R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3484.74
+ ,13830.14
+ ,9349.44
+ ,7977
+ ,-5.6
+ ,6
+ ,1
+ ,2.77
+ ,3411.13
+ ,14153.22
+ ,9327.78
+ ,8241
+ ,-6.2
+ ,3
+ ,1
+ ,2.76
+ ,3288.18
+ ,15418.03
+ ,9753.63
+ ,8444
+ ,-7.1
+ ,2
+ ,1.2
+ ,2.76
+ ,3280.37
+ ,16666.97
+ ,10443.5
+ ,8490
+ ,-1.4
+ ,2
+ ,1.2
+ ,2.46
+ ,3173.95
+ ,16505.21
+ ,10853.87
+ ,8388
+ ,-0.1
+ ,2
+ ,0.8
+ ,2.46
+ ,3165.26
+ ,17135.96
+ ,10704.02
+ ,8099
+ ,-0.9
+ ,-8
+ ,0.7
+ ,2.47
+ ,3092.71
+ ,18033.25
+ ,11052.23
+ ,7984
+ ,0
+ ,0
+ ,0.7
+ ,2.71
+ ,3053.05
+ ,17671
+ ,10935.47
+ ,7786
+ ,0.1
+ ,-2
+ ,0.9
+ ,2.8
+ ,3181.96
+ ,17544.22
+ ,10714.03
+ ,8086
+ ,2.6
+ ,3
+ ,1.2
+ ,2.89
+ ,2999.93
+ ,17677.9
+ ,10394.48
+ ,9315
+ ,6
+ ,5
+ ,1.3
+ ,3.36
+ ,3249.57
+ ,18470.97
+ ,10817.9
+ ,9113
+ ,6.4
+ ,8
+ ,1.5
+ ,3.31
+ ,3210.52
+ ,18409.96
+ ,11251.2
+ ,9023
+ ,8.6
+ ,8
+ ,1.9
+ ,3.5
+ ,3030.29
+ ,18941.6
+ ,11281.26
+ ,9026
+ ,6.4
+ ,9
+ ,1.8
+ ,3.51
+ ,2803.47
+ ,19685.53
+ ,10539.68
+ ,9787
+ ,7.7
+ ,11
+ ,1.9
+ ,3.71
+ ,2767.63
+ ,19834.71
+ ,10483.39
+ ,9536
+ ,9.2
+ ,13
+ ,2.2
+ ,3.71
+ ,2882.6
+ ,19598.93
+ ,10947.43
+ ,9490
+ ,8.6
+ ,12
+ ,2.1
+ ,3.71
+ ,2863.36
+ ,17039.97
+ ,10580.27
+ ,9736
+ ,7.4
+ ,13
+ ,2.2
+ ,4.21
+ ,2897.06
+ ,16969.28
+ ,10582.92
+ ,9694
+ ,8.6
+ ,15
+ ,2.7
+ ,4.21
+ ,3012.61
+ ,16973.38
+ ,10654.41
+ ,9647
+ ,6.2
+ ,13
+ ,2.8
+ ,4.21
+ ,3142.95
+ ,16329.89
+ ,11014.51
+ ,9753
+ ,6
+ ,16
+ ,2.9
+ ,4.5
+ ,3032.93
+ ,16153.34
+ ,10967.87
+ ,10070
+ ,6.6
+ ,10
+ ,3.4
+ ,4.51
+ ,3045.78
+ ,15311.7
+ ,10433.56
+ ,10137
+ ,5.1
+ ,14
+ ,3
+ ,4.51
+ ,3110.52
+ ,14760.87
+ ,10665.78
+ ,9984
+ ,4.7
+ ,14
+ ,3.1
+ ,4.51
+ ,3013.24
+ ,14452.93
+ ,10666.71
+ ,9732
+ ,5
+ ,15
+ ,2.5
+ ,4.32
+ ,2987.1
+ ,13720.95
+ ,10682.74
+ ,9103
+ ,3.6
+ ,13
+ ,2.2
+ ,4.02
+ ,2995.55
+ ,13266.27
+ ,10777.22
+ ,9155
+ ,1.9
+ ,8
+ ,2.3
+ ,4.02
+ ,2833.18
+ ,12708.47
+ ,10052.6
+ ,9308
+ ,-0.1
+ ,7
+ ,2.1
+ ,3.85
+ ,2848.96
+ ,13411.84
+ ,10213.97
+ ,9394
+ ,-5.7
+ ,3
+ ,2.8
+ ,3.84
+ ,2794.83
+ ,13975.55
+ ,10546.82
+ ,9948
+ ,-5.6
+ ,3
+ ,3.1
+ ,4.02
+ ,2845.26
+ ,12974.89
+ ,10767.2
+ ,10177
+ ,-6.4
+ ,4
+ ,2.9
+ ,3.82
+ ,2915.02
+ ,12151.11
+ ,10444.5
+ ,10002
+ ,-7.7
+ ,4
+ ,2.6
+ ,3.75
+ ,2892.63
+ ,11576.21
+ ,10314.68
+ ,9728
+ ,-8
+ ,0
+ ,2.7
+ ,3.74
+ ,2604.42
+ ,9996.83
+ ,9042.56
+ ,10002
+ ,-11.9
+ ,-4
+ ,2.3
+ ,3.14
+ ,2641.65
+ ,10438.9
+ ,9220.75
+ ,10063
+ ,-15.4
+ ,-14
+ ,2.3
+ ,2.91
+ ,2659.81
+ ,10511.22
+ ,9721.84
+ ,10018
+ ,-15.5
+ ,-18
+ ,2.1
+ ,2.84
+ ,2638.53
+ ,10496.2
+ ,9978.53
+ ,9960
+ ,-13.4
+ ,-8
+ ,2.2
+ ,2.85
+ ,2720.25
+ ,10300.79
+ ,9923.81
+ ,10236
+ ,-10.9
+ ,-1
+ ,2.9
+ ,2.85
+ ,2745.88
+ ,9981.65
+ ,9892.56
+ ,10893
+ ,-10.8
+ ,1
+ ,2.6
+ ,3.08
+ ,2735.7
+ ,11448.79
+ ,10500.98
+ ,10756
+ ,-7.3
+ ,2
+ ,2.7
+ ,3.3
+ ,2811.7
+ ,11384.49
+ ,10179.35
+ ,10940
+ ,-6.5
+ ,0
+ ,1.8
+ ,3.29
+ ,2799.43
+ ,11717.46
+ ,10080.48
+ ,10997
+ ,-5.1
+ ,1
+ ,1.3
+ ,3.26
+ ,2555.28
+ ,10965.88
+ ,9492.44
+ ,10827
+ ,-5.3
+ ,0
+ ,0.9
+ ,3.26
+ ,2304.98
+ ,10352.27
+ ,8616.49
+ ,10166
+ ,-6.8
+ ,-1
+ ,1.3
+ ,3.11
+ ,2214.95
+ ,9751.2
+ ,8685.4
+ ,10186
+ ,-8.4
+ ,-3
+ ,1.3
+ ,2.84
+ ,2065.81
+ ,9354.01
+ ,8160.67
+ ,10457
+ ,-8.4
+ ,-3
+ ,1.3
+ ,2.71
+ ,1940.49
+ ,8792.5
+ ,8048.1
+ ,10368
+ ,-9.7
+ ,-3
+ ,1.3
+ ,2.69
+ ,2042
+ ,8721.14
+ ,8641.21
+ ,10244
+ ,-8.8
+ ,-4
+ ,1.1
+ ,2.65
+ ,1995.37
+ ,8692.94
+ ,8526.63
+ ,10511
+ ,-9.6
+ ,-8
+ ,1.4
+ ,2.57
+ ,1946.81
+ ,8570.73
+ ,8474.21
+ ,10812
+ ,-11.5
+ ,-9
+ ,1.2
+ ,2.32
+ ,1765.9
+ ,8538.47
+ ,7916.13
+ ,10738
+ ,-11
+ ,-13
+ ,1.7
+ ,2.12
+ ,1635.25
+ ,8169.75
+ ,7977.64
+ ,10171
+ ,-14.9
+ ,-18
+ ,1.8
+ ,2.05)
+ ,dim=c(8
+ ,51)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_1j')
+ ,1:51))
> y <- array(NA,dim=c(8,51),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_1j'),1:51))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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 3484.74 13830.14 9349.44 7977 -5.6 6
2 3411.13 14153.22 9327.78 8241 -6.2 3
3 3288.18 15418.03 9753.63 8444 -7.1 2
4 3280.37 16666.97 10443.50 8490 -1.4 2
5 3173.95 16505.21 10853.87 8388 -0.1 2
6 3165.26 17135.96 10704.02 8099 -0.9 -8
7 3092.71 18033.25 11052.23 7984 0.0 0
8 3053.05 17671.00 10935.47 7786 0.1 -2
9 3181.96 17544.22 10714.03 8086 2.6 3
10 2999.93 17677.90 10394.48 9315 6.0 5
11 3249.57 18470.97 10817.90 9113 6.4 8
12 3210.52 18409.96 11251.20 9023 8.6 8
13 3030.29 18941.60 11281.26 9026 6.4 9
14 2803.47 19685.53 10539.68 9787 7.7 11
15 2767.63 19834.71 10483.39 9536 9.2 13
16 2882.60 19598.93 10947.43 9490 8.6 12
17 2863.36 17039.97 10580.27 9736 7.4 13
18 2897.06 16969.28 10582.92 9694 8.6 15
19 3012.61 16973.38 10654.41 9647 6.2 13
20 3142.95 16329.89 11014.51 9753 6.0 16
21 3032.93 16153.34 10967.87 10070 6.6 10
22 3045.78 15311.70 10433.56 10137 5.1 14
23 3110.52 14760.87 10665.78 9984 4.7 14
24 3013.24 14452.93 10666.71 9732 5.0 15
25 2987.10 13720.95 10682.74 9103 3.6 13
26 2995.55 13266.27 10777.22 9155 1.9 8
27 2833.18 12708.47 10052.60 9308 -0.1 7
28 2848.96 13411.84 10213.97 9394 -5.7 3
29 2794.83 13975.55 10546.82 9948 -5.6 3
30 2845.26 12974.89 10767.20 10177 -6.4 4
31 2915.02 12151.11 10444.50 10002 -7.7 4
32 2892.63 11576.21 10314.68 9728 -8.0 0
33 2604.42 9996.83 9042.56 10002 -11.9 -4
34 2641.65 10438.90 9220.75 10063 -15.4 -14
35 2659.81 10511.22 9721.84 10018 -15.5 -18
36 2638.53 10496.20 9978.53 9960 -13.4 -8
37 2720.25 10300.79 9923.81 10236 -10.9 -1
38 2745.88 9981.65 9892.56 10893 -10.8 1
39 2735.70 11448.79 10500.98 10756 -7.3 2
40 2811.70 11384.49 10179.35 10940 -6.5 0
41 2799.43 11717.46 10080.48 10997 -5.1 1
42 2555.28 10965.88 9492.44 10827 -5.3 0
43 2304.98 10352.27 8616.49 10166 -6.8 -1
44 2214.95 9751.20 8685.40 10186 -8.4 -3
45 2065.81 9354.01 8160.67 10457 -8.4 -3
46 1940.49 8792.50 8048.10 10368 -9.7 -3
47 2042.00 8721.14 8641.21 10244 -8.8 -4
48 1995.37 8692.94 8526.63 10511 -9.6 -8
49 1946.81 8570.73 8474.21 10812 -11.5 -9
50 1765.90 8538.47 7916.13 10738 -11.0 -13
51 1635.25 8169.75 7977.64 10171 -14.9 -18
Alg_consumptie_index_BE Gem_rente_kasbon_1j M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 1.0 2.77 1 0 0 0 0 0 0 0 0 0
2 1.0 2.76 0 1 0 0 0 0 0 0 0 0
3 1.2 2.76 0 0 1 0 0 0 0 0 0 0
4 1.2 2.46 0 0 0 1 0 0 0 0 0 0
5 0.8 2.46 0 0 0 0 1 0 0 0 0 0
6 0.7 2.47 0 0 0 0 0 1 0 0 0 0
7 0.7 2.71 0 0 0 0 0 0 1 0 0 0
8 0.9 2.80 0 0 0 0 0 0 0 1 0 0
9 1.2 2.89 0 0 0 0 0 0 0 0 1 0
10 1.3 3.36 0 0 0 0 0 0 0 0 0 1
11 1.5 3.31 0 0 0 0 0 0 0 0 0 0
12 1.9 3.50 0 0 0 0 0 0 0 0 0 0
13 1.8 3.51 1 0 0 0 0 0 0 0 0 0
14 1.9 3.71 0 1 0 0 0 0 0 0 0 0
15 2.2 3.71 0 0 1 0 0 0 0 0 0 0
16 2.1 3.71 0 0 0 1 0 0 0 0 0 0
17 2.2 4.21 0 0 0 0 1 0 0 0 0 0
18 2.7 4.21 0 0 0 0 0 1 0 0 0 0
19 2.8 4.21 0 0 0 0 0 0 1 0 0 0
20 2.9 4.50 0 0 0 0 0 0 0 1 0 0
21 3.4 4.51 0 0 0 0 0 0 0 0 1 0
22 3.0 4.51 0 0 0 0 0 0 0 0 0 1
23 3.1 4.51 0 0 0 0 0 0 0 0 0 0
24 2.5 4.32 0 0 0 0 0 0 0 0 0 0
25 2.2 4.02 1 0 0 0 0 0 0 0 0 0
26 2.3 4.02 0 1 0 0 0 0 0 0 0 0
27 2.1 3.85 0 0 1 0 0 0 0 0 0 0
28 2.8 3.84 0 0 0 1 0 0 0 0 0 0
29 3.1 4.02 0 0 0 0 1 0 0 0 0 0
30 2.9 3.82 0 0 0 0 0 1 0 0 0 0
31 2.6 3.75 0 0 0 0 0 0 1 0 0 0
32 2.7 3.74 0 0 0 0 0 0 0 1 0 0
33 2.3 3.14 0 0 0 0 0 0 0 0 1 0
34 2.3 2.91 0 0 0 0 0 0 0 0 0 1
35 2.1 2.84 0 0 0 0 0 0 0 0 0 0
36 2.2 2.85 0 0 0 0 0 0 0 0 0 0
37 2.9 2.85 1 0 0 0 0 0 0 0 0 0
38 2.6 3.08 0 1 0 0 0 0 0 0 0 0
39 2.7 3.30 0 0 1 0 0 0 0 0 0 0
40 1.8 3.29 0 0 0 1 0 0 0 0 0 0
41 1.3 3.26 0 0 0 0 1 0 0 0 0 0
42 0.9 3.26 0 0 0 0 0 1 0 0 0 0
43 1.3 3.11 0 0 0 0 0 0 1 0 0 0
44 1.3 2.84 0 0 0 0 0 0 0 1 0 0
45 1.3 2.71 0 0 0 0 0 0 0 0 1 0
46 1.3 2.69 0 0 0 0 0 0 0 0 0 1
47 1.1 2.65 0 0 0 0 0 0 0 0 0 0
48 1.4 2.57 0 0 0 0 0 0 0 0 0 0
49 1.2 2.32 1 0 0 0 0 0 0 0 0 0
50 1.7 2.12 0 1 0 0 0 0 0 0 0 0
51 1.8 2.05 0 0 1 0 0 0 0 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
305.04884 -0.08794 0.27326
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
0.16609 -12.15814 4.00557
Alg_consumptie_index_BE Gem_rente_kasbon_1j M1
-120.44520 176.26212 23.09845
M2 M3 M4
-0.34417 -3.60817 58.26220
M5 M6 M7
-80.49088 -53.38570 22.65211
M8 M9 M10
18.20958 39.15621 -25.20268
M11 t
39.83616 -40.48872
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-123.29 -48.07 -5.36 40.44 200.20
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 305.04884 498.91067 0.611 0.545373
Nikkei -0.08794 0.02164 -4.065 0.000305 ***
DJ_Indust 0.27326 0.03430 7.968 5.38e-09 ***
Goudprijs 0.16609 0.04921 3.375 0.002000 **
Conjunct_Seizoenzuiver -12.15814 8.73718 -1.392 0.173967
Cons_vertrouw 4.00557 5.91425 0.677 0.503255
Alg_consumptie_index_BE -120.44520 41.61713 -2.894 0.006903 **
Gem_rente_kasbon_1j 176.26212 72.13287 2.444 0.020433 *
M1 23.09845 66.46141 0.348 0.730529
M2 -0.34417 65.87617 -0.005 0.995865
M3 -3.60817 66.96787 -0.054 0.957377
M4 58.26220 70.59571 0.825 0.415511
M5 -80.49088 70.24190 -1.146 0.260602
M6 -53.38570 69.22554 -0.771 0.446436
M7 22.65211 72.08573 0.314 0.755445
M8 18.20958 70.31967 0.259 0.797382
M9 39.15621 68.59894 0.571 0.572251
M10 -25.20268 72.76848 -0.346 0.731427
M11 39.83616 67.90693 0.587 0.561701
t -40.48872 4.71398 -8.589 1.06e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 93.73 on 31 degrees of freedom
Multiple R-squared: 0.9707, Adjusted R-squared: 0.9528
F-statistic: 54.08 on 19 and 31 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,] 0.7548723 0.4902554 0.2451277
[2,] 0.5906977 0.8186046 0.4093023
[3,] 0.4744955 0.9489909 0.5255045
[4,] 0.3140796 0.6281592 0.6859204
[5,] 0.2597794 0.5195589 0.7402206
[6,] 0.1316839 0.2633677 0.8683161
> postscript(file="/var/www/rcomp/tmp/1xjn61291647019.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2xjn61291647019.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3lpy21291647019.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4lpy21291647019.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5lpy21291647019.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 = 51
Frequency = 1
1 2 3 4 5 6
73.6546952 60.9434470 -39.9562040 -33.2882659 -102.2616398 63.3728114
7 8 9 10 11 12
-105.2733380 -49.6145199 129.0090492 -90.6960748 147.7332343 121.6228240
13 14 15 16 17 18
-47.7362326 -84.3659607 40.0967719 -21.5480370 -121.7976253 -7.8777965
19 20 21 22 23 24
51.6306709 0.7844602 -55.3377718 40.7846397 1.6696104 -39.9008472
25 26 27 28 29 30
-5.1962232 4.1483283 -5.3595809 26.5257349 23.8634162 -101.1490337
31 32 33 34 35 36
-61.7364677 17.4127648 -61.8525149 98.3202546 -28.1087315 -35.1367538
37 38 39 40 41 42
102.5679978 -19.9891425 10.9190896 28.3105679 200.1958489 45.6540187
43 44 45 46 47 48
115.3791347 31.4172949 -11.8187625 -48.4088195 -121.2941133 -46.5852230
49 50 51
-123.2902372 39.2633278 -5.7000766
> postscript(file="/var/www/rcomp/tmp/6vgx51291647019.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 = 51
Frequency = 1
lag(myerror, k = 1) myerror
0 73.6546952 NA
1 60.9434470 73.6546952
2 -39.9562040 60.9434470
3 -33.2882659 -39.9562040
4 -102.2616398 -33.2882659
5 63.3728114 -102.2616398
6 -105.2733380 63.3728114
7 -49.6145199 -105.2733380
8 129.0090492 -49.6145199
9 -90.6960748 129.0090492
10 147.7332343 -90.6960748
11 121.6228240 147.7332343
12 -47.7362326 121.6228240
13 -84.3659607 -47.7362326
14 40.0967719 -84.3659607
15 -21.5480370 40.0967719
16 -121.7976253 -21.5480370
17 -7.8777965 -121.7976253
18 51.6306709 -7.8777965
19 0.7844602 51.6306709
20 -55.3377718 0.7844602
21 40.7846397 -55.3377718
22 1.6696104 40.7846397
23 -39.9008472 1.6696104
24 -5.1962232 -39.9008472
25 4.1483283 -5.1962232
26 -5.3595809 4.1483283
27 26.5257349 -5.3595809
28 23.8634162 26.5257349
29 -101.1490337 23.8634162
30 -61.7364677 -101.1490337
31 17.4127648 -61.7364677
32 -61.8525149 17.4127648
33 98.3202546 -61.8525149
34 -28.1087315 98.3202546
35 -35.1367538 -28.1087315
36 102.5679978 -35.1367538
37 -19.9891425 102.5679978
38 10.9190896 -19.9891425
39 28.3105679 10.9190896
40 200.1958489 28.3105679
41 45.6540187 200.1958489
42 115.3791347 45.6540187
43 31.4172949 115.3791347
44 -11.8187625 31.4172949
45 -48.4088195 -11.8187625
46 -121.2941133 -48.4088195
47 -46.5852230 -121.2941133
48 -123.2902372 -46.5852230
49 39.2633278 -123.2902372
50 -5.7000766 39.2633278
51 NA -5.7000766
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 60.9434470 73.6546952
[2,] -39.9562040 60.9434470
[3,] -33.2882659 -39.9562040
[4,] -102.2616398 -33.2882659
[5,] 63.3728114 -102.2616398
[6,] -105.2733380 63.3728114
[7,] -49.6145199 -105.2733380
[8,] 129.0090492 -49.6145199
[9,] -90.6960748 129.0090492
[10,] 147.7332343 -90.6960748
[11,] 121.6228240 147.7332343
[12,] -47.7362326 121.6228240
[13,] -84.3659607 -47.7362326
[14,] 40.0967719 -84.3659607
[15,] -21.5480370 40.0967719
[16,] -121.7976253 -21.5480370
[17,] -7.8777965 -121.7976253
[18,] 51.6306709 -7.8777965
[19,] 0.7844602 51.6306709
[20,] -55.3377718 0.7844602
[21,] 40.7846397 -55.3377718
[22,] 1.6696104 40.7846397
[23,] -39.9008472 1.6696104
[24,] -5.1962232 -39.9008472
[25,] 4.1483283 -5.1962232
[26,] -5.3595809 4.1483283
[27,] 26.5257349 -5.3595809
[28,] 23.8634162 26.5257349
[29,] -101.1490337 23.8634162
[30,] -61.7364677 -101.1490337
[31,] 17.4127648 -61.7364677
[32,] -61.8525149 17.4127648
[33,] 98.3202546 -61.8525149
[34,] -28.1087315 98.3202546
[35,] -35.1367538 -28.1087315
[36,] 102.5679978 -35.1367538
[37,] -19.9891425 102.5679978
[38,] 10.9190896 -19.9891425
[39,] 28.3105679 10.9190896
[40,] 200.1958489 28.3105679
[41,] 45.6540187 200.1958489
[42,] 115.3791347 45.6540187
[43,] 31.4172949 115.3791347
[44,] -11.8187625 31.4172949
[45,] -48.4088195 -11.8187625
[46,] -121.2941133 -48.4088195
[47,] -46.5852230 -121.2941133
[48,] -123.2902372 -46.5852230
[49,] 39.2633278 -123.2902372
[50,] -5.7000766 39.2633278
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 60.9434470 73.6546952
2 -39.9562040 60.9434470
3 -33.2882659 -39.9562040
4 -102.2616398 -33.2882659
5 63.3728114 -102.2616398
6 -105.2733380 63.3728114
7 -49.6145199 -105.2733380
8 129.0090492 -49.6145199
9 -90.6960748 129.0090492
10 147.7332343 -90.6960748
11 121.6228240 147.7332343
12 -47.7362326 121.6228240
13 -84.3659607 -47.7362326
14 40.0967719 -84.3659607
15 -21.5480370 40.0967719
16 -121.7976253 -21.5480370
17 -7.8777965 -121.7976253
18 51.6306709 -7.8777965
19 0.7844602 51.6306709
20 -55.3377718 0.7844602
21 40.7846397 -55.3377718
22 1.6696104 40.7846397
23 -39.9008472 1.6696104
24 -5.1962232 -39.9008472
25 4.1483283 -5.1962232
26 -5.3595809 4.1483283
27 26.5257349 -5.3595809
28 23.8634162 26.5257349
29 -101.1490337 23.8634162
30 -61.7364677 -101.1490337
31 17.4127648 -61.7364677
32 -61.8525149 17.4127648
33 98.3202546 -61.8525149
34 -28.1087315 98.3202546
35 -35.1367538 -28.1087315
36 102.5679978 -35.1367538
37 -19.9891425 102.5679978
38 10.9190896 -19.9891425
39 28.3105679 10.9190896
40 200.1958489 28.3105679
41 45.6540187 200.1958489
42 115.3791347 45.6540187
43 31.4172949 115.3791347
44 -11.8187625 31.4172949
45 -48.4088195 -11.8187625
46 -121.2941133 -48.4088195
47 -46.5852230 -121.2941133
48 -123.2902372 -46.5852230
49 39.2633278 -123.2902372
50 -5.7000766 39.2633278
> 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/rcomp/tmp/76pwq1291647019.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/86pwq1291647019.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9zhdb1291647019.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10zhdb1291647019.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11kzch1291647019.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/rcomp/tmp/12n0sn1291647019.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/rcomp/tmp/132rqe1291647019.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/rcomp/tmp/145ap21291647019.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/rcomp/tmp/15qbnq1291647019.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/rcomp/tmp/16m2ly1291647019.tab")
+ }
>
> try(system("convert tmp/1xjn61291647019.ps tmp/1xjn61291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xjn61291647019.ps tmp/2xjn61291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lpy21291647019.ps tmp/3lpy21291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lpy21291647019.ps tmp/4lpy21291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lpy21291647019.ps tmp/5lpy21291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vgx51291647019.ps tmp/6vgx51291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/76pwq1291647019.ps tmp/76pwq1291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/86pwq1291647019.ps tmp/86pwq1291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zhdb1291647019.ps tmp/9zhdb1291647019.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zhdb1291647019.ps tmp/10zhdb1291647019.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.97 1.66 4.64