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(95.1,136,97,133,112.7,126,102.9,120,97.4,114,111.4,116,87.4,153,96.8,162,114.1,161,110.3,149,103.9,139,101.6,135,94.6,130,95.9,127,104.7,122,102.8,117,98.1,112,113.9,113,80.9,149,95.7,157,113.2,157,105.9,147,108.8,137,102.3,132,99,125,100.7,123,115.5,117,100.7,114,109.9,111,114.6,112,85.4,144,100.5,150,114.8,149,116.5,134,112.9,123,102,116,106,117,105.3,111,118.8,105,106.1,102,109.3,95,117.2,93,92.5,124,104.2,130,112.5,124,122.4,115,113.3,106,100,105,110.7,105,112.8,101,109.8,95,117.3,93,109.1,84,115.9,87,96,116,99.8,120,116.8,117,115.7,109,99.4,105,94.3,107,91,109),dim=c(2,61),dimnames=list(c('tip','wrk'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('tip','wrk'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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
tip wrk M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 95.1 136 1 0 0 0 0 0 0 0 0 0 0
2 97.0 133 0 1 0 0 0 0 0 0 0 0 0
3 112.7 126 0 0 1 0 0 0 0 0 0 0 0
4 102.9 120 0 0 0 1 0 0 0 0 0 0 0
5 97.4 114 0 0 0 0 1 0 0 0 0 0 0
6 111.4 116 0 0 0 0 0 1 0 0 0 0 0
7 87.4 153 0 0 0 0 0 0 1 0 0 0 0
8 96.8 162 0 0 0 0 0 0 0 1 0 0 0
9 114.1 161 0 0 0 0 0 0 0 0 1 0 0
10 110.3 149 0 0 0 0 0 0 0 0 0 1 0
11 103.9 139 0 0 0 0 0 0 0 0 0 0 1
12 101.6 135 0 0 0 0 0 0 0 0 0 0 0
13 94.6 130 1 0 0 0 0 0 0 0 0 0 0
14 95.9 127 0 1 0 0 0 0 0 0 0 0 0
15 104.7 122 0 0 1 0 0 0 0 0 0 0 0
16 102.8 117 0 0 0 1 0 0 0 0 0 0 0
17 98.1 112 0 0 0 0 1 0 0 0 0 0 0
18 113.9 113 0 0 0 0 0 1 0 0 0 0 0
19 80.9 149 0 0 0 0 0 0 1 0 0 0 0
20 95.7 157 0 0 0 0 0 0 0 1 0 0 0
21 113.2 157 0 0 0 0 0 0 0 0 1 0 0
22 105.9 147 0 0 0 0 0 0 0 0 0 1 0
23 108.8 137 0 0 0 0 0 0 0 0 0 0 1
24 102.3 132 0 0 0 0 0 0 0 0 0 0 0
25 99.0 125 1 0 0 0 0 0 0 0 0 0 0
26 100.7 123 0 1 0 0 0 0 0 0 0 0 0
27 115.5 117 0 0 1 0 0 0 0 0 0 0 0
28 100.7 114 0 0 0 1 0 0 0 0 0 0 0
29 109.9 111 0 0 0 0 1 0 0 0 0 0 0
30 114.6 112 0 0 0 0 0 1 0 0 0 0 0
31 85.4 144 0 0 0 0 0 0 1 0 0 0 0
32 100.5 150 0 0 0 0 0 0 0 1 0 0 0
33 114.8 149 0 0 0 0 0 0 0 0 1 0 0
34 116.5 134 0 0 0 0 0 0 0 0 0 1 0
35 112.9 123 0 0 0 0 0 0 0 0 0 0 1
36 102.0 116 0 0 0 0 0 0 0 0 0 0 0
37 106.0 117 1 0 0 0 0 0 0 0 0 0 0
38 105.3 111 0 1 0 0 0 0 0 0 0 0 0
39 118.8 105 0 0 1 0 0 0 0 0 0 0 0
40 106.1 102 0 0 0 1 0 0 0 0 0 0 0
41 109.3 95 0 0 0 0 1 0 0 0 0 0 0
42 117.2 93 0 0 0 0 0 1 0 0 0 0 0
43 92.5 124 0 0 0 0 0 0 1 0 0 0 0
44 104.2 130 0 0 0 0 0 0 0 1 0 0 0
45 112.5 124 0 0 0 0 0 0 0 0 1 0 0
46 122.4 115 0 0 0 0 0 0 0 0 0 1 0
47 113.3 106 0 0 0 0 0 0 0 0 0 0 1
48 100.0 105 0 0 0 0 0 0 0 0 0 0 0
49 110.7 105 1 0 0 0 0 0 0 0 0 0 0
50 112.8 101 0 1 0 0 0 0 0 0 0 0 0
51 109.8 95 0 0 1 0 0 0 0 0 0 0 0
52 117.3 93 0 0 0 1 0 0 0 0 0 0 0
53 109.1 84 0 0 0 0 1 0 0 0 0 0 0
54 115.9 87 0 0 0 0 0 1 0 0 0 0 0
55 96.0 116 0 0 0 0 0 0 1 0 0 0 0
56 99.8 120 0 0 0 0 0 0 0 1 0 0 0
57 116.8 117 0 0 0 0 0 0 0 0 1 0 0
58 115.7 109 0 0 0 0 0 0 0 0 0 1 0
59 99.4 105 0 0 0 0 0 0 0 0 0 0 1
60 94.3 107 0 0 0 0 0 0 0 0 0 0 0
61 91.0 109 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) wrk M1 M2 M3 M4
121.3948 -0.1795 -0.4007 2.3000 11.1833 4.1614
M5 M6 M7 M8 M9 M10
1.8847 11.9041 -8.3340 3.8104 18.2956 16.2375
M11
8.1584
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.3107 -2.5523 0.5901 2.9142 8.5484
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 121.39482 5.92024 20.505 < 2e-16 ***
wrk -0.17945 0.04627 -3.879 0.000319 ***
M1 -0.40073 2.94741 -0.136 0.892421
M2 2.30000 3.07779 0.747 0.458535
M3 11.18329 3.09029 3.619 0.000710 ***
M4 4.16137 3.11101 1.338 0.187322
M5 1.88465 3.16342 0.596 0.554131
M6 11.90411 3.15305 3.775 0.000440 ***
M7 -8.33397 3.19090 -2.612 0.011986 *
M8 3.81042 3.28472 1.160 0.251769
M9 18.29562 3.25056 5.628 9.22e-07 ***
M10 16.23754 3.12584 5.195 4.14e-06 ***
M11 8.15836 3.08092 2.648 0.010923 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.866 on 48 degrees of freedom
Multiple R-squared: 0.7719, Adjusted R-squared: 0.7149
F-statistic: 13.53 on 12 and 48 DF, p-value: 1.362e-11
> 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,] 0.1558077422 0.3116154844 0.8441923
[2,] 0.0768466260 0.1536932519 0.9231534
[3,] 0.0474884736 0.0949769471 0.9525115
[4,] 0.0477199698 0.0954399396 0.9522800
[5,] 0.0219261381 0.0438522763 0.9780739
[6,] 0.0090807352 0.0181614704 0.9909193
[7,] 0.0078687700 0.0157375401 0.9921312
[8,] 0.0095581292 0.0191162584 0.9904419
[9,] 0.0051398591 0.0102797183 0.9948601
[10,] 0.0076127015 0.0152254030 0.9923873
[11,] 0.0075653231 0.0151306461 0.9924347
[12,] 0.0093121472 0.0186242944 0.9906879
[13,] 0.0092491813 0.0184983626 0.9907508
[14,] 0.0526308169 0.1052616339 0.9473692
[15,] 0.0315075174 0.0630150348 0.9684925
[16,] 0.0255704308 0.0511408616 0.9744296
[17,] 0.0149995939 0.0299991878 0.9850004
[18,] 0.0081272411 0.0162544822 0.9918728
[19,] 0.0061144886 0.0122289772 0.9938855
[20,] 0.0037862582 0.0075725163 0.9962137
[21,] 0.0035809188 0.0071618376 0.9964191
[22,] 0.0040277806 0.0080555612 0.9959722
[23,] 0.0025094536 0.0050189071 0.9974905
[24,] 0.0027891725 0.0055783450 0.9972108
[25,] 0.0027148640 0.0054297281 0.9972851
[26,] 0.0011893771 0.0023787543 0.9988106
[27,] 0.0005913908 0.0011827816 0.9994086
[28,] 0.0002181016 0.0004362033 0.9997819
[29,] 0.0001420874 0.0002841748 0.9998579
[30,] 0.0002300496 0.0004600993 0.9997700
> postscript(file="/var/www/html/rcomp/tmp/15vjs1260971632.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2t06m1260971632.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32all1260971632.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wp421260971632.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xyij1260971632.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 = 61
Frequency = 1
1 2 3 4 5 6
-1.4885815 -2.8276686 2.7328792 -1.1219158 -5.4219158 -1.0824635
7 8 9 10 11 12
1.7953455 0.6660309 3.3013735 -0.5939691 -0.7093118 4.4312359
13 14 15 16 17 18
-3.0652950 -5.0043820 -5.9849298 -1.7602725 -5.0808202 0.8791798
19 20 21 22 23 24
-5.4224635 -1.3312304 1.6835646 -5.3528736 3.8317837 4.5928792
25 26 27 28 29 30
0.4374438 -0.9221910 3.9178090 -4.3986292 6.5397275 1.3997275
31 32 33 34 35 36
-1.8197247 2.2126039 1.8479466 2.9142472 5.4194522 1.4216433
37 38 39 40 41 42
6.0018259 1.5243820 5.0643820 -1.1520562 3.0684916 0.5901349
43 44 45 46 47 48
1.6912304 2.3235590 -4.9383595 5.4046545 2.7687641 -2.5523314
49 50 51 52 53 54
8.5483989 7.2298596 -5.7301404 8.4328736 0.8945169 -1.7865786
55 56 57 58 59 60
3.7556124 -3.8709634 -1.8945252 -2.3720589 -11.3106882 -7.8934269
61
-10.4337921
> postscript(file="/var/www/html/rcomp/tmp/61cfr1260971632.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.4885815 NA
1 -2.8276686 -1.4885815
2 2.7328792 -2.8276686
3 -1.1219158 2.7328792
4 -5.4219158 -1.1219158
5 -1.0824635 -5.4219158
6 1.7953455 -1.0824635
7 0.6660309 1.7953455
8 3.3013735 0.6660309
9 -0.5939691 3.3013735
10 -0.7093118 -0.5939691
11 4.4312359 -0.7093118
12 -3.0652950 4.4312359
13 -5.0043820 -3.0652950
14 -5.9849298 -5.0043820
15 -1.7602725 -5.9849298
16 -5.0808202 -1.7602725
17 0.8791798 -5.0808202
18 -5.4224635 0.8791798
19 -1.3312304 -5.4224635
20 1.6835646 -1.3312304
21 -5.3528736 1.6835646
22 3.8317837 -5.3528736
23 4.5928792 3.8317837
24 0.4374438 4.5928792
25 -0.9221910 0.4374438
26 3.9178090 -0.9221910
27 -4.3986292 3.9178090
28 6.5397275 -4.3986292
29 1.3997275 6.5397275
30 -1.8197247 1.3997275
31 2.2126039 -1.8197247
32 1.8479466 2.2126039
33 2.9142472 1.8479466
34 5.4194522 2.9142472
35 1.4216433 5.4194522
36 6.0018259 1.4216433
37 1.5243820 6.0018259
38 5.0643820 1.5243820
39 -1.1520562 5.0643820
40 3.0684916 -1.1520562
41 0.5901349 3.0684916
42 1.6912304 0.5901349
43 2.3235590 1.6912304
44 -4.9383595 2.3235590
45 5.4046545 -4.9383595
46 2.7687641 5.4046545
47 -2.5523314 2.7687641
48 8.5483989 -2.5523314
49 7.2298596 8.5483989
50 -5.7301404 7.2298596
51 8.4328736 -5.7301404
52 0.8945169 8.4328736
53 -1.7865786 0.8945169
54 3.7556124 -1.7865786
55 -3.8709634 3.7556124
56 -1.8945252 -3.8709634
57 -2.3720589 -1.8945252
58 -11.3106882 -2.3720589
59 -7.8934269 -11.3106882
60 -10.4337921 -7.8934269
61 NA -10.4337921
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.8276686 -1.4885815
[2,] 2.7328792 -2.8276686
[3,] -1.1219158 2.7328792
[4,] -5.4219158 -1.1219158
[5,] -1.0824635 -5.4219158
[6,] 1.7953455 -1.0824635
[7,] 0.6660309 1.7953455
[8,] 3.3013735 0.6660309
[9,] -0.5939691 3.3013735
[10,] -0.7093118 -0.5939691
[11,] 4.4312359 -0.7093118
[12,] -3.0652950 4.4312359
[13,] -5.0043820 -3.0652950
[14,] -5.9849298 -5.0043820
[15,] -1.7602725 -5.9849298
[16,] -5.0808202 -1.7602725
[17,] 0.8791798 -5.0808202
[18,] -5.4224635 0.8791798
[19,] -1.3312304 -5.4224635
[20,] 1.6835646 -1.3312304
[21,] -5.3528736 1.6835646
[22,] 3.8317837 -5.3528736
[23,] 4.5928792 3.8317837
[24,] 0.4374438 4.5928792
[25,] -0.9221910 0.4374438
[26,] 3.9178090 -0.9221910
[27,] -4.3986292 3.9178090
[28,] 6.5397275 -4.3986292
[29,] 1.3997275 6.5397275
[30,] -1.8197247 1.3997275
[31,] 2.2126039 -1.8197247
[32,] 1.8479466 2.2126039
[33,] 2.9142472 1.8479466
[34,] 5.4194522 2.9142472
[35,] 1.4216433 5.4194522
[36,] 6.0018259 1.4216433
[37,] 1.5243820 6.0018259
[38,] 5.0643820 1.5243820
[39,] -1.1520562 5.0643820
[40,] 3.0684916 -1.1520562
[41,] 0.5901349 3.0684916
[42,] 1.6912304 0.5901349
[43,] 2.3235590 1.6912304
[44,] -4.9383595 2.3235590
[45,] 5.4046545 -4.9383595
[46,] 2.7687641 5.4046545
[47,] -2.5523314 2.7687641
[48,] 8.5483989 -2.5523314
[49,] 7.2298596 8.5483989
[50,] -5.7301404 7.2298596
[51,] 8.4328736 -5.7301404
[52,] 0.8945169 8.4328736
[53,] -1.7865786 0.8945169
[54,] 3.7556124 -1.7865786
[55,] -3.8709634 3.7556124
[56,] -1.8945252 -3.8709634
[57,] -2.3720589 -1.8945252
[58,] -11.3106882 -2.3720589
[59,] -7.8934269 -11.3106882
[60,] -10.4337921 -7.8934269
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.8276686 -1.4885815
2 2.7328792 -2.8276686
3 -1.1219158 2.7328792
4 -5.4219158 -1.1219158
5 -1.0824635 -5.4219158
6 1.7953455 -1.0824635
7 0.6660309 1.7953455
8 3.3013735 0.6660309
9 -0.5939691 3.3013735
10 -0.7093118 -0.5939691
11 4.4312359 -0.7093118
12 -3.0652950 4.4312359
13 -5.0043820 -3.0652950
14 -5.9849298 -5.0043820
15 -1.7602725 -5.9849298
16 -5.0808202 -1.7602725
17 0.8791798 -5.0808202
18 -5.4224635 0.8791798
19 -1.3312304 -5.4224635
20 1.6835646 -1.3312304
21 -5.3528736 1.6835646
22 3.8317837 -5.3528736
23 4.5928792 3.8317837
24 0.4374438 4.5928792
25 -0.9221910 0.4374438
26 3.9178090 -0.9221910
27 -4.3986292 3.9178090
28 6.5397275 -4.3986292
29 1.3997275 6.5397275
30 -1.8197247 1.3997275
31 2.2126039 -1.8197247
32 1.8479466 2.2126039
33 2.9142472 1.8479466
34 5.4194522 2.9142472
35 1.4216433 5.4194522
36 6.0018259 1.4216433
37 1.5243820 6.0018259
38 5.0643820 1.5243820
39 -1.1520562 5.0643820
40 3.0684916 -1.1520562
41 0.5901349 3.0684916
42 1.6912304 0.5901349
43 2.3235590 1.6912304
44 -4.9383595 2.3235590
45 5.4046545 -4.9383595
46 2.7687641 5.4046545
47 -2.5523314 2.7687641
48 8.5483989 -2.5523314
49 7.2298596 8.5483989
50 -5.7301404 7.2298596
51 8.4328736 -5.7301404
52 0.8945169 8.4328736
53 -1.7865786 0.8945169
54 3.7556124 -1.7865786
55 -3.8709634 3.7556124
56 -1.8945252 -3.8709634
57 -2.3720589 -1.8945252
58 -11.3106882 -2.3720589
59 -7.8934269 -11.3106882
60 -10.4337921 -7.8934269
> 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/741851260971632.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8597p1260971632.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bn2e1260971632.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10khg91260971632.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qq0b1260971632.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/1238wn1260971632.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/1304od1260971632.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/14yuun1260971632.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/15i9t71260971632.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/16syts1260971632.tab")
+ }
> try(system("convert tmp/15vjs1260971632.ps tmp/15vjs1260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t06m1260971632.ps tmp/2t06m1260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/32all1260971632.ps tmp/32all1260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wp421260971632.ps tmp/4wp421260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xyij1260971632.ps tmp/5xyij1260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/61cfr1260971632.ps tmp/61cfr1260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/741851260971632.ps tmp/741851260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/8597p1260971632.ps tmp/8597p1260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bn2e1260971632.ps tmp/9bn2e1260971632.png",intern=TRUE))
character(0)
> try(system("convert tmp/10khg91260971632.ps tmp/10khg91260971632.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.395 1.557 2.991