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(97.6,82.9,96.9,83.8,105.6,86.2,102.8,86.1,101.7,86.2,104.2,88.8,92.7,89.6,91.9,87.8,106.5,88.3,112.3,88.6,102.8,91,96.5,91.5,101,95.4,98.9,98.7,105.1,99.9,103,98.6,99,100.3,104.3,100.2,94.6,100.4,90.4,101.4,108.9,103,111.4,109.1,100.8,111.4,102.5,114.1,98.2,121.8,98.7,127.6,113.3,129.9,104.6,128,99.3,123.5,111.8,124,97.3,127.4,97.7,127.6,115.6,128.4,111.9,131.4,107,135.1,107.1,134,100.6,144.5,99.2,147.3,108.4,150.9,103,148.7,99.8,141.4,115,138.9,90.8,139.8,95.9,145.6,114.4,147.9,108.2,148.5,112.6,151.1,109.1,157.5,105,167.5,105,172.3,118.5,173.5,103.7,187.5,112.5,205.5,116.6,195.1,96.6,204.5,101.9,204.5,116.5,201.7,119.3,207,115.4,206.6,108.5,210.6,111.5,211.1,108.8,215,121.8,223.9,109.6,238.2,112.2,238.9,119.6,229.6,104.1,232.2,105.3,222.1,115,221.6,124.1,227.3,116.8,221,107.5,213.6,115.6,243.4),dim=c(2,73),dimnames=list(c('tot_indus','prijsindex'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('tot_indus','prijsindex'),1:73))
> 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
tot_indus prijsindex M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 97.6 82.9 1 0 0 0 0 0 0 0 0 0 0
2 96.9 83.8 0 1 0 0 0 0 0 0 0 0 0
3 105.6 86.2 0 0 1 0 0 0 0 0 0 0 0
4 102.8 86.1 0 0 0 1 0 0 0 0 0 0 0
5 101.7 86.2 0 0 0 0 1 0 0 0 0 0 0
6 104.2 88.8 0 0 0 0 0 1 0 0 0 0 0
7 92.7 89.6 0 0 0 0 0 0 1 0 0 0 0
8 91.9 87.8 0 0 0 0 0 0 0 1 0 0 0
9 106.5 88.3 0 0 0 0 0 0 0 0 1 0 0
10 112.3 88.6 0 0 0 0 0 0 0 0 0 1 0
11 102.8 91.0 0 0 0 0 0 0 0 0 0 0 1
12 96.5 91.5 0 0 0 0 0 0 0 0 0 0 0
13 101.0 95.4 1 0 0 0 0 0 0 0 0 0 0
14 98.9 98.7 0 1 0 0 0 0 0 0 0 0 0
15 105.1 99.9 0 0 1 0 0 0 0 0 0 0 0
16 103.0 98.6 0 0 0 1 0 0 0 0 0 0 0
17 99.0 100.3 0 0 0 0 1 0 0 0 0 0 0
18 104.3 100.2 0 0 0 0 0 1 0 0 0 0 0
19 94.6 100.4 0 0 0 0 0 0 1 0 0 0 0
20 90.4 101.4 0 0 0 0 0 0 0 1 0 0 0
21 108.9 103.0 0 0 0 0 0 0 0 0 1 0 0
22 111.4 109.1 0 0 0 0 0 0 0 0 0 1 0
23 100.8 111.4 0 0 0 0 0 0 0 0 0 0 1
24 102.5 114.1 0 0 0 0 0 0 0 0 0 0 0
25 98.2 121.8 1 0 0 0 0 0 0 0 0 0 0
26 98.7 127.6 0 1 0 0 0 0 0 0 0 0 0
27 113.3 129.9 0 0 1 0 0 0 0 0 0 0 0
28 104.6 128.0 0 0 0 1 0 0 0 0 0 0 0
29 99.3 123.5 0 0 0 0 1 0 0 0 0 0 0
30 111.8 124.0 0 0 0 0 0 1 0 0 0 0 0
31 97.3 127.4 0 0 0 0 0 0 1 0 0 0 0
32 97.7 127.6 0 0 0 0 0 0 0 1 0 0 0
33 115.6 128.4 0 0 0 0 0 0 0 0 1 0 0
34 111.9 131.4 0 0 0 0 0 0 0 0 0 1 0
35 107.0 135.1 0 0 0 0 0 0 0 0 0 0 1
36 107.1 134.0 0 0 0 0 0 0 0 0 0 0 0
37 100.6 144.5 1 0 0 0 0 0 0 0 0 0 0
38 99.2 147.3 0 1 0 0 0 0 0 0 0 0 0
39 108.4 150.9 0 0 1 0 0 0 0 0 0 0 0
40 103.0 148.7 0 0 0 1 0 0 0 0 0 0 0
41 99.8 141.4 0 0 0 0 1 0 0 0 0 0 0
42 115.0 138.9 0 0 0 0 0 1 0 0 0 0 0
43 90.8 139.8 0 0 0 0 0 0 1 0 0 0 0
44 95.9 145.6 0 0 0 0 0 0 0 1 0 0 0
45 114.4 147.9 0 0 0 0 0 0 0 0 1 0 0
46 108.2 148.5 0 0 0 0 0 0 0 0 0 1 0
47 112.6 151.1 0 0 0 0 0 0 0 0 0 0 1
48 109.1 157.5 0 0 0 0 0 0 0 0 0 0 0
49 105.0 167.5 1 0 0 0 0 0 0 0 0 0 0
50 105.0 172.3 0 1 0 0 0 0 0 0 0 0 0
51 118.5 173.5 0 0 1 0 0 0 0 0 0 0 0
52 103.7 187.5 0 0 0 1 0 0 0 0 0 0 0
53 112.5 205.5 0 0 0 0 1 0 0 0 0 0 0
54 116.6 195.1 0 0 0 0 0 1 0 0 0 0 0
55 96.6 204.5 0 0 0 0 0 0 1 0 0 0 0
56 101.9 204.5 0 0 0 0 0 0 0 1 0 0 0
57 116.5 201.7 0 0 0 0 0 0 0 0 1 0 0
58 119.3 207.0 0 0 0 0 0 0 0 0 0 1 0
59 115.4 206.6 0 0 0 0 0 0 0 0 0 0 1
60 108.5 210.6 0 0 0 0 0 0 0 0 0 0 0
61 111.5 211.1 1 0 0 0 0 0 0 0 0 0 0
62 108.8 215.0 0 1 0 0 0 0 0 0 0 0 0
63 121.8 223.9 0 0 1 0 0 0 0 0 0 0 0
64 109.6 238.2 0 0 0 1 0 0 0 0 0 0 0
65 112.2 238.9 0 0 0 0 1 0 0 0 0 0 0
66 119.6 229.6 0 0 0 0 0 1 0 0 0 0 0
67 104.1 232.2 0 0 0 0 0 0 1 0 0 0 0
68 105.3 222.1 0 0 0 0 0 0 0 1 0 0 0
69 115.0 221.6 0 0 0 0 0 0 0 0 1 0 0
70 124.1 227.3 0 0 0 0 0 0 0 0 0 1 0
71 116.8 221.0 0 0 0 0 0 0 0 0 0 0 1
72 107.5 213.6 0 0 0 0 0 0 0 0 0 0 0
73 115.6 243.4 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) prijsindex M1 M2 M3 M4
91.50941 0.08916 -0.88063 -2.81172 7.76369 -0.24179
M5 M6 M7 M8 M9 M10
-0.73773 7.38091 -8.77617 -7.53669 8.06841 9.47302
M11
4.10912
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0228 -2.4830 0.1386 2.0494 4.5740
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.509413 1.587977 57.626 < 2e-16 ***
prijsindex 0.089160 0.006837 13.041 < 2e-16 ***
M1 -0.880632 1.623658 -0.542 0.5896
M2 -2.811718 1.687188 -1.667 0.1008
M3 7.763691 1.686180 4.604 2.20e-05 ***
M4 -0.241785 1.685379 -0.143 0.8864
M5 -0.737735 1.685179 -0.438 0.6631
M6 7.380912 1.685698 4.379 4.86e-05 ***
M7 -8.776167 1.685218 -5.208 2.46e-06 ***
M8 -7.536686 1.685330 -4.472 3.51e-05 ***
M9 8.068413 1.685285 4.788 1.14e-05 ***
M10 9.473018 1.684963 5.622 5.20e-07 ***
M11 4.109120 1.684939 2.439 0.0177 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.918 on 60 degrees of freedom
Multiple R-squared: 0.8848, Adjusted R-squared: 0.8617
F-statistic: 38.39 on 12 and 60 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,] 0.087650426 0.17530085 0.91234957
[2,] 0.124866993 0.24973399 0.87513301
[3,] 0.061406238 0.12281248 0.93859376
[4,] 0.036209902 0.07241980 0.96379010
[5,] 0.023846745 0.04769349 0.97615326
[6,] 0.014737176 0.02947435 0.98526282
[7,] 0.008273015 0.01654603 0.99172698
[8,] 0.008173297 0.01634659 0.99182670
[9,] 0.032356649 0.06471330 0.96764335
[10,] 0.028151435 0.05630287 0.97184857
[11,] 0.015133524 0.03026705 0.98486648
[12,] 0.076528619 0.15305724 0.92347138
[13,] 0.061347898 0.12269580 0.93865210
[14,] 0.055432119 0.11086424 0.94456788
[15,] 0.105960128 0.21192026 0.89403987
[16,] 0.111346452 0.22269290 0.88865355
[17,] 0.122237559 0.24447512 0.87776244
[18,] 0.227355380 0.45471076 0.77264462
[19,] 0.205233764 0.41046753 0.79476624
[20,] 0.168868954 0.33773791 0.83113105
[21,] 0.236195946 0.47239189 0.76380405
[22,] 0.237419590 0.47483918 0.76258041
[23,] 0.231099357 0.46219871 0.76890064
[24,] 0.370651506 0.74130301 0.62934849
[25,] 0.381014125 0.76202825 0.61898587
[26,] 0.435673844 0.87134769 0.56432616
[27,] 0.543795253 0.91240949 0.45620475
[28,] 0.609053586 0.78189283 0.39094641
[29,] 0.533012670 0.93397466 0.46698733
[30,] 0.573552476 0.85289505 0.42644752
[31,] 0.863437275 0.27312545 0.13656273
[32,] 0.871600632 0.25679874 0.12839937
[33,] 0.975748349 0.04850330 0.02425165
[34,] 0.970199916 0.05960017 0.02980008
[35,] 0.947629411 0.10474118 0.05237059
[36,] 0.938433891 0.12313222 0.06156611
[37,] 0.911086547 0.17782691 0.08891345
[38,] 0.943813063 0.11237387 0.05618694
[39,] 0.911942545 0.17611491 0.08805745
[40,] 0.935682569 0.12863486 0.06431743
[41,] 0.878758431 0.24248314 0.12124157
[42,] 0.951517084 0.09696583 0.04848292
> postscript(file="/var/www/html/rcomp/tmp/1a0oc1258644167.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29bq11258644167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35ofh1258644167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hzhw1258644167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wezg1258644167.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 = 73
Frequency = 1
1 2 3 4 5 6
-0.42018199 0.73065955 -1.35873473 3.85565769 3.24269096 -2.60777297
7 8 9 10 11 12
1.97797796 0.09898574 -0.95069368 3.41795309 -0.93213367 -3.16759418
13 14 15 16 17 18
1.86531240 1.40216887 -3.08023287 2.94115208 -0.71447136 -3.52420208
19 20 21 22 23 24
2.91504512 -2.61359636 0.13864773 0.69016390 -4.75100681 0.81737969
25 26 27 28 29 30
-3.28852343 -1.37456809 2.44495368 1.91983490 -2.48299376 1.85377924
31 32 33 34 35 36
3.20771301 2.35039989 4.57397234 -0.79811410 -0.66410944 3.64308677
37 38 39 40 41 42
-2.91246561 -2.63102892 -4.32741574 -1.52578638 -3.57896579 3.72528856
43 44 45 46 47 48
-4.39787655 -1.05448818 1.63534359 -6.02275777 3.50932338 3.54781623
49 50 51 52 53 54
-0.56315593 0.93995987 3.75755813 -4.28521178 3.40584947 0.31447136
55 56 57 58 59 60
-4.36655756 -0.30603859 -1.06148853 -0.13864400 1.36091850 -1.78660358
61 62 63 64 65 66
2.04944852 0.93280872 2.56387153 -2.90564651 0.12789049 0.23843589
67 68 69 70 71 72
0.66369802 1.52473751 -4.33578145 2.85139889 1.47700804 -3.05408493
73
3.26956604
> postscript(file="/var/www/html/rcomp/tmp/6k0t61258644167.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.42018199 NA
1 0.73065955 -0.42018199
2 -1.35873473 0.73065955
3 3.85565769 -1.35873473
4 3.24269096 3.85565769
5 -2.60777297 3.24269096
6 1.97797796 -2.60777297
7 0.09898574 1.97797796
8 -0.95069368 0.09898574
9 3.41795309 -0.95069368
10 -0.93213367 3.41795309
11 -3.16759418 -0.93213367
12 1.86531240 -3.16759418
13 1.40216887 1.86531240
14 -3.08023287 1.40216887
15 2.94115208 -3.08023287
16 -0.71447136 2.94115208
17 -3.52420208 -0.71447136
18 2.91504512 -3.52420208
19 -2.61359636 2.91504512
20 0.13864773 -2.61359636
21 0.69016390 0.13864773
22 -4.75100681 0.69016390
23 0.81737969 -4.75100681
24 -3.28852343 0.81737969
25 -1.37456809 -3.28852343
26 2.44495368 -1.37456809
27 1.91983490 2.44495368
28 -2.48299376 1.91983490
29 1.85377924 -2.48299376
30 3.20771301 1.85377924
31 2.35039989 3.20771301
32 4.57397234 2.35039989
33 -0.79811410 4.57397234
34 -0.66410944 -0.79811410
35 3.64308677 -0.66410944
36 -2.91246561 3.64308677
37 -2.63102892 -2.91246561
38 -4.32741574 -2.63102892
39 -1.52578638 -4.32741574
40 -3.57896579 -1.52578638
41 3.72528856 -3.57896579
42 -4.39787655 3.72528856
43 -1.05448818 -4.39787655
44 1.63534359 -1.05448818
45 -6.02275777 1.63534359
46 3.50932338 -6.02275777
47 3.54781623 3.50932338
48 -0.56315593 3.54781623
49 0.93995987 -0.56315593
50 3.75755813 0.93995987
51 -4.28521178 3.75755813
52 3.40584947 -4.28521178
53 0.31447136 3.40584947
54 -4.36655756 0.31447136
55 -0.30603859 -4.36655756
56 -1.06148853 -0.30603859
57 -0.13864400 -1.06148853
58 1.36091850 -0.13864400
59 -1.78660358 1.36091850
60 2.04944852 -1.78660358
61 0.93280872 2.04944852
62 2.56387153 0.93280872
63 -2.90564651 2.56387153
64 0.12789049 -2.90564651
65 0.23843589 0.12789049
66 0.66369802 0.23843589
67 1.52473751 0.66369802
68 -4.33578145 1.52473751
69 2.85139889 -4.33578145
70 1.47700804 2.85139889
71 -3.05408493 1.47700804
72 3.26956604 -3.05408493
73 NA 3.26956604
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.73065955 -0.42018199
[2,] -1.35873473 0.73065955
[3,] 3.85565769 -1.35873473
[4,] 3.24269096 3.85565769
[5,] -2.60777297 3.24269096
[6,] 1.97797796 -2.60777297
[7,] 0.09898574 1.97797796
[8,] -0.95069368 0.09898574
[9,] 3.41795309 -0.95069368
[10,] -0.93213367 3.41795309
[11,] -3.16759418 -0.93213367
[12,] 1.86531240 -3.16759418
[13,] 1.40216887 1.86531240
[14,] -3.08023287 1.40216887
[15,] 2.94115208 -3.08023287
[16,] -0.71447136 2.94115208
[17,] -3.52420208 -0.71447136
[18,] 2.91504512 -3.52420208
[19,] -2.61359636 2.91504512
[20,] 0.13864773 -2.61359636
[21,] 0.69016390 0.13864773
[22,] -4.75100681 0.69016390
[23,] 0.81737969 -4.75100681
[24,] -3.28852343 0.81737969
[25,] -1.37456809 -3.28852343
[26,] 2.44495368 -1.37456809
[27,] 1.91983490 2.44495368
[28,] -2.48299376 1.91983490
[29,] 1.85377924 -2.48299376
[30,] 3.20771301 1.85377924
[31,] 2.35039989 3.20771301
[32,] 4.57397234 2.35039989
[33,] -0.79811410 4.57397234
[34,] -0.66410944 -0.79811410
[35,] 3.64308677 -0.66410944
[36,] -2.91246561 3.64308677
[37,] -2.63102892 -2.91246561
[38,] -4.32741574 -2.63102892
[39,] -1.52578638 -4.32741574
[40,] -3.57896579 -1.52578638
[41,] 3.72528856 -3.57896579
[42,] -4.39787655 3.72528856
[43,] -1.05448818 -4.39787655
[44,] 1.63534359 -1.05448818
[45,] -6.02275777 1.63534359
[46,] 3.50932338 -6.02275777
[47,] 3.54781623 3.50932338
[48,] -0.56315593 3.54781623
[49,] 0.93995987 -0.56315593
[50,] 3.75755813 0.93995987
[51,] -4.28521178 3.75755813
[52,] 3.40584947 -4.28521178
[53,] 0.31447136 3.40584947
[54,] -4.36655756 0.31447136
[55,] -0.30603859 -4.36655756
[56,] -1.06148853 -0.30603859
[57,] -0.13864400 -1.06148853
[58,] 1.36091850 -0.13864400
[59,] -1.78660358 1.36091850
[60,] 2.04944852 -1.78660358
[61,] 0.93280872 2.04944852
[62,] 2.56387153 0.93280872
[63,] -2.90564651 2.56387153
[64,] 0.12789049 -2.90564651
[65,] 0.23843589 0.12789049
[66,] 0.66369802 0.23843589
[67,] 1.52473751 0.66369802
[68,] -4.33578145 1.52473751
[69,] 2.85139889 -4.33578145
[70,] 1.47700804 2.85139889
[71,] -3.05408493 1.47700804
[72,] 3.26956604 -3.05408493
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.73065955 -0.42018199
2 -1.35873473 0.73065955
3 3.85565769 -1.35873473
4 3.24269096 3.85565769
5 -2.60777297 3.24269096
6 1.97797796 -2.60777297
7 0.09898574 1.97797796
8 -0.95069368 0.09898574
9 3.41795309 -0.95069368
10 -0.93213367 3.41795309
11 -3.16759418 -0.93213367
12 1.86531240 -3.16759418
13 1.40216887 1.86531240
14 -3.08023287 1.40216887
15 2.94115208 -3.08023287
16 -0.71447136 2.94115208
17 -3.52420208 -0.71447136
18 2.91504512 -3.52420208
19 -2.61359636 2.91504512
20 0.13864773 -2.61359636
21 0.69016390 0.13864773
22 -4.75100681 0.69016390
23 0.81737969 -4.75100681
24 -3.28852343 0.81737969
25 -1.37456809 -3.28852343
26 2.44495368 -1.37456809
27 1.91983490 2.44495368
28 -2.48299376 1.91983490
29 1.85377924 -2.48299376
30 3.20771301 1.85377924
31 2.35039989 3.20771301
32 4.57397234 2.35039989
33 -0.79811410 4.57397234
34 -0.66410944 -0.79811410
35 3.64308677 -0.66410944
36 -2.91246561 3.64308677
37 -2.63102892 -2.91246561
38 -4.32741574 -2.63102892
39 -1.52578638 -4.32741574
40 -3.57896579 -1.52578638
41 3.72528856 -3.57896579
42 -4.39787655 3.72528856
43 -1.05448818 -4.39787655
44 1.63534359 -1.05448818
45 -6.02275777 1.63534359
46 3.50932338 -6.02275777
47 3.54781623 3.50932338
48 -0.56315593 3.54781623
49 0.93995987 -0.56315593
50 3.75755813 0.93995987
51 -4.28521178 3.75755813
52 3.40584947 -4.28521178
53 0.31447136 3.40584947
54 -4.36655756 0.31447136
55 -0.30603859 -4.36655756
56 -1.06148853 -0.30603859
57 -0.13864400 -1.06148853
58 1.36091850 -0.13864400
59 -1.78660358 1.36091850
60 2.04944852 -1.78660358
61 0.93280872 2.04944852
62 2.56387153 0.93280872
63 -2.90564651 2.56387153
64 0.12789049 -2.90564651
65 0.23843589 0.12789049
66 0.66369802 0.23843589
67 1.52473751 0.66369802
68 -4.33578145 1.52473751
69 2.85139889 -4.33578145
70 1.47700804 2.85139889
71 -3.05408493 1.47700804
72 3.26956604 -3.05408493
> 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/7h4vc1258644167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8uced1258644167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9me811258644167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vaut1258644167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11v6eh1258644167.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/12nfgg1258644167.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/13kcrz1258644167.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/145q181258644167.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/15vshz1258644167.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/16o0qj1258644167.tab")
+ }
>
> system("convert tmp/1a0oc1258644167.ps tmp/1a0oc1258644167.png")
> system("convert tmp/29bq11258644167.ps tmp/29bq11258644167.png")
> system("convert tmp/35ofh1258644167.ps tmp/35ofh1258644167.png")
> system("convert tmp/4hzhw1258644167.ps tmp/4hzhw1258644167.png")
> system("convert tmp/5wezg1258644167.ps tmp/5wezg1258644167.png")
> system("convert tmp/6k0t61258644167.ps tmp/6k0t61258644167.png")
> system("convert tmp/7h4vc1258644167.ps tmp/7h4vc1258644167.png")
> system("convert tmp/8uced1258644167.ps tmp/8uced1258644167.png")
> system("convert tmp/9me811258644167.ps tmp/9me811258644167.png")
> system("convert tmp/10vaut1258644167.ps tmp/10vaut1258644167.png")
>
>
> proc.time()
user system elapsed
2.623 1.602 3.483