R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8.4,99,8.4,98.6,8.4,98.6,8.6,98.5,8.9,98.9,8.8,99.4,8.3,99.8,7.5,99.9,7.2,100,7.4,100.1,8.8,100.1,9.3,100.2,9.3,100.3,8.7,100,8.2,99.9,8.3,99.4,8.5,99.8,8.6,99.6,8.5,100,8.2,99.9,8.1,100.3,7.9,100.6,8.6,100.7,8.7,100.8,8.7,100.8,8.5,100.6,8.4,101.1,8.5,101.1,8.7,100.9,8.7,101.1,8.6,101.2,8.5,101.4,8.3,101.9,8,102.1,8.2,102.1,8.1,103,8.1,103.4,8,103.2,7.9,103.1,7.9,103,8,103.7,8,103.4,7.9,103.5,8,103.8,7.7,104,7.2,104.2,7.5,104.4,7.3,104.4,7,104.9,7,105.3,7,105.2,7.2,105.4,7.3,105.4,7.1,105.5,6.8,105.7,6.4,105.6,6.1,105.8,6.5,105.4,7.7,105.5,7.9,105.8,7.5,106.1,6.9,106,6.6,105.5,6.9,105.4,7.7,106,8,106.1,8,106.4,7.7,106,7.3,106,7.4,106,8.1,106,8.3,106.1,8.2,106.1),dim=c(2,73),dimnames=list(c('werkl','afzetp'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('werkl','afzetp'),1:73))
> 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'
> 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
werkl afzetp
1 8.4 99.0
2 8.4 98.6
3 8.4 98.6
4 8.6 98.5
5 8.9 98.9
6 8.8 99.4
7 8.3 99.8
8 7.5 99.9
9 7.2 100.0
10 7.4 100.1
11 8.8 100.1
12 9.3 100.2
13 9.3 100.3
14 8.7 100.0
15 8.2 99.9
16 8.3 99.4
17 8.5 99.8
18 8.6 99.6
19 8.5 100.0
20 8.2 99.9
21 8.1 100.3
22 7.9 100.6
23 8.6 100.7
24 8.7 100.8
25 8.7 100.8
26 8.5 100.6
27 8.4 101.1
28 8.5 101.1
29 8.7 100.9
30 8.7 101.1
31 8.6 101.2
32 8.5 101.4
33 8.3 101.9
34 8.0 102.1
35 8.2 102.1
36 8.1 103.0
37 8.1 103.4
38 8.0 103.2
39 7.9 103.1
40 7.9 103.0
41 8.0 103.7
42 8.0 103.4
43 7.9 103.5
44 8.0 103.8
45 7.7 104.0
46 7.2 104.2
47 7.5 104.4
48 7.3 104.4
49 7.0 104.9
50 7.0 105.3
51 7.0 105.2
52 7.2 105.4
53 7.3 105.4
54 7.1 105.5
55 6.8 105.7
56 6.4 105.6
57 6.1 105.8
58 6.5 105.4
59 7.7 105.5
60 7.9 105.8
61 7.5 106.1
62 6.9 106.0
63 6.6 105.5
64 6.9 105.4
65 7.7 106.0
66 8.0 106.1
67 8.0 106.4
68 7.7 106.0
69 7.3 106.0
70 7.4 106.0
71 8.1 106.0
72 8.3 106.1
73 8.2 106.1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) afzetp
26.9283 -0.1849
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.26163 -0.29321 0.07535 0.29451 0.99385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.92835 2.35187 11.45 < 2e-16 ***
afzetp -0.18494 0.02289 -8.08 1.19e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5051 on 71 degrees of freedom
Multiple R-squared: 0.4791, Adjusted R-squared: 0.4717
F-statistic: 65.29 on 1 and 71 DF, p-value: 1.186e-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,] 0.12030804 0.24061608 0.87969196
[2,] 0.04597748 0.09195497 0.95402252
[3,] 0.05121549 0.10243098 0.94878451
[4,] 0.24338869 0.48677739 0.75661131
[5,] 0.41345767 0.82691534 0.58654233
[6,] 0.39106954 0.78213907 0.60893046
[7,] 0.71646550 0.56706900 0.28353450
[8,] 0.94407183 0.11185635 0.05592817
[9,] 0.98148158 0.03703683 0.01851842
[10,] 0.97284186 0.05431629 0.02715814
[11,] 0.96060525 0.07878949 0.03939475
[12,] 0.94410719 0.11178561 0.05589281
[13,] 0.91869494 0.16261012 0.08130506
[14,] 0.88765984 0.22468032 0.11234016
[15,] 0.84721368 0.30557264 0.15278632
[16,] 0.81371570 0.37256860 0.18628430
[17,] 0.78323841 0.43352318 0.21676159
[18,] 0.77726212 0.44547577 0.22273788
[19,] 0.73627890 0.52744219 0.26372110
[20,] 0.70043370 0.59913259 0.29956630
[21,] 0.65667667 0.68664666 0.34332333
[22,] 0.59007409 0.81985182 0.40992591
[23,] 0.51917453 0.96165094 0.48082547
[24,] 0.45022452 0.90044903 0.54977548
[25,] 0.39984974 0.79969949 0.60015026
[26,] 0.35363288 0.70726576 0.64636712
[27,] 0.30099152 0.60198304 0.69900848
[28,] 0.24983497 0.49966994 0.75016503
[29,] 0.20602841 0.41205682 0.79397159
[30,] 0.18082920 0.36165840 0.81917080
[31,] 0.14433946 0.28867893 0.85566054
[32,] 0.11677668 0.23355336 0.88322332
[33,] 0.09483046 0.18966091 0.90516954
[34,] 0.07539014 0.15078028 0.92460986
[35,] 0.05956563 0.11913126 0.94043437
[36,] 0.04635429 0.09270859 0.95364571
[37,] 0.03756427 0.07512853 0.96243573
[38,] 0.03239289 0.06478578 0.96760711
[39,] 0.02994984 0.05989968 0.97005016
[40,] 0.03817931 0.07635862 0.96182069
[41,] 0.04768746 0.09537493 0.95231254
[42,] 0.05797020 0.11594039 0.94202980
[43,] 0.07789096 0.15578191 0.92210904
[44,] 0.14424634 0.28849269 0.85575366
[45,] 0.17259595 0.34519191 0.82740405
[46,] 0.15428574 0.30857147 0.84571426
[47,] 0.14521322 0.29042644 0.85478678
[48,] 0.12546830 0.25093659 0.87453170
[49,] 0.12404039 0.24808079 0.87595961
[50,] 0.10022067 0.20044134 0.89977933
[51,] 0.08846992 0.17693984 0.91153008
[52,] 0.12900726 0.25801453 0.87099274
[53,] 0.53577152 0.92845695 0.46422848
[54,] 0.55721743 0.88556515 0.44278257
[55,] 0.65407480 0.69185041 0.34592520
[56,] 0.72804936 0.54390128 0.27195064
[57,] 0.68462095 0.63075809 0.31537905
[58,] 0.86004189 0.27991623 0.13995811
[59,] 0.86293426 0.27413149 0.13706574
[60,] 0.79146267 0.41707467 0.20853733
[61,] 0.70481824 0.59036351 0.29518176
[62,] 0.61672510 0.76654981 0.38327490
[63,] 0.79260823 0.41478355 0.20739177
[64,] 0.64969964 0.70060072 0.35030036
> postscript(file="/var/www/html/rcomp/tmp/174wb1258762637.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/2soxr1258762637.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3bmw51258762637.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xv9k1258762637.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c71z1258762637.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 73
Frequency = 1
1 2 3 4 5 6
-0.219230655 -0.293206887 -0.293206887 -0.111700945 0.262275287 0.254745577
7 8 9 10 11 12
-0.171278190 -0.952784132 -1.234290074 -1.015796016 0.384203984 0.902698042
13 14 15 16 17 18
0.921192100 0.265709926 -0.252784132 -0.245254423 0.028721810 0.091733693
19 20 21 22 23 24
0.065709926 -0.252784132 -0.278807900 -0.423325726 0.295168332 0.413662390
25 26 27 28 29 30
0.413662390 0.176674274 0.169144564 0.269144564 0.432156448 0.469144564
31 32 33 34 35 36
0.387638622 0.324626738 0.217097028 -0.045914856 0.154085144 0.220531666
37 38 39 40 41 42
0.294507898 0.157519782 0.039025724 0.020531666 0.249990072 0.194507898
43 44 45 46 47 48
0.113001956 0.268484130 0.005472246 -0.457539638 -0.120551522 -0.320551522
49 50 51 52 53 54
-0.528081232 -0.454105000 -0.472599058 -0.235610942 -0.135610942 -0.317116884
55 56 57 58 59 60
-0.580128768 -0.998622826 -1.261634710 -0.935610942 0.282883116 0.538365290
61 62 63 64 65 66
0.193847464 -0.424646594 -0.817116884 -0.535610942 0.375353406 0.693847464
67 68 69 70 71 72
0.749329638 0.375353406 -0.024646594 0.075353406 0.775353406 0.993847464
73
0.893847464
> postscript(file="/var/www/html/rcomp/tmp/6sa2w1258762637.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.219230655 NA
1 -0.293206887 -0.219230655
2 -0.293206887 -0.293206887
3 -0.111700945 -0.293206887
4 0.262275287 -0.111700945
5 0.254745577 0.262275287
6 -0.171278190 0.254745577
7 -0.952784132 -0.171278190
8 -1.234290074 -0.952784132
9 -1.015796016 -1.234290074
10 0.384203984 -1.015796016
11 0.902698042 0.384203984
12 0.921192100 0.902698042
13 0.265709926 0.921192100
14 -0.252784132 0.265709926
15 -0.245254423 -0.252784132
16 0.028721810 -0.245254423
17 0.091733693 0.028721810
18 0.065709926 0.091733693
19 -0.252784132 0.065709926
20 -0.278807900 -0.252784132
21 -0.423325726 -0.278807900
22 0.295168332 -0.423325726
23 0.413662390 0.295168332
24 0.413662390 0.413662390
25 0.176674274 0.413662390
26 0.169144564 0.176674274
27 0.269144564 0.169144564
28 0.432156448 0.269144564
29 0.469144564 0.432156448
30 0.387638622 0.469144564
31 0.324626738 0.387638622
32 0.217097028 0.324626738
33 -0.045914856 0.217097028
34 0.154085144 -0.045914856
35 0.220531666 0.154085144
36 0.294507898 0.220531666
37 0.157519782 0.294507898
38 0.039025724 0.157519782
39 0.020531666 0.039025724
40 0.249990072 0.020531666
41 0.194507898 0.249990072
42 0.113001956 0.194507898
43 0.268484130 0.113001956
44 0.005472246 0.268484130
45 -0.457539638 0.005472246
46 -0.120551522 -0.457539638
47 -0.320551522 -0.120551522
48 -0.528081232 -0.320551522
49 -0.454105000 -0.528081232
50 -0.472599058 -0.454105000
51 -0.235610942 -0.472599058
52 -0.135610942 -0.235610942
53 -0.317116884 -0.135610942
54 -0.580128768 -0.317116884
55 -0.998622826 -0.580128768
56 -1.261634710 -0.998622826
57 -0.935610942 -1.261634710
58 0.282883116 -0.935610942
59 0.538365290 0.282883116
60 0.193847464 0.538365290
61 -0.424646594 0.193847464
62 -0.817116884 -0.424646594
63 -0.535610942 -0.817116884
64 0.375353406 -0.535610942
65 0.693847464 0.375353406
66 0.749329638 0.693847464
67 0.375353406 0.749329638
68 -0.024646594 0.375353406
69 0.075353406 -0.024646594
70 0.775353406 0.075353406
71 0.993847464 0.775353406
72 0.893847464 0.993847464
73 NA 0.893847464
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.293206887 -0.219230655
[2,] -0.293206887 -0.293206887
[3,] -0.111700945 -0.293206887
[4,] 0.262275287 -0.111700945
[5,] 0.254745577 0.262275287
[6,] -0.171278190 0.254745577
[7,] -0.952784132 -0.171278190
[8,] -1.234290074 -0.952784132
[9,] -1.015796016 -1.234290074
[10,] 0.384203984 -1.015796016
[11,] 0.902698042 0.384203984
[12,] 0.921192100 0.902698042
[13,] 0.265709926 0.921192100
[14,] -0.252784132 0.265709926
[15,] -0.245254423 -0.252784132
[16,] 0.028721810 -0.245254423
[17,] 0.091733693 0.028721810
[18,] 0.065709926 0.091733693
[19,] -0.252784132 0.065709926
[20,] -0.278807900 -0.252784132
[21,] -0.423325726 -0.278807900
[22,] 0.295168332 -0.423325726
[23,] 0.413662390 0.295168332
[24,] 0.413662390 0.413662390
[25,] 0.176674274 0.413662390
[26,] 0.169144564 0.176674274
[27,] 0.269144564 0.169144564
[28,] 0.432156448 0.269144564
[29,] 0.469144564 0.432156448
[30,] 0.387638622 0.469144564
[31,] 0.324626738 0.387638622
[32,] 0.217097028 0.324626738
[33,] -0.045914856 0.217097028
[34,] 0.154085144 -0.045914856
[35,] 0.220531666 0.154085144
[36,] 0.294507898 0.220531666
[37,] 0.157519782 0.294507898
[38,] 0.039025724 0.157519782
[39,] 0.020531666 0.039025724
[40,] 0.249990072 0.020531666
[41,] 0.194507898 0.249990072
[42,] 0.113001956 0.194507898
[43,] 0.268484130 0.113001956
[44,] 0.005472246 0.268484130
[45,] -0.457539638 0.005472246
[46,] -0.120551522 -0.457539638
[47,] -0.320551522 -0.120551522
[48,] -0.528081232 -0.320551522
[49,] -0.454105000 -0.528081232
[50,] -0.472599058 -0.454105000
[51,] -0.235610942 -0.472599058
[52,] -0.135610942 -0.235610942
[53,] -0.317116884 -0.135610942
[54,] -0.580128768 -0.317116884
[55,] -0.998622826 -0.580128768
[56,] -1.261634710 -0.998622826
[57,] -0.935610942 -1.261634710
[58,] 0.282883116 -0.935610942
[59,] 0.538365290 0.282883116
[60,] 0.193847464 0.538365290
[61,] -0.424646594 0.193847464
[62,] -0.817116884 -0.424646594
[63,] -0.535610942 -0.817116884
[64,] 0.375353406 -0.535610942
[65,] 0.693847464 0.375353406
[66,] 0.749329638 0.693847464
[67,] 0.375353406 0.749329638
[68,] -0.024646594 0.375353406
[69,] 0.075353406 -0.024646594
[70,] 0.775353406 0.075353406
[71,] 0.993847464 0.775353406
[72,] 0.893847464 0.993847464
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.293206887 -0.219230655
2 -0.293206887 -0.293206887
3 -0.111700945 -0.293206887
4 0.262275287 -0.111700945
5 0.254745577 0.262275287
6 -0.171278190 0.254745577
7 -0.952784132 -0.171278190
8 -1.234290074 -0.952784132
9 -1.015796016 -1.234290074
10 0.384203984 -1.015796016
11 0.902698042 0.384203984
12 0.921192100 0.902698042
13 0.265709926 0.921192100
14 -0.252784132 0.265709926
15 -0.245254423 -0.252784132
16 0.028721810 -0.245254423
17 0.091733693 0.028721810
18 0.065709926 0.091733693
19 -0.252784132 0.065709926
20 -0.278807900 -0.252784132
21 -0.423325726 -0.278807900
22 0.295168332 -0.423325726
23 0.413662390 0.295168332
24 0.413662390 0.413662390
25 0.176674274 0.413662390
26 0.169144564 0.176674274
27 0.269144564 0.169144564
28 0.432156448 0.269144564
29 0.469144564 0.432156448
30 0.387638622 0.469144564
31 0.324626738 0.387638622
32 0.217097028 0.324626738
33 -0.045914856 0.217097028
34 0.154085144 -0.045914856
35 0.220531666 0.154085144
36 0.294507898 0.220531666
37 0.157519782 0.294507898
38 0.039025724 0.157519782
39 0.020531666 0.039025724
40 0.249990072 0.020531666
41 0.194507898 0.249990072
42 0.113001956 0.194507898
43 0.268484130 0.113001956
44 0.005472246 0.268484130
45 -0.457539638 0.005472246
46 -0.120551522 -0.457539638
47 -0.320551522 -0.120551522
48 -0.528081232 -0.320551522
49 -0.454105000 -0.528081232
50 -0.472599058 -0.454105000
51 -0.235610942 -0.472599058
52 -0.135610942 -0.235610942
53 -0.317116884 -0.135610942
54 -0.580128768 -0.317116884
55 -0.998622826 -0.580128768
56 -1.261634710 -0.998622826
57 -0.935610942 -1.261634710
58 0.282883116 -0.935610942
59 0.538365290 0.282883116
60 0.193847464 0.538365290
61 -0.424646594 0.193847464
62 -0.817116884 -0.424646594
63 -0.535610942 -0.817116884
64 0.375353406 -0.535610942
65 0.693847464 0.375353406
66 0.749329638 0.693847464
67 0.375353406 0.749329638
68 -0.024646594 0.375353406
69 0.075353406 -0.024646594
70 0.775353406 0.075353406
71 0.993847464 0.775353406
72 0.893847464 0.993847464
> 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/7ch3a1258762637.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/8bvyo1258762637.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/9c1621258762637.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/10gi8x1258762637.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11z4al1258762637.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12q50x1258762637.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13yiv51258762637.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/144yft1258762637.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/15pdnh1258762638.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/16u2mj1258762638.tab")
+ }
>
> system("convert tmp/174wb1258762637.ps tmp/174wb1258762637.png")
> system("convert tmp/2soxr1258762637.ps tmp/2soxr1258762637.png")
> system("convert tmp/3bmw51258762637.ps tmp/3bmw51258762637.png")
> system("convert tmp/4xv9k1258762637.ps tmp/4xv9k1258762637.png")
> system("convert tmp/5c71z1258762637.ps tmp/5c71z1258762637.png")
> system("convert tmp/6sa2w1258762637.ps tmp/6sa2w1258762637.png")
> system("convert tmp/7ch3a1258762637.ps tmp/7ch3a1258762637.png")
> system("convert tmp/8bvyo1258762637.ps tmp/8bvyo1258762637.png")
> system("convert tmp/9c1621258762637.ps tmp/9c1621258762637.png")
> system("convert tmp/10gi8x1258762637.ps tmp/10gi8x1258762637.png")
>
>
> proc.time()
user system elapsed
2.603 1.573 3.049