R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(631923.00
+ ,-12
+ ,-10.8
+ ,654294.00
+ ,-13
+ ,-12.2
+ ,671833.00
+ ,-16
+ ,-14.1
+ ,586840.00
+ ,-10
+ ,-15.2
+ ,600969.00
+ ,-4
+ ,-15.8
+ ,625568.00
+ ,-9
+ ,-15.8
+ ,558110.00
+ ,-8
+ ,-14.9
+ ,630577.00
+ ,-9
+ ,-12.6
+ ,628654.00
+ ,-3
+ ,-9.9
+ ,603184.00
+ ,-13
+ ,-7.8
+ ,656255.00
+ ,-3
+ ,-6
+ ,600730.00
+ ,-1
+ ,-5
+ ,670326.00
+ ,-2
+ ,-4.5
+ ,678423.00
+ ,0
+ ,-3.9
+ ,641502.00
+ ,0
+ ,-2.9
+ ,625311.00
+ ,-3
+ ,-1.5
+ ,628177.00
+ ,0
+ ,-0.5
+ ,589767.00
+ ,5
+ ,0
+ ,582471.00
+ ,3
+ ,0.5
+ ,636248.00
+ ,4
+ ,0.9
+ ,599885.00
+ ,3
+ ,0.8
+ ,621694.00
+ ,1
+ ,0.1
+ ,637406.00
+ ,-1
+ ,-1
+ ,595994.00
+ ,0
+ ,-2
+ ,696308.00
+ ,-2
+ ,-3
+ ,674201.00
+ ,-1
+ ,-3.7
+ ,648861.00
+ ,2
+ ,-4.7
+ ,649605.00
+ ,0
+ ,-6.4
+ ,672392.00
+ ,-6
+ ,-7.5
+ ,598396.00
+ ,-7
+ ,-7.8
+ ,613177.00
+ ,-6
+ ,-7.7
+ ,638104.00
+ ,-4
+ ,-6.6
+ ,615632.00
+ ,-9
+ ,-4.2
+ ,634465.00
+ ,-2
+ ,-2
+ ,638686.00
+ ,-3
+ ,-0.7
+ ,604243.00
+ ,2
+ ,0.1
+ ,706669.00
+ ,3
+ ,0.9
+ ,677185.00
+ ,1
+ ,2.1
+ ,644328.00
+ ,0
+ ,3.5
+ ,644825.00
+ ,1
+ ,4.9
+ ,605707.00
+ ,1
+ ,5.7
+ ,600136.00
+ ,3
+ ,6.2
+ ,612166.00
+ ,5
+ ,6.5
+ ,599659.00
+ ,5
+ ,6.5
+ ,634210.00
+ ,4
+ ,6.3
+ ,618234.00
+ ,11
+ ,6.2
+ ,613576.00
+ ,8
+ ,6.4
+ ,627200.00
+ ,-1
+ ,6.3
+ ,668973.00
+ ,4
+ ,5.8
+ ,651479.00
+ ,4
+ ,5.1
+ ,619661.00
+ ,4
+ ,5.1
+ ,644260.00
+ ,6
+ ,5.8
+ ,579936.00
+ ,6
+ ,6.7
+ ,601752.00
+ ,6
+ ,7.1
+ ,595376.00
+ ,6
+ ,6.7
+ ,588902.00
+ ,4
+ ,5.5
+ ,634341.00
+ ,1
+ ,4.2
+ ,594305.00
+ ,6
+ ,3
+ ,606200.00
+ ,0
+ ,2.2
+ ,610926.00
+ ,2
+ ,2
+ ,633685.00
+ ,-2
+ ,1.8
+ ,639696.00
+ ,0
+ ,1.8
+ ,659451.00
+ ,1
+ ,1.5
+ ,593248.00
+ ,-3
+ ,0.4
+ ,606677.00
+ ,-3
+ ,-0.9
+ ,599434.00
+ ,-5
+ ,-1.7
+ ,569578.00
+ ,-7
+ ,-2.6
+ ,629873.00
+ ,-7
+ ,-4.4
+ ,613438.00
+ ,-5
+ ,-8.3
+ ,604172.00
+ ,-13
+ ,-14.4
+ ,658328.00
+ ,-16
+ ,-21.3
+ ,612633.00
+ ,-20
+ ,-26.5
+ ,707372.00
+ ,-18
+ ,-29.2
+ ,739770.00
+ ,-21
+ ,-30.8
+ ,777535.00
+ ,-20
+ ,-30.9
+ ,685030.00
+ ,-16
+ ,-29.5
+ ,730234.00
+ ,-14
+ ,-27.1
+ ,714154.00
+ ,-12
+ ,-24.4
+ ,630872.00
+ ,-10
+ ,-21.9
+ ,719492.00
+ ,-3
+ ,-19.3
+ ,677023.00
+ ,-4
+ ,-17
+ ,679272.00
+ ,-4
+ ,-13.8
+ ,718317.00
+ ,-1
+ ,-9.9
+ ,645672.00
+ ,-8
+ ,-7.9)
+ ,dim=c(3
+ ,84)
+ ,dimnames=list(c('Werkloosheid'
+ ,'Consumentenvertrouwen'
+ ,'Producentenvertrouwen')
+ ,1:84))
> y <- array(NA,dim=c(3,84),dimnames=list(c('Werkloosheid','Consumentenvertrouwen','Producentenvertrouwen'),1:84))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
Werkloosheid Consumentenvertrouwen Producentenvertrouwen
1 631923 -12 -10.8
2 654294 -13 -12.2
3 671833 -16 -14.1
4 586840 -10 -15.2
5 600969 -4 -15.8
6 625568 -9 -15.8
7 558110 -8 -14.9
8 630577 -9 -12.6
9 628654 -3 -9.9
10 603184 -13 -7.8
11 656255 -3 -6.0
12 600730 -1 -5.0
13 670326 -2 -4.5
14 678423 0 -3.9
15 641502 0 -2.9
16 625311 -3 -1.5
17 628177 0 -0.5
18 589767 5 0.0
19 582471 3 0.5
20 636248 4 0.9
21 599885 3 0.8
22 621694 1 0.1
23 637406 -1 -1.0
24 595994 0 -2.0
25 696308 -2 -3.0
26 674201 -1 -3.7
27 648861 2 -4.7
28 649605 0 -6.4
29 672392 -6 -7.5
30 598396 -7 -7.8
31 613177 -6 -7.7
32 638104 -4 -6.6
33 615632 -9 -4.2
34 634465 -2 -2.0
35 638686 -3 -0.7
36 604243 2 0.1
37 706669 3 0.9
38 677185 1 2.1
39 644328 0 3.5
40 644825 1 4.9
41 605707 1 5.7
42 600136 3 6.2
43 612166 5 6.5
44 599659 5 6.5
45 634210 4 6.3
46 618234 11 6.2
47 613576 8 6.4
48 627200 -1 6.3
49 668973 4 5.8
50 651479 4 5.1
51 619661 4 5.1
52 644260 6 5.8
53 579936 6 6.7
54 601752 6 7.1
55 595376 6 6.7
56 588902 4 5.5
57 634341 1 4.2
58 594305 6 3.0
59 606200 0 2.2
60 610926 2 2.0
61 633685 -2 1.8
62 639696 0 1.8
63 659451 1 1.5
64 593248 -3 0.4
65 606677 -3 -0.9
66 599434 -5 -1.7
67 569578 -7 -2.6
68 629873 -7 -4.4
69 613438 -5 -8.3
70 604172 -13 -14.4
71 658328 -16 -21.3
72 612633 -20 -26.5
73 707372 -18 -29.2
74 739770 -21 -30.8
75 777535 -20 -30.9
76 685030 -16 -29.5
77 730234 -14 -27.1
78 714154 -12 -24.4
79 630872 -10 -21.9
80 719492 -3 -19.3
81 677023 -4 -17.0
82 679272 -4 -13.8
83 718317 -1 -9.9
84 645672 -8 -7.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Consumentenvertrouwen Producentenvertrouwen
625608 2020 -3338
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-101076.6 -24197.5 -350.4 22324.5 89177.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 625608.2 4315.6 144.963 < 2e-16 ***
Consumentenvertrouwen 2019.8 1181.2 1.710 0.091106 .
Producentenvertrouwen -3338.1 820.8 -4.067 0.000110 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35380 on 81 degrees of freedom
Multiple R-squared: 0.2907, Adjusted R-squared: 0.2732
F-statistic: 16.6 on 2 and 81 DF, p-value: 9.08e-07
> 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,] 0.34212882 0.68425763 0.65787118
[2,] 0.59480461 0.81039078 0.40519539
[3,] 0.48634026 0.97268051 0.51365974
[4,] 0.42125051 0.84250101 0.57874949
[5,] 0.58171766 0.83656469 0.41828234
[6,] 0.56609638 0.86780724 0.43390362
[7,] 0.53590138 0.92819724 0.46409862
[8,] 0.56407591 0.87184818 0.43592409
[9,] 0.57865928 0.84268145 0.42134072
[10,] 0.49380893 0.98761785 0.50619107
[11,] 0.47875366 0.95750732 0.52124634
[12,] 0.42284160 0.84568320 0.57715840
[13,] 0.49648171 0.99296341 0.50351829
[14,] 0.58527278 0.82945443 0.41472722
[15,] 0.51508780 0.96982440 0.48491220
[16,] 0.49523544 0.99047088 0.50476456
[17,] 0.42278962 0.84557923 0.57721038
[18,] 0.35360435 0.70720870 0.64639565
[19,] 0.35485309 0.70970617 0.64514691
[20,] 0.57653326 0.84693348 0.42346674
[21,] 0.62622572 0.74754855 0.37377428
[22,] 0.60151781 0.79696438 0.39848219
[23,] 0.56955779 0.86088442 0.43044221
[24,] 0.58248800 0.83502400 0.41751200
[25,] 0.60113531 0.79772938 0.39886469
[26,] 0.56768089 0.86463821 0.43231911
[27,] 0.50516193 0.98967615 0.49483807
[28,] 0.46608393 0.93216786 0.53391607
[29,] 0.40083329 0.80166658 0.59916671
[30,] 0.34383352 0.68766705 0.65616648
[31,] 0.33045564 0.66091127 0.66954436
[32,] 0.57038674 0.85922653 0.42961326
[33,] 0.62567196 0.74865608 0.37432804
[34,] 0.59473478 0.81053044 0.40526522
[35,] 0.57518856 0.84962289 0.42481144
[36,] 0.56487226 0.87025549 0.43512774
[37,] 0.55094484 0.89811032 0.44905516
[38,] 0.50320385 0.99359230 0.49679615
[39,] 0.47597487 0.95194974 0.52402513
[40,] 0.42990847 0.85981694 0.57009153
[41,] 0.37964188 0.75928376 0.62035812
[42,] 0.32924595 0.65849190 0.67075405
[43,] 0.31713866 0.63427732 0.68286134
[44,] 0.40083343 0.80166686 0.59916657
[45,] 0.40440260 0.80880521 0.59559740
[46,] 0.34977519 0.69955038 0.65022481
[47,] 0.32824086 0.65648171 0.67175914
[48,] 0.35180551 0.70361103 0.64819449
[49,] 0.30791909 0.61583818 0.69208091
[50,] 0.27697907 0.55395814 0.72302093
[51,] 0.26140670 0.52281339 0.73859330
[52,] 0.23929519 0.47859038 0.76070481
[53,] 0.24952447 0.49904893 0.75047553
[54,] 0.20553278 0.41106556 0.79446722
[55,] 0.16619507 0.33239014 0.83380493
[56,] 0.14736710 0.29473421 0.85263290
[57,] 0.13003517 0.26007034 0.86996483
[58,] 0.16139513 0.32279026 0.83860487
[59,] 0.13412205 0.26824410 0.86587795
[60,] 0.10238269 0.20476538 0.89761731
[61,] 0.07869693 0.15739385 0.92130307
[62,] 0.07970867 0.15941735 0.92029133
[63,] 0.06565510 0.13131019 0.93434490
[64,] 0.04857732 0.09715465 0.95142268
[65,] 0.04497755 0.08995510 0.95502245
[66,] 0.03138394 0.06276788 0.96861606
[67,] 0.14317229 0.28634458 0.85682771
[68,] 0.13113563 0.26227126 0.86886437
[69,] 0.12975609 0.25951218 0.87024391
[70,] 0.44473748 0.88947496 0.55526252
[71,] 0.32888619 0.65777237 0.67111381
[72,] 0.40767066 0.81534133 0.59232934
[73,] 0.92999397 0.14001206 0.07000603
> postscript(file="/var/www/html/rcomp/tmp/1vrad1292961499.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vrad1292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35iag1292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45iag1292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55iag1292961499.ps",horizontal=F,onefile=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 = 84
Frequency = 1
1 2 3 4 5 6
-5498.2625 14219.2894 31475.4675 -69308.3494 -69301.1397 -34603.0077
7 8 9 10 11 12
-101076.5862 -18912.2375 -23941.4522 -22203.2765 16677.9553 -39548.6443
13 14 15 16 17 18
33736.2087 39796.3878 6213.4410 755.1947 899.7687 -45940.3367
19 20 21 22 23 24
-47527.6573 5564.7376 -29112.2413 -5600.2258 10479.5685 -36290.3111
25 26 27 28 29 30
64725.2885 38261.8249 3524.2924 2633.2548 33867.3547 -39110.2349
31 32 33 34 35 36
-26015.2560 -1456.0502 -5817.5905 6220.3417 16800.6373 -25071.0522
37 38 39 40 41 42
78005.5640 56566.8806 30402.9815 33553.4296 -2894.1278 -10835.7540
43 44 45 46 47 48
-1843.9909 -14350.9909 21552.2249 -8896.3652 -6827.2754 24641.3569
49 50 51 52 53 54
54646.1983 34815.5611 2997.5611 25893.5455 -35426.2066 -12274.9853
55 56 57 58 59 60
-19986.2066 -26426.2177 20732.7924 -33408.0035 -12064.4876 -12045.7511
61 62 63 64 65 66
18124.9439 20096.2911 36830.0487 -24965.5042 -15875.9734 -21749.7631
67 68 69 70 71 72
-50570.3582 3716.1460 -29776.9143 -43246.4276 -6063.5155 -61037.0866
73 74 75 76 77 78
20649.5169 53766.1110 89177.4793 -6733.5518 42442.1231 31335.2139
79 80 81 82 83 84
-47641.3059 35518.8477 2747.1964 15677.9667 61681.8950 9851.7862
> postscript(file="/var/www/html/rcomp/tmp/6ysr11292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 -5498.2625 NA
1 14219.2894 -5498.2625
2 31475.4675 14219.2894
3 -69308.3494 31475.4675
4 -69301.1397 -69308.3494
5 -34603.0077 -69301.1397
6 -101076.5862 -34603.0077
7 -18912.2375 -101076.5862
8 -23941.4522 -18912.2375
9 -22203.2765 -23941.4522
10 16677.9553 -22203.2765
11 -39548.6443 16677.9553
12 33736.2087 -39548.6443
13 39796.3878 33736.2087
14 6213.4410 39796.3878
15 755.1947 6213.4410
16 899.7687 755.1947
17 -45940.3367 899.7687
18 -47527.6573 -45940.3367
19 5564.7376 -47527.6573
20 -29112.2413 5564.7376
21 -5600.2258 -29112.2413
22 10479.5685 -5600.2258
23 -36290.3111 10479.5685
24 64725.2885 -36290.3111
25 38261.8249 64725.2885
26 3524.2924 38261.8249
27 2633.2548 3524.2924
28 33867.3547 2633.2548
29 -39110.2349 33867.3547
30 -26015.2560 -39110.2349
31 -1456.0502 -26015.2560
32 -5817.5905 -1456.0502
33 6220.3417 -5817.5905
34 16800.6373 6220.3417
35 -25071.0522 16800.6373
36 78005.5640 -25071.0522
37 56566.8806 78005.5640
38 30402.9815 56566.8806
39 33553.4296 30402.9815
40 -2894.1278 33553.4296
41 -10835.7540 -2894.1278
42 -1843.9909 -10835.7540
43 -14350.9909 -1843.9909
44 21552.2249 -14350.9909
45 -8896.3652 21552.2249
46 -6827.2754 -8896.3652
47 24641.3569 -6827.2754
48 54646.1983 24641.3569
49 34815.5611 54646.1983
50 2997.5611 34815.5611
51 25893.5455 2997.5611
52 -35426.2066 25893.5455
53 -12274.9853 -35426.2066
54 -19986.2066 -12274.9853
55 -26426.2177 -19986.2066
56 20732.7924 -26426.2177
57 -33408.0035 20732.7924
58 -12064.4876 -33408.0035
59 -12045.7511 -12064.4876
60 18124.9439 -12045.7511
61 20096.2911 18124.9439
62 36830.0487 20096.2911
63 -24965.5042 36830.0487
64 -15875.9734 -24965.5042
65 -21749.7631 -15875.9734
66 -50570.3582 -21749.7631
67 3716.1460 -50570.3582
68 -29776.9143 3716.1460
69 -43246.4276 -29776.9143
70 -6063.5155 -43246.4276
71 -61037.0866 -6063.5155
72 20649.5169 -61037.0866
73 53766.1110 20649.5169
74 89177.4793 53766.1110
75 -6733.5518 89177.4793
76 42442.1231 -6733.5518
77 31335.2139 42442.1231
78 -47641.3059 31335.2139
79 35518.8477 -47641.3059
80 2747.1964 35518.8477
81 15677.9667 2747.1964
82 61681.8950 15677.9667
83 9851.7862 61681.8950
84 NA 9851.7862
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 14219.2894 -5498.2625
[2,] 31475.4675 14219.2894
[3,] -69308.3494 31475.4675
[4,] -69301.1397 -69308.3494
[5,] -34603.0077 -69301.1397
[6,] -101076.5862 -34603.0077
[7,] -18912.2375 -101076.5862
[8,] -23941.4522 -18912.2375
[9,] -22203.2765 -23941.4522
[10,] 16677.9553 -22203.2765
[11,] -39548.6443 16677.9553
[12,] 33736.2087 -39548.6443
[13,] 39796.3878 33736.2087
[14,] 6213.4410 39796.3878
[15,] 755.1947 6213.4410
[16,] 899.7687 755.1947
[17,] -45940.3367 899.7687
[18,] -47527.6573 -45940.3367
[19,] 5564.7376 -47527.6573
[20,] -29112.2413 5564.7376
[21,] -5600.2258 -29112.2413
[22,] 10479.5685 -5600.2258
[23,] -36290.3111 10479.5685
[24,] 64725.2885 -36290.3111
[25,] 38261.8249 64725.2885
[26,] 3524.2924 38261.8249
[27,] 2633.2548 3524.2924
[28,] 33867.3547 2633.2548
[29,] -39110.2349 33867.3547
[30,] -26015.2560 -39110.2349
[31,] -1456.0502 -26015.2560
[32,] -5817.5905 -1456.0502
[33,] 6220.3417 -5817.5905
[34,] 16800.6373 6220.3417
[35,] -25071.0522 16800.6373
[36,] 78005.5640 -25071.0522
[37,] 56566.8806 78005.5640
[38,] 30402.9815 56566.8806
[39,] 33553.4296 30402.9815
[40,] -2894.1278 33553.4296
[41,] -10835.7540 -2894.1278
[42,] -1843.9909 -10835.7540
[43,] -14350.9909 -1843.9909
[44,] 21552.2249 -14350.9909
[45,] -8896.3652 21552.2249
[46,] -6827.2754 -8896.3652
[47,] 24641.3569 -6827.2754
[48,] 54646.1983 24641.3569
[49,] 34815.5611 54646.1983
[50,] 2997.5611 34815.5611
[51,] 25893.5455 2997.5611
[52,] -35426.2066 25893.5455
[53,] -12274.9853 -35426.2066
[54,] -19986.2066 -12274.9853
[55,] -26426.2177 -19986.2066
[56,] 20732.7924 -26426.2177
[57,] -33408.0035 20732.7924
[58,] -12064.4876 -33408.0035
[59,] -12045.7511 -12064.4876
[60,] 18124.9439 -12045.7511
[61,] 20096.2911 18124.9439
[62,] 36830.0487 20096.2911
[63,] -24965.5042 36830.0487
[64,] -15875.9734 -24965.5042
[65,] -21749.7631 -15875.9734
[66,] -50570.3582 -21749.7631
[67,] 3716.1460 -50570.3582
[68,] -29776.9143 3716.1460
[69,] -43246.4276 -29776.9143
[70,] -6063.5155 -43246.4276
[71,] -61037.0866 -6063.5155
[72,] 20649.5169 -61037.0866
[73,] 53766.1110 20649.5169
[74,] 89177.4793 53766.1110
[75,] -6733.5518 89177.4793
[76,] 42442.1231 -6733.5518
[77,] 31335.2139 42442.1231
[78,] -47641.3059 31335.2139
[79,] 35518.8477 -47641.3059
[80,] 2747.1964 35518.8477
[81,] 15677.9667 2747.1964
[82,] 61681.8950 15677.9667
[83,] 9851.7862 61681.8950
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 14219.2894 -5498.2625
2 31475.4675 14219.2894
3 -69308.3494 31475.4675
4 -69301.1397 -69308.3494
5 -34603.0077 -69301.1397
6 -101076.5862 -34603.0077
7 -18912.2375 -101076.5862
8 -23941.4522 -18912.2375
9 -22203.2765 -23941.4522
10 16677.9553 -22203.2765
11 -39548.6443 16677.9553
12 33736.2087 -39548.6443
13 39796.3878 33736.2087
14 6213.4410 39796.3878
15 755.1947 6213.4410
16 899.7687 755.1947
17 -45940.3367 899.7687
18 -47527.6573 -45940.3367
19 5564.7376 -47527.6573
20 -29112.2413 5564.7376
21 -5600.2258 -29112.2413
22 10479.5685 -5600.2258
23 -36290.3111 10479.5685
24 64725.2885 -36290.3111
25 38261.8249 64725.2885
26 3524.2924 38261.8249
27 2633.2548 3524.2924
28 33867.3547 2633.2548
29 -39110.2349 33867.3547
30 -26015.2560 -39110.2349
31 -1456.0502 -26015.2560
32 -5817.5905 -1456.0502
33 6220.3417 -5817.5905
34 16800.6373 6220.3417
35 -25071.0522 16800.6373
36 78005.5640 -25071.0522
37 56566.8806 78005.5640
38 30402.9815 56566.8806
39 33553.4296 30402.9815
40 -2894.1278 33553.4296
41 -10835.7540 -2894.1278
42 -1843.9909 -10835.7540
43 -14350.9909 -1843.9909
44 21552.2249 -14350.9909
45 -8896.3652 21552.2249
46 -6827.2754 -8896.3652
47 24641.3569 -6827.2754
48 54646.1983 24641.3569
49 34815.5611 54646.1983
50 2997.5611 34815.5611
51 25893.5455 2997.5611
52 -35426.2066 25893.5455
53 -12274.9853 -35426.2066
54 -19986.2066 -12274.9853
55 -26426.2177 -19986.2066
56 20732.7924 -26426.2177
57 -33408.0035 20732.7924
58 -12064.4876 -33408.0035
59 -12045.7511 -12064.4876
60 18124.9439 -12045.7511
61 20096.2911 18124.9439
62 36830.0487 20096.2911
63 -24965.5042 36830.0487
64 -15875.9734 -24965.5042
65 -21749.7631 -15875.9734
66 -50570.3582 -21749.7631
67 3716.1460 -50570.3582
68 -29776.9143 3716.1460
69 -43246.4276 -29776.9143
70 -6063.5155 -43246.4276
71 -61037.0866 -6063.5155
72 20649.5169 -61037.0866
73 53766.1110 20649.5169
74 89177.4793 53766.1110
75 -6733.5518 89177.4793
76 42442.1231 -6733.5518
77 31335.2139 42442.1231
78 -47641.3059 31335.2139
79 35518.8477 -47641.3059
80 2747.1964 35518.8477
81 15677.9667 2747.1964
82 61681.8950 15677.9667
83 9851.7862 61681.8950
> 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/79jqm1292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89jqm1292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99jqm1292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10japp1292961499.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115b6v1292961499.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/128tn11292961499.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/13m3kr1292961499.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/148l1f1292961499.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/15b4z31292961499.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/16f5gr1292961499.tab")
+ }
>
> try(system("convert tmp/1vrad1292961499.ps tmp/1vrad1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vrad1292961499.ps tmp/2vrad1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/35iag1292961499.ps tmp/35iag1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/45iag1292961499.ps tmp/45iag1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/55iag1292961499.ps tmp/55iag1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ysr11292961499.ps tmp/6ysr11292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/79jqm1292961499.ps tmp/79jqm1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/89jqm1292961499.ps tmp/89jqm1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/99jqm1292961499.ps tmp/99jqm1292961499.png",intern=TRUE))
character(0)
> try(system("convert tmp/10japp1292961499.ps tmp/10japp1292961499.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.808 1.668 6.451