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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(104.2,97.4,103.2,97,112.7,105.4,106.4,102.7,102.6,98.1,110.6,104.5,95.2,87.4,89,89.9,112.5,109.8,116.8,111.7,107.2,98.6,113.6,96.9,101.8,95.1,102.6,97,122.7,112.7,110.3,102.9,110.5,97.4,121.6,111.4,100.3,87.4,100.7,96.8,123.4,114.1,127.1,110.3,124.1,103.9,131.2,101.6,111.6,94.6,114.2,95.9,130.1,104.7,125.9,102.8,119,98.1,133.8,113.9,107.5,80.9,113.5,95.7,134.4,113.2,126.8,105.9,135.6,108.8,139.9,102.3,129.8,99,131,100.7,153.1,115.5,134.1,100.7,144.1,109.9,155.9,114.6,123.3,85.4,128.1,100.5,144.3,114.8,153,116.5,149.9,112.9,150.9,102,141,106,138.9,105.3,157.4,118.8,142.9,106.1,151.7,109.3,161,117.2,138.5,92.5,135.9,104.2,151.5,112.5,164,122.4,159.1,113.3,157,100,142.1,110.7,144.8,112.8,152.1,109.8,154.9,117.3,148.4,109.1,157.3,115.9,145.7,96,133.8,99.8,156.8,116.8),dim=c(2,69),dimnames=list(c('Omzet','Productie'),1:69))
> y <- array(NA,dim=c(2,69),dimnames=list(c('Omzet','Productie'),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 = '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
Omzet Productie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 104.2 97.4 1 0 0 0 0 0 0 0 0 0 0 1
2 103.2 97.0 0 1 0 0 0 0 0 0 0 0 0 2
3 112.7 105.4 0 0 1 0 0 0 0 0 0 0 0 3
4 106.4 102.7 0 0 0 1 0 0 0 0 0 0 0 4
5 102.6 98.1 0 0 0 0 1 0 0 0 0 0 0 5
6 110.6 104.5 0 0 0 0 0 1 0 0 0 0 0 6
7 95.2 87.4 0 0 0 0 0 0 1 0 0 0 0 7
8 89.0 89.9 0 0 0 0 0 0 0 1 0 0 0 8
9 112.5 109.8 0 0 0 0 0 0 0 0 1 0 0 9
10 116.8 111.7 0 0 0 0 0 0 0 0 0 1 0 10
11 107.2 98.6 0 0 0 0 0 0 0 0 0 0 1 11
12 113.6 96.9 0 0 0 0 0 0 0 0 0 0 0 12
13 101.8 95.1 1 0 0 0 0 0 0 0 0 0 0 13
14 102.6 97.0 0 1 0 0 0 0 0 0 0 0 0 14
15 122.7 112.7 0 0 1 0 0 0 0 0 0 0 0 15
16 110.3 102.9 0 0 0 1 0 0 0 0 0 0 0 16
17 110.5 97.4 0 0 0 0 1 0 0 0 0 0 0 17
18 121.6 111.4 0 0 0 0 0 1 0 0 0 0 0 18
19 100.3 87.4 0 0 0 0 0 0 1 0 0 0 0 19
20 100.7 96.8 0 0 0 0 0 0 0 1 0 0 0 20
21 123.4 114.1 0 0 0 0 0 0 0 0 1 0 0 21
22 127.1 110.3 0 0 0 0 0 0 0 0 0 1 0 22
23 124.1 103.9 0 0 0 0 0 0 0 0 0 0 1 23
24 131.2 101.6 0 0 0 0 0 0 0 0 0 0 0 24
25 111.6 94.6 1 0 0 0 0 0 0 0 0 0 0 25
26 114.2 95.9 0 1 0 0 0 0 0 0 0 0 0 26
27 130.1 104.7 0 0 1 0 0 0 0 0 0 0 0 27
28 125.9 102.8 0 0 0 1 0 0 0 0 0 0 0 28
29 119.0 98.1 0 0 0 0 1 0 0 0 0 0 0 29
30 133.8 113.9 0 0 0 0 0 1 0 0 0 0 0 30
31 107.5 80.9 0 0 0 0 0 0 1 0 0 0 0 31
32 113.5 95.7 0 0 0 0 0 0 0 1 0 0 0 32
33 134.4 113.2 0 0 0 0 0 0 0 0 1 0 0 33
34 126.8 105.9 0 0 0 0 0 0 0 0 0 1 0 34
35 135.6 108.8 0 0 0 0 0 0 0 0 0 0 1 35
36 139.9 102.3 0 0 0 0 0 0 0 0 0 0 0 36
37 129.8 99.0 1 0 0 0 0 0 0 0 0 0 0 37
38 131.0 100.7 0 1 0 0 0 0 0 0 0 0 0 38
39 153.1 115.5 0 0 1 0 0 0 0 0 0 0 0 39
40 134.1 100.7 0 0 0 1 0 0 0 0 0 0 0 40
41 144.1 109.9 0 0 0 0 1 0 0 0 0 0 0 41
42 155.9 114.6 0 0 0 0 0 1 0 0 0 0 0 42
43 123.3 85.4 0 0 0 0 0 0 1 0 0 0 0 43
44 128.1 100.5 0 0 0 0 0 0 0 1 0 0 0 44
45 144.3 114.8 0 0 0 0 0 0 0 0 1 0 0 45
46 153.0 116.5 0 0 0 0 0 0 0 0 0 1 0 46
47 149.9 112.9 0 0 0 0 0 0 0 0 0 0 1 47
48 150.9 102.0 0 0 0 0 0 0 0 0 0 0 0 48
49 141.0 106.0 1 0 0 0 0 0 0 0 0 0 0 49
50 138.9 105.3 0 1 0 0 0 0 0 0 0 0 0 50
51 157.4 118.8 0 0 1 0 0 0 0 0 0 0 0 51
52 142.9 106.1 0 0 0 1 0 0 0 0 0 0 0 52
53 151.7 109.3 0 0 0 0 1 0 0 0 0 0 0 53
54 161.0 117.2 0 0 0 0 0 1 0 0 0 0 0 54
55 138.5 92.5 0 0 0 0 0 0 1 0 0 0 0 55
56 135.9 104.2 0 0 0 0 0 0 0 1 0 0 0 56
57 151.5 112.5 0 0 0 0 0 0 0 0 1 0 0 57
58 164.0 122.4 0 0 0 0 0 0 0 0 0 1 0 58
59 159.1 113.3 0 0 0 0 0 0 0 0 0 0 1 59
60 157.0 100.0 0 0 0 0 0 0 0 0 0 0 0 60
61 142.1 110.7 1 0 0 0 0 0 0 0 0 0 0 61
62 144.8 112.8 0 1 0 0 0 0 0 0 0 0 0 62
63 152.1 109.8 0 0 1 0 0 0 0 0 0 0 0 63
64 154.9 117.3 0 0 0 1 0 0 0 0 0 0 0 64
65 148.4 109.1 0 0 0 0 1 0 0 0 0 0 0 65
66 157.3 115.9 0 0 0 0 0 1 0 0 0 0 0 66
67 145.7 96.0 0 0 0 0 0 0 1 0 0 0 0 67
68 133.8 99.8 0 0 0 0 0 0 0 1 0 0 0 68
69 156.8 116.8 0 0 0 0 0 0 0 0 1 0 0 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Productie M1 M2 M3 M4
29.6444 0.8328 -13.2023 -14.0192 -7.2289 -12.0854
M5 M6 M7 M8 M9 M10
-11.0121 -8.7775 -10.5633 -20.7980 -14.2684 -10.2440
M11 t
-8.4217 0.6980
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.1123 -2.8643 0.2940 2.6174 10.2768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.64445 14.96341 1.981 0.052585 .
Productie 0.83281 0.15775 5.279 2.27e-06 ***
M1 -13.20231 2.64101 -4.999 6.22e-06 ***
M2 -14.01923 2.64909 -5.292 2.17e-06 ***
M3 -7.22886 3.16846 -2.282 0.026412 *
M4 -12.08538 2.76101 -4.377 5.42e-05 ***
M5 -11.01207 2.68473 -4.102 0.000137 ***
M6 -8.77748 3.27711 -2.678 0.009735 **
M7 -10.56326 3.28878 -3.212 0.002205 **
M8 -20.79797 2.68036 -7.759 2.15e-10 ***
M9 -14.26836 3.28353 -4.345 6.04e-05 ***
M10 -10.24404 3.44838 -2.971 0.004399 **
M11 -8.42174 2.97241 -2.833 0.006428 **
t 0.69799 0.03954 17.652 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.351 on 55 degrees of freedom
Multiple R-squared: 0.9598, Adjusted R-squared: 0.9503
F-statistic: 101 on 13 and 55 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,] 0.25994505 0.51989010 0.74005495
[2,] 0.14310634 0.28621268 0.85689366
[3,] 0.08970239 0.17940478 0.91029761
[4,] 0.04745900 0.09491800 0.95254100
[5,] 0.03217308 0.06434615 0.96782692
[6,] 0.08788950 0.17577901 0.91211050
[7,] 0.11945426 0.23890851 0.88054574
[8,] 0.16668589 0.33337178 0.83331411
[9,] 0.11722475 0.23444950 0.88277525
[10,] 0.08851620 0.17703240 0.91148380
[11,] 0.10260570 0.20521141 0.89739430
[12,] 0.11493795 0.22987590 0.88506205
[13,] 0.08737740 0.17475480 0.91262260
[14,] 0.11342316 0.22684632 0.88657684
[15,] 0.11681986 0.23363972 0.88318014
[16,] 0.11293302 0.22586605 0.88706698
[17,] 0.10736219 0.21472438 0.89263781
[18,] 0.26679290 0.53358580 0.73320710
[19,] 0.37743555 0.75487109 0.62256445
[20,] 0.60442153 0.79115695 0.39557847
[21,] 0.56962266 0.86075467 0.43037734
[22,] 0.51873296 0.96253408 0.48126704
[23,] 0.54385146 0.91229707 0.45614854
[24,] 0.48599915 0.97199830 0.51400085
[25,] 0.42246729 0.84493458 0.57753271
[26,] 0.59381550 0.81236901 0.40618450
[27,] 0.66737986 0.66524028 0.33262014
[28,] 0.57407918 0.85184165 0.42592082
[29,] 0.63996650 0.72006701 0.36003350
[30,] 0.61770265 0.76459470 0.38229735
[31,] 0.75723992 0.48552016 0.24276008
[32,] 0.89676908 0.20646184 0.10323092
[33,] 0.90032023 0.19935953 0.09967977
[34,] 0.85338227 0.29323546 0.14661773
[35,] 0.82921706 0.34156588 0.17078294
[36,] 0.69698618 0.60602765 0.30301382
> postscript(file="/var/www/html/rcomp/tmp/1m96z1261063239.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24kby1261063239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/367kt1261063239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hg9p1261063239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5k2z81261063239.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
5.94375166 5.39581147 0.41180342 0.51893332 -1.22142611 -1.48402505
7 8 9 10 11 12
-1.55510651 -0.30043221 -0.60103879 -2.60569803 -3.81612235 -5.12006939
13 14 15 16 17 18
-2.91669050 -3.58010362 -4.04365622 -4.12354464 -1.11437118 -4.60635897
19 20 21 22 23 24
-4.83102161 -2.72276613 -1.65805547 0.48432692 0.29404665 0.18978820
25 26 27 28 29 30
-1.07619843 0.56007703 1.64294325 3.18382169 -1.57325630 -2.86430987
31 32 33 34 35 36
-0.59364362 2.61741453 1.71556232 -4.52720517 -0.66265862 -0.06909692
37 38 39 40 41 42
5.08350345 4.98665319 7.27263349 4.75681666 5.32361961 10.27680501
43 44 45 46 47 48
3.08277684 4.84399069 1.90714431 4.46904793 1.84688757 2.80483228
49 50 51 52 53 54
2.07788811 0.67979222 0.44843113 0.68370423 5.04739311 4.83557268
55 56 57 58 59 60
3.99388006 1.18666260 2.64670215 2.17952834 2.33784674 2.19454582
61 62 63 64 65 66
-9.11225430 -8.04223029 -5.73215508 -5.01973126 -6.46195913 -6.15768380
67 68 69
-0.09688516 -5.62486948 -4.01031453
> postscript(file="/var/www/html/rcomp/tmp/6nmpj1261063239.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 5.94375166 NA
1 5.39581147 5.94375166
2 0.41180342 5.39581147
3 0.51893332 0.41180342
4 -1.22142611 0.51893332
5 -1.48402505 -1.22142611
6 -1.55510651 -1.48402505
7 -0.30043221 -1.55510651
8 -0.60103879 -0.30043221
9 -2.60569803 -0.60103879
10 -3.81612235 -2.60569803
11 -5.12006939 -3.81612235
12 -2.91669050 -5.12006939
13 -3.58010362 -2.91669050
14 -4.04365622 -3.58010362
15 -4.12354464 -4.04365622
16 -1.11437118 -4.12354464
17 -4.60635897 -1.11437118
18 -4.83102161 -4.60635897
19 -2.72276613 -4.83102161
20 -1.65805547 -2.72276613
21 0.48432692 -1.65805547
22 0.29404665 0.48432692
23 0.18978820 0.29404665
24 -1.07619843 0.18978820
25 0.56007703 -1.07619843
26 1.64294325 0.56007703
27 3.18382169 1.64294325
28 -1.57325630 3.18382169
29 -2.86430987 -1.57325630
30 -0.59364362 -2.86430987
31 2.61741453 -0.59364362
32 1.71556232 2.61741453
33 -4.52720517 1.71556232
34 -0.66265862 -4.52720517
35 -0.06909692 -0.66265862
36 5.08350345 -0.06909692
37 4.98665319 5.08350345
38 7.27263349 4.98665319
39 4.75681666 7.27263349
40 5.32361961 4.75681666
41 10.27680501 5.32361961
42 3.08277684 10.27680501
43 4.84399069 3.08277684
44 1.90714431 4.84399069
45 4.46904793 1.90714431
46 1.84688757 4.46904793
47 2.80483228 1.84688757
48 2.07788811 2.80483228
49 0.67979222 2.07788811
50 0.44843113 0.67979222
51 0.68370423 0.44843113
52 5.04739311 0.68370423
53 4.83557268 5.04739311
54 3.99388006 4.83557268
55 1.18666260 3.99388006
56 2.64670215 1.18666260
57 2.17952834 2.64670215
58 2.33784674 2.17952834
59 2.19454582 2.33784674
60 -9.11225430 2.19454582
61 -8.04223029 -9.11225430
62 -5.73215508 -8.04223029
63 -5.01973126 -5.73215508
64 -6.46195913 -5.01973126
65 -6.15768380 -6.46195913
66 -0.09688516 -6.15768380
67 -5.62486948 -0.09688516
68 -4.01031453 -5.62486948
69 NA -4.01031453
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.39581147 5.94375166
[2,] 0.41180342 5.39581147
[3,] 0.51893332 0.41180342
[4,] -1.22142611 0.51893332
[5,] -1.48402505 -1.22142611
[6,] -1.55510651 -1.48402505
[7,] -0.30043221 -1.55510651
[8,] -0.60103879 -0.30043221
[9,] -2.60569803 -0.60103879
[10,] -3.81612235 -2.60569803
[11,] -5.12006939 -3.81612235
[12,] -2.91669050 -5.12006939
[13,] -3.58010362 -2.91669050
[14,] -4.04365622 -3.58010362
[15,] -4.12354464 -4.04365622
[16,] -1.11437118 -4.12354464
[17,] -4.60635897 -1.11437118
[18,] -4.83102161 -4.60635897
[19,] -2.72276613 -4.83102161
[20,] -1.65805547 -2.72276613
[21,] 0.48432692 -1.65805547
[22,] 0.29404665 0.48432692
[23,] 0.18978820 0.29404665
[24,] -1.07619843 0.18978820
[25,] 0.56007703 -1.07619843
[26,] 1.64294325 0.56007703
[27,] 3.18382169 1.64294325
[28,] -1.57325630 3.18382169
[29,] -2.86430987 -1.57325630
[30,] -0.59364362 -2.86430987
[31,] 2.61741453 -0.59364362
[32,] 1.71556232 2.61741453
[33,] -4.52720517 1.71556232
[34,] -0.66265862 -4.52720517
[35,] -0.06909692 -0.66265862
[36,] 5.08350345 -0.06909692
[37,] 4.98665319 5.08350345
[38,] 7.27263349 4.98665319
[39,] 4.75681666 7.27263349
[40,] 5.32361961 4.75681666
[41,] 10.27680501 5.32361961
[42,] 3.08277684 10.27680501
[43,] 4.84399069 3.08277684
[44,] 1.90714431 4.84399069
[45,] 4.46904793 1.90714431
[46,] 1.84688757 4.46904793
[47,] 2.80483228 1.84688757
[48,] 2.07788811 2.80483228
[49,] 0.67979222 2.07788811
[50,] 0.44843113 0.67979222
[51,] 0.68370423 0.44843113
[52,] 5.04739311 0.68370423
[53,] 4.83557268 5.04739311
[54,] 3.99388006 4.83557268
[55,] 1.18666260 3.99388006
[56,] 2.64670215 1.18666260
[57,] 2.17952834 2.64670215
[58,] 2.33784674 2.17952834
[59,] 2.19454582 2.33784674
[60,] -9.11225430 2.19454582
[61,] -8.04223029 -9.11225430
[62,] -5.73215508 -8.04223029
[63,] -5.01973126 -5.73215508
[64,] -6.46195913 -5.01973126
[65,] -6.15768380 -6.46195913
[66,] -0.09688516 -6.15768380
[67,] -5.62486948 -0.09688516
[68,] -4.01031453 -5.62486948
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.39581147 5.94375166
2 0.41180342 5.39581147
3 0.51893332 0.41180342
4 -1.22142611 0.51893332
5 -1.48402505 -1.22142611
6 -1.55510651 -1.48402505
7 -0.30043221 -1.55510651
8 -0.60103879 -0.30043221
9 -2.60569803 -0.60103879
10 -3.81612235 -2.60569803
11 -5.12006939 -3.81612235
12 -2.91669050 -5.12006939
13 -3.58010362 -2.91669050
14 -4.04365622 -3.58010362
15 -4.12354464 -4.04365622
16 -1.11437118 -4.12354464
17 -4.60635897 -1.11437118
18 -4.83102161 -4.60635897
19 -2.72276613 -4.83102161
20 -1.65805547 -2.72276613
21 0.48432692 -1.65805547
22 0.29404665 0.48432692
23 0.18978820 0.29404665
24 -1.07619843 0.18978820
25 0.56007703 -1.07619843
26 1.64294325 0.56007703
27 3.18382169 1.64294325
28 -1.57325630 3.18382169
29 -2.86430987 -1.57325630
30 -0.59364362 -2.86430987
31 2.61741453 -0.59364362
32 1.71556232 2.61741453
33 -4.52720517 1.71556232
34 -0.66265862 -4.52720517
35 -0.06909692 -0.66265862
36 5.08350345 -0.06909692
37 4.98665319 5.08350345
38 7.27263349 4.98665319
39 4.75681666 7.27263349
40 5.32361961 4.75681666
41 10.27680501 5.32361961
42 3.08277684 10.27680501
43 4.84399069 3.08277684
44 1.90714431 4.84399069
45 4.46904793 1.90714431
46 1.84688757 4.46904793
47 2.80483228 1.84688757
48 2.07788811 2.80483228
49 0.67979222 2.07788811
50 0.44843113 0.67979222
51 0.68370423 0.44843113
52 5.04739311 0.68370423
53 4.83557268 5.04739311
54 3.99388006 4.83557268
55 1.18666260 3.99388006
56 2.64670215 1.18666260
57 2.17952834 2.64670215
58 2.33784674 2.17952834
59 2.19454582 2.33784674
60 -9.11225430 2.19454582
61 -8.04223029 -9.11225430
62 -5.73215508 -8.04223029
63 -5.01973126 -5.73215508
64 -6.46195913 -5.01973126
65 -6.15768380 -6.46195913
66 -0.09688516 -6.15768380
67 -5.62486948 -0.09688516
68 -4.01031453 -5.62486948
> 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/7992y1261063239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86qa71261063239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mwyg1261063239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tsr11261063239.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g6pe1261063239.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/12hvqj1261063239.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/13pco21261063239.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/14n9r61261063239.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/15qqxh1261063239.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/16rbii1261063239.tab")
+ }
>
> try(system("convert tmp/1m96z1261063239.ps tmp/1m96z1261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/24kby1261063239.ps tmp/24kby1261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/367kt1261063239.ps tmp/367kt1261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hg9p1261063239.ps tmp/4hg9p1261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k2z81261063239.ps tmp/5k2z81261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nmpj1261063239.ps tmp/6nmpj1261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/7992y1261063239.ps tmp/7992y1261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/86qa71261063239.ps tmp/86qa71261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mwyg1261063239.ps tmp/9mwyg1261063239.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tsr11261063239.ps tmp/10tsr11261063239.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.504 1.579 4.300