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(99.29,0,98.69,0,107.92,0,101.03,0,97.55,0,103.02,0,94.08,0,94.12,0,115.08,0,116.48,0,103.42,0,112.51,0,95.55,0,97.53,0,119.26,0,100.94,0,97.73,0,115.25,0,92.8,0,99.2,0,118.69,0,110.12,0,110.26,0,112.9,0,102.17,1,99.38,1,116.1,1,103.77,1,101.81,1,113.74,1,89.67,1,99.5,1,122.89,1,108.61,1,114.37,1,110.5,1,104.08,1,103.64,1,121.61,1,101.14,1,115.97,1,120.12,1,95.97,1,105.01,1,124.68,1,123.89,1,123.61,1,114.76,1,108.75,1,106.09,1,123.17,1,106.16,1,115.18,1,120.6,1,109.48,1,114.44,1,121.44,1,129.48,1,124.32,1,112.59,1),dim=c(2,60),dimnames=list(c('omzet','dummievariabele'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('omzet','dummievariabele'),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 dummievariabele M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 99.29 0 1 0 0 0 0 0 0 0 0 0 0 1
2 98.69 0 0 1 0 0 0 0 0 0 0 0 0 2
3 107.92 0 0 0 1 0 0 0 0 0 0 0 0 3
4 101.03 0 0 0 0 1 0 0 0 0 0 0 0 4
5 97.55 0 0 0 0 0 1 0 0 0 0 0 0 5
6 103.02 0 0 0 0 0 0 1 0 0 0 0 0 6
7 94.08 0 0 0 0 0 0 0 1 0 0 0 0 7
8 94.12 0 0 0 0 0 0 0 0 1 0 0 0 8
9 115.08 0 0 0 0 0 0 0 0 0 1 0 0 9
10 116.48 0 0 0 0 0 0 0 0 0 0 1 0 10
11 103.42 0 0 0 0 0 0 0 0 0 0 0 1 11
12 112.51 0 0 0 0 0 0 0 0 0 0 0 0 12
13 95.55 0 1 0 0 0 0 0 0 0 0 0 0 13
14 97.53 0 0 1 0 0 0 0 0 0 0 0 0 14
15 119.26 0 0 0 1 0 0 0 0 0 0 0 0 15
16 100.94 0 0 0 0 1 0 0 0 0 0 0 0 16
17 97.73 0 0 0 0 0 1 0 0 0 0 0 0 17
18 115.25 0 0 0 0 0 0 1 0 0 0 0 0 18
19 92.80 0 0 0 0 0 0 0 1 0 0 0 0 19
20 99.20 0 0 0 0 0 0 0 0 1 0 0 0 20
21 118.69 0 0 0 0 0 0 0 0 0 1 0 0 21
22 110.12 0 0 0 0 0 0 0 0 0 0 1 0 22
23 110.26 0 0 0 0 0 0 0 0 0 0 0 1 23
24 112.90 0 0 0 0 0 0 0 0 0 0 0 0 24
25 102.17 1 1 0 0 0 0 0 0 0 0 0 0 25
26 99.38 1 0 1 0 0 0 0 0 0 0 0 0 26
27 116.10 1 0 0 1 0 0 0 0 0 0 0 0 27
28 103.77 1 0 0 0 1 0 0 0 0 0 0 0 28
29 101.81 1 0 0 0 0 1 0 0 0 0 0 0 29
30 113.74 1 0 0 0 0 0 1 0 0 0 0 0 30
31 89.67 1 0 0 0 0 0 0 1 0 0 0 0 31
32 99.50 1 0 0 0 0 0 0 0 1 0 0 0 32
33 122.89 1 0 0 0 0 0 0 0 0 1 0 0 33
34 108.61 1 0 0 0 0 0 0 0 0 0 1 0 34
35 114.37 1 0 0 0 0 0 0 0 0 0 0 1 35
36 110.50 1 0 0 0 0 0 0 0 0 0 0 0 36
37 104.08 1 1 0 0 0 0 0 0 0 0 0 0 37
38 103.64 1 0 1 0 0 0 0 0 0 0 0 0 38
39 121.61 1 0 0 1 0 0 0 0 0 0 0 0 39
40 101.14 1 0 0 0 1 0 0 0 0 0 0 0 40
41 115.97 1 0 0 0 0 1 0 0 0 0 0 0 41
42 120.12 1 0 0 0 0 0 1 0 0 0 0 0 42
43 95.97 1 0 0 0 0 0 0 1 0 0 0 0 43
44 105.01 1 0 0 0 0 0 0 0 1 0 0 0 44
45 124.68 1 0 0 0 0 0 0 0 0 1 0 0 45
46 123.89 1 0 0 0 0 0 0 0 0 0 1 0 46
47 123.61 1 0 0 0 0 0 0 0 0 0 0 1 47
48 114.76 1 0 0 0 0 0 0 0 0 0 0 0 48
49 108.75 1 1 0 0 0 0 0 0 0 0 0 0 49
50 106.09 1 0 1 0 0 0 0 0 0 0 0 0 50
51 123.17 1 0 0 1 0 0 0 0 0 0 0 0 51
52 106.16 1 0 0 0 1 0 0 0 0 0 0 0 52
53 115.18 1 0 0 0 0 1 0 0 0 0 0 0 53
54 120.60 1 0 0 0 0 0 1 0 0 0 0 0 54
55 109.48 1 0 0 0 0 0 0 1 0 0 0 0 55
56 114.44 1 0 0 0 0 0 0 0 1 0 0 0 56
57 121.44 1 0 0 0 0 0 0 0 0 1 0 0 57
58 129.48 1 0 0 0 0 0 0 0 0 0 1 0 58
59 124.32 1 0 0 0 0 0 0 0 0 0 0 1 59
60 112.59 1 0 0 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) dummievariabele M1 M2
102.2072 -3.0436 -6.9345 -8.1774
M3 M4 M5 M6
8.0278 -7.3171 -4.6180 3.9392
M7 M8 M9 M10
-14.5477 -8.8346 8.9266 5.7457
M11 t
2.8849 0.3409
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.8886 -2.1398 -0.2967 2.7679 7.4491
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 102.20717 2.27338 44.958 < 2e-16 ***
dummievariabele -3.04361 2.18756 -1.391 0.17082
M1 -6.93453 2.71542 -2.554 0.01404 *
M2 -8.17739 2.69996 -3.029 0.00402 **
M3 8.02775 2.68589 2.989 0.00448 **
M4 -7.31711 2.67324 -2.737 0.00878 **
M5 -4.61797 2.66203 -1.735 0.08948 .
M6 3.93917 2.65227 1.485 0.14431
M7 -14.54769 2.64399 -5.502 1.60e-06 ***
M8 -8.83456 2.63719 -3.350 0.00162 **
M9 8.92658 2.63189 3.392 0.00144 **
M10 5.74572 2.62810 2.186 0.03392 *
M11 2.88486 2.62583 1.099 0.27764
t 0.34086 0.06315 5.398 2.29e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.151 on 46 degrees of freedom
Multiple R-squared: 0.8609, Adjusted R-squared: 0.8216
F-statistic: 21.91 on 13 and 46 DF, p-value: 1.97e-15
> 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,] 0.7576276 0.4847448 0.2423724
[2,] 0.8584606 0.2830788 0.1415394
[3,] 0.7938528 0.4122944 0.2061472
[4,] 0.6988033 0.6023934 0.3011967
[5,] 0.5864338 0.8271323 0.4135662
[6,] 0.6622166 0.6755668 0.3377834
[7,] 0.6690037 0.6619926 0.3309963
[8,] 0.5698444 0.8603111 0.4301556
[9,] 0.4700145 0.9400290 0.5299855
[10,] 0.3837635 0.7675270 0.6162365
[11,] 0.2883502 0.5767004 0.7116498
[12,] 0.2705645 0.5411291 0.7294355
[13,] 0.2414401 0.4828801 0.7585599
[14,] 0.1723148 0.3446296 0.8276852
[15,] 0.2252771 0.4505543 0.7747229
[16,] 0.1733035 0.3466071 0.8266965
[17,] 0.1799730 0.3599460 0.8200270
[18,] 0.5241093 0.9517815 0.4758907
[19,] 0.5501476 0.8997048 0.4498524
[20,] 0.4827009 0.9654017 0.5172991
[21,] 0.3826028 0.7652057 0.6173972
[22,] 0.2810463 0.5620927 0.7189537
[23,] 0.2054017 0.4108034 0.7945983
[24,] 0.1571461 0.3142922 0.8428539
[25,] 0.2346010 0.4692021 0.7653990
[26,] 0.1655182 0.3310364 0.8344818
[27,] 0.3139635 0.6279270 0.6860365
> postscript(file="/var/www/html/rcomp/tmp/13xll1227269247.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2re201227269248.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3o32u1227269248.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xehw1227269248.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gn511227269248.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 7
3.6765000 3.9785000 -3.3375000 4.7765000 -1.7435000 -5.1715000 4.0345000
8 9 10 11 12 13 14
-1.9795000 0.8785000 5.1185000 -5.4215000 6.2125000 -4.1538333 -1.2718333
15 16 17 18 19 20 21
3.9121667 0.5961667 -5.6538333 2.9681667 -1.3358333 -0.9898333 0.3981667
22 23 24 25 26 27 28
-5.3318333 -2.6718333 2.5121667 1.4194444 -0.4685556 -0.2945556 2.3794444
29 30 31 32 33 34 35
-2.6205556 0.4114444 -5.5125556 -1.7365556 3.5514444 -7.8885556 0.3914444
36 37 38 39 40 41 42
-0.9345556 -0.7608889 -0.2988889 1.1251111 -4.3408889 7.4491111 2.7011111
43 44 45 46 47 48 49
-3.3028889 -0.3168889 1.2511111 3.3011111 5.5411111 -0.7648889 -0.1812222
50 51 52 53 54 55 56
-1.9392222 -1.4052222 -3.4112222 2.5687778 -0.9092222 6.1167778 5.0227778
57 58 59 60
-6.0792222 4.8007778 2.1607778 -7.0252222
> postscript(file="/var/www/html/rcomp/tmp/687px1227269248.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 3.6765000 NA
1 3.9785000 3.6765000
2 -3.3375000 3.9785000
3 4.7765000 -3.3375000
4 -1.7435000 4.7765000
5 -5.1715000 -1.7435000
6 4.0345000 -5.1715000
7 -1.9795000 4.0345000
8 0.8785000 -1.9795000
9 5.1185000 0.8785000
10 -5.4215000 5.1185000
11 6.2125000 -5.4215000
12 -4.1538333 6.2125000
13 -1.2718333 -4.1538333
14 3.9121667 -1.2718333
15 0.5961667 3.9121667
16 -5.6538333 0.5961667
17 2.9681667 -5.6538333
18 -1.3358333 2.9681667
19 -0.9898333 -1.3358333
20 0.3981667 -0.9898333
21 -5.3318333 0.3981667
22 -2.6718333 -5.3318333
23 2.5121667 -2.6718333
24 1.4194444 2.5121667
25 -0.4685556 1.4194444
26 -0.2945556 -0.4685556
27 2.3794444 -0.2945556
28 -2.6205556 2.3794444
29 0.4114444 -2.6205556
30 -5.5125556 0.4114444
31 -1.7365556 -5.5125556
32 3.5514444 -1.7365556
33 -7.8885556 3.5514444
34 0.3914444 -7.8885556
35 -0.9345556 0.3914444
36 -0.7608889 -0.9345556
37 -0.2988889 -0.7608889
38 1.1251111 -0.2988889
39 -4.3408889 1.1251111
40 7.4491111 -4.3408889
41 2.7011111 7.4491111
42 -3.3028889 2.7011111
43 -0.3168889 -3.3028889
44 1.2511111 -0.3168889
45 3.3011111 1.2511111
46 5.5411111 3.3011111
47 -0.7648889 5.5411111
48 -0.1812222 -0.7648889
49 -1.9392222 -0.1812222
50 -1.4052222 -1.9392222
51 -3.4112222 -1.4052222
52 2.5687778 -3.4112222
53 -0.9092222 2.5687778
54 6.1167778 -0.9092222
55 5.0227778 6.1167778
56 -6.0792222 5.0227778
57 4.8007778 -6.0792222
58 2.1607778 4.8007778
59 -7.0252222 2.1607778
60 NA -7.0252222
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.9785000 3.6765000
[2,] -3.3375000 3.9785000
[3,] 4.7765000 -3.3375000
[4,] -1.7435000 4.7765000
[5,] -5.1715000 -1.7435000
[6,] 4.0345000 -5.1715000
[7,] -1.9795000 4.0345000
[8,] 0.8785000 -1.9795000
[9,] 5.1185000 0.8785000
[10,] -5.4215000 5.1185000
[11,] 6.2125000 -5.4215000
[12,] -4.1538333 6.2125000
[13,] -1.2718333 -4.1538333
[14,] 3.9121667 -1.2718333
[15,] 0.5961667 3.9121667
[16,] -5.6538333 0.5961667
[17,] 2.9681667 -5.6538333
[18,] -1.3358333 2.9681667
[19,] -0.9898333 -1.3358333
[20,] 0.3981667 -0.9898333
[21,] -5.3318333 0.3981667
[22,] -2.6718333 -5.3318333
[23,] 2.5121667 -2.6718333
[24,] 1.4194444 2.5121667
[25,] -0.4685556 1.4194444
[26,] -0.2945556 -0.4685556
[27,] 2.3794444 -0.2945556
[28,] -2.6205556 2.3794444
[29,] 0.4114444 -2.6205556
[30,] -5.5125556 0.4114444
[31,] -1.7365556 -5.5125556
[32,] 3.5514444 -1.7365556
[33,] -7.8885556 3.5514444
[34,] 0.3914444 -7.8885556
[35,] -0.9345556 0.3914444
[36,] -0.7608889 -0.9345556
[37,] -0.2988889 -0.7608889
[38,] 1.1251111 -0.2988889
[39,] -4.3408889 1.1251111
[40,] 7.4491111 -4.3408889
[41,] 2.7011111 7.4491111
[42,] -3.3028889 2.7011111
[43,] -0.3168889 -3.3028889
[44,] 1.2511111 -0.3168889
[45,] 3.3011111 1.2511111
[46,] 5.5411111 3.3011111
[47,] -0.7648889 5.5411111
[48,] -0.1812222 -0.7648889
[49,] -1.9392222 -0.1812222
[50,] -1.4052222 -1.9392222
[51,] -3.4112222 -1.4052222
[52,] 2.5687778 -3.4112222
[53,] -0.9092222 2.5687778
[54,] 6.1167778 -0.9092222
[55,] 5.0227778 6.1167778
[56,] -6.0792222 5.0227778
[57,] 4.8007778 -6.0792222
[58,] 2.1607778 4.8007778
[59,] -7.0252222 2.1607778
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.9785000 3.6765000
2 -3.3375000 3.9785000
3 4.7765000 -3.3375000
4 -1.7435000 4.7765000
5 -5.1715000 -1.7435000
6 4.0345000 -5.1715000
7 -1.9795000 4.0345000
8 0.8785000 -1.9795000
9 5.1185000 0.8785000
10 -5.4215000 5.1185000
11 6.2125000 -5.4215000
12 -4.1538333 6.2125000
13 -1.2718333 -4.1538333
14 3.9121667 -1.2718333
15 0.5961667 3.9121667
16 -5.6538333 0.5961667
17 2.9681667 -5.6538333
18 -1.3358333 2.9681667
19 -0.9898333 -1.3358333
20 0.3981667 -0.9898333
21 -5.3318333 0.3981667
22 -2.6718333 -5.3318333
23 2.5121667 -2.6718333
24 1.4194444 2.5121667
25 -0.4685556 1.4194444
26 -0.2945556 -0.4685556
27 2.3794444 -0.2945556
28 -2.6205556 2.3794444
29 0.4114444 -2.6205556
30 -5.5125556 0.4114444
31 -1.7365556 -5.5125556
32 3.5514444 -1.7365556
33 -7.8885556 3.5514444
34 0.3914444 -7.8885556
35 -0.9345556 0.3914444
36 -0.7608889 -0.9345556
37 -0.2988889 -0.7608889
38 1.1251111 -0.2988889
39 -4.3408889 1.1251111
40 7.4491111 -4.3408889
41 2.7011111 7.4491111
42 -3.3028889 2.7011111
43 -0.3168889 -3.3028889
44 1.2511111 -0.3168889
45 3.3011111 1.2511111
46 5.5411111 3.3011111
47 -0.7648889 5.5411111
48 -0.1812222 -0.7648889
49 -1.9392222 -0.1812222
50 -1.4052222 -1.9392222
51 -3.4112222 -1.4052222
52 2.5687778 -3.4112222
53 -0.9092222 2.5687778
54 6.1167778 -0.9092222
55 5.0227778 6.1167778
56 -6.0792222 5.0227778
57 4.8007778 -6.0792222
58 2.1607778 4.8007778
59 -7.0252222 2.1607778
> 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/733mk1227269248.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ocuo1227269248.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jah81227269248.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ld511227269248.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gwxe1227269248.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/12h86w1227269248.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/13s8uo1227269248.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/14cesr1227269248.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/153bky1227269248.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/16vzvz1227269248.tab")
+ }
>
> system("convert tmp/13xll1227269247.ps tmp/13xll1227269247.png")
> system("convert tmp/2re201227269248.ps tmp/2re201227269248.png")
> system("convert tmp/3o32u1227269248.ps tmp/3o32u1227269248.png")
> system("convert tmp/4xehw1227269248.ps tmp/4xehw1227269248.png")
> system("convert tmp/5gn511227269248.ps tmp/5gn511227269248.png")
> system("convert tmp/687px1227269248.ps tmp/687px1227269248.png")
> system("convert tmp/733mk1227269248.ps tmp/733mk1227269248.png")
> system("convert tmp/8ocuo1227269248.ps tmp/8ocuo1227269248.png")
> system("convert tmp/9jah81227269248.ps tmp/9jah81227269248.png")
> system("convert tmp/10ld511227269248.ps tmp/10ld511227269248.png")
>
>
> proc.time()
user system elapsed
2.342 1.523 2.800