -31.1869681 -32.4899083
[56,] -28.8840279 -31.1869681
[57,] -32.5810877 -28.8840279
[58,] -42.1781475 -32.5810877
[59,] -50.6752073 -42.1781475
[60,] -51.8722671 -50.6752073
[61,] -42.5693269 -51.8722671
[62,] -41.5663867 -42.5693269
[63,] -46.1634465 -41.5663867
[64,] -53.7605063 -46.1634465
[65,] -54.4575661 -53.7605063
[66,] -52.0546259 -54.4575661
[67,] -42.9516857 -52.0546259
[68,] -36.3487455 -42.9516857
[69,] -38.6458053 -36.3487455
[70,] -43.9428651 -38.6458053
[71,] -43.4399249 -43.9428651
[72,] -35.5369847 -43.4399249
[73,] -29.9340445 -35.5369847
[74,] -26.2311043 -29.9340445
[75,] -16.6281641 -26.2311043
[76,] 0.2747760 -16.6281641
[77,] -1.8222838 0.2747760
[78,] 7.8806564 -1.8222838
[79,] 40.0835966 7.8806564
[80,] 40.9865368 40.0835966
[81,] 51.3894770 40.9865368
[82,] 47.4924172 51.3894770
[83,] 42.1953574 47.4924172
[84,] 44.6982976 42.1953574
[85,] 43.6012378 44.6982976
[86,] 47.4041780 43.6012378
[87,] 48.0071182 47.4041780
[88,] 49.9100584 48.0071182
[89,] 45.7129986 49.9100584
[90,] 43.5159388 45.7129986
[91,] 34.1188790 43.5159388
[92,] 23.7218192 34.1188790
[93,] 23.0247594 23.7218192
[94,] 23.9276996 23.0247594
[95,] 11.0306398 23.9276996
[96,] 27.5335800 11.0306398
[97,] 34.0365202 27.5335800
[98,] 41.3394604 34.0365202
[99,] 56.7424005 41.3394604
[100,] 65.2453407 56.7424005
[101,] 76.4482809 65.2453407
[102,] 63.0512211 76.4482809
[103,] 51.4541613 63.0512211
[104,] 24.9571015 51.4541613
[105,] -10.1399583 24.9571015
[106,] -28.7370181 -10.1399583
[107,] -45.3340779 -28.7370181
[108,] -48.2311377 -45.3340779
[109,] 6.4397093 -48.2311377
[110,] -7.5573505 6.4397093
[111,] -4.9544103 -7.5573505
[112,] 0.8485299 -4.9544103
[113,] -0.1485299 0.8485299
[114,] -4.5455897 -0.1485299
[115,] 4.1573505 -4.5455897
[116,] 5.7602907 4.1573505
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 53.5542614 57.0513212
2 58.6572016 53.5542614
3 55.1601418 58.6572016
4 54.2630820 55.1601418
5 51.9660222 54.2630820
6 49.1689624 51.9660222
7 51.0719025 49.1689624
8 51.2748427 51.0719025
9 46.0777829 51.2748427
10 40.5807231 46.0777829
11 35.3836633 40.5807231
12 34.5866035 35.3836633
13 34.7895437 34.5866035
14 31.8924839 34.7895437
15 29.4954241 31.8924839
16 30.7983643 29.4954241
17 27.1013045 30.7983643
18 17.5042447 27.1013045
19 11.5071849 17.5042447
20 7.6101251 11.5071849
21 1.3130653 7.6101251
22 -6.2839945 1.3130653
23 -11.5810543 -6.2839945
24 -7.9781141 -11.5810543
25 -9.9751739 -7.9781141
26 -10.5722337 -9.9751739
27 -12.0692935 -10.5722337
28 -12.8663533 -12.0692935
29 -16.0634131 -12.8663533
30 -19.2604730 -16.0634131
31 -22.0575328 -19.2604730
32 -25.5545926 -22.0575328
33 -30.4516524 -25.5545926
34 -31.4487122 -30.4516524
35 -32.6457720 -31.4487122
36 -33.4428318 -32.6457720
37 -27.9398916 -33.4428318
38 -31.2369514 -27.9398916
39 -38.2340112 -31.2369514
40 -43.4310710 -38.2340112
41 -46.1281308 -43.4310710
42 -48.2251906 -46.1281308
43 -43.0222504 -48.2251906
44 -41.3193102 -43.0222504
45 -41.7163700 -41.3193102
46 -48.5134298 -41.7163700
47 -52.9104896 -48.5134298
48 -49.4075494 -52.9104896
49 -44.5046092 -49.4075494
50 -24.5016690 -44.5046092
51 -19.8987288 -24.5016690
52 -21.8957886 -19.8987288
53 -27.0928485 -21.8957886
54 -32.4899083 -27.0928485
55 -31.1869681 -32.4899083
56 -28.8840279 -31.1869681
57 -32.5810877 -28.8840279
58 -42.1781475 -32.5810877
59 -50.6752073 -42.1781475
60 -51.8722671 -50.6752073
61 -42.5693269 -51.8722671
62 -41.5663867 -42.5693269
63 -46.1634465 -41.5663867
64 -53.7605063 -46.1634465
65 -54.4575661 -53.7605063
66 -52.0546259 -54.4575661
67 -42.9516857 -52.0546259
68 -36.3487455 -42.9516857
69 -38.6458053 -36.3487455
70 -43.9428651 -38.6458053
71 -43.4399249 -43.9428651
72 -35.5369847 -43.4399249
73 -29.9340445 -35.5369847
74 -26.2311043 -29.9340445
75 -16.6281641 -26.2311043
76 0.2747760 -16.6281641
77 -1.8222838 0.2747760
78 7.8806564 -1.8222838
79 40.0835966 7.8806564
80 40.9865368 40.0835966
81 51.3894770 40.9865368
82 47.4924172 51.3894770
83 42.1953574 47.4924172
84 44.6982976 42.1953574
85 43.6012378 44.6982976
86 47.4041780 43.6012378
87 48.0071182 47.4041780
88 49.9100584 48.0071182
89 45.7129986 49.9100584
90 43.5159388 45.7129986
91 34.1188790 43.5159388
92 23.7218192 34.1188790
93 23.0247594 23.7218192
94 23.9276996 23.0247594
95 11.0306398 23.9276996
96 27.5335800 11.0306398
97 34.0365202 27.5335800
98 41.3394604 34.0365202
99 56.7424005 41.3394604
100 65.2453407 56.7424005
101 76.4482809 65.2453407
102 63.0512211 76.4482809
103 51.4541613 63.0512211
104 24.9571015 51.4541613
105 -10.1399583 24.9571015
106 -28.7370181 -10.1399583
107 -45.3340779 -28.7370181
108 -48.2311377 -45.3340779
109 6.4397093 -48.2311377
110 -7.5573505 6.4397093
111 -4.9544103 -7.5573505
112 0.8485299 -4.9544103
113 -0.1485299 0.8485299
114 -4.5455897 -0.1485299
115 4.1573505 -4.5455897
116 5.7602907 4.1573505
> 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/79fpz1258470133.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8idho1258470133.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r6yx1258470133.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gb7u1258470133.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u54v1258470133.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/12o7181258470133.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/13zfg11258470133.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/148vqz1258470133.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/15fhir1258470133.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/16l5rv1258470133.tab")
+ }
>
> system("convert tmp/1xvbo1258470133.ps tmp/1xvbo1258470133.png")
> system("convert tmp/2d29s1258470133.ps tmp/2d29s1258470133.png")
> system("convert tmp/31wia1258470133.ps tmp/31wia1258470133.png")
> system("convert tmp/4iw7f1258470133.ps tmp/4iw7f1258470133.png")
> system("convert tmp/5h2zd1258470133.ps tmp/5h2zd1258470133.png")
> system("convert tmp/6yeof1258470133.ps tmp/6yeof1258470133.png")
> system("convert tmp/79fpz1258470133.ps tmp/79fpz1258470133.png")
> system("convert tmp/8idho1258470133.ps tmp/8idho1258470133.png")
> system("convert tmp/9r6yx1258470133.ps tmp/9r6yx1258470133.png")
> system("convert tmp/10gb7u1258470133.ps tmp/10gb7u1258470133.png")
>
>
> proc.time()
user system elapsed
3.179 1.667 5.193