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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(16643
+ ,16196.7
+ ,18252.1
+ ,17570.4
+ ,89.1
+ ,17729
+ ,16643
+ ,16196.7
+ ,18252.1
+ ,82.6
+ ,16446.1
+ ,17729
+ ,16643
+ ,16196.7
+ ,102.7
+ ,15993.8
+ ,16446.1
+ ,17729
+ ,16643
+ ,91.8
+ ,16373.5
+ ,15993.8
+ ,16446.1
+ ,17729
+ ,94.1
+ ,17842.2
+ ,16373.5
+ ,15993.8
+ ,16446.1
+ ,103.1
+ ,22321.5
+ ,17842.2
+ ,16373.5
+ ,15993.8
+ ,93.2
+ ,22786.7
+ ,22321.5
+ ,17842.2
+ ,16373.5
+ ,91
+ ,18274.1
+ ,22786.7
+ ,22321.5
+ ,17842.2
+ ,94.3
+ ,22392.9
+ ,18274.1
+ ,22786.7
+ ,22321.5
+ ,99.4
+ ,23899.3
+ ,22392.9
+ ,18274.1
+ ,22786.7
+ ,115.7
+ ,21343.5
+ ,23899.3
+ ,22392.9
+ ,18274.1
+ ,116.8
+ ,22952.3
+ ,21343.5
+ ,23899.3
+ ,22392.9
+ ,99.8
+ ,21374.4
+ ,22952.3
+ ,21343.5
+ ,23899.3
+ ,96
+ ,21164.1
+ ,21374.4
+ ,22952.3
+ ,21343.5
+ ,115.9
+ ,20906.5
+ ,21164.1
+ ,21374.4
+ ,22952.3
+ ,109.1
+ ,17877.4
+ ,20906.5
+ ,21164.1
+ ,21374.4
+ ,117.3
+ ,20664.3
+ ,17877.4
+ ,20906.5
+ ,21164.1
+ ,109.8
+ ,22160
+ ,20664.3
+ ,17877.4
+ ,20906.5
+ ,112.8
+ ,19813.6
+ ,22160
+ ,20664.3
+ ,17877.4
+ ,110.7
+ ,17735.4
+ ,19813.6
+ ,22160
+ ,20664.3
+ ,100
+ ,19640.2
+ ,17735.4
+ ,19813.6
+ ,22160
+ ,113.3
+ ,20844.4
+ ,19640.2
+ ,17735.4
+ ,19813.6
+ ,122.4
+ ,19823.1
+ ,20844.4
+ ,19640.2
+ ,17735.4
+ ,112.5
+ ,18594.6
+ ,19823.1
+ ,20844.4
+ ,19640.2
+ ,104.2
+ ,21350.6
+ ,18594.6
+ ,19823.1
+ ,20844.4
+ ,92.5
+ ,18574.1
+ ,21350.6
+ ,18594.6
+ ,19823.1
+ ,117.2
+ ,18924.2
+ ,18574.1
+ ,21350.6
+ ,18594.6
+ ,109.3
+ ,17343.4
+ ,18924.2
+ ,18574.1
+ ,21350.6
+ ,106.1
+ ,19961.2
+ ,17343.4
+ ,18924.2
+ ,18574.1
+ ,118.8
+ ,19932.1
+ ,19961.2
+ ,17343.4
+ ,18924.2
+ ,105.3
+ ,19464.6
+ ,19932.1
+ ,19961.2
+ ,17343.4
+ ,106
+ ,16165.4
+ ,19464.6
+ ,19932.1
+ ,19961.2
+ ,102
+ ,17574.9
+ ,16165.4
+ ,19464.6
+ ,19932.1
+ ,112.9
+ ,19795.4
+ ,17574.9
+ ,16165.4
+ ,19464.6
+ ,116.5
+ ,19439.5
+ ,19795.4
+ ,17574.9
+ ,16165.4
+ ,114.8
+ ,17170
+ ,19439.5
+ ,19795.4
+ ,17574.9
+ ,100.5
+ ,21072.4
+ ,17170
+ ,19439.5
+ ,19795.4
+ ,85.4
+ ,17751.8
+ ,21072.4
+ ,17170
+ ,19439.5
+ ,114.6
+ ,17515.5
+ ,17751.8
+ ,21072.4
+ ,17170
+ ,109.9
+ ,18040.3
+ ,17515.5
+ ,17751.8
+ ,21072.4
+ ,100.7
+ ,19090.1
+ ,18040.3
+ ,17515.5
+ ,17751.8
+ ,115.5
+ ,17746.5
+ ,19090.1
+ ,18040.3
+ ,17515.5
+ ,100.7
+ ,19202.1
+ ,17746.5
+ ,19090.1
+ ,18040.3
+ ,99
+ ,15141.6
+ ,19202.1
+ ,17746.5
+ ,19090.1
+ ,102.3
+ ,16258.1
+ ,15141.6
+ ,19202.1
+ ,17746.5
+ ,108.8
+ ,18586.5
+ ,16258.1
+ ,15141.6
+ ,19202.1
+ ,105.9
+ ,17209.4
+ ,18586.5
+ ,16258.1
+ ,15141.6
+ ,113.2
+ ,17838.7
+ ,17209.4
+ ,18586.5
+ ,16258.1
+ ,95.7
+ ,19123.5
+ ,17838.7
+ ,17209.4
+ ,18586.5
+ ,80.9
+ ,16583.6
+ ,19123.5
+ ,17838.7
+ ,17209.4
+ ,113.9
+ ,15991.2
+ ,16583.6
+ ,19123.5
+ ,17838.7
+ ,98.1
+ ,16704.4
+ ,15991.2
+ ,16583.6
+ ,19123.5
+ ,102.8
+ ,17420.4
+ ,16704.4
+ ,15991.2
+ ,16583.6
+ ,104.7
+ ,17872
+ ,17420.4
+ ,16704.4
+ ,15991.2
+ ,95.9
+ ,17823.2
+ ,17872
+ ,17420.4
+ ,16704.4
+ ,94.6)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('uitvoer'
+ ,'uitvoer1'
+ ,'uitvoer2'
+ ,'uitvoer3'
+ ,'indproc')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('uitvoer','uitvoer1','uitvoer2','uitvoer3','indproc'),1:56))
> 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
uitvoer uitvoer1 uitvoer2 uitvoer3 indproc M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 16643.0 16196.7 18252.1 17570.4 89.1 1 0 0 0 0 0 0 0 0 0
2 17729.0 16643.0 16196.7 18252.1 82.6 0 1 0 0 0 0 0 0 0 0
3 16446.1 17729.0 16643.0 16196.7 102.7 0 0 1 0 0 0 0 0 0 0
4 15993.8 16446.1 17729.0 16643.0 91.8 0 0 0 1 0 0 0 0 0 0
5 16373.5 15993.8 16446.1 17729.0 94.1 0 0 0 0 1 0 0 0 0 0
6 17842.2 16373.5 15993.8 16446.1 103.1 0 0 0 0 0 1 0 0 0 0
7 22321.5 17842.2 16373.5 15993.8 93.2 0 0 0 0 0 0 1 0 0 0
8 22786.7 22321.5 17842.2 16373.5 91.0 0 0 0 0 0 0 0 1 0 0
9 18274.1 22786.7 22321.5 17842.2 94.3 0 0 0 0 0 0 0 0 1 0
10 22392.9 18274.1 22786.7 22321.5 99.4 0 0 0 0 0 0 0 0 0 1
11 23899.3 22392.9 18274.1 22786.7 115.7 0 0 0 0 0 0 0 0 0 0
12 21343.5 23899.3 22392.9 18274.1 116.8 0 0 0 0 0 0 0 0 0 0
13 22952.3 21343.5 23899.3 22392.9 99.8 1 0 0 0 0 0 0 0 0 0
14 21374.4 22952.3 21343.5 23899.3 96.0 0 1 0 0 0 0 0 0 0 0
15 21164.1 21374.4 22952.3 21343.5 115.9 0 0 1 0 0 0 0 0 0 0
16 20906.5 21164.1 21374.4 22952.3 109.1 0 0 0 1 0 0 0 0 0 0
17 17877.4 20906.5 21164.1 21374.4 117.3 0 0 0 0 1 0 0 0 0 0
18 20664.3 17877.4 20906.5 21164.1 109.8 0 0 0 0 0 1 0 0 0 0
19 22160.0 20664.3 17877.4 20906.5 112.8 0 0 0 0 0 0 1 0 0 0
20 19813.6 22160.0 20664.3 17877.4 110.7 0 0 0 0 0 0 0 1 0 0
21 17735.4 19813.6 22160.0 20664.3 100.0 0 0 0 0 0 0 0 0 1 0
22 19640.2 17735.4 19813.6 22160.0 113.3 0 0 0 0 0 0 0 0 0 1
23 20844.4 19640.2 17735.4 19813.6 122.4 0 0 0 0 0 0 0 0 0 0
24 19823.1 20844.4 19640.2 17735.4 112.5 0 0 0 0 0 0 0 0 0 0
25 18594.6 19823.1 20844.4 19640.2 104.2 1 0 0 0 0 0 0 0 0 0
26 21350.6 18594.6 19823.1 20844.4 92.5 0 1 0 0 0 0 0 0 0 0
27 18574.1 21350.6 18594.6 19823.1 117.2 0 0 1 0 0 0 0 0 0 0
28 18924.2 18574.1 21350.6 18594.6 109.3 0 0 0 1 0 0 0 0 0 0
29 17343.4 18924.2 18574.1 21350.6 106.1 0 0 0 0 1 0 0 0 0 0
30 19961.2 17343.4 18924.2 18574.1 118.8 0 0 0 0 0 1 0 0 0 0
31 19932.1 19961.2 17343.4 18924.2 105.3 0 0 0 0 0 0 1 0 0 0
32 19464.6 19932.1 19961.2 17343.4 106.0 0 0 0 0 0 0 0 1 0 0
33 16165.4 19464.6 19932.1 19961.2 102.0 0 0 0 0 0 0 0 0 1 0
34 17574.9 16165.4 19464.6 19932.1 112.9 0 0 0 0 0 0 0 0 0 1
35 19795.4 17574.9 16165.4 19464.6 116.5 0 0 0 0 0 0 0 0 0 0
36 19439.5 19795.4 17574.9 16165.4 114.8 0 0 0 0 0 0 0 0 0 0
37 17170.0 19439.5 19795.4 17574.9 100.5 1 0 0 0 0 0 0 0 0 0
38 21072.4 17170.0 19439.5 19795.4 85.4 0 1 0 0 0 0 0 0 0 0
39 17751.8 21072.4 17170.0 19439.5 114.6 0 0 1 0 0 0 0 0 0 0
40 17515.5 17751.8 21072.4 17170.0 109.9 0 0 0 1 0 0 0 0 0 0
41 18040.3 17515.5 17751.8 21072.4 100.7 0 0 0 0 1 0 0 0 0 0
42 19090.1 18040.3 17515.5 17751.8 115.5 0 0 0 0 0 1 0 0 0 0
43 17746.5 19090.1 18040.3 17515.5 100.7 0 0 0 0 0 0 1 0 0 0
44 19202.1 17746.5 19090.1 18040.3 99.0 0 0 0 0 0 0 0 1 0 0
45 15141.6 19202.1 17746.5 19090.1 102.3 0 0 0 0 0 0 0 0 1 0
46 16258.1 15141.6 19202.1 17746.5 108.8 0 0 0 0 0 0 0 0 0 1
47 18586.5 16258.1 15141.6 19202.1 105.9 0 0 0 0 0 0 0 0 0 0
48 17209.4 18586.5 16258.1 15141.6 113.2 0 0 0 0 0 0 0 0 0 0
49 17838.7 17209.4 18586.5 16258.1 95.7 1 0 0 0 0 0 0 0 0 0
50 19123.5 17838.7 17209.4 18586.5 80.9 0 1 0 0 0 0 0 0 0 0
51 16583.6 19123.5 17838.7 17209.4 113.9 0 0 1 0 0 0 0 0 0 0
52 15991.2 16583.6 19123.5 17838.7 98.1 0 0 0 1 0 0 0 0 0 0
53 16704.4 15991.2 16583.6 19123.5 102.8 0 0 0 0 1 0 0 0 0 0
54 17420.4 16704.4 15991.2 16583.6 104.7 0 0 0 0 0 1 0 0 0 0
55 17872.0 17420.4 16704.4 15991.2 95.9 0 0 0 0 0 0 1 0 0 0
56 17823.2 17872.0 17420.4 16704.4 94.6 0 0 0 0 0 0 0 1 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) uitvoer1 uitvoer2 uitvoer3 indproc M1
9486.8777 0.3185 0.3158 0.3473 -70.0797 -2475.7242
M2 M3 M4 M5 M6 M7
-1731.6135 -1874.8436 -2511.5793 -2856.9582 289.2147 420.3258
M8 M9 M10 M11 t
-504.4890 -4938.9226 -1376.6297 1409.5134 -15.8575
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2283.53 -653.57 -28.07 605.72 2647.41
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9486.8777 2815.2405 3.370 0.00171 **
uitvoer1 0.3185 0.1455 2.189 0.03462 *
uitvoer2 0.3158 0.1365 2.313 0.02606 *
uitvoer3 0.3473 0.1360 2.554 0.01468 *
indproc -70.0797 32.5094 -2.156 0.03734 *
M1 -2475.7242 993.9006 -2.491 0.01711 *
M2 -1731.6135 1280.4939 -1.352 0.18407
M3 -1874.8436 788.0418 -2.379 0.02234 *
M4 -2511.5793 949.3788 -2.645 0.01170 *
M5 -2856.9582 1048.2769 -2.725 0.00957 **
M6 289.2147 909.4291 0.318 0.75217
M7 420.3258 871.2041 0.482 0.63217
M8 -504.4890 851.2743 -0.593 0.55685
M9 -4938.9226 995.4248 -4.962 1.41e-05 ***
M10 -1376.6297 1177.1471 -1.169 0.24932
M11 1409.5134 1043.0096 1.351 0.18436
t -15.8575 10.6792 -1.485 0.14561
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1065 on 39 degrees of freedom
Multiple R-squared: 0.8124, Adjusted R-squared: 0.7354
F-statistic: 10.55 on 16 and 39 DF, p-value: 1.307e-09
> 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,] 0.9577403 0.08451942 0.04225971
[2,] 0.9605745 0.07885105 0.03942553
[3,] 0.9654417 0.06911658 0.03455829
[4,] 0.9457874 0.10842527 0.05421264
[5,] 0.9301623 0.13967538 0.06983769
[6,] 0.8949634 0.21007311 0.10503656
[7,] 0.9273627 0.14527458 0.07263729
[8,] 0.8929315 0.21413696 0.10706848
[9,] 0.8616987 0.27660261 0.13830130
[10,] 0.9314996 0.13700080 0.06850040
[11,] 0.9489435 0.10211310 0.05105655
[12,] 0.9733556 0.05328889 0.02664444
[13,] 0.9674975 0.06500491 0.03250245
[14,] 0.9424284 0.11514328 0.05757164
[15,] 0.8812841 0.23743172 0.11871586
[16,] 0.7780039 0.44399212 0.22199606
[17,] 0.7489530 0.50209400 0.25104700
> postscript(file="/var/www/html/rcomp/tmp/1voh71258481096.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2es6h1258481096.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34avh1258481096.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kuhx1258481096.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53gwu1258481096.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 = 56
Frequency = 1
1 2 3 4 5 6
-1134.22958 -961.81584 -450.01422 -1102.92141 -28.76780 -592.19185
7 8 9 10 11 12
2647.41423 1876.58712 -27.36551 637.14728 467.13026 -799.48289
13 14 15 16 17 18
1017.36472 -1783.60929 441.99278 366.97627 -1029.72318 -779.46641
19 20 21 22 23 24
969.58594 -887.79817 41.53842 215.46919 151.63968 -401.40728
25 26 27 28 29 30
-436.54723 1066.87871 45.24594 935.03490 -700.67718 1034.13710
31 32 33 34 35 36
-512.51770 -258.70091 -139.04575 -303.45307 154.36899 1098.15067
37 38 39 40 41 42
-759.34979 1420.67541 -97.21746 603.25950 613.11969 630.56360
43 44 45 46 47 48
-2283.53228 -92.22482 124.87284 -549.16340 -773.13894 102.73950
49 50 51 52 53 54
1312.76188 257.87100 59.99296 -802.34925 1146.04848 -293.04243
55 56
-820.95019 -637.86323
> postscript(file="/var/www/html/rcomp/tmp/6kcxt1258481096.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -1134.22958 NA
1 -961.81584 -1134.22958
2 -450.01422 -961.81584
3 -1102.92141 -450.01422
4 -28.76780 -1102.92141
5 -592.19185 -28.76780
6 2647.41423 -592.19185
7 1876.58712 2647.41423
8 -27.36551 1876.58712
9 637.14728 -27.36551
10 467.13026 637.14728
11 -799.48289 467.13026
12 1017.36472 -799.48289
13 -1783.60929 1017.36472
14 441.99278 -1783.60929
15 366.97627 441.99278
16 -1029.72318 366.97627
17 -779.46641 -1029.72318
18 969.58594 -779.46641
19 -887.79817 969.58594
20 41.53842 -887.79817
21 215.46919 41.53842
22 151.63968 215.46919
23 -401.40728 151.63968
24 -436.54723 -401.40728
25 1066.87871 -436.54723
26 45.24594 1066.87871
27 935.03490 45.24594
28 -700.67718 935.03490
29 1034.13710 -700.67718
30 -512.51770 1034.13710
31 -258.70091 -512.51770
32 -139.04575 -258.70091
33 -303.45307 -139.04575
34 154.36899 -303.45307
35 1098.15067 154.36899
36 -759.34979 1098.15067
37 1420.67541 -759.34979
38 -97.21746 1420.67541
39 603.25950 -97.21746
40 613.11969 603.25950
41 630.56360 613.11969
42 -2283.53228 630.56360
43 -92.22482 -2283.53228
44 124.87284 -92.22482
45 -549.16340 124.87284
46 -773.13894 -549.16340
47 102.73950 -773.13894
48 1312.76188 102.73950
49 257.87100 1312.76188
50 59.99296 257.87100
51 -802.34925 59.99296
52 1146.04848 -802.34925
53 -293.04243 1146.04848
54 -820.95019 -293.04243
55 -637.86323 -820.95019
56 NA -637.86323
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -961.81584 -1134.22958
[2,] -450.01422 -961.81584
[3,] -1102.92141 -450.01422
[4,] -28.76780 -1102.92141
[5,] -592.19185 -28.76780
[6,] 2647.41423 -592.19185
[7,] 1876.58712 2647.41423
[8,] -27.36551 1876.58712
[9,] 637.14728 -27.36551
[10,] 467.13026 637.14728
[11,] -799.48289 467.13026
[12,] 1017.36472 -799.48289
[13,] -1783.60929 1017.36472
[14,] 441.99278 -1783.60929
[15,] 366.97627 441.99278
[16,] -1029.72318 366.97627
[17,] -779.46641 -1029.72318
[18,] 969.58594 -779.46641
[19,] -887.79817 969.58594
[20,] 41.53842 -887.79817
[21,] 215.46919 41.53842
[22,] 151.63968 215.46919
[23,] -401.40728 151.63968
[24,] -436.54723 -401.40728
[25,] 1066.87871 -436.54723
[26,] 45.24594 1066.87871
[27,] 935.03490 45.24594
[28,] -700.67718 935.03490
[29,] 1034.13710 -700.67718
[30,] -512.51770 1034.13710
[31,] -258.70091 -512.51770
[32,] -139.04575 -258.70091
[33,] -303.45307 -139.04575
[34,] 154.36899 -303.45307
[35,] 1098.15067 154.36899
[36,] -759.34979 1098.15067
[37,] 1420.67541 -759.34979
[38,] -97.21746 1420.67541
[39,] 603.25950 -97.21746
[40,] 613.11969 603.25950
[41,] 630.56360 613.11969
[42,] -2283.53228 630.56360
[43,] -92.22482 -2283.53228
[44,] 124.87284 -92.22482
[45,] -549.16340 124.87284
[46,] -773.13894 -549.16340
[47,] 102.73950 -773.13894
[48,] 1312.76188 102.73950
[49,] 257.87100 1312.76188
[50,] 59.99296 257.87100
[51,] -802.34925 59.99296
[52,] 1146.04848 -802.34925
[53,] -293.04243 1146.04848
[54,] -820.95019 -293.04243
[55,] -637.86323 -820.95019
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -961.81584 -1134.22958
2 -450.01422 -961.81584
3 -1102.92141 -450.01422
4 -28.76780 -1102.92141
5 -592.19185 -28.76780
6 2647.41423 -592.19185
7 1876.58712 2647.41423
8 -27.36551 1876.58712
9 637.14728 -27.36551
10 467.13026 637.14728
11 -799.48289 467.13026
12 1017.36472 -799.48289
13 -1783.60929 1017.36472
14 441.99278 -1783.60929
15 366.97627 441.99278
16 -1029.72318 366.97627
17 -779.46641 -1029.72318
18 969.58594 -779.46641
19 -887.79817 969.58594
20 41.53842 -887.79817
21 215.46919 41.53842
22 151.63968 215.46919
23 -401.40728 151.63968
24 -436.54723 -401.40728
25 1066.87871 -436.54723
26 45.24594 1066.87871
27 935.03490 45.24594
28 -700.67718 935.03490
29 1034.13710 -700.67718
30 -512.51770 1034.13710
31 -258.70091 -512.51770
32 -139.04575 -258.70091
33 -303.45307 -139.04575
34 154.36899 -303.45307
35 1098.15067 154.36899
36 -759.34979 1098.15067
37 1420.67541 -759.34979
38 -97.21746 1420.67541
39 603.25950 -97.21746
40 613.11969 603.25950
41 630.56360 613.11969
42 -2283.53228 630.56360
43 -92.22482 -2283.53228
44 124.87284 -92.22482
45 -549.16340 124.87284
46 -773.13894 -549.16340
47 102.73950 -773.13894
48 1312.76188 102.73950
49 257.87100 1312.76188
50 59.99296 257.87100
51 -802.34925 59.99296
52 1146.04848 -802.34925
53 -293.04243 1146.04848
54 -820.95019 -293.04243
55 -637.86323 -820.95019
> 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/7aiw21258481096.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/825ms1258481096.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qs021258481096.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107ra61258481096.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ptk31258481096.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/12x7p61258481096.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/132z761258481097.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/14qfqr1258481097.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/153ri01258481097.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/16rqw61258481097.tab")
+ }
>
> system("convert tmp/1voh71258481096.ps tmp/1voh71258481096.png")
> system("convert tmp/2es6h1258481096.ps tmp/2es6h1258481096.png")
> system("convert tmp/34avh1258481096.ps tmp/34avh1258481096.png")
> system("convert tmp/4kuhx1258481096.ps tmp/4kuhx1258481096.png")
> system("convert tmp/53gwu1258481096.ps tmp/53gwu1258481096.png")
> system("convert tmp/6kcxt1258481096.ps tmp/6kcxt1258481096.png")
> system("convert tmp/7aiw21258481096.ps tmp/7aiw21258481096.png")
> system("convert tmp/825ms1258481096.ps tmp/825ms1258481096.png")
> system("convert tmp/9qs021258481096.ps tmp/9qs021258481096.png")
> system("convert tmp/107ra61258481096.ps tmp/107ra61258481096.png")
>
>
> proc.time()
user system elapsed
2.330 1.547 2.885