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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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(604.4,0,883.9,0,527.9,0,756.2,0,812.9,0,655.6,0,707.6,0,612.6,0,659.2,0,833.4,0,727.8,0,797.2,0,753,0,762,1,613.7,0,759.2,0,816.4,0,736.8,0,680.1,1,736.5,0,637.2,0,801.9,1,772.3,1,897.3,1,792.1,1,826.8,0,666.8,0,906.6,1,871.4,1,891,1,739.2,0,833.6,1,715.6,1,871.6,1,751.6,0,1005.5,0,681.2,0,837.3,0,674.7,0,806.3,0,860.2,0,689.8,0,691.6,0,682.6,0,800.1,0,1023.7,0,733.5,0,875.3,0,770.2,0,1005.7,0,982.3,1,742.9,1,974.2,1,822.3,1,773.2,1,750.9,1,708,1,690,1,652.8,1,620.7,1,461.9,1),dim=c(2,61),dimnames=list(c('UitvoerBEVS','Dummy'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('UitvoerBEVS','Dummy'),1:61))
> 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
UitvoerBEVS Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 604.4 0 1 0 0 0 0 0 0 0 0 0 0 1
2 883.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 527.9 0 0 0 1 0 0 0 0 0 0 0 0 3
4 756.2 0 0 0 0 1 0 0 0 0 0 0 0 4
5 812.9 0 0 0 0 0 1 0 0 0 0 0 0 5
6 655.6 0 0 0 0 0 0 1 0 0 0 0 0 6
7 707.6 0 0 0 0 0 0 0 1 0 0 0 0 7
8 612.6 0 0 0 0 0 0 0 0 1 0 0 0 8
9 659.2 0 0 0 0 0 0 0 0 0 1 0 0 9
10 833.4 0 0 0 0 0 0 0 0 0 0 1 0 10
11 727.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 797.2 0 0 0 0 0 0 0 0 0 0 0 0 12
13 753.0 0 1 0 0 0 0 0 0 0 0 0 0 13
14 762.0 1 0 1 0 0 0 0 0 0 0 0 0 14
15 613.7 0 0 0 1 0 0 0 0 0 0 0 0 15
16 759.2 0 0 0 0 1 0 0 0 0 0 0 0 16
17 816.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 736.8 0 0 0 0 0 0 1 0 0 0 0 0 18
19 680.1 1 0 0 0 0 0 0 1 0 0 0 0 19
20 736.5 0 0 0 0 0 0 0 0 1 0 0 0 20
21 637.2 0 0 0 0 0 0 0 0 0 1 0 0 21
22 801.9 1 0 0 0 0 0 0 0 0 0 1 0 22
23 772.3 1 0 0 0 0 0 0 0 0 0 0 1 23
24 897.3 1 0 0 0 0 0 0 0 0 0 0 0 24
25 792.1 1 1 0 0 0 0 0 0 0 0 0 0 25
26 826.8 0 0 1 0 0 0 0 0 0 0 0 0 26
27 666.8 0 0 0 1 0 0 0 0 0 0 0 0 27
28 906.6 1 0 0 0 1 0 0 0 0 0 0 0 28
29 871.4 1 0 0 0 0 1 0 0 0 0 0 0 29
30 891.0 1 0 0 0 0 0 1 0 0 0 0 0 30
31 739.2 0 0 0 0 0 0 0 1 0 0 0 0 31
32 833.6 1 0 0 0 0 0 0 0 1 0 0 0 32
33 715.6 1 0 0 0 0 0 0 0 0 1 0 0 33
34 871.6 1 0 0 0 0 0 0 0 0 0 1 0 34
35 751.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 1005.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 681.2 0 1 0 0 0 0 0 0 0 0 0 0 37
38 837.3 0 0 1 0 0 0 0 0 0 0 0 0 38
39 674.7 0 0 0 1 0 0 0 0 0 0 0 0 39
40 806.3 0 0 0 0 1 0 0 0 0 0 0 0 40
41 860.2 0 0 0 0 0 1 0 0 0 0 0 0 41
42 689.8 0 0 0 0 0 0 1 0 0 0 0 0 42
43 691.6 0 0 0 0 0 0 0 1 0 0 0 0 43
44 682.6 0 0 0 0 0 0 0 0 1 0 0 0 44
45 800.1 0 0 0 0 0 0 0 0 0 1 0 0 45
46 1023.7 0 0 0 0 0 0 0 0 0 0 1 0 46
47 733.5 0 0 0 0 0 0 0 0 0 0 0 1 47
48 875.3 0 0 0 0 0 0 0 0 0 0 0 0 48
49 770.2 0 1 0 0 0 0 0 0 0 0 0 0 49
50 1005.7 0 0 1 0 0 0 0 0 0 0 0 0 50
51 982.3 1 0 0 1 0 0 0 0 0 0 0 0 51
52 742.9 1 0 0 0 1 0 0 0 0 0 0 0 52
53 974.2 1 0 0 0 0 1 0 0 0 0 0 0 53
54 822.3 1 0 0 0 0 0 1 0 0 0 0 0 54
55 773.2 1 0 0 0 0 0 0 1 0 0 0 0 55
56 750.9 1 0 0 0 0 0 0 0 1 0 0 0 56
57 708.0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 690.0 1 0 0 0 0 0 0 0 0 0 1 0 58
59 652.8 1 0 0 0 0 0 0 0 0 0 0 1 59
60 620.7 1 0 0 0 0 0 0 0 0 0 0 0 60
61 461.9 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
802.1835 4.9337 -156.8707 34.6609 -136.3725 -37.1727
M5 M6 M7 M8 M9 M10
34.6339 -74.2595 -115.9929 -112.0663 -132.2598 5.8801
M11 t
-110.6266 0.9734
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-247.725 -36.534 -3.808 36.878 261.911
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 802.1835 52.6038 15.250 <2e-16 ***
Dummy 4.9337 29.8638 0.165 0.8695
M1 -156.8707 61.3494 -2.557 0.0138 *
M2 34.6609 64.4464 0.538 0.5932
M3 -136.3725 64.3788 -2.118 0.0395 *
M4 -37.1727 64.2906 -0.578 0.5659
M5 34.6339 64.2128 0.539 0.5922
M6 -74.2595 64.1452 -1.158 0.2528
M7 -115.9929 64.0880 -1.810 0.0767 .
M8 -112.0663 64.0411 -1.750 0.0867 .
M9 -132.2598 64.0046 -2.066 0.0443 *
M10 5.8801 64.3184 0.091 0.9275
M11 -110.6266 63.9629 -1.730 0.0903 .
t 0.9734 0.8168 1.192 0.2393
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 101.1 on 47 degrees of freedom
Multiple R-squared: 0.3847, Adjusted R-squared: 0.2145
F-statistic: 2.26 on 13 and 47 DF, p-value: 0.02093
> 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,] 1.061285e-01 2.122570e-01 0.8938715
[2,] 3.971523e-02 7.943047e-02 0.9602848
[3,] 2.316738e-02 4.633476e-02 0.9768326
[4,] 1.120683e-02 2.241367e-02 0.9887932
[5,] 1.043485e-02 2.086970e-02 0.9895652
[6,] 4.687373e-03 9.374746e-03 0.9953126
[7,] 3.835454e-03 7.670909e-03 0.9961645
[8,] 4.796458e-03 9.592917e-03 0.9952035
[9,] 3.833353e-03 7.666706e-03 0.9961666
[10,] 3.874438e-03 7.748875e-03 0.9961256
[11,] 2.869193e-03 5.738386e-03 0.9971308
[12,] 2.737199e-03 5.474399e-03 0.9972628
[13,] 1.279632e-03 2.559263e-03 0.9987204
[14,] 1.878149e-03 3.756299e-03 0.9981219
[15,] 8.698867e-04 1.739773e-03 0.9991301
[16,] 5.928124e-04 1.185625e-03 0.9994072
[17,] 2.561766e-04 5.123532e-04 0.9997438
[18,] 1.030701e-04 2.061402e-04 0.9998969
[19,] 4.875381e-05 9.750762e-05 0.9999512
[20,] 1.019555e-04 2.039110e-04 0.9998980
[21,] 1.805207e-04 3.610413e-04 0.9998195
[22,] 8.039658e-05 1.607932e-04 0.9999196
[23,] 5.160974e-04 1.032195e-03 0.9994839
[24,] 2.425192e-04 4.850383e-04 0.9997575
[25,] 2.763658e-04 5.527315e-04 0.9997236
[26,] 1.535663e-03 3.071326e-03 0.9984643
[27,] 4.872344e-03 9.744689e-03 0.9951277
[28,] 5.281142e-02 1.056228e-01 0.9471886
> postscript(file="/var/www/html/rcomp/tmp/102f21227522598.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/298to1227522598.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b2sz1227522598.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pq0t1227522598.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dgzd1227522598.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 = 61
Frequency = 1
1 2 3 4 5 6
-41.8862745 45.1087323 -140.8312677 -12.7045235 -28.7845235 -78.1645235
7 8 9 10 11 12
14.5954765 -85.3045235 -19.4845235 15.6022207 25.5354765 -16.6645235
13 14 15 16 17 18
95.0327314 -93.4059827 -66.7122617 -21.3855176 -36.9655176 -8.6455176
19 20 21 22 23 24
-29.5192385 26.9144824 -53.1655176 -32.5124943 53.4207615 66.8207615
25 26 27 28 29 30
117.5180164 -35.3532558 -25.2932558 109.3997674 1.4197674 128.9397674
31 32 33 34 35 36
22.8334884 107.3997674 8.6197674 25.5065116 25.9734884 168.2734884
37 38 39 40 41 42
-0.1292567 -36.5342499 -29.0742499 2.3524943 -16.5275057 -79.0075057
43 44 45 46 47 48
-36.4475057 -50.3475057 86.3724943 170.8592385 -3.8075057 26.3924943
49 50 51 52 53 54
77.1897492 120.1847560 261.9110351 -77.6622207 80.8577793 36.8777793
55 56 57 58 59 60
28.5377793 1.3377793 -22.3422207 -179.4554765 -101.1222207 -244.8222207
61
-247.7249658
> postscript(file="/var/www/html/rcomp/tmp/6u2et1227522598.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -41.8862745 NA
1 45.1087323 -41.8862745
2 -140.8312677 45.1087323
3 -12.7045235 -140.8312677
4 -28.7845235 -12.7045235
5 -78.1645235 -28.7845235
6 14.5954765 -78.1645235
7 -85.3045235 14.5954765
8 -19.4845235 -85.3045235
9 15.6022207 -19.4845235
10 25.5354765 15.6022207
11 -16.6645235 25.5354765
12 95.0327314 -16.6645235
13 -93.4059827 95.0327314
14 -66.7122617 -93.4059827
15 -21.3855176 -66.7122617
16 -36.9655176 -21.3855176
17 -8.6455176 -36.9655176
18 -29.5192385 -8.6455176
19 26.9144824 -29.5192385
20 -53.1655176 26.9144824
21 -32.5124943 -53.1655176
22 53.4207615 -32.5124943
23 66.8207615 53.4207615
24 117.5180164 66.8207615
25 -35.3532558 117.5180164
26 -25.2932558 -35.3532558
27 109.3997674 -25.2932558
28 1.4197674 109.3997674
29 128.9397674 1.4197674
30 22.8334884 128.9397674
31 107.3997674 22.8334884
32 8.6197674 107.3997674
33 25.5065116 8.6197674
34 25.9734884 25.5065116
35 168.2734884 25.9734884
36 -0.1292567 168.2734884
37 -36.5342499 -0.1292567
38 -29.0742499 -36.5342499
39 2.3524943 -29.0742499
40 -16.5275057 2.3524943
41 -79.0075057 -16.5275057
42 -36.4475057 -79.0075057
43 -50.3475057 -36.4475057
44 86.3724943 -50.3475057
45 170.8592385 86.3724943
46 -3.8075057 170.8592385
47 26.3924943 -3.8075057
48 77.1897492 26.3924943
49 120.1847560 77.1897492
50 261.9110351 120.1847560
51 -77.6622207 261.9110351
52 80.8577793 -77.6622207
53 36.8777793 80.8577793
54 28.5377793 36.8777793
55 1.3377793 28.5377793
56 -22.3422207 1.3377793
57 -179.4554765 -22.3422207
58 -101.1222207 -179.4554765
59 -244.8222207 -101.1222207
60 -247.7249658 -244.8222207
61 NA -247.7249658
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 45.1087323 -41.8862745
[2,] -140.8312677 45.1087323
[3,] -12.7045235 -140.8312677
[4,] -28.7845235 -12.7045235
[5,] -78.1645235 -28.7845235
[6,] 14.5954765 -78.1645235
[7,] -85.3045235 14.5954765
[8,] -19.4845235 -85.3045235
[9,] 15.6022207 -19.4845235
[10,] 25.5354765 15.6022207
[11,] -16.6645235 25.5354765
[12,] 95.0327314 -16.6645235
[13,] -93.4059827 95.0327314
[14,] -66.7122617 -93.4059827
[15,] -21.3855176 -66.7122617
[16,] -36.9655176 -21.3855176
[17,] -8.6455176 -36.9655176
[18,] -29.5192385 -8.6455176
[19,] 26.9144824 -29.5192385
[20,] -53.1655176 26.9144824
[21,] -32.5124943 -53.1655176
[22,] 53.4207615 -32.5124943
[23,] 66.8207615 53.4207615
[24,] 117.5180164 66.8207615
[25,] -35.3532558 117.5180164
[26,] -25.2932558 -35.3532558
[27,] 109.3997674 -25.2932558
[28,] 1.4197674 109.3997674
[29,] 128.9397674 1.4197674
[30,] 22.8334884 128.9397674
[31,] 107.3997674 22.8334884
[32,] 8.6197674 107.3997674
[33,] 25.5065116 8.6197674
[34,] 25.9734884 25.5065116
[35,] 168.2734884 25.9734884
[36,] -0.1292567 168.2734884
[37,] -36.5342499 -0.1292567
[38,] -29.0742499 -36.5342499
[39,] 2.3524943 -29.0742499
[40,] -16.5275057 2.3524943
[41,] -79.0075057 -16.5275057
[42,] -36.4475057 -79.0075057
[43,] -50.3475057 -36.4475057
[44,] 86.3724943 -50.3475057
[45,] 170.8592385 86.3724943
[46,] -3.8075057 170.8592385
[47,] 26.3924943 -3.8075057
[48,] 77.1897492 26.3924943
[49,] 120.1847560 77.1897492
[50,] 261.9110351 120.1847560
[51,] -77.6622207 261.9110351
[52,] 80.8577793 -77.6622207
[53,] 36.8777793 80.8577793
[54,] 28.5377793 36.8777793
[55,] 1.3377793 28.5377793
[56,] -22.3422207 1.3377793
[57,] -179.4554765 -22.3422207
[58,] -101.1222207 -179.4554765
[59,] -244.8222207 -101.1222207
[60,] -247.7249658 -244.8222207
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 45.1087323 -41.8862745
2 -140.8312677 45.1087323
3 -12.7045235 -140.8312677
4 -28.7845235 -12.7045235
5 -78.1645235 -28.7845235
6 14.5954765 -78.1645235
7 -85.3045235 14.5954765
8 -19.4845235 -85.3045235
9 15.6022207 -19.4845235
10 25.5354765 15.6022207
11 -16.6645235 25.5354765
12 95.0327314 -16.6645235
13 -93.4059827 95.0327314
14 -66.7122617 -93.4059827
15 -21.3855176 -66.7122617
16 -36.9655176 -21.3855176
17 -8.6455176 -36.9655176
18 -29.5192385 -8.6455176
19 26.9144824 -29.5192385
20 -53.1655176 26.9144824
21 -32.5124943 -53.1655176
22 53.4207615 -32.5124943
23 66.8207615 53.4207615
24 117.5180164 66.8207615
25 -35.3532558 117.5180164
26 -25.2932558 -35.3532558
27 109.3997674 -25.2932558
28 1.4197674 109.3997674
29 128.9397674 1.4197674
30 22.8334884 128.9397674
31 107.3997674 22.8334884
32 8.6197674 107.3997674
33 25.5065116 8.6197674
34 25.9734884 25.5065116
35 168.2734884 25.9734884
36 -0.1292567 168.2734884
37 -36.5342499 -0.1292567
38 -29.0742499 -36.5342499
39 2.3524943 -29.0742499
40 -16.5275057 2.3524943
41 -79.0075057 -16.5275057
42 -36.4475057 -79.0075057
43 -50.3475057 -36.4475057
44 86.3724943 -50.3475057
45 170.8592385 86.3724943
46 -3.8075057 170.8592385
47 26.3924943 -3.8075057
48 77.1897492 26.3924943
49 120.1847560 77.1897492
50 261.9110351 120.1847560
51 -77.6622207 261.9110351
52 80.8577793 -77.6622207
53 36.8777793 80.8577793
54 28.5377793 36.8777793
55 1.3377793 28.5377793
56 -22.3422207 1.3377793
57 -179.4554765 -22.3422207
58 -101.1222207 -179.4554765
59 -244.8222207 -101.1222207
60 -247.7249658 -244.8222207
> 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/7uhsx1227522598.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88hyu1227522598.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r9i11227522598.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nwl61227522598.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ax2m1227522598.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/12b9w01227522598.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/13v6yp1227522599.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/14xs021227522599.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/1582a71227522599.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/16ehov1227522599.tab")
+ }
>
> system("convert tmp/102f21227522598.ps tmp/102f21227522598.png")
> system("convert tmp/298to1227522598.ps tmp/298to1227522598.png")
> system("convert tmp/3b2sz1227522598.ps tmp/3b2sz1227522598.png")
> system("convert tmp/4pq0t1227522598.ps tmp/4pq0t1227522598.png")
> system("convert tmp/5dgzd1227522598.ps tmp/5dgzd1227522598.png")
> system("convert tmp/6u2et1227522598.ps tmp/6u2et1227522598.png")
> system("convert tmp/7uhsx1227522598.ps tmp/7uhsx1227522598.png")
> system("convert tmp/88hyu1227522598.ps tmp/88hyu1227522598.png")
> system("convert tmp/9r9i11227522598.ps tmp/9r9i11227522598.png")
> system("convert tmp/10nwl61227522598.ps tmp/10nwl61227522598.png")
>
>
> proc.time()
user system elapsed
2.467 1.614 4.103