R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(14929388,0,0,14717825,0,0,15826281,0,0,16301310,0,0,15033017,0,0,16998461,0,0,14066463,0,0,13328937,0,0,17319718,0,0,17586427,0,0,15887037,0,0,17935679,0,0,15869489,0,0,15892511,0,0,17556558,0,0,16791643,0,0,15953689,0,0,18144914,0,1,14390881,0,1,13885709,0,1,17332572,0,1,17152596,0,1,16003877,0,1,16841467,0,1,14783398,0,1,14667848,0,1,17714362,0,1,16282088,0,1,15014866,1,0,17722582,1,0,13876509,1,0,15495490,1,0,17799521,1,0,17920079,1,0,17248022,1,0,18813782,1,0,16249688,1,0,17823359,0,0,20424438,0,0,17814219,0,0,19699960,0,0,19776328,0,0,15679833,0,0,17119267,0,0,20092613,0,0,20863688,0,0,20925203,0,0,21032593,0,0,20664684,0,0,19711511,0,0,22553293,0,0,19498333,0,0,20722828,0,0,21321275,0,0,17960848,0,0,17789655,0,0,20003709,0,0,21169852,0,0,20422839,0,0,19810562,0,0),dim=c(3,60),dimnames=list(c('Omzet_Industriële_Sector','Dummy_1_tijdenscrisis','Dummy_2_voorcrisis'),1:60))
> y <- array(NA,dim=c(3,60),dimnames=list(c('Omzet_Industriële_Sector','Dummy_1_tijdenscrisis','Dummy_2_voorcrisis'),1:60))
> 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_Industri\353le_Sector Dummy_1_tijdenscrisis Dummy_2_voorcrisis M1 M2
1 14929388 0 0 1 0
2 14717825 0 0 0 1
3 15826281 0 0 0 0
4 16301310 0 0 0 0
5 15033017 0 0 0 0
6 16998461 0 0 0 0
7 14066463 0 0 0 0
8 13328937 0 0 0 0
9 17319718 0 0 0 0
10 17586427 0 0 0 0
11 15887037 0 0 0 0
12 17935679 0 0 0 0
13 15869489 0 0 1 0
14 15892511 0 0 0 1
15 17556558 0 0 0 0
16 16791643 0 0 0 0
17 15953689 0 0 0 0
18 18144914 0 1 0 0
19 14390881 0 1 0 0
20 13885709 0 1 0 0
21 17332572 0 1 0 0
22 17152596 0 1 0 0
23 16003877 0 1 0 0
24 16841467 0 1 0 0
25 14783398 0 1 1 0
26 14667848 0 1 0 1
27 17714362 0 1 0 0
28 16282088 0 1 0 0
29 15014866 1 0 0 0
30 17722582 1 0 0 0
31 13876509 1 0 0 0
32 15495490 1 0 0 0
33 17799521 1 0 0 0
34 17920079 1 0 0 0
35 17248022 1 0 0 0
36 18813782 1 0 0 0
37 16249688 1 0 1 0
38 17823359 0 0 0 1
39 20424438 0 0 0 0
40 17814219 0 0 0 0
41 19699960 0 0 0 0
42 19776328 0 0 0 0
43 15679833 0 0 0 0
44 17119267 0 0 0 0
45 20092613 0 0 0 0
46 20863688 0 0 0 0
47 20925203 0 0 0 0
48 21032593 0 0 0 0
49 20664684 0 0 1 0
50 19711511 0 0 0 1
51 22553293 0 0 0 0
52 19498333 0 0 0 0
53 20722828 0 0 0 0
54 21321275 0 0 0 0
55 17960848 0 0 0 0
56 17789655 0 0 0 0
57 20003709 0 0 0 0
58 21169852 0 0 0 0
59 20422839 0 0 0 0
60 19810562 0 0 0 0
M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 0 0 0 0 0 0 0 0 0 1
2 0 0 0 0 0 0 0 0 0 2
3 1 0 0 0 0 0 0 0 0 3
4 0 1 0 0 0 0 0 0 0 4
5 0 0 1 0 0 0 0 0 0 5
6 0 0 0 1 0 0 0 0 0 6
7 0 0 0 0 1 0 0 0 0 7
8 0 0 0 0 0 1 0 0 0 8
9 0 0 0 0 0 0 1 0 0 9
10 0 0 0 0 0 0 0 1 0 10
11 0 0 0 0 0 0 0 0 1 11
12 0 0 0 0 0 0 0 0 0 12
13 0 0 0 0 0 0 0 0 0 13
14 0 0 0 0 0 0 0 0 0 14
15 1 0 0 0 0 0 0 0 0 15
16 0 1 0 0 0 0 0 0 0 16
17 0 0 1 0 0 0 0 0 0 17
18 0 0 0 1 0 0 0 0 0 18
19 0 0 0 0 1 0 0 0 0 19
20 0 0 0 0 0 1 0 0 0 20
21 0 0 0 0 0 0 1 0 0 21
22 0 0 0 0 0 0 0 1 0 22
23 0 0 0 0 0 0 0 0 1 23
24 0 0 0 0 0 0 0 0 0 24
25 0 0 0 0 0 0 0 0 0 25
26 0 0 0 0 0 0 0 0 0 26
27 1 0 0 0 0 0 0 0 0 27
28 0 1 0 0 0 0 0 0 0 28
29 0 0 1 0 0 0 0 0 0 29
30 0 0 0 1 0 0 0 0 0 30
31 0 0 0 0 1 0 0 0 0 31
32 0 0 0 0 0 1 0 0 0 32
33 0 0 0 0 0 0 1 0 0 33
34 0 0 0 0 0 0 0 1 0 34
35 0 0 0 0 0 0 0 0 1 35
36 0 0 0 0 0 0 0 0 0 36
37 0 0 0 0 0 0 0 0 0 37
38 0 0 0 0 0 0 0 0 0 38
39 1 0 0 0 0 0 0 0 0 39
40 0 1 0 0 0 0 0 0 0 40
41 0 0 1 0 0 0 0 0 0 41
42 0 0 0 1 0 0 0 0 0 42
43 0 0 0 0 1 0 0 0 0 43
44 0 0 0 0 0 1 0 0 0 44
45 0 0 0 0 0 0 1 0 0 45
46 0 0 0 0 0 0 0 1 0 46
47 0 0 0 0 0 0 0 0 1 47
48 0 0 0 0 0 0 0 0 0 48
49 0 0 0 0 0 0 0 0 0 49
50 0 0 0 0 0 0 0 0 0 50
51 1 0 0 0 0 0 0 0 0 51
52 0 1 0 0 0 0 0 0 0 52
53 0 0 1 0 0 0 0 0 0 53
54 0 0 0 1 0 0 0 0 0 54
55 0 0 0 0 1 0 0 0 0 55
56 0 0 0 0 0 1 0 0 0 56
57 0 0 0 0 0 0 1 0 0 57
58 0 0 0 0 0 0 0 1 0 58
59 0 0 0 0 0 0 0 0 1 59
60 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy_1_tijdenscrisis Dummy_2_voorcrisis
16230758 -1476067 -1233878
M1 M2 M3
-1410306 -1731073 432468
M4 M5 M6
-1133834 -1226878 438903
M7 M8 M9
-3247737 -3007666 -110686
M10 M11 t
229381 -700586 88835
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1750275 -409892 -40035 415500 1491334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16230758 433828 37.413 < 2e-16 ***
Dummy_1_tijdenscrisis -1476067 301162 -4.901 1.28e-05 ***
Dummy_2_voorcrisis -1233878 278799 -4.426 6.05e-05 ***
M1 -1410306 505209 -2.792 0.00767 **
M2 -1731073 508073 -3.407 0.00139 **
M3 432468 507354 0.852 0.39851
M4 -1133834 506709 -2.238 0.03024 *
M5 -1226878 506590 -2.422 0.01953 *
M6 438903 502000 0.874 0.38659
M7 -3247737 501583 -6.475 6.14e-08 ***
M8 -3007666 501242 -6.000 3.12e-07 ***
M9 -110686 500976 -0.221 0.82614
M10 229381 500786 0.458 0.64913
M11 -700586 500672 -1.399 0.16858
t 88835 6167 14.405 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 791600 on 45 degrees of freedom
Multiple R-squared: 0.9069, Adjusted R-squared: 0.8779
F-statistic: 31.29 on 14 and 45 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,] 0.09872559 0.19745117 0.9012744
[2,] 0.07265901 0.14531803 0.9273410
[3,] 0.02840430 0.05680859 0.9715957
[4,] 0.02210473 0.04420945 0.9778953
[5,] 0.02572434 0.05144869 0.9742757
[6,] 0.01107087 0.02214174 0.9889291
[7,] 0.02900452 0.05800905 0.9709955
[8,] 0.05091551 0.10183101 0.9490845
[9,] 0.05544435 0.11088869 0.9445557
[10,] 0.05237781 0.10475561 0.9476222
[11,] 0.03462133 0.06924265 0.9653787
[12,] 0.05359820 0.10719640 0.9464018
[13,] 0.02999362 0.05998723 0.9700064
[14,] 0.01754868 0.03509735 0.9824513
[15,] 0.05848726 0.11697452 0.9415127
[16,] 0.03948763 0.07897526 0.9605124
[17,] 0.02153793 0.04307586 0.9784621
[18,] 0.01565138 0.03130276 0.9843486
[19,] 0.05357298 0.10714596 0.9464270
[20,] 0.02927710 0.05855420 0.9707229
[21,] 0.02266621 0.04533241 0.9773338
[22,] 0.02976990 0.05953979 0.9702301
[23,] 0.04495364 0.08990729 0.9550464
[24,] 0.05024237 0.10048474 0.9497576
[25,] 0.05468836 0.10937672 0.9453116
> postscript(file="/var/www/html/freestat/rcomp/tmp/1kp401228336428.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2rmnw1228336428.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3bp371228336428.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4ul0h1228336428.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5eccu1228336428.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 = 60
Frequency = 1
1 2 3 4 5 6
20101.22 40470.17 -1103449.43 849047.37 -415036.77 -204208.38
7 8 9 10 11 12
461598.82 -604831.98 400134.02 237941.22 -620315.98 638905.02
13 14 15 16 17 18
-105813.57 149140.38 -439188.22 273364.58 -560380.56 1110106.90
19 20 21 22 23 24
953879.10 119802.30 580850.30 -28027.50 -335613.70 -287444.70
25 26 27 28 29 30
-1024042.29 -907660.34 -113521.94 -68328.14 -1089152.59 -136052.20
31 32 33 34 35 36
-384320.00 905756.20 223972.20 -84371.60 84704.20 861043.20
37 38 39 40 41 42
-381579.40 -52043.21 296660.19 -836091.01 1053858.85 -624388.76
43 44 45 46 47 48
-1123078.56 -12549.36 -25018.36 317154.84 1219802.64 537771.64
49 50 51 52 53 54
1491334.04 770093.00 1359499.40 -217992.80 1010711.06 -145457.56
55 56 57 58 59 60
91920.64 -408177.16 -1179938.16 -442696.96 -348577.16 -1750275.16
> postscript(file="/var/www/html/freestat/rcomp/tmp/6xj0f1228336428.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 20101.22 NA
1 40470.17 20101.22
2 -1103449.43 40470.17
3 849047.37 -1103449.43
4 -415036.77 849047.37
5 -204208.38 -415036.77
6 461598.82 -204208.38
7 -604831.98 461598.82
8 400134.02 -604831.98
9 237941.22 400134.02
10 -620315.98 237941.22
11 638905.02 -620315.98
12 -105813.57 638905.02
13 149140.38 -105813.57
14 -439188.22 149140.38
15 273364.58 -439188.22
16 -560380.56 273364.58
17 1110106.90 -560380.56
18 953879.10 1110106.90
19 119802.30 953879.10
20 580850.30 119802.30
21 -28027.50 580850.30
22 -335613.70 -28027.50
23 -287444.70 -335613.70
24 -1024042.29 -287444.70
25 -907660.34 -1024042.29
26 -113521.94 -907660.34
27 -68328.14 -113521.94
28 -1089152.59 -68328.14
29 -136052.20 -1089152.59
30 -384320.00 -136052.20
31 905756.20 -384320.00
32 223972.20 905756.20
33 -84371.60 223972.20
34 84704.20 -84371.60
35 861043.20 84704.20
36 -381579.40 861043.20
37 -52043.21 -381579.40
38 296660.19 -52043.21
39 -836091.01 296660.19
40 1053858.85 -836091.01
41 -624388.76 1053858.85
42 -1123078.56 -624388.76
43 -12549.36 -1123078.56
44 -25018.36 -12549.36
45 317154.84 -25018.36
46 1219802.64 317154.84
47 537771.64 1219802.64
48 1491334.04 537771.64
49 770093.00 1491334.04
50 1359499.40 770093.00
51 -217992.80 1359499.40
52 1010711.06 -217992.80
53 -145457.56 1010711.06
54 91920.64 -145457.56
55 -408177.16 91920.64
56 -1179938.16 -408177.16
57 -442696.96 -1179938.16
58 -348577.16 -442696.96
59 -1750275.16 -348577.16
60 NA -1750275.16
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 40470.17 20101.22
[2,] -1103449.43 40470.17
[3,] 849047.37 -1103449.43
[4,] -415036.77 849047.37
[5,] -204208.38 -415036.77
[6,] 461598.82 -204208.38
[7,] -604831.98 461598.82
[8,] 400134.02 -604831.98
[9,] 237941.22 400134.02
[10,] -620315.98 237941.22
[11,] 638905.02 -620315.98
[12,] -105813.57 638905.02
[13,] 149140.38 -105813.57
[14,] -439188.22 149140.38
[15,] 273364.58 -439188.22
[16,] -560380.56 273364.58
[17,] 1110106.90 -560380.56
[18,] 953879.10 1110106.90
[19,] 119802.30 953879.10
[20,] 580850.30 119802.30
[21,] -28027.50 580850.30
[22,] -335613.70 -28027.50
[23,] -287444.70 -335613.70
[24,] -1024042.29 -287444.70
[25,] -907660.34 -1024042.29
[26,] -113521.94 -907660.34
[27,] -68328.14 -113521.94
[28,] -1089152.59 -68328.14
[29,] -136052.20 -1089152.59
[30,] -384320.00 -136052.20
[31,] 905756.20 -384320.00
[32,] 223972.20 905756.20
[33,] -84371.60 223972.20
[34,] 84704.20 -84371.60
[35,] 861043.20 84704.20
[36,] -381579.40 861043.20
[37,] -52043.21 -381579.40
[38,] 296660.19 -52043.21
[39,] -836091.01 296660.19
[40,] 1053858.85 -836091.01
[41,] -624388.76 1053858.85
[42,] -1123078.56 -624388.76
[43,] -12549.36 -1123078.56
[44,] -25018.36 -12549.36
[45,] 317154.84 -25018.36
[46,] 1219802.64 317154.84
[47,] 537771.64 1219802.64
[48,] 1491334.04 537771.64
[49,] 770093.00 1491334.04
[50,] 1359499.40 770093.00
[51,] -217992.80 1359499.40
[52,] 1010711.06 -217992.80
[53,] -145457.56 1010711.06
[54,] 91920.64 -145457.56
[55,] -408177.16 91920.64
[56,] -1179938.16 -408177.16
[57,] -442696.96 -1179938.16
[58,] -348577.16 -442696.96
[59,] -1750275.16 -348577.16
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 40470.17 20101.22
2 -1103449.43 40470.17
3 849047.37 -1103449.43
4 -415036.77 849047.37
5 -204208.38 -415036.77
6 461598.82 -204208.38
7 -604831.98 461598.82
8 400134.02 -604831.98
9 237941.22 400134.02
10 -620315.98 237941.22
11 638905.02 -620315.98
12 -105813.57 638905.02
13 149140.38 -105813.57
14 -439188.22 149140.38
15 273364.58 -439188.22
16 -560380.56 273364.58
17 1110106.90 -560380.56
18 953879.10 1110106.90
19 119802.30 953879.10
20 580850.30 119802.30
21 -28027.50 580850.30
22 -335613.70 -28027.50
23 -287444.70 -335613.70
24 -1024042.29 -287444.70
25 -907660.34 -1024042.29
26 -113521.94 -907660.34
27 -68328.14 -113521.94
28 -1089152.59 -68328.14
29 -136052.20 -1089152.59
30 -384320.00 -136052.20
31 905756.20 -384320.00
32 223972.20 905756.20
33 -84371.60 223972.20
34 84704.20 -84371.60
35 861043.20 84704.20
36 -381579.40 861043.20
37 -52043.21 -381579.40
38 296660.19 -52043.21
39 -836091.01 296660.19
40 1053858.85 -836091.01
41 -624388.76 1053858.85
42 -1123078.56 -624388.76
43 -12549.36 -1123078.56
44 -25018.36 -12549.36
45 317154.84 -25018.36
46 1219802.64 317154.84
47 537771.64 1219802.64
48 1491334.04 537771.64
49 770093.00 1491334.04
50 1359499.40 770093.00
51 -217992.80 1359499.40
52 1010711.06 -217992.80
53 -145457.56 1010711.06
54 91920.64 -145457.56
55 -408177.16 91920.64
56 -1179938.16 -408177.16
57 -442696.96 -1179938.16
58 -348577.16 -442696.96
59 -1750275.16 -348577.16
> 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/freestat/rcomp/tmp/7993v1228336428.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8fe721228336428.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/92jvi1228336428.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10oxmw1228336428.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/1151f71228336428.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/freestat/rcomp/tmp/12ioue1228336429.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/freestat/rcomp/tmp/13tg0j1228336429.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/freestat/rcomp/tmp/14zqft1228336429.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/freestat/rcomp/tmp/1509c61228336429.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/freestat/rcomp/tmp/16vpqd1228336429.tab")
+ }
>
> system("convert tmp/1kp401228336428.ps tmp/1kp401228336428.png")
> system("convert tmp/2rmnw1228336428.ps tmp/2rmnw1228336428.png")
> system("convert tmp/3bp371228336428.ps tmp/3bp371228336428.png")
> system("convert tmp/4ul0h1228336428.ps tmp/4ul0h1228336428.png")
> system("convert tmp/5eccu1228336428.ps tmp/5eccu1228336428.png")
> system("convert tmp/6xj0f1228336428.ps tmp/6xj0f1228336428.png")
> system("convert tmp/7993v1228336428.ps tmp/7993v1228336428.png")
> system("convert tmp/8fe721228336428.ps tmp/8fe721228336428.png")
> system("convert tmp/92jvi1228336428.ps tmp/92jvi1228336428.png")
> system("convert tmp/10oxmw1228336428.ps tmp/10oxmw1228336428.png")
>
>
> proc.time()
user system elapsed
3.725 2.538 4.801