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(216234,213587,209465,204045,200237,203666,241476,260307,243324,244460,233575,237217,235243,230354,227184,221678,217142,219452,256446,265845,248624,241114,229245,231805,219277,219313,212610,214771,211142,211457,240048,240636,230580,208795,197922,194596,194581,185686,178106,172608,167302,168053,202300,202388,182516,173476,166444,171297,169701,164182,161914,159612,151001,158114,186530,187069,174330,169362,166827,178037,186412,189226,191563,188906,186005,195309,223532,226899,214126),dim=c(1,69),dimnames=list(c('Werkl'),1:69))
> y <- array(NA,dim=c(1,69),dimnames=list(c('Werkl'),1:69))
> 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
Werkl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 216234 1 0 0 0 0 0 0 0 0 0 0
2 213587 0 1 0 0 0 0 0 0 0 0 0
3 209465 0 0 1 0 0 0 0 0 0 0 0
4 204045 0 0 0 1 0 0 0 0 0 0 0
5 200237 0 0 0 0 1 0 0 0 0 0 0
6 203666 0 0 0 0 0 1 0 0 0 0 0
7 241476 0 0 0 0 0 0 1 0 0 0 0
8 260307 0 0 0 0 0 0 0 1 0 0 0
9 243324 0 0 0 0 0 0 0 0 1 0 0
10 244460 0 0 0 0 0 0 0 0 0 1 0
11 233575 0 0 0 0 0 0 0 0 0 0 1
12 237217 0 0 0 0 0 0 0 0 0 0 0
13 235243 1 0 0 0 0 0 0 0 0 0 0
14 230354 0 1 0 0 0 0 0 0 0 0 0
15 227184 0 0 1 0 0 0 0 0 0 0 0
16 221678 0 0 0 1 0 0 0 0 0 0 0
17 217142 0 0 0 0 1 0 0 0 0 0 0
18 219452 0 0 0 0 0 1 0 0 0 0 0
19 256446 0 0 0 0 0 0 1 0 0 0 0
20 265845 0 0 0 0 0 0 0 1 0 0 0
21 248624 0 0 0 0 0 0 0 0 1 0 0
22 241114 0 0 0 0 0 0 0 0 0 1 0
23 229245 0 0 0 0 0 0 0 0 0 0 1
24 231805 0 0 0 0 0 0 0 0 0 0 0
25 219277 1 0 0 0 0 0 0 0 0 0 0
26 219313 0 1 0 0 0 0 0 0 0 0 0
27 212610 0 0 1 0 0 0 0 0 0 0 0
28 214771 0 0 0 1 0 0 0 0 0 0 0
29 211142 0 0 0 0 1 0 0 0 0 0 0
30 211457 0 0 0 0 0 1 0 0 0 0 0
31 240048 0 0 0 0 0 0 1 0 0 0 0
32 240636 0 0 0 0 0 0 0 1 0 0 0
33 230580 0 0 0 0 0 0 0 0 1 0 0
34 208795 0 0 0 0 0 0 0 0 0 1 0
35 197922 0 0 0 0 0 0 0 0 0 0 1
36 194596 0 0 0 0 0 0 0 0 0 0 0
37 194581 1 0 0 0 0 0 0 0 0 0 0
38 185686 0 1 0 0 0 0 0 0 0 0 0
39 178106 0 0 1 0 0 0 0 0 0 0 0
40 172608 0 0 0 1 0 0 0 0 0 0 0
41 167302 0 0 0 0 1 0 0 0 0 0 0
42 168053 0 0 0 0 0 1 0 0 0 0 0
43 202300 0 0 0 0 0 0 1 0 0 0 0
44 202388 0 0 0 0 0 0 0 1 0 0 0
45 182516 0 0 0 0 0 0 0 0 1 0 0
46 173476 0 0 0 0 0 0 0 0 0 1 0
47 166444 0 0 0 0 0 0 0 0 0 0 1
48 171297 0 0 0 0 0 0 0 0 0 0 0
49 169701 1 0 0 0 0 0 0 0 0 0 0
50 164182 0 1 0 0 0 0 0 0 0 0 0
51 161914 0 0 1 0 0 0 0 0 0 0 0
52 159612 0 0 0 1 0 0 0 0 0 0 0
53 151001 0 0 0 0 1 0 0 0 0 0 0
54 158114 0 0 0 0 0 1 0 0 0 0 0
55 186530 0 0 0 0 0 0 1 0 0 0 0
56 187069 0 0 0 0 0 0 0 1 0 0 0
57 174330 0 0 0 0 0 0 0 0 1 0 0
58 169362 0 0 0 0 0 0 0 0 0 1 0
59 166827 0 0 0 0 0 0 0 0 0 0 1
60 178037 0 0 0 0 0 0 0 0 0 0 0
61 186412 1 0 0 0 0 0 0 0 0 0 0
62 189226 0 1 0 0 0 0 0 0 0 0 0
63 191563 0 0 1 0 0 0 0 0 0 0 0
64 188906 0 0 0 1 0 0 0 0 0 0 0
65 186005 0 0 0 0 1 0 0 0 0 0 0
66 195309 0 0 0 0 0 1 0 0 0 0 0
67 223532 0 0 0 0 0 0 1 0 0 0 0
68 226899 0 0 0 0 0 0 0 1 0 0 0
69 214126 0 0 0 0 0 0 0 0 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
202590.4 984.3 -2199.1 -5783.4 -8987.1 -13785.6
M6 M7 M8 M9 M10 M11
-9915.2 22464.9 27933.6 12992.9 4851.0 -3787.8
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-43455 -24553 1354 22337 37019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 202590.4 12502.3 16.204 <2e-16 ***
M1 984.3 16928.2 0.058 0.954
M2 -2199.1 16928.2 -0.130 0.897
M3 -5783.4 16928.2 -0.342 0.734
M4 -8987.1 16928.2 -0.531 0.598
M5 -13785.6 16928.2 -0.814 0.419
M6 -9915.2 16928.2 -0.586 0.560
M7 22464.9 16928.2 1.327 0.190
M8 27933.6 16928.2 1.650 0.104
M9 12992.9 16928.2 0.768 0.446
M10 4851.0 17681.0 0.274 0.785
M11 -3787.8 17681.0 -0.214 0.831
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 27960 on 57 degrees of freedom
Multiple R-squared: 0.1987, Adjusted R-squared: 0.04407
F-statistic: 1.285 on 11 and 57 DF, p-value: 0.2567
> 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,] 0.1090192616 0.2180385233 0.8909807
[2,] 0.0675551524 0.1351103047 0.9324448
[3,] 0.0423156532 0.0846313064 0.9576843
[4,] 0.0258775438 0.0517550875 0.9741225
[5,] 0.0164839116 0.0329678232 0.9835161
[6,] 0.0088508433 0.0177016866 0.9911492
[7,] 0.0047456769 0.0094913538 0.9952543
[8,] 0.0028903818 0.0057807636 0.9971096
[9,] 0.0017993816 0.0035987632 0.9982006
[10,] 0.0012195412 0.0024390825 0.9987805
[11,] 0.0007163082 0.0014326163 0.9992837
[12,] 0.0004228453 0.0008456906 0.9995772
[13,] 0.0002620368 0.0005240736 0.9997380
[14,] 0.0001802463 0.0003604927 0.9998198
[15,] 0.0001490247 0.0002980495 0.9998510
[16,] 0.0001117728 0.0002235457 0.9998882
[17,] 0.0001214860 0.0002429719 0.9998785
[18,] 0.0006275651 0.0012551302 0.9993724
[19,] 0.0016577499 0.0033154998 0.9983423
[20,] 0.0203014104 0.0406028207 0.9796986
[21,] 0.0791467689 0.1582935378 0.9208532
[22,] 0.1986362085 0.3972724169 0.8013638
[23,] 0.2557347325 0.5114694650 0.7442653
[24,] 0.3311772735 0.6623545471 0.6688227
[25,] 0.4005720303 0.8011440607 0.5994280
[26,] 0.4643599641 0.9287199282 0.5356400
[27,] 0.5115953618 0.9768092763 0.4884046
[28,] 0.5520540695 0.8958918610 0.4479459
[29,] 0.5664493560 0.8671012881 0.4335506
[30,] 0.6062470146 0.7875059708 0.3937530
[31,] 0.6525117158 0.6949765684 0.3474883
[32,] 0.6600261008 0.6799477985 0.3399739
[33,] 0.6366220530 0.7267558941 0.3633779
[34,] 0.5935927608 0.8128144783 0.4064072
[35,] 0.5516872784 0.8966254432 0.4483127
[36,] 0.5261646660 0.9476706680 0.4738353
[37,] 0.5025203900 0.9949592200 0.4974796
[38,] 0.4641636879 0.9283273757 0.5358363
[39,] 0.4486218162 0.8972436323 0.5513782
[40,] 0.4274801957 0.8549603914 0.5725198
> postscript(file="/var/www/html/rcomp/tmp/1f26t1259924351.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29sm31259924351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ahrq1259924351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45akz1259924351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nrsb1259924351.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 = 69
Frequency = 1
1 2 3 4 5 6 7
12659.333 13195.667 12658.000 10441.667 11432.167 10990.833 16420.667
8 9 10 11 12 13 14
29783.000 27740.667 37018.600 34772.400 34626.600 31668.333 29962.667
15 16 17 18 19 20 21
30377.000 28074.667 28337.167 26776.833 31390.667 35321.000 33040.667
22 23 24 25 26 27 28
33672.600 30442.400 29214.600 15702.333 18921.667 15803.000 21167.667
29 30 31 32 33 34 35
22337.167 18781.833 14992.667 10112.000 14996.667 1353.600 -880.600
36 37 38 39 40 41 42
-7994.400 -8993.667 -14705.333 -18701.000 -20995.333 -21502.833 -24622.167
43 44 45 46 47 48 49
-22755.333 -28136.000 -33067.333 -33965.400 -32358.600 -31293.400 -33873.667
50 51 52 53 54 55 56
-36209.333 -34893.000 -33991.333 -37803.833 -34561.167 -38525.333 -43455.000
57 58 59 60 61 62 63
-41253.333 -38079.400 -31975.600 -24553.400 -17162.667 -11165.333 -5244.000
64 65 66 67 68 69
-4697.333 -2799.833 2633.833 -1523.333 -3625.000 -1457.333
> postscript(file="/var/www/html/rcomp/tmp/6fzts1259924351.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 = 69
Frequency = 1
lag(myerror, k = 1) myerror
0 12659.333 NA
1 13195.667 12659.333
2 12658.000 13195.667
3 10441.667 12658.000
4 11432.167 10441.667
5 10990.833 11432.167
6 16420.667 10990.833
7 29783.000 16420.667
8 27740.667 29783.000
9 37018.600 27740.667
10 34772.400 37018.600
11 34626.600 34772.400
12 31668.333 34626.600
13 29962.667 31668.333
14 30377.000 29962.667
15 28074.667 30377.000
16 28337.167 28074.667
17 26776.833 28337.167
18 31390.667 26776.833
19 35321.000 31390.667
20 33040.667 35321.000
21 33672.600 33040.667
22 30442.400 33672.600
23 29214.600 30442.400
24 15702.333 29214.600
25 18921.667 15702.333
26 15803.000 18921.667
27 21167.667 15803.000
28 22337.167 21167.667
29 18781.833 22337.167
30 14992.667 18781.833
31 10112.000 14992.667
32 14996.667 10112.000
33 1353.600 14996.667
34 -880.600 1353.600
35 -7994.400 -880.600
36 -8993.667 -7994.400
37 -14705.333 -8993.667
38 -18701.000 -14705.333
39 -20995.333 -18701.000
40 -21502.833 -20995.333
41 -24622.167 -21502.833
42 -22755.333 -24622.167
43 -28136.000 -22755.333
44 -33067.333 -28136.000
45 -33965.400 -33067.333
46 -32358.600 -33965.400
47 -31293.400 -32358.600
48 -33873.667 -31293.400
49 -36209.333 -33873.667
50 -34893.000 -36209.333
51 -33991.333 -34893.000
52 -37803.833 -33991.333
53 -34561.167 -37803.833
54 -38525.333 -34561.167
55 -43455.000 -38525.333
56 -41253.333 -43455.000
57 -38079.400 -41253.333
58 -31975.600 -38079.400
59 -24553.400 -31975.600
60 -17162.667 -24553.400
61 -11165.333 -17162.667
62 -5244.000 -11165.333
63 -4697.333 -5244.000
64 -2799.833 -4697.333
65 2633.833 -2799.833
66 -1523.333 2633.833
67 -3625.000 -1523.333
68 -1457.333 -3625.000
69 NA -1457.333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 13195.667 12659.333
[2,] 12658.000 13195.667
[3,] 10441.667 12658.000
[4,] 11432.167 10441.667
[5,] 10990.833 11432.167
[6,] 16420.667 10990.833
[7,] 29783.000 16420.667
[8,] 27740.667 29783.000
[9,] 37018.600 27740.667
[10,] 34772.400 37018.600
[11,] 34626.600 34772.400
[12,] 31668.333 34626.600
[13,] 29962.667 31668.333
[14,] 30377.000 29962.667
[15,] 28074.667 30377.000
[16,] 28337.167 28074.667
[17,] 26776.833 28337.167
[18,] 31390.667 26776.833
[19,] 35321.000 31390.667
[20,] 33040.667 35321.000
[21,] 33672.600 33040.667
[22,] 30442.400 33672.600
[23,] 29214.600 30442.400
[24,] 15702.333 29214.600
[25,] 18921.667 15702.333
[26,] 15803.000 18921.667
[27,] 21167.667 15803.000
[28,] 22337.167 21167.667
[29,] 18781.833 22337.167
[30,] 14992.667 18781.833
[31,] 10112.000 14992.667
[32,] 14996.667 10112.000
[33,] 1353.600 14996.667
[34,] -880.600 1353.600
[35,] -7994.400 -880.600
[36,] -8993.667 -7994.400
[37,] -14705.333 -8993.667
[38,] -18701.000 -14705.333
[39,] -20995.333 -18701.000
[40,] -21502.833 -20995.333
[41,] -24622.167 -21502.833
[42,] -22755.333 -24622.167
[43,] -28136.000 -22755.333
[44,] -33067.333 -28136.000
[45,] -33965.400 -33067.333
[46,] -32358.600 -33965.400
[47,] -31293.400 -32358.600
[48,] -33873.667 -31293.400
[49,] -36209.333 -33873.667
[50,] -34893.000 -36209.333
[51,] -33991.333 -34893.000
[52,] -37803.833 -33991.333
[53,] -34561.167 -37803.833
[54,] -38525.333 -34561.167
[55,] -43455.000 -38525.333
[56,] -41253.333 -43455.000
[57,] -38079.400 -41253.333
[58,] -31975.600 -38079.400
[59,] -24553.400 -31975.600
[60,] -17162.667 -24553.400
[61,] -11165.333 -17162.667
[62,] -5244.000 -11165.333
[63,] -4697.333 -5244.000
[64,] -2799.833 -4697.333
[65,] 2633.833 -2799.833
[66,] -1523.333 2633.833
[67,] -3625.000 -1523.333
[68,] -1457.333 -3625.000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 13195.667 12659.333
2 12658.000 13195.667
3 10441.667 12658.000
4 11432.167 10441.667
5 10990.833 11432.167
6 16420.667 10990.833
7 29783.000 16420.667
8 27740.667 29783.000
9 37018.600 27740.667
10 34772.400 37018.600
11 34626.600 34772.400
12 31668.333 34626.600
13 29962.667 31668.333
14 30377.000 29962.667
15 28074.667 30377.000
16 28337.167 28074.667
17 26776.833 28337.167
18 31390.667 26776.833
19 35321.000 31390.667
20 33040.667 35321.000
21 33672.600 33040.667
22 30442.400 33672.600
23 29214.600 30442.400
24 15702.333 29214.600
25 18921.667 15702.333
26 15803.000 18921.667
27 21167.667 15803.000
28 22337.167 21167.667
29 18781.833 22337.167
30 14992.667 18781.833
31 10112.000 14992.667
32 14996.667 10112.000
33 1353.600 14996.667
34 -880.600 1353.600
35 -7994.400 -880.600
36 -8993.667 -7994.400
37 -14705.333 -8993.667
38 -18701.000 -14705.333
39 -20995.333 -18701.000
40 -21502.833 -20995.333
41 -24622.167 -21502.833
42 -22755.333 -24622.167
43 -28136.000 -22755.333
44 -33067.333 -28136.000
45 -33965.400 -33067.333
46 -32358.600 -33965.400
47 -31293.400 -32358.600
48 -33873.667 -31293.400
49 -36209.333 -33873.667
50 -34893.000 -36209.333
51 -33991.333 -34893.000
52 -37803.833 -33991.333
53 -34561.167 -37803.833
54 -38525.333 -34561.167
55 -43455.000 -38525.333
56 -41253.333 -43455.000
57 -38079.400 -41253.333
58 -31975.600 -38079.400
59 -24553.400 -31975.600
60 -17162.667 -24553.400
61 -11165.333 -17162.667
62 -5244.000 -11165.333
63 -4697.333 -5244.000
64 -2799.833 -4697.333
65 2633.833 -2799.833
66 -1523.333 2633.833
67 -3625.000 -1523.333
68 -1457.333 -3625.000
> 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/7uy991259924351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8o73j1259924351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9luo01259924351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10t1q91259924351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qhn41259924351.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/12md5s1259924351.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/13vwd81259924351.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/14xxfp1259924351.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/15co761259924351.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/16yv4m1259924351.tab")
+ }
>
> system("convert tmp/1f26t1259924351.ps tmp/1f26t1259924351.png")
> system("convert tmp/29sm31259924351.ps tmp/29sm31259924351.png")
> system("convert tmp/3ahrq1259924351.ps tmp/3ahrq1259924351.png")
> system("convert tmp/45akz1259924351.ps tmp/45akz1259924351.png")
> system("convert tmp/5nrsb1259924351.ps tmp/5nrsb1259924351.png")
> system("convert tmp/6fzts1259924351.ps tmp/6fzts1259924351.png")
> system("convert tmp/7uy991259924351.ps tmp/7uy991259924351.png")
> system("convert tmp/8o73j1259924351.ps tmp/8o73j1259924351.png")
> system("convert tmp/9luo01259924351.ps tmp/9luo01259924351.png")
> system("convert tmp/10t1q91259924351.ps tmp/10t1q91259924351.png")
>
>
> proc.time()
user system elapsed
2.462 1.600 3.017