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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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2454.62
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+ ,3504.37
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+ ,19644
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+ ,-17
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+ ,4.53
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,2014.45
+ ,8531.45
+ ,8614.55
+ ,19195
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+ ,2962.34
+ ,3047.03
+ ,1905.41
+ ,8331.49
+ ,8396.2
+ ,20830
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+ ,-20
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+ ,2014.45
+ ,2197.82
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+ ,7690.5
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+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,2070.83
+ ,9691.12
+ ,8679.75
+ ,21383
+ ,-22.8
+ ,-17
+ ,-1.7
+ ,3.54
+ ,2029.6
+ ,2052.02
+ ,1864.44
+ ,1670.07
+ ,2293.41
+ ,10430.35
+ ,9374.63
+ ,21467
+ ,-18.2
+ ,-11
+ ,-0.8
+ ,3.52
+ ,2070.83
+ ,2029.6
+ ,2052.02
+ ,1864.44
+ ,2443.27
+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
+ ,-11
+ ,-1.2
+ ,3.53
+ ,2293.41
+ ,2070.83
+ ,2029.6
+ ,2052.02
+ ,2513.17
+ ,10066.24
+ ,9857.34
+ ,22680
+ ,-14.2
+ ,-12
+ ,-1
+ ,3.55
+ ,2443.27
+ ,2293.41
+ ,2070.83
+ ,2029.6
+ ,2466.92
+ ,9633.83
+ ,10238.83
+ ,24320
+ ,-8.8
+ ,-10
+ ,-0.1
+ ,3.37
+ ,2513.17
+ ,2443.27
+ ,2293.41
+ ,2070.83
+ ,2502.66
+ ,10169.02
+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,3.36
+ ,2466.92
+ ,2513.17
+ ,2443.27
+ ,2293.41)
+ ,dim=c(12
+ ,67)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_5j'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:67))
> y <- array(NA,dim=c(12,67),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_5j','Y1','Y2','Y3','Y4'),1:67))
> 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 = '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
BEL_20 Nikkei DJ_Indust Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1 2454.62 11527.72 10364.91 10383 -0.4 0
2 2448.05 11383.89 10152.09 10431 3.0 -4
3 2497.84 10989.34 10032.80 10574 0.4 -2
4 2645.64 11079.42 10204.59 10653 1.2 -2
5 2756.76 11028.93 10001.60 10805 0.6 -6
6 2849.27 10973.00 10411.75 10872 -1.3 -7
7 2921.44 11068.05 10673.38 10625 -3.2 -6
8 2981.85 11394.84 10539.51 10407 -1.8 -6
9 3080.58 11545.71 10723.78 10463 -3.6 -3
10 3106.22 11809.38 10682.06 10556 -4.2 -2
11 3119.31 11395.64 10283.19 10646 -6.9 -5
12 3061.26 11082.38 10377.18 10702 -8.0 -11
13 3097.31 11402.75 10486.64 11353 -7.5 -11
14 3161.69 11716.87 10545.38 11346 -8.2 -11
15 3257.16 12204.98 10554.27 11451 -7.6 -10
16 3277.01 12986.62 10532.54 11964 -3.7 -14
17 3295.32 13392.79 10324.31 12574 -1.7 -8
18 3363.99 14368.05 10695.25 13031 -0.7 -9
19 3494.17 15650.83 10827.81 13812 0.2 -5
20 3667.03 16102.64 10872.48 14544 0.6 -1
21 3813.06 16187.64 10971.19 14931 2.2 -2
22 3917.96 16311.54 11145.65 14886 3.3 -5
23 3895.51 17232.97 11234.68 16005 5.3 -4
24 3801.06 16397.83 11333.88 17064 5.5 -6
25 3570.12 14990.31 10997.97 15168 6.3 -2
26 3701.61 15147.55 11036.89 16050 7.7 -2
27 3862.27 15786.78 11257.35 15839 6.5 -2
28 3970.10 15934.09 11533.59 15137 5.5 -2
29 4138.52 16519.44 11963.12 14954 6.9 2
30 4199.75 16101.07 12185.15 15648 5.7 1
31 4290.89 16775.08 12377.62 15305 6.9 -8
32 4443.91 17286.32 12512.89 15579 6.1 -1
33 4502.64 17741.23 12631.48 16348 4.8 1
34 4356.98 17128.37 12268.53 15928 3.7 -1
35 4591.27 17460.53 12754.80 16171 5.8 2
36 4696.96 17611.14 13407.75 15937 6.8 2
37 4621.40 18001.37 13480.21 15713 8.5 1
38 4562.84 17974.77 13673.28 15594 7.2 -1
39 4202.52 16460.95 13239.71 15683 5.0 -2
40 4296.49 16235.39 13557.69 16438 4.7 -2
41 4435.23 16903.36 13901.28 17032 2.3 -1
42 4105.18 15543.76 13200.58 17696 2.4 -8
43 4116.68 15532.18 13406.97 17745 0.1 -4
44 3844.49 13731.31 12538.12 19394 1.9 -6
45 3720.98 13547.84 12419.57 20148 1.7 -3
46 3674.40 12602.93 12193.88 20108 2.0 -3
47 3857.62 13357.70 12656.63 18584 -1.9 -7
48 3801.06 13995.33 12812.48 18441 0.5 -9
49 3504.37 14084.60 12056.67 18391 -1.3 -11
50 3032.60 13168.91 11322.38 19178 -3.3 -13
51 3047.03 12989.35 11530.75 18079 -2.8 -11
52 2962.34 12123.53 11114.08 18483 -8.0 -9
53 2197.82 9117.03 9181.73 19644 -13.9 -17
54 2014.45 8531.45 8614.55 19195 -21.9 -22
55 1862.83 8460.94 8595.56 19650 -28.8 -25
56 1905.41 8331.49 8396.20 20830 -27.6 -20
57 1810.99 7694.78 7690.50 23595 -31.4 -24
58 1670.07 7764.58 7235.47 22937 -31.8 -24
59 1864.44 8767.96 7992.12 21814 -29.4 -22
60 2052.02 9304.43 8398.37 21928 -27.6 -19
61 2029.60 9810.31 8593.00 21777 -23.6 -18
62 2070.83 9691.12 8679.75 21383 -22.8 -17
63 2293.41 10430.35 9374.63 21467 -18.2 -11
64 2443.27 10302.87 9634.97 22052 -17.8 -11
65 2513.17 10066.24 9857.34 22680 -14.2 -12
66 2466.92 9633.83 10238.83 24320 -8.8 -10
67 2502.66 10169.02 10433.44 24977 -7.9 -15
Alg_consumptie_index_BE Gem_rente_kasbon_5j Y1 Y2 Y3 Y4
1 2.3 3.19 2407.60 2472.81 2408.64 2440.25
2 2.4 3.35 2454.62 2407.60 2472.81 2408.64
3 2.2 3.24 2448.05 2454.62 2407.60 2472.81
4 2.0 3.23 2497.84 2448.05 2454.62 2407.60
5 2.9 3.31 2645.64 2497.84 2448.05 2454.62
6 2.6 3.25 2756.76 2645.64 2497.84 2448.05
7 2.3 3.20 2849.27 2756.76 2645.64 2497.84
8 2.3 3.10 2921.44 2849.27 2756.76 2645.64
9 2.6 2.93 2981.85 2921.44 2849.27 2756.76
10 3.1 2.92 3080.58 2981.85 2921.44 2849.27
11 2.8 2.90 3106.22 3080.58 2981.85 2921.44
12 2.5 2.87 3119.31 3106.22 3080.58 2981.85
13 2.9 2.76 3061.26 3119.31 3106.22 3080.58
14 3.1 2.67 3097.31 3061.26 3119.31 3106.22
15 3.1 2.75 3161.69 3097.31 3061.26 3119.31
16 3.2 2.72 3257.16 3161.69 3097.31 3061.26
17 2.5 2.72 3277.01 3257.16 3161.69 3097.31
18 2.6 2.86 3295.32 3277.01 3257.16 3161.69
19 2.9 2.99 3363.99 3295.32 3277.01 3257.16
20 2.6 3.07 3494.17 3363.99 3295.32 3277.01
21 2.4 2.96 3667.03 3494.17 3363.99 3295.32
22 1.7 3.04 3813.06 3667.03 3494.17 3363.99
23 2.0 3.30 3917.96 3813.06 3667.03 3494.17
24 2.2 3.48 3895.51 3917.96 3813.06 3667.03
25 1.9 3.46 3801.06 3895.51 3917.96 3813.06
26 1.6 3.57 3570.12 3801.06 3895.51 3917.96
27 1.6 3.60 3701.61 3570.12 3801.06 3895.51
28 1.2 3.51 3862.27 3701.61 3570.12 3801.06
29 1.2 3.52 3970.10 3862.27 3701.61 3570.12
30 1.5 3.49 4138.52 3970.10 3862.27 3701.61
31 1.6 3.50 4199.75 4138.52 3970.10 3862.27
32 1.7 3.64 4290.89 4199.75 4138.52 3970.10
33 1.8 3.94 4443.91 4290.89 4199.75 4138.52
34 1.8 3.94 4502.64 4443.91 4290.89 4199.75
35 1.8 3.91 4356.98 4502.64 4443.91 4290.89
36 1.3 3.88 4591.27 4356.98 4502.64 4443.91
37 1.3 4.21 4696.96 4591.27 4356.98 4502.64
38 1.4 4.39 4621.40 4696.96 4591.27 4356.98
39 1.1 4.33 4562.84 4621.40 4696.96 4591.27
40 1.5 4.27 4202.52 4562.84 4621.40 4696.96
41 2.2 4.29 4296.49 4202.52 4562.84 4621.40
42 2.9 4.18 4435.23 4296.49 4202.52 4562.84
43 3.1 4.14 4105.18 4435.23 4296.49 4202.52
44 3.5 4.23 4116.68 4105.18 4435.23 4296.49
45 3.6 4.07 3844.49 4116.68 4105.18 4435.23
46 4.4 3.74 3720.98 3844.49 4116.68 4105.18
47 4.2 3.66 3674.40 3720.98 3844.49 4116.68
48 5.2 3.92 3857.62 3674.40 3720.98 3844.49
49 5.8 4.45 3801.06 3857.62 3674.40 3720.98
50 5.9 4.92 3504.37 3801.06 3857.62 3674.40
51 5.4 4.90 3032.60 3504.37 3801.06 3857.62
52 5.5 4.54 3047.03 3032.60 3504.37 3801.06
53 4.7 4.53 2962.34 3047.03 3032.60 3504.37
54 3.1 4.14 2197.82 2962.34 3047.03 3032.60
55 2.6 4.05 2014.45 2197.82 2962.34 3047.03
56 2.3 3.92 1862.83 2014.45 2197.82 2962.34
57 1.9 3.68 1905.41 1862.83 2014.45 2197.82
58 0.6 3.35 1810.99 1905.41 1862.83 2014.45
59 0.6 3.38 1670.07 1810.99 1905.41 1862.83
60 -0.4 3.44 1864.44 1670.07 1810.99 1905.41
61 -1.1 3.50 2052.02 1864.44 1670.07 1810.99
62 -1.7 3.54 2029.60 2052.02 1864.44 1670.07
63 -0.8 3.52 2070.83 2029.60 2052.02 1864.44
64 -1.2 3.53 2293.41 2070.83 2029.60 2052.02
65 -1.0 3.55 2443.27 2293.41 2070.83 2029.60
66 -0.1 3.37 2513.17 2443.27 2293.41 2070.83
67 0.3 3.36 2466.92 2513.17 2443.27 2293.41
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-2.032e+02 8.834e-02 1.368e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
-8.152e-03 -7.961e+00 9.820e+00
Alg_consumptie_index_BE Gem_rente_kasbon_5j Y1
-8.465e+00 -2.427e+02 4.486e-01
Y2 Y3 Y4
-6.142e-02 7.482e-02 7.771e-02
t
3.320e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-212.9923 -50.7767 0.3945 56.2120 173.0581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.032e+02 3.650e+02 -0.557 0.5800
Nikkei 8.834e-02 1.287e-02 6.863 6.89e-09 ***
DJ_Indust 1.368e-01 2.790e-02 4.904 8.99e-06 ***
Goudprijs -8.152e-03 1.353e-02 -0.603 0.5493
Conjunct_Seizoenzuiver -7.961e+00 4.121e+00 -1.932 0.0586 .
Cons_vertrouw 9.820e+00 4.578e+00 2.145 0.0365 *
Alg_consumptie_index_BE -8.465e+00 1.134e+01 -0.747 0.4586
Gem_rente_kasbon_5j -2.427e+02 4.029e+01 -6.024 1.56e-07 ***
Y1 4.486e-01 9.533e-02 4.705 1.80e-05 ***
Y2 -6.142e-02 1.118e-01 -0.549 0.5850
Y3 7.482e-02 1.102e-01 0.679 0.5002
Y4 7.772e-02 7.633e-02 1.018 0.3131
t 3.320e+00 3.587e+00 0.925 0.3589
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 88.51 on 54 degrees of freedom
Multiple R-squared: 0.9911, Adjusted R-squared: 0.9891
F-statistic: 498.5 on 12 and 54 DF, p-value: < 2.2e-16
> 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,] 0.044713787 0.089427575 0.95528621
[2,] 0.061757068 0.123514137 0.93824293
[3,] 0.040667330 0.081334659 0.95933267
[4,] 0.028145827 0.056291654 0.97185417
[5,] 0.015359221 0.030718442 0.98464078
[6,] 0.007861784 0.015723569 0.99213822
[7,] 0.003324311 0.006648622 0.99667569
[8,] 0.005649168 0.011298337 0.99435083
[9,] 0.005795930 0.011591860 0.99420407
[10,] 0.033589141 0.067178282 0.96641086
[11,] 0.486502655 0.973005310 0.51349735
[12,] 0.448537037 0.897074075 0.55146296
[13,] 0.532422534 0.935154933 0.46757747
[14,] 0.496174564 0.992349127 0.50382544
[15,] 0.616651276 0.766697447 0.38334872
[16,] 0.636818647 0.726362705 0.36318135
[17,] 0.656854213 0.686291574 0.34314579
[18,] 0.613672464 0.772655071 0.38632754
[19,] 0.614032042 0.771935916 0.38596796
[20,] 0.803667496 0.392665008 0.19633250
[21,] 0.771227858 0.457544284 0.22877214
[22,] 0.774400498 0.451199003 0.22559950
[23,] 0.849000932 0.301998135 0.15099907
[24,] 0.968676336 0.062647329 0.03132366
[25,] 0.952633391 0.094733218 0.04736661
[26,] 0.938536441 0.122927119 0.06146356
[27,] 0.953527124 0.092945753 0.04647288
[28,] 0.970675609 0.058648783 0.02932439
[29,] 0.969737457 0.060525086 0.03026254
[30,] 0.970485603 0.059028795 0.02951440
[31,] 0.941883757 0.116232487 0.05811624
[32,] 0.895308552 0.209382896 0.10469145
[33,] 0.824230436 0.351539129 0.17576956
[34,] 0.713621894 0.572756212 0.28637811
[35,] 0.885103115 0.229793770 0.11489689
[36,] 0.811012178 0.377975644 0.18898782
> postscript(file="/var/www/html/rcomp/tmp/10edf1291660450.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20edf1291660450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3o25c1291660450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4o25c1291660450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5o25c1291660450.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 = 67
Frequency = 1
1 2 3 4 5 6
-204.7992036 -93.8855626 -58.0761345 36.6475501 173.0580973 145.2302028
7 8 9 10 11 12
78.6535460 63.8002135 0.3944756 -60.0684400 26.5668445 4.4341058
13 14 15 16 17 18
-2.8850434 -26.0993931 13.6039463 -4.9201450 -51.9315604 -86.2986889
19 20 21 22 23 24
-121.1406087 -67.9675466 -25.0503655 23.0947805 -76.5258486 -46.6921177
25 26 27 28 29 30
-144.6212120 98.1427404 100.5006241 75.9178738 72.9475520 47.4596460
31 32 33 34 35 36
110.1439470 100.0164386 68.2364455 1.9942906 169.4598134 33.4019549
37 38 39 40 41 42
-14.9311983 -13.8487130 -212.9923426 2.1358473 -35.7999843 -123.9607033
43 44 45 46 47 48
-30.6640885 1.3247877 -19.2574941 37.4142135 94.8000596 11.7632352
49 50 51 52 53 54
-4.4036676 -53.5649288 94.5789079 -13.0615000 -121.8298329 67.6839263
55 56 57 58 59 60
-85.7590725 48.7229770 118.4434212 1.2156237 63.7010219 44.7931872
61 62 63 64 65 66
-77.1567489 -24.6327887 -33.6132081 -14.6450672 37.6706239 -67.2477793
67
-49.6219325
> postscript(file="/var/www/html/rcomp/tmp/6hb4f1291660450.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -204.7992036 NA
1 -93.8855626 -204.7992036
2 -58.0761345 -93.8855626
3 36.6475501 -58.0761345
4 173.0580973 36.6475501
5 145.2302028 173.0580973
6 78.6535460 145.2302028
7 63.8002135 78.6535460
8 0.3944756 63.8002135
9 -60.0684400 0.3944756
10 26.5668445 -60.0684400
11 4.4341058 26.5668445
12 -2.8850434 4.4341058
13 -26.0993931 -2.8850434
14 13.6039463 -26.0993931
15 -4.9201450 13.6039463
16 -51.9315604 -4.9201450
17 -86.2986889 -51.9315604
18 -121.1406087 -86.2986889
19 -67.9675466 -121.1406087
20 -25.0503655 -67.9675466
21 23.0947805 -25.0503655
22 -76.5258486 23.0947805
23 -46.6921177 -76.5258486
24 -144.6212120 -46.6921177
25 98.1427404 -144.6212120
26 100.5006241 98.1427404
27 75.9178738 100.5006241
28 72.9475520 75.9178738
29 47.4596460 72.9475520
30 110.1439470 47.4596460
31 100.0164386 110.1439470
32 68.2364455 100.0164386
33 1.9942906 68.2364455
34 169.4598134 1.9942906
35 33.4019549 169.4598134
36 -14.9311983 33.4019549
37 -13.8487130 -14.9311983
38 -212.9923426 -13.8487130
39 2.1358473 -212.9923426
40 -35.7999843 2.1358473
41 -123.9607033 -35.7999843
42 -30.6640885 -123.9607033
43 1.3247877 -30.6640885
44 -19.2574941 1.3247877
45 37.4142135 -19.2574941
46 94.8000596 37.4142135
47 11.7632352 94.8000596
48 -4.4036676 11.7632352
49 -53.5649288 -4.4036676
50 94.5789079 -53.5649288
51 -13.0615000 94.5789079
52 -121.8298329 -13.0615000
53 67.6839263 -121.8298329
54 -85.7590725 67.6839263
55 48.7229770 -85.7590725
56 118.4434212 48.7229770
57 1.2156237 118.4434212
58 63.7010219 1.2156237
59 44.7931872 63.7010219
60 -77.1567489 44.7931872
61 -24.6327887 -77.1567489
62 -33.6132081 -24.6327887
63 -14.6450672 -33.6132081
64 37.6706239 -14.6450672
65 -67.2477793 37.6706239
66 -49.6219325 -67.2477793
67 NA -49.6219325
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -93.8855626 -204.7992036
[2,] -58.0761345 -93.8855626
[3,] 36.6475501 -58.0761345
[4,] 173.0580973 36.6475501
[5,] 145.2302028 173.0580973
[6,] 78.6535460 145.2302028
[7,] 63.8002135 78.6535460
[8,] 0.3944756 63.8002135
[9,] -60.0684400 0.3944756
[10,] 26.5668445 -60.0684400
[11,] 4.4341058 26.5668445
[12,] -2.8850434 4.4341058
[13,] -26.0993931 -2.8850434
[14,] 13.6039463 -26.0993931
[15,] -4.9201450 13.6039463
[16,] -51.9315604 -4.9201450
[17,] -86.2986889 -51.9315604
[18,] -121.1406087 -86.2986889
[19,] -67.9675466 -121.1406087
[20,] -25.0503655 -67.9675466
[21,] 23.0947805 -25.0503655
[22,] -76.5258486 23.0947805
[23,] -46.6921177 -76.5258486
[24,] -144.6212120 -46.6921177
[25,] 98.1427404 -144.6212120
[26,] 100.5006241 98.1427404
[27,] 75.9178738 100.5006241
[28,] 72.9475520 75.9178738
[29,] 47.4596460 72.9475520
[30,] 110.1439470 47.4596460
[31,] 100.0164386 110.1439470
[32,] 68.2364455 100.0164386
[33,] 1.9942906 68.2364455
[34,] 169.4598134 1.9942906
[35,] 33.4019549 169.4598134
[36,] -14.9311983 33.4019549
[37,] -13.8487130 -14.9311983
[38,] -212.9923426 -13.8487130
[39,] 2.1358473 -212.9923426
[40,] -35.7999843 2.1358473
[41,] -123.9607033 -35.7999843
[42,] -30.6640885 -123.9607033
[43,] 1.3247877 -30.6640885
[44,] -19.2574941 1.3247877
[45,] 37.4142135 -19.2574941
[46,] 94.8000596 37.4142135
[47,] 11.7632352 94.8000596
[48,] -4.4036676 11.7632352
[49,] -53.5649288 -4.4036676
[50,] 94.5789079 -53.5649288
[51,] -13.0615000 94.5789079
[52,] -121.8298329 -13.0615000
[53,] 67.6839263 -121.8298329
[54,] -85.7590725 67.6839263
[55,] 48.7229770 -85.7590725
[56,] 118.4434212 48.7229770
[57,] 1.2156237 118.4434212
[58,] 63.7010219 1.2156237
[59,] 44.7931872 63.7010219
[60,] -77.1567489 44.7931872
[61,] -24.6327887 -77.1567489
[62,] -33.6132081 -24.6327887
[63,] -14.6450672 -33.6132081
[64,] 37.6706239 -14.6450672
[65,] -67.2477793 37.6706239
[66,] -49.6219325 -67.2477793
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -93.8855626 -204.7992036
2 -58.0761345 -93.8855626
3 36.6475501 -58.0761345
4 173.0580973 36.6475501
5 145.2302028 173.0580973
6 78.6535460 145.2302028
7 63.8002135 78.6535460
8 0.3944756 63.8002135
9 -60.0684400 0.3944756
10 26.5668445 -60.0684400
11 4.4341058 26.5668445
12 -2.8850434 4.4341058
13 -26.0993931 -2.8850434
14 13.6039463 -26.0993931
15 -4.9201450 13.6039463
16 -51.9315604 -4.9201450
17 -86.2986889 -51.9315604
18 -121.1406087 -86.2986889
19 -67.9675466 -121.1406087
20 -25.0503655 -67.9675466
21 23.0947805 -25.0503655
22 -76.5258486 23.0947805
23 -46.6921177 -76.5258486
24 -144.6212120 -46.6921177
25 98.1427404 -144.6212120
26 100.5006241 98.1427404
27 75.9178738 100.5006241
28 72.9475520 75.9178738
29 47.4596460 72.9475520
30 110.1439470 47.4596460
31 100.0164386 110.1439470
32 68.2364455 100.0164386
33 1.9942906 68.2364455
34 169.4598134 1.9942906
35 33.4019549 169.4598134
36 -14.9311983 33.4019549
37 -13.8487130 -14.9311983
38 -212.9923426 -13.8487130
39 2.1358473 -212.9923426
40 -35.7999843 2.1358473
41 -123.9607033 -35.7999843
42 -30.6640885 -123.9607033
43 1.3247877 -30.6640885
44 -19.2574941 1.3247877
45 37.4142135 -19.2574941
46 94.8000596 37.4142135
47 11.7632352 94.8000596
48 -4.4036676 11.7632352
49 -53.5649288 -4.4036676
50 94.5789079 -53.5649288
51 -13.0615000 94.5789079
52 -121.8298329 -13.0615000
53 67.6839263 -121.8298329
54 -85.7590725 67.6839263
55 48.7229770 -85.7590725
56 118.4434212 48.7229770
57 1.2156237 118.4434212
58 63.7010219 1.2156237
59 44.7931872 63.7010219
60 -77.1567489 44.7931872
61 -24.6327887 -77.1567489
62 -33.6132081 -24.6327887
63 -14.6450672 -33.6132081
64 37.6706239 -14.6450672
65 -67.2477793 37.6706239
66 -49.6219325 -67.2477793
> 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/7r33h1291660450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8r33h1291660450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r33h1291660450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kc3k1291660450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ncjq1291660450.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/129v0e1291660450.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/1355xn1291660450.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/14q5wt1291660450.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/15cocz1291660450.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/16qfsp1291660450.tab")
+ }
>
> try(system("convert tmp/10edf1291660450.ps tmp/10edf1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/20edf1291660450.ps tmp/20edf1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/3o25c1291660450.ps tmp/3o25c1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/4o25c1291660450.ps tmp/4o25c1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o25c1291660450.ps tmp/5o25c1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hb4f1291660450.ps tmp/6hb4f1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/7r33h1291660450.ps tmp/7r33h1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/8r33h1291660450.ps tmp/8r33h1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/9r33h1291660450.ps tmp/9r33h1291660450.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kc3k1291660450.ps tmp/10kc3k1291660450.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.757 1.664 6.405