R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(101.0,0,98.7,0,105.1,0,98.4,0,101.7,0,102.9,0,92.2,0,94.9,0,92.8,0,98.5,0,94.3,0,87.4,0,103.4,0,101.2,0,109.6,0,111.9,0,108.9,0,105.6,0,107.8,0,97.5,0,102.4,0,105.6,0,99.8,0,96.2,0,113.1,0,107.4,0,116.8,0,112.9,0,105.3,0,109.3,0,107.9,0,101.1,0,114.7,0,116.2,0,108.4,0,113.4,0,108.7,0,112.6,0,124.2,1,114.9,1,110.5,1,121.5,1,118.1,1,111.7,1,132.7,1,119.0,1,116.7,1,120.1,1,113.4,1,106.6,1,116.3,1,112.6,1,111.6,1,125.1,1,110.7,1,109.6,1,114.2,1,113.4,1,116.0,1,109.6,1,117.8,1,115.8,1,125.3,1,113.0,1,120.5,1,116.6,1,111.8,1,115.2,1,118.6,1,122.4,1,116.4,1,114.5,1,119.8,1,115.8,1,127.8,1,118.8,1,119.7,1,118.6,1,120.8,1,115.9,1,109.7,1,114.8,1,116.2,1,112.2,1),dim=c(2,84),dimnames=list(c('Y','DUM'),1:84))
> y <- array(NA,dim=c(2,84),dimnames=list(c('Y','DUM'),1:84))
> 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 DUM M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 101.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 98.7 0 0 1 0 0 0 0 0 0 0 0 0 2
3 105.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 98.4 0 0 0 0 1 0 0 0 0 0 0 0 4
5 101.7 0 0 0 0 0 1 0 0 0 0 0 0 5
6 102.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 92.2 0 0 0 0 0 0 0 1 0 0 0 0 7
8 94.9 0 0 0 0 0 0 0 0 1 0 0 0 8
9 92.8 0 0 0 0 0 0 0 0 0 1 0 0 9
10 98.5 0 0 0 0 0 0 0 0 0 0 1 0 10
11 94.3 0 0 0 0 0 0 0 0 0 0 0 1 11
12 87.4 0 0 0 0 0 0 0 0 0 0 0 0 12
13 103.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 101.2 0 0 1 0 0 0 0 0 0 0 0 0 14
15 109.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 111.9 0 0 0 0 1 0 0 0 0 0 0 0 16
17 108.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 105.6 0 0 0 0 0 0 1 0 0 0 0 0 18
19 107.8 0 0 0 0 0 0 0 1 0 0 0 0 19
20 97.5 0 0 0 0 0 0 0 0 1 0 0 0 20
21 102.4 0 0 0 0 0 0 0 0 0 1 0 0 21
22 105.6 0 0 0 0 0 0 0 0 0 0 1 0 22
23 99.8 0 0 0 0 0 0 0 0 0 0 0 1 23
24 96.2 0 0 0 0 0 0 0 0 0 0 0 0 24
25 113.1 0 1 0 0 0 0 0 0 0 0 0 0 25
26 107.4 0 0 1 0 0 0 0 0 0 0 0 0 26
27 116.8 0 0 0 1 0 0 0 0 0 0 0 0 27
28 112.9 0 0 0 0 1 0 0 0 0 0 0 0 28
29 105.3 0 0 0 0 0 1 0 0 0 0 0 0 29
30 109.3 0 0 0 0 0 0 1 0 0 0 0 0 30
31 107.9 0 0 0 0 0 0 0 1 0 0 0 0 31
32 101.1 0 0 0 0 0 0 0 0 1 0 0 0 32
33 114.7 0 0 0 0 0 0 0 0 0 1 0 0 33
34 116.2 0 0 0 0 0 0 0 0 0 0 1 0 34
35 108.4 0 0 0 0 0 0 0 0 0 0 0 1 35
36 113.4 0 0 0 0 0 0 0 0 0 0 0 0 36
37 108.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 112.6 0 0 1 0 0 0 0 0 0 0 0 0 38
39 124.2 1 0 0 1 0 0 0 0 0 0 0 0 39
40 114.9 1 0 0 0 1 0 0 0 0 0 0 0 40
41 110.5 1 0 0 0 0 1 0 0 0 0 0 0 41
42 121.5 1 0 0 0 0 0 1 0 0 0 0 0 42
43 118.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 111.7 1 0 0 0 0 0 0 0 1 0 0 0 44
45 132.7 1 0 0 0 0 0 0 0 0 1 0 0 45
46 119.0 1 0 0 0 0 0 0 0 0 0 1 0 46
47 116.7 1 0 0 0 0 0 0 0 0 0 0 1 47
48 120.1 1 0 0 0 0 0 0 0 0 0 0 0 48
49 113.4 1 1 0 0 0 0 0 0 0 0 0 0 49
50 106.6 1 0 1 0 0 0 0 0 0 0 0 0 50
51 116.3 1 0 0 1 0 0 0 0 0 0 0 0 51
52 112.6 1 0 0 0 1 0 0 0 0 0 0 0 52
53 111.6 1 0 0 0 0 1 0 0 0 0 0 0 53
54 125.1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 110.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 109.6 1 0 0 0 0 0 0 0 1 0 0 0 56
57 114.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 113.4 1 0 0 0 0 0 0 0 0 0 1 0 58
59 116.0 1 0 0 0 0 0 0 0 0 0 0 1 59
60 109.6 1 0 0 0 0 0 0 0 0 0 0 0 60
61 117.8 1 1 0 0 0 0 0 0 0 0 0 0 61
62 115.8 1 0 1 0 0 0 0 0 0 0 0 0 62
63 125.3 1 0 0 1 0 0 0 0 0 0 0 0 63
64 113.0 1 0 0 0 1 0 0 0 0 0 0 0 64
65 120.5 1 0 0 0 0 1 0 0 0 0 0 0 65
66 116.6 1 0 0 0 0 0 1 0 0 0 0 0 66
67 111.8 1 0 0 0 0 0 0 1 0 0 0 0 67
68 115.2 1 0 0 0 0 0 0 0 1 0 0 0 68
69 118.6 1 0 0 0 0 0 0 0 0 1 0 0 69
70 122.4 1 0 0 0 0 0 0 0 0 0 1 0 70
71 116.4 1 0 0 0 0 0 0 0 0 0 0 1 71
72 114.5 1 0 0 0 0 0 0 0 0 0 0 0 72
73 119.8 1 1 0 0 0 0 0 0 0 0 0 0 73
74 115.8 1 0 1 0 0 0 0 0 0 0 0 0 74
75 127.8 1 0 0 1 0 0 0 0 0 0 0 0 75
76 118.8 1 0 0 0 1 0 0 0 0 0 0 0 76
77 119.7 1 0 0 0 0 1 0 0 0 0 0 0 77
78 118.6 1 0 0 0 0 0 1 0 0 0 0 0 78
79 120.8 1 0 0 0 0 0 0 1 0 0 0 0 79
80 115.9 1 0 0 0 0 0 0 0 1 0 0 0 80
81 109.7 1 0 0 0 0 0 0 0 0 1 0 0 81
82 114.8 1 0 0 0 0 0 0 0 0 0 1 0 82
83 116.2 1 0 0 0 0 0 0 0 0 0 0 1 83
84 112.2 1 0 0 0 0 0 0 0 0 0 0 0 84
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) DUM M1 M2 M3 M4
96.0952 4.4333 6.0958 3.1798 11.9304 5.6571
M5 M6 M7 M8 M9 M10
4.8554 7.7250 3.2089 -0.3214 5.0911 5.5893
M11 t
2.2446 0.1875
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.1071 -2.9732 -0.5929 3.1786 18.6429
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 96.09524 2.45175 39.195 < 2e-16 ***
DUM 4.43333 2.37997 1.863 0.066692 .
M1 6.09583 2.90037 2.102 0.039176 *
M2 3.17976 2.89663 1.098 0.276077
M3 11.93036 2.91858 4.088 0.000115 ***
M4 5.65714 2.91155 1.943 0.056039 .
M5 4.85536 2.90534 1.671 0.099151 .
M6 7.72500 2.89995 2.664 0.009581 **
M7 3.20893 2.89538 1.108 0.271529
M8 -0.32143 2.89163 -0.111 0.911809
M9 5.09107 2.88872 1.762 0.082367 .
M10 5.58929 2.88663 1.936 0.056874 .
M11 2.24464 2.88538 0.778 0.439228
t 0.18750 0.04907 3.821 0.000285 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.397 on 70 degrees of freedom
Multiple R-squared: 0.6769, Adjusted R-squared: 0.6169
F-statistic: 11.28 on 13 and 70 DF, p-value: 1.485e-12
> 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,] 0.36773931 0.73547862 0.63226069
[2,] 0.24922359 0.49844717 0.75077641
[3,] 0.39406560 0.78813119 0.60593440
[4,] 0.32050291 0.64100582 0.67949709
[5,] 0.25898650 0.51797301 0.74101350
[6,] 0.17749032 0.35498065 0.82250968
[7,] 0.13278290 0.26556580 0.86721710
[8,] 0.12500577 0.25001154 0.87499423
[9,] 0.08003963 0.16007927 0.91996037
[10,] 0.05614838 0.11229677 0.94385162
[11,] 0.03291029 0.06582057 0.96708971
[12,] 0.02176549 0.04353098 0.97823451
[13,] 0.07087699 0.14175397 0.92912301
[14,] 0.05981132 0.11962264 0.94018868
[15,] 0.03836761 0.07673521 0.96163239
[16,] 0.03592437 0.07184875 0.96407563
[17,] 0.07329764 0.14659528 0.92670236
[18,] 0.07169535 0.14339069 0.92830465
[19,] 0.05493136 0.10986271 0.94506864
[20,] 0.15598250 0.31196501 0.84401750
[21,] 0.24983050 0.49966100 0.75016950
[22,] 0.19571785 0.39143571 0.80428215
[23,] 0.14753267 0.29506534 0.85246733
[24,] 0.12564137 0.25128275 0.87435863
[25,] 0.12277351 0.24554701 0.87722649
[26,] 0.11462688 0.22925376 0.88537312
[27,] 0.10160256 0.20320512 0.89839744
[28,] 0.07489541 0.14979081 0.92510459
[29,] 0.69817854 0.60364293 0.30182146
[30,] 0.66289613 0.67420774 0.33710387
[31,] 0.60941401 0.78117198 0.39058599
[32,] 0.80045474 0.39909052 0.19954526
[33,] 0.82770062 0.34459877 0.17229938
[34,] 0.91644159 0.16711681 0.08355841
[35,] 0.96137628 0.07724745 0.03862372
[36,] 0.95890051 0.08219899 0.04109949
[37,] 0.97021402 0.05957196 0.02978598
[38,] 0.98710432 0.02579136 0.01289568
[39,] 0.98540185 0.02919630 0.01459815
[40,] 0.98250898 0.03498204 0.01749102
[41,] 0.97473455 0.05053089 0.02526545
[42,] 0.97088779 0.05822442 0.02911221
[43,] 0.94863527 0.10272946 0.05136473
[44,] 0.92915893 0.14168214 0.07084107
[45,] 0.88843561 0.22312877 0.11156439
[46,] 0.82364802 0.35270396 0.17635198
[47,] 0.74667717 0.50664566 0.25332283
[48,] 0.74534522 0.50930957 0.25465478
[49,] 0.61996350 0.76007301 0.38003650
[50,] 0.51360902 0.97278196 0.48639098
[51,] 0.75585155 0.48829690 0.24414845
> postscript(file="/var/www/html/rcomp/tmp/12w5l1261321869.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mkx91261321869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3c9v71261321869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4atcz1261321869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5q3ee1261321869.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 = 84
Frequency = 1
1 2 3 4 5 6
-1.37857143 -0.95000000 -3.48809524 -4.10238095 -0.18809524 -2.04523810
7 8 9 10 11 12
-8.41666667 -2.37380952 -10.07380952 -5.05952381 -6.10238095 -10.94523810
13 14 15 16 17 18
-1.22857143 -0.70000000 -1.23809524 7.14761905 4.76190476 -1.59523810
19 20 21 22 23 24
4.93333333 -2.02380952 -2.72380952 -0.20952381 -2.85238095 -4.39523810
25 26 27 28 29 30
6.22142857 3.25000000 3.71190476 5.89761905 -1.08809524 -0.14523810
31 32 33 34 35 36
2.78333333 -0.67380952 7.32619048 8.14047619 3.49761905 10.55476190
37 38 39 40 41 42
-0.42857143 6.20000000 4.42857143 1.21428571 -2.57142857 5.37142857
43 44 45 46 47 48
6.30000000 3.24285714 18.64285714 4.25714286 5.11428571 10.57142857
49 50 51 52 53 54
-2.41190476 -6.48333333 -5.72142857 -3.33571429 -3.72142857 6.72142857
55 56 57 58 59 60
-3.35000000 -1.10714286 -2.10714286 -3.59285714 2.16428571 -2.17857143
61 62 63 64 65 66
-0.26190476 0.46666667 1.02857143 -5.18571429 2.92857143 -4.02857143
67 68 69 70 71 72
-4.50000000 2.24285714 0.04285714 3.15714286 0.31428571 0.47142857
73 74 75 76 77 78
-0.51190476 -1.78333333 1.27857143 -1.63571429 -0.12142857 -4.27857143
79 80 81 82 83 84
2.25000000 0.69285714 -11.10714286 -6.69285714 -2.13571429 -4.07857143
> postscript(file="/var/www/html/rcomp/tmp/6okzs1261321869.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.37857143 NA
1 -0.95000000 -1.37857143
2 -3.48809524 -0.95000000
3 -4.10238095 -3.48809524
4 -0.18809524 -4.10238095
5 -2.04523810 -0.18809524
6 -8.41666667 -2.04523810
7 -2.37380952 -8.41666667
8 -10.07380952 -2.37380952
9 -5.05952381 -10.07380952
10 -6.10238095 -5.05952381
11 -10.94523810 -6.10238095
12 -1.22857143 -10.94523810
13 -0.70000000 -1.22857143
14 -1.23809524 -0.70000000
15 7.14761905 -1.23809524
16 4.76190476 7.14761905
17 -1.59523810 4.76190476
18 4.93333333 -1.59523810
19 -2.02380952 4.93333333
20 -2.72380952 -2.02380952
21 -0.20952381 -2.72380952
22 -2.85238095 -0.20952381
23 -4.39523810 -2.85238095
24 6.22142857 -4.39523810
25 3.25000000 6.22142857
26 3.71190476 3.25000000
27 5.89761905 3.71190476
28 -1.08809524 5.89761905
29 -0.14523810 -1.08809524
30 2.78333333 -0.14523810
31 -0.67380952 2.78333333
32 7.32619048 -0.67380952
33 8.14047619 7.32619048
34 3.49761905 8.14047619
35 10.55476190 3.49761905
36 -0.42857143 10.55476190
37 6.20000000 -0.42857143
38 4.42857143 6.20000000
39 1.21428571 4.42857143
40 -2.57142857 1.21428571
41 5.37142857 -2.57142857
42 6.30000000 5.37142857
43 3.24285714 6.30000000
44 18.64285714 3.24285714
45 4.25714286 18.64285714
46 5.11428571 4.25714286
47 10.57142857 5.11428571
48 -2.41190476 10.57142857
49 -6.48333333 -2.41190476
50 -5.72142857 -6.48333333
51 -3.33571429 -5.72142857
52 -3.72142857 -3.33571429
53 6.72142857 -3.72142857
54 -3.35000000 6.72142857
55 -1.10714286 -3.35000000
56 -2.10714286 -1.10714286
57 -3.59285714 -2.10714286
58 2.16428571 -3.59285714
59 -2.17857143 2.16428571
60 -0.26190476 -2.17857143
61 0.46666667 -0.26190476
62 1.02857143 0.46666667
63 -5.18571429 1.02857143
64 2.92857143 -5.18571429
65 -4.02857143 2.92857143
66 -4.50000000 -4.02857143
67 2.24285714 -4.50000000
68 0.04285714 2.24285714
69 3.15714286 0.04285714
70 0.31428571 3.15714286
71 0.47142857 0.31428571
72 -0.51190476 0.47142857
73 -1.78333333 -0.51190476
74 1.27857143 -1.78333333
75 -1.63571429 1.27857143
76 -0.12142857 -1.63571429
77 -4.27857143 -0.12142857
78 2.25000000 -4.27857143
79 0.69285714 2.25000000
80 -11.10714286 0.69285714
81 -6.69285714 -11.10714286
82 -2.13571429 -6.69285714
83 -4.07857143 -2.13571429
84 NA -4.07857143
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.95000000 -1.37857143
[2,] -3.48809524 -0.95000000
[3,] -4.10238095 -3.48809524
[4,] -0.18809524 -4.10238095
[5,] -2.04523810 -0.18809524
[6,] -8.41666667 -2.04523810
[7,] -2.37380952 -8.41666667
[8,] -10.07380952 -2.37380952
[9,] -5.05952381 -10.07380952
[10,] -6.10238095 -5.05952381
[11,] -10.94523810 -6.10238095
[12,] -1.22857143 -10.94523810
[13,] -0.70000000 -1.22857143
[14,] -1.23809524 -0.70000000
[15,] 7.14761905 -1.23809524
[16,] 4.76190476 7.14761905
[17,] -1.59523810 4.76190476
[18,] 4.93333333 -1.59523810
[19,] -2.02380952 4.93333333
[20,] -2.72380952 -2.02380952
[21,] -0.20952381 -2.72380952
[22,] -2.85238095 -0.20952381
[23,] -4.39523810 -2.85238095
[24,] 6.22142857 -4.39523810
[25,] 3.25000000 6.22142857
[26,] 3.71190476 3.25000000
[27,] 5.89761905 3.71190476
[28,] -1.08809524 5.89761905
[29,] -0.14523810 -1.08809524
[30,] 2.78333333 -0.14523810
[31,] -0.67380952 2.78333333
[32,] 7.32619048 -0.67380952
[33,] 8.14047619 7.32619048
[34,] 3.49761905 8.14047619
[35,] 10.55476190 3.49761905
[36,] -0.42857143 10.55476190
[37,] 6.20000000 -0.42857143
[38,] 4.42857143 6.20000000
[39,] 1.21428571 4.42857143
[40,] -2.57142857 1.21428571
[41,] 5.37142857 -2.57142857
[42,] 6.30000000 5.37142857
[43,] 3.24285714 6.30000000
[44,] 18.64285714 3.24285714
[45,] 4.25714286 18.64285714
[46,] 5.11428571 4.25714286
[47,] 10.57142857 5.11428571
[48,] -2.41190476 10.57142857
[49,] -6.48333333 -2.41190476
[50,] -5.72142857 -6.48333333
[51,] -3.33571429 -5.72142857
[52,] -3.72142857 -3.33571429
[53,] 6.72142857 -3.72142857
[54,] -3.35000000 6.72142857
[55,] -1.10714286 -3.35000000
[56,] -2.10714286 -1.10714286
[57,] -3.59285714 -2.10714286
[58,] 2.16428571 -3.59285714
[59,] -2.17857143 2.16428571
[60,] -0.26190476 -2.17857143
[61,] 0.46666667 -0.26190476
[62,] 1.02857143 0.46666667
[63,] -5.18571429 1.02857143
[64,] 2.92857143 -5.18571429
[65,] -4.02857143 2.92857143
[66,] -4.50000000 -4.02857143
[67,] 2.24285714 -4.50000000
[68,] 0.04285714 2.24285714
[69,] 3.15714286 0.04285714
[70,] 0.31428571 3.15714286
[71,] 0.47142857 0.31428571
[72,] -0.51190476 0.47142857
[73,] -1.78333333 -0.51190476
[74,] 1.27857143 -1.78333333
[75,] -1.63571429 1.27857143
[76,] -0.12142857 -1.63571429
[77,] -4.27857143 -0.12142857
[78,] 2.25000000 -4.27857143
[79,] 0.69285714 2.25000000
[80,] -11.10714286 0.69285714
[81,] -6.69285714 -11.10714286
[82,] -2.13571429 -6.69285714
[83,] -4.07857143 -2.13571429
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.95000000 -1.37857143
2 -3.48809524 -0.95000000
3 -4.10238095 -3.48809524
4 -0.18809524 -4.10238095
5 -2.04523810 -0.18809524
6 -8.41666667 -2.04523810
7 -2.37380952 -8.41666667
8 -10.07380952 -2.37380952
9 -5.05952381 -10.07380952
10 -6.10238095 -5.05952381
11 -10.94523810 -6.10238095
12 -1.22857143 -10.94523810
13 -0.70000000 -1.22857143
14 -1.23809524 -0.70000000
15 7.14761905 -1.23809524
16 4.76190476 7.14761905
17 -1.59523810 4.76190476
18 4.93333333 -1.59523810
19 -2.02380952 4.93333333
20 -2.72380952 -2.02380952
21 -0.20952381 -2.72380952
22 -2.85238095 -0.20952381
23 -4.39523810 -2.85238095
24 6.22142857 -4.39523810
25 3.25000000 6.22142857
26 3.71190476 3.25000000
27 5.89761905 3.71190476
28 -1.08809524 5.89761905
29 -0.14523810 -1.08809524
30 2.78333333 -0.14523810
31 -0.67380952 2.78333333
32 7.32619048 -0.67380952
33 8.14047619 7.32619048
34 3.49761905 8.14047619
35 10.55476190 3.49761905
36 -0.42857143 10.55476190
37 6.20000000 -0.42857143
38 4.42857143 6.20000000
39 1.21428571 4.42857143
40 -2.57142857 1.21428571
41 5.37142857 -2.57142857
42 6.30000000 5.37142857
43 3.24285714 6.30000000
44 18.64285714 3.24285714
45 4.25714286 18.64285714
46 5.11428571 4.25714286
47 10.57142857 5.11428571
48 -2.41190476 10.57142857
49 -6.48333333 -2.41190476
50 -5.72142857 -6.48333333
51 -3.33571429 -5.72142857
52 -3.72142857 -3.33571429
53 6.72142857 -3.72142857
54 -3.35000000 6.72142857
55 -1.10714286 -3.35000000
56 -2.10714286 -1.10714286
57 -3.59285714 -2.10714286
58 2.16428571 -3.59285714
59 -2.17857143 2.16428571
60 -0.26190476 -2.17857143
61 0.46666667 -0.26190476
62 1.02857143 0.46666667
63 -5.18571429 1.02857143
64 2.92857143 -5.18571429
65 -4.02857143 2.92857143
66 -4.50000000 -4.02857143
67 2.24285714 -4.50000000
68 0.04285714 2.24285714
69 3.15714286 0.04285714
70 0.31428571 3.15714286
71 0.47142857 0.31428571
72 -0.51190476 0.47142857
73 -1.78333333 -0.51190476
74 1.27857143 -1.78333333
75 -1.63571429 1.27857143
76 -0.12142857 -1.63571429
77 -4.27857143 -0.12142857
78 2.25000000 -4.27857143
79 0.69285714 2.25000000
80 -11.10714286 0.69285714
81 -6.69285714 -11.10714286
82 -2.13571429 -6.69285714
83 -4.07857143 -2.13571429
> 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/7z7wp1261321869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8frlw1261321869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94zm71261321869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10astq1261321869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xjpj1261321869.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/12kg9g1261321869.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/13bxp51261321869.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/14iw1y1261321869.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/1512pm1261321869.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/1628051261321869.tab")
+ }
> try(system("convert tmp/12w5l1261321869.ps tmp/12w5l1261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mkx91261321869.ps tmp/2mkx91261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/3c9v71261321869.ps tmp/3c9v71261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/4atcz1261321869.ps tmp/4atcz1261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q3ee1261321869.ps tmp/5q3ee1261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/6okzs1261321869.ps tmp/6okzs1261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z7wp1261321869.ps tmp/7z7wp1261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/8frlw1261321869.ps tmp/8frlw1261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/94zm71261321869.ps tmp/94zm71261321869.png",intern=TRUE))
character(0)
> try(system("convert tmp/10astq1261321869.ps tmp/10astq1261321869.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.729 1.583 3.910