R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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.
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'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(1
+ ,2981.85
+ ,10407
+ ,0.762253
+ ,14448.9
+ ,13953.3
+ ,2
+ ,3080.58
+ ,10463
+ ,0.768403
+ ,15023.9
+ ,14657.7
+ ,3
+ ,3106.22
+ ,10556
+ ,0.757518
+ ,17319.2
+ ,16686.2
+ ,4
+ ,3119.31
+ ,10646
+ ,0.772917
+ ,16080.7
+ ,15232.4
+ ,5
+ ,3061.26
+ ,10702
+ ,0.787774
+ ,15486.3
+ ,15014.1
+ ,6
+ ,3097.31
+ ,11353
+ ,0.82203
+ ,17046.4
+ ,16688.6
+ ,7
+ ,3161.69
+ ,11346
+ ,0.830772
+ ,14793.9
+ ,13969.6
+ ,8
+ ,3257.16
+ ,11451
+ ,0.813537
+ ,13666.7
+ ,14546.8
+ ,9
+ ,3277.01
+ ,11964
+ ,0.815927
+ ,17358.8
+ ,16292
+ ,10
+ ,3295.32
+ ,12574
+ ,0.832293
+ ,16091.8
+ ,15039
+ ,11
+ ,3363.99
+ ,13031
+ ,0.848464
+ ,17401.7
+ ,17433.8
+ ,12
+ ,3494.17
+ ,13812
+ ,0.843455
+ ,16467
+ ,17798.4
+ ,13
+ ,3667.03
+ ,14544
+ ,0.826241
+ ,16103.8
+ ,16870.9
+ ,14
+ ,3813.06
+ ,14931
+ ,0.837661
+ ,16422.6
+ ,16659.3
+ ,15
+ ,3917.96
+ ,14886
+ ,0.831947
+ ,19435.5
+ ,19620.4
+ ,16
+ ,3895.51
+ ,16005
+ ,0.81493
+ ,15810.1
+ ,15953.5
+ ,17
+ ,3801.06
+ ,17064
+ ,0.783085
+ ,17914.8
+ ,17420.9
+ ,18
+ ,3570.12
+ ,15168
+ ,0.790514
+ ,18197.2
+ ,17647.5
+ ,19
+ ,3701.61
+ ,16050
+ ,0.788395
+ ,16183.5
+ ,15200.8
+ ,20
+ ,3862.27
+ ,15839
+ ,0.780579
+ ,14781
+ ,15637.3
+ ,21
+ ,3970.1
+ ,15137
+ ,0.785731
+ ,18091.5
+ ,17124.5
+ ,22
+ ,4138.52
+ ,14954
+ ,0.792959
+ ,18318.8
+ ,17659.4
+ ,23
+ ,4199.75
+ ,15648
+ ,0.776337
+ ,18392.2
+ ,17815
+ ,24
+ ,4290.89
+ ,15305
+ ,0.75683
+ ,15952.5
+ ,16165.6
+ ,25
+ ,4443.91
+ ,15579
+ ,0.76929
+ ,17434.3
+ ,17416.6
+ ,26
+ ,4502.64
+ ,16348
+ ,0.764877
+ ,17214
+ ,16823.9
+ ,27
+ ,4356.98
+ ,15928
+ ,0.755173
+ ,19680.5
+ ,19171.2
+ ,28
+ ,4591.27
+ ,16171
+ ,0.739864
+ ,17216.8
+ ,16806.8
+ ,29
+ ,4696.96
+ ,15937
+ ,0.740138
+ ,18325.3
+ ,18112.8
+ ,30
+ ,4621.4
+ ,15713
+ ,0.745212
+ ,19303.5
+ ,18485.5
+ ,31
+ ,4562.84
+ ,15594
+ ,0.729076
+ ,18090.7
+ ,17668
+ ,32
+ ,4202.52
+ ,15683
+ ,0.734107
+ ,16166.3
+ ,16324.3
+ ,33
+ ,4296.49
+ ,16438
+ ,0.719632
+ ,18304.7
+ ,17877.5
+ ,34
+ ,4435.23
+ ,17032
+ ,0.702889
+ ,20380.1
+ ,20136.7
+ ,35
+ ,4105.18
+ ,17696
+ ,0.681013
+ ,18887.7
+ ,19307
+ ,36
+ ,4116.68
+ ,17745
+ ,0.686342
+ ,16316.5
+ ,17776.3
+ ,37
+ ,3844.49
+ ,19394
+ ,0.67944
+ ,18471.5
+ ,19861.3
+ ,38
+ ,3720.98
+ ,20148
+ ,0.678058
+ ,18754.9
+ ,18757
+ ,39
+ ,3674.4
+ ,20108
+ ,0.644039
+ ,18940.7
+ ,19879.3
+ ,40
+ ,3857.62
+ ,18584
+ ,0.63488
+ ,20228.5
+ ,21068.4
+ ,41
+ ,3801.06
+ ,18441
+ ,0.642797
+ ,19060.4
+ ,19358
+ ,42
+ ,3504.37
+ ,18391
+ ,0.642963
+ ,20262.9
+ ,20639.2
+ ,43
+ ,3032.6
+ ,19178
+ ,0.634115
+ ,19928.7
+ ,20008.1
+ ,44
+ ,3047.03
+ ,18079
+ ,0.66778
+ ,16058.8
+ ,18150.1
+ ,45
+ ,2962.34
+ ,18483
+ ,0.695894
+ ,20157.4
+ ,21180.4
+ ,46
+ ,2197.82
+ ,19644
+ ,0.750638
+ ,19663.3
+ ,20428.9
+ ,47
+ ,2014.45
+ ,19195
+ ,0.785423
+ ,15648.9
+ ,17241.2
+ ,48
+ ,1862.83
+ ,19650
+ ,0.74355
+ ,14380.5
+ ,15969.3
+ ,49
+ ,1905.41
+ ,20830
+ ,0.755344
+ ,13654.4
+ ,14972.4
+ ,50
+ ,1810.99
+ ,23595
+ ,0.782167
+ ,14085.9
+ ,14488.3
+ ,51
+ ,1670.07
+ ,22937
+ ,0.766284
+ ,15070.6
+ ,15885.1
+ ,52
+ ,1864.44
+ ,21814
+ ,0.75815
+ ,14206.9
+ ,14305.3
+ ,53
+ ,2052.02
+ ,21928
+ ,0.732601
+ ,13585.6
+ ,13891.5
+ ,54
+ ,2029.6
+ ,21777
+ ,0.71347
+ ,15413.2
+ ,15431.6
+ ,55
+ ,2070.83
+ ,21383
+ ,0.709824
+ ,14809.6
+ ,14199.3
+ ,56
+ ,2293.41
+ ,21467
+ ,0.700869
+ ,12625.3
+ ,13542.6
+ ,57
+ ,2443.27
+ ,22052
+ ,0.686719
+ ,16314.7
+ ,16226.3
+ ,58
+ ,2513.17
+ ,22680
+ ,0.674946
+ ,16045.9
+ ,16786.1
+ ,59
+ ,2466.92
+ ,24320
+ ,0.670511
+ ,16063.6
+ ,16034.3
+ ,60
+ ,2502.66
+ ,24977
+ ,0.684275
+ ,15851.3
+ ,16744.5
+ ,61
+ ,2539.91
+ ,25204
+ ,0.700673
+ ,14925.2
+ ,15955.4)
+ ,dim=c(6
+ ,61)
+ ,dimnames=list(c('Periodes'
+ ,'BEL20'
+ ,'GoudkoersTeBrussel'
+ ,'EurosPerUSdollar'
+ ,'Uitvoer'
+ ,'Invoer')
+ ,1:61))
> y <- array(NA,dim=c(6,61),dimnames=list(c('Periodes','BEL20','GoudkoersTeBrussel','EurosPerUSdollar','Uitvoer','Invoer'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
EurosPerUSdollar Periodes BEL20 GoudkoersTeBrussel Uitvoer Invoer
1 0.762253 1 2981.85 10407 14448.9 13953.3
2 0.768403 2 3080.58 10463 15023.9 14657.7
3 0.757518 3 3106.22 10556 17319.2 16686.2
4 0.772917 4 3119.31 10646 16080.7 15232.4
5 0.787774 5 3061.26 10702 15486.3 15014.1
6 0.822030 6 3097.31 11353 17046.4 16688.6
7 0.830772 7 3161.69 11346 14793.9 13969.6
8 0.813537 8 3257.16 11451 13666.7 14546.8
9 0.815927 9 3277.01 11964 17358.8 16292.0
10 0.832293 10 3295.32 12574 16091.8 15039.0
11 0.848464 11 3363.99 13031 17401.7 17433.8
12 0.843455 12 3494.17 13812 16467.0 17798.4
13 0.826241 13 3667.03 14544 16103.8 16870.9
14 0.837661 14 3813.06 14931 16422.6 16659.3
15 0.831947 15 3917.96 14886 19435.5 19620.4
16 0.814930 16 3895.51 16005 15810.1 15953.5
17 0.783085 17 3801.06 17064 17914.8 17420.9
18 0.790514 18 3570.12 15168 18197.2 17647.5
19 0.788395 19 3701.61 16050 16183.5 15200.8
20 0.780579 20 3862.27 15839 14781.0 15637.3
21 0.785731 21 3970.10 15137 18091.5 17124.5
22 0.792959 22 4138.52 14954 18318.8 17659.4
23 0.776337 23 4199.75 15648 18392.2 17815.0
24 0.756830 24 4290.89 15305 15952.5 16165.6
25 0.769290 25 4443.91 15579 17434.3 17416.6
26 0.764877 26 4502.64 16348 17214.0 16823.9
27 0.755173 27 4356.98 15928 19680.5 19171.2
28 0.739864 28 4591.27 16171 17216.8 16806.8
29 0.740138 29 4696.96 15937 18325.3 18112.8
30 0.745212 30 4621.40 15713 19303.5 18485.5
31 0.729076 31 4562.84 15594 18090.7 17668.0
32 0.734107 32 4202.52 15683 16166.3 16324.3
33 0.719632 33 4296.49 16438 18304.7 17877.5
34 0.702889 34 4435.23 17032 20380.1 20136.7
35 0.681013 35 4105.18 17696 18887.7 19307.0
36 0.686342 36 4116.68 17745 16316.5 17776.3
37 0.679440 37 3844.49 19394 18471.5 19861.3
38 0.678058 38 3720.98 20148 18754.9 18757.0
39 0.644039 39 3674.40 20108 18940.7 19879.3
40 0.634880 40 3857.62 18584 20228.5 21068.4
41 0.642797 41 3801.06 18441 19060.4 19358.0
42 0.642963 42 3504.37 18391 20262.9 20639.2
43 0.634115 43 3032.60 19178 19928.7 20008.1
44 0.667780 44 3047.03 18079 16058.8 18150.1
45 0.695894 45 2962.34 18483 20157.4 21180.4
46 0.750638 46 2197.82 19644 19663.3 20428.9
47 0.785423 47 2014.45 19195 15648.9 17241.2
48 0.743550 48 1862.83 19650 14380.5 15969.3
49 0.755344 49 1905.41 20830 13654.4 14972.4
50 0.782167 50 1810.99 23595 14085.9 14488.3
51 0.766284 51 1670.07 22937 15070.6 15885.1
52 0.758150 52 1864.44 21814 14206.9 14305.3
53 0.732601 53 2052.02 21928 13585.6 13891.5
54 0.713470 54 2029.60 21777 15413.2 15431.6
55 0.709824 55 2070.83 21383 14809.6 14199.3
56 0.700869 56 2293.41 21467 12625.3 13542.6
57 0.686719 57 2443.27 22052 16314.7 16226.3
58 0.674946 58 2513.17 22680 16045.9 16786.1
59 0.670511 59 2466.92 24320 16063.6 16034.3
60 0.684275 60 2502.66 24977 15851.3 16744.5
61 0.700673 61 2539.91 25204 14925.2 15955.4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Periodes BEL20 GoudkoersTeBrussel
8.882e-01 -4.533e-03 -1.150e-05 9.512e-06
Uitvoer Invoer
-2.517e-06 -4.899e-06
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.081419 -0.027546 0.007701 0.023799 0.074687
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.882e-01 6.623e-02 13.411 < 2e-16 ***
Periodes -4.533e-03 9.879e-04 -4.589 2.63e-05 ***
BEL20 -1.150e-05 8.047e-06 -1.429 0.159
GoudkoersTeBrussel 9.512e-06 4.374e-06 2.175 0.034 *
Uitvoer -2.517e-06 7.440e-06 -0.338 0.736
Invoer -4.899e-06 7.123e-06 -0.688 0.494
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03631 on 55 degrees of freedom
Multiple R-squared: 0.6475, Adjusted R-squared: 0.6154
F-statistic: 20.2 on 5 and 55 DF, p-value: 2.193e-11
> 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,] 0.134426944 0.2688538878 0.8655730561
[2,] 0.101222222 0.2024444440 0.8987777780
[3,] 0.043713580 0.0874271592 0.9562864204
[4,] 0.018581851 0.0371637012 0.9814181494
[5,] 0.007173238 0.0143464768 0.9928267616
[6,] 0.006366835 0.0127336703 0.9936331649
[7,] 0.004672277 0.0093445534 0.9953277233
[8,] 0.014427381 0.0288547622 0.9855726189
[9,] 0.070330959 0.1406619177 0.9296690412
[10,] 0.398901118 0.7978022369 0.6010988815
[11,] 0.459579437 0.9191588732 0.5404205634
[12,] 0.723559318 0.5528813639 0.2764406820
[13,] 0.728074577 0.5438508453 0.2719254227
[14,] 0.663096670 0.6738066598 0.3369033299
[15,] 0.615817903 0.7683641946 0.3841820973
[16,] 0.612587723 0.7748245533 0.3874122767
[17,] 0.537920292 0.9241594155 0.4620797077
[18,] 0.456511109 0.9130222185 0.5434888907
[19,] 0.456550397 0.9131007943 0.5434496029
[20,] 0.387115733 0.7742314658 0.6128842671
[21,] 0.360000644 0.7200012873 0.6399993563
[22,] 0.387438793 0.7748775863 0.6125612069
[23,] 0.400875474 0.8017509478 0.5991245261
[24,] 0.395650638 0.7913012756 0.6043493622
[25,] 0.498325869 0.9966517388 0.5016741306
[26,] 0.869891167 0.2602176656 0.1301088328
[27,] 0.941519455 0.1169610903 0.0584805451
[28,] 0.967351127 0.0652977465 0.0326488732
[29,] 0.965317992 0.0693640161 0.0346820081
[30,] 0.969846203 0.0603075936 0.0301537968
[31,] 0.960931825 0.0781363506 0.0390681753
[32,] 0.941477359 0.1170452812 0.0585226406
[33,] 0.955660076 0.0886798480 0.0443399240
[34,] 0.955173101 0.0896537975 0.0448268988
[35,] 0.981701568 0.0365968646 0.0182984323
[36,] 0.991816076 0.0163678475 0.0081839238
[37,] 0.994440035 0.0111199300 0.0055599650
[38,] 0.996369707 0.0072605852 0.0036302926
[39,] 0.999840292 0.0003194157 0.0001597079
[40,] 0.999405889 0.0011882222 0.0005941111
[41,] 0.997738200 0.0045236009 0.0022618005
[42,] 0.992230301 0.0155393984 0.0077696992
[43,] 0.977160746 0.0456785073 0.0228392537
[44,] 0.950034407 0.0999311864 0.0499655932
> postscript(file="/var/fisher/rcomp/tmp/155b41353252111.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/fisher/rcomp/tmp/2jfbb1353252111.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3xtny1353252111.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4jjbq1353252111.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5oq791353252111.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 61
Frequency = 1
1 2 3 4 5
-0.0814189913 -0.0652348821 -0.0564609812 -0.0474743490 -0.0318501116
6 7 8 9 10
0.0132919822 0.0083832278 -0.0042289887 0.0158862438 0.0218657583
11 12 13 14 15
0.0540424162 0.0470682727 0.0239542025 0.0376712179 0.0602154860
16 17 18 19 20
0.0097391804 -0.0162450681 0.0129168767 -0.0086020718 -0.0094221682
21 22 23 24 25
0.0237991843 0.0424304115 0.0253914468 0.0005064381 0.0265116715
26 27 28 29 30
0.0165341982 0.0313916555 0.0032134510 0.0206502858 0.0358073061
31 32 33 34 35
0.0176050691 0.0107521829 0.0077014327 0.0077292099 -0.0275463307
36 37 38 39 40
-0.0319892587 -0.0375340272 -0.0476720637 -0.0713470386 -0.0503027483
41 42 43 44 45
-0.0484627220 -0.0373960765 -0.0585549949 -0.0285810492 0.0244120454
46 47 48 49 50
0.0589289009 0.0746873148 0.0218519659 0.0207329845 0.0234173713
51 52 53 54 55
0.0260276238 0.0254298603 0.0018954828 0.0006215947 -0.0018261576
56 57 58 59 60
-0.0132029224 -0.0042263713 -0.0145699233 -0.0342418471 -0.0188380002
61
-0.0058348086
> postscript(file="/var/fisher/rcomp/tmp/6k5oj1353252111.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0814189913 NA
1 -0.0652348821 -0.0814189913
2 -0.0564609812 -0.0652348821
3 -0.0474743490 -0.0564609812
4 -0.0318501116 -0.0474743490
5 0.0132919822 -0.0318501116
6 0.0083832278 0.0132919822
7 -0.0042289887 0.0083832278
8 0.0158862438 -0.0042289887
9 0.0218657583 0.0158862438
10 0.0540424162 0.0218657583
11 0.0470682727 0.0540424162
12 0.0239542025 0.0470682727
13 0.0376712179 0.0239542025
14 0.0602154860 0.0376712179
15 0.0097391804 0.0602154860
16 -0.0162450681 0.0097391804
17 0.0129168767 -0.0162450681
18 -0.0086020718 0.0129168767
19 -0.0094221682 -0.0086020718
20 0.0237991843 -0.0094221682
21 0.0424304115 0.0237991843
22 0.0253914468 0.0424304115
23 0.0005064381 0.0253914468
24 0.0265116715 0.0005064381
25 0.0165341982 0.0265116715
26 0.0313916555 0.0165341982
27 0.0032134510 0.0313916555
28 0.0206502858 0.0032134510
29 0.0358073061 0.0206502858
30 0.0176050691 0.0358073061
31 0.0107521829 0.0176050691
32 0.0077014327 0.0107521829
33 0.0077292099 0.0077014327
34 -0.0275463307 0.0077292099
35 -0.0319892587 -0.0275463307
36 -0.0375340272 -0.0319892587
37 -0.0476720637 -0.0375340272
38 -0.0713470386 -0.0476720637
39 -0.0503027483 -0.0713470386
40 -0.0484627220 -0.0503027483
41 -0.0373960765 -0.0484627220
42 -0.0585549949 -0.0373960765
43 -0.0285810492 -0.0585549949
44 0.0244120454 -0.0285810492
45 0.0589289009 0.0244120454
46 0.0746873148 0.0589289009
47 0.0218519659 0.0746873148
48 0.0207329845 0.0218519659
49 0.0234173713 0.0207329845
50 0.0260276238 0.0234173713
51 0.0254298603 0.0260276238
52 0.0018954828 0.0254298603
53 0.0006215947 0.0018954828
54 -0.0018261576 0.0006215947
55 -0.0132029224 -0.0018261576
56 -0.0042263713 -0.0132029224
57 -0.0145699233 -0.0042263713
58 -0.0342418471 -0.0145699233
59 -0.0188380002 -0.0342418471
60 -0.0058348086 -0.0188380002
61 NA -0.0058348086
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0652348821 -0.0814189913
[2,] -0.0564609812 -0.0652348821
[3,] -0.0474743490 -0.0564609812
[4,] -0.0318501116 -0.0474743490
[5,] 0.0132919822 -0.0318501116
[6,] 0.0083832278 0.0132919822
[7,] -0.0042289887 0.0083832278
[8,] 0.0158862438 -0.0042289887
[9,] 0.0218657583 0.0158862438
[10,] 0.0540424162 0.0218657583
[11,] 0.0470682727 0.0540424162
[12,] 0.0239542025 0.0470682727
[13,] 0.0376712179 0.0239542025
[14,] 0.0602154860 0.0376712179
[15,] 0.0097391804 0.0602154860
[16,] -0.0162450681 0.0097391804
[17,] 0.0129168767 -0.0162450681
[18,] -0.0086020718 0.0129168767
[19,] -0.0094221682 -0.0086020718
[20,] 0.0237991843 -0.0094221682
[21,] 0.0424304115 0.0237991843
[22,] 0.0253914468 0.0424304115
[23,] 0.0005064381 0.0253914468
[24,] 0.0265116715 0.0005064381
[25,] 0.0165341982 0.0265116715
[26,] 0.0313916555 0.0165341982
[27,] 0.0032134510 0.0313916555
[28,] 0.0206502858 0.0032134510
[29,] 0.0358073061 0.0206502858
[30,] 0.0176050691 0.0358073061
[31,] 0.0107521829 0.0176050691
[32,] 0.0077014327 0.0107521829
[33,] 0.0077292099 0.0077014327
[34,] -0.0275463307 0.0077292099
[35,] -0.0319892587 -0.0275463307
[36,] -0.0375340272 -0.0319892587
[37,] -0.0476720637 -0.0375340272
[38,] -0.0713470386 -0.0476720637
[39,] -0.0503027483 -0.0713470386
[40,] -0.0484627220 -0.0503027483
[41,] -0.0373960765 -0.0484627220
[42,] -0.0585549949 -0.0373960765
[43,] -0.0285810492 -0.0585549949
[44,] 0.0244120454 -0.0285810492
[45,] 0.0589289009 0.0244120454
[46,] 0.0746873148 0.0589289009
[47,] 0.0218519659 0.0746873148
[48,] 0.0207329845 0.0218519659
[49,] 0.0234173713 0.0207329845
[50,] 0.0260276238 0.0234173713
[51,] 0.0254298603 0.0260276238
[52,] 0.0018954828 0.0254298603
[53,] 0.0006215947 0.0018954828
[54,] -0.0018261576 0.0006215947
[55,] -0.0132029224 -0.0018261576
[56,] -0.0042263713 -0.0132029224
[57,] -0.0145699233 -0.0042263713
[58,] -0.0342418471 -0.0145699233
[59,] -0.0188380002 -0.0342418471
[60,] -0.0058348086 -0.0188380002
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0652348821 -0.0814189913
2 -0.0564609812 -0.0652348821
3 -0.0474743490 -0.0564609812
4 -0.0318501116 -0.0474743490
5 0.0132919822 -0.0318501116
6 0.0083832278 0.0132919822
7 -0.0042289887 0.0083832278
8 0.0158862438 -0.0042289887
9 0.0218657583 0.0158862438
10 0.0540424162 0.0218657583
11 0.0470682727 0.0540424162
12 0.0239542025 0.0470682727
13 0.0376712179 0.0239542025
14 0.0602154860 0.0376712179
15 0.0097391804 0.0602154860
16 -0.0162450681 0.0097391804
17 0.0129168767 -0.0162450681
18 -0.0086020718 0.0129168767
19 -0.0094221682 -0.0086020718
20 0.0237991843 -0.0094221682
21 0.0424304115 0.0237991843
22 0.0253914468 0.0424304115
23 0.0005064381 0.0253914468
24 0.0265116715 0.0005064381
25 0.0165341982 0.0265116715
26 0.0313916555 0.0165341982
27 0.0032134510 0.0313916555
28 0.0206502858 0.0032134510
29 0.0358073061 0.0206502858
30 0.0176050691 0.0358073061
31 0.0107521829 0.0176050691
32 0.0077014327 0.0107521829
33 0.0077292099 0.0077014327
34 -0.0275463307 0.0077292099
35 -0.0319892587 -0.0275463307
36 -0.0375340272 -0.0319892587
37 -0.0476720637 -0.0375340272
38 -0.0713470386 -0.0476720637
39 -0.0503027483 -0.0713470386
40 -0.0484627220 -0.0503027483
41 -0.0373960765 -0.0484627220
42 -0.0585549949 -0.0373960765
43 -0.0285810492 -0.0585549949
44 0.0244120454 -0.0285810492
45 0.0589289009 0.0244120454
46 0.0746873148 0.0589289009
47 0.0218519659 0.0746873148
48 0.0207329845 0.0218519659
49 0.0234173713 0.0207329845
50 0.0260276238 0.0234173713
51 0.0254298603 0.0260276238
52 0.0018954828 0.0254298603
53 0.0006215947 0.0018954828
54 -0.0018261576 0.0006215947
55 -0.0132029224 -0.0018261576
56 -0.0042263713 -0.0132029224
57 -0.0145699233 -0.0042263713
58 -0.0342418471 -0.0145699233
59 -0.0188380002 -0.0342418471
60 -0.0058348086 -0.0188380002
> 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/fisher/rcomp/tmp/7jiex1353252111.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/fisher/rcomp/tmp/8iggp1353252111.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/fisher/rcomp/tmp/96h7w1353252111.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/fisher/rcomp/tmp/10rd691353252111.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11k59u1353252111.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12zkz71353252111.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13yjho1353252111.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1476111353252111.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/fisher/rcomp/tmp/15j9aw1353252111.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/fisher/rcomp/tmp/16wp331353252111.tab")
+ }
>
> try(system("convert tmp/155b41353252111.ps tmp/155b41353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jfbb1353252111.ps tmp/2jfbb1353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xtny1353252111.ps tmp/3xtny1353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jjbq1353252111.ps tmp/4jjbq1353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oq791353252111.ps tmp/5oq791353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/6k5oj1353252111.ps tmp/6k5oj1353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jiex1353252111.ps tmp/7jiex1353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/8iggp1353252111.ps tmp/8iggp1353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/96h7w1353252111.ps tmp/96h7w1353252111.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rd691353252111.ps tmp/10rd691353252111.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.167 1.272 7.439