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(2293.41
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+ ,3097.31
+ ,3161.69
+ ,3257.16
+ ,3106.22
+ ,11809.38
+ ,10682.06
+ ,10556
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+ ,3097.31
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+ ,10723.78
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+ ,3061.26
+ ,3097.31
+ ,2981.85
+ ,11394.84
+ ,10539.51
+ ,10407
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+ ,3.1
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+ ,10625
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+ ,3080.58
+ ,3106.22
+ ,3119.31
+ ,2849.27
+ ,10973
+ ,10411.75
+ ,10872
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+ ,11079.42
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+ ,2849.27
+ ,2921.44
+ ,2448.05
+ ,11383.89
+ ,10152.09
+ ,10431
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+ ,2645.64
+ ,2756.76
+ ,2849.27
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+ ,11527.72
+ ,10364.91
+ ,10383
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+ ,2497.84
+ ,2645.64
+ ,2756.76
+ ,2407.6
+ ,11037.54
+ ,10092.96
+ ,10296
+ ,0
+ ,-6
+ ,2.6
+ ,3.17
+ ,2454.62
+ ,2448.05
+ ,2497.84
+ ,2645.64
+ ,2472.81
+ ,11950.95
+ ,10418.4
+ ,10872
+ ,-1.3
+ ,-4
+ ,1.9
+ ,3.06
+ ,2407.6
+ ,2454.62
+ ,2448.05
+ ,2497.84
+ ,2408.64
+ ,11441.08
+ ,10323.73
+ ,10635
+ ,-3.1
+ ,-3
+ ,1.1
+ ,3.22
+ ,2472.81
+ ,2407.6
+ ,2454.62
+ ,2448.05
+ ,2440.25
+ ,10631.92
+ ,10601.61
+ ,10297
+ ,-4
+ ,-1
+ ,1.3
+ ,3.35
+ ,2408.64
+ ,2472.81
+ ,2407.6
+ ,2454.62
+ ,2350.44
+ ,10892.76
+ ,10540.05
+ ,10570
+ ,-4.9
+ ,-3
+ ,1.6
+ ,3.38
+ ,2440.25
+ ,2408.64
+ ,2472.81
+ ,2407.6)
+ ,dim=c(12
+ ,68)
+ ,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')
+ ,1:68))
> y <- array(NA,dim=c(12,68),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'),1:68))
> 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
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 2293.41 10430.35 9374.63 21467 -18.2 -11
2 2070.83 9691.12 8679.75 21383 -22.8 -17
3 2029.60 9810.31 8593.00 21777 -23.6 -18
4 2052.02 9304.43 8398.37 21928 -27.6 -19
5 1864.44 8767.96 7992.12 21814 -29.4 -22
6 1670.07 7764.58 7235.47 22937 -31.8 -24
7 1810.99 7694.78 7690.50 23595 -31.4 -24
8 1905.41 8331.49 8396.20 20830 -27.6 -20
9 1862.83 8460.94 8595.56 19650 -28.8 -25
10 2014.45 8531.45 8614.55 19195 -21.9 -22
11 2197.82 9117.03 9181.73 19644 -13.9 -17
12 2962.34 12123.53 11114.08 18483 -8.0 -9
13 3047.03 12989.35 11530.75 18079 -2.8 -11
14 3032.60 13168.91 11322.38 19178 -3.3 -13
15 3504.37 14084.60 12056.67 18391 -1.3 -11
16 3801.06 13995.33 12812.48 18441 0.5 -9
17 3857.62 13357.70 12656.63 18584 -1.9 -7
18 3674.40 12602.93 12193.88 20108 2.0 -3
19 3720.98 13547.84 12419.57 20148 1.7 -3
20 3844.49 13731.31 12538.12 19394 1.9 -6
21 4116.68 15532.18 13406.97 17745 0.1 -4
22 4105.18 15543.76 13200.58 17696 2.4 -8
23 4435.23 16903.36 13901.28 17032 2.3 -1
24 4296.49 16235.39 13557.69 16438 4.7 -2
25 4202.52 16460.95 13239.71 15683 5.0 -2
26 4562.84 17974.77 13673.28 15594 7.2 -1
27 4621.40 18001.37 13480.21 15713 8.5 1
28 4696.96 17611.14 13407.75 15937 6.8 2
29 4591.27 17460.53 12754.80 16171 5.8 2
30 4356.98 17128.37 12268.53 15928 3.7 -1
31 4502.64 17741.23 12631.48 16348 4.8 1
32 4443.91 17286.32 12512.89 15579 6.1 -1
33 4290.89 16775.08 12377.62 15305 6.9 -8
34 4199.75 16101.07 12185.15 15648 5.7 1
35 4138.52 16519.44 11963.12 14954 6.9 2
36 3970.10 15934.09 11533.59 15137 5.5 -2
37 3862.27 15786.78 11257.35 15839 6.5 -2
38 3701.61 15147.55 11036.89 16050 7.7 -2
39 3570.12 14990.31 10997.97 15168 6.3 -2
40 3801.06 16397.83 11333.88 17064 5.5 -6
41 3895.51 17232.97 11234.68 16005 5.3 -4
42 3917.96 16311.54 11145.65 14886 3.3 -5
43 3813.06 16187.64 10971.19 14931 2.2 -2
44 3667.03 16102.64 10872.48 14544 0.6 -1
45 3494.17 15650.83 10827.81 13812 0.2 -5
46 3363.99 14368.05 10695.25 13031 -0.7 -9
47 3295.32 13392.79 10324.31 12574 -1.7 -8
48 3277.01 12986.62 10532.54 11964 -3.7 -14
49 3257.16 12204.98 10554.27 11451 -7.6 -10
50 3161.69 11716.87 10545.38 11346 -8.2 -11
51 3097.31 11402.75 10486.64 11353 -7.5 -11
52 3061.26 11082.38 10377.18 10702 -8.0 -11
53 3119.31 11395.64 10283.19 10646 -6.9 -5
54 3106.22 11809.38 10682.06 10556 -4.2 -2
55 3080.58 11545.71 10723.78 10463 -3.6 -3
56 2981.85 11394.84 10539.51 10407 -1.8 -6
57 2921.44 11068.05 10673.38 10625 -3.2 -6
58 2849.27 10973.00 10411.75 10872 -1.3 -7
59 2756.76 11028.93 10001.60 10805 0.6 -6
60 2645.64 11079.42 10204.59 10653 1.2 -2
61 2497.84 10989.34 10032.80 10574 0.4 -2
62 2448.05 11383.89 10152.09 10431 3.0 -4
63 2454.62 11527.72 10364.91 10383 -0.4 0
64 2407.60 11037.54 10092.96 10296 0.0 -6
65 2472.81 11950.95 10418.40 10872 -1.3 -4
66 2408.64 11441.08 10323.73 10635 -3.1 -3
67 2440.25 10631.92 10601.61 10297 -4.0 -1
68 2350.44 10892.76 10540.05 10570 -4.9 -3
Alg_consumptie_index_BE Gem_rente_kasbon_5j Y1 Y2 Y3 Y4
1 -0.8 3.52 2443.27 2513.17 2466.92 2502.66
2 -1.7 3.54 2293.41 2443.27 2513.17 2466.92
3 -1.1 3.50 2070.83 2293.41 2443.27 2513.17
4 -0.4 3.44 2029.60 2070.83 2293.41 2443.27
5 0.6 3.38 2052.02 2029.60 2070.83 2293.41
6 0.6 3.35 1864.44 2052.02 2029.60 2070.83
7 1.9 3.68 1670.07 1864.44 2052.02 2029.60
8 2.3 3.92 1810.99 1670.07 1864.44 2052.02
9 2.6 4.05 1905.41 1810.99 1670.07 1864.44
10 3.1 4.14 1862.83 1905.41 1810.99 1670.07
11 4.7 4.53 2014.45 1862.83 1905.41 1810.99
12 5.5 4.54 2197.82 2014.45 1862.83 1905.41
13 5.4 4.90 2962.34 2197.82 2014.45 1862.83
14 5.9 4.92 3047.03 2962.34 2197.82 2014.45
15 5.8 4.45 3032.60 3047.03 2962.34 2197.82
16 5.2 3.92 3504.37 3032.60 3047.03 2962.34
17 4.2 3.66 3801.06 3504.37 3032.60 3047.03
18 4.4 3.74 3857.62 3801.06 3504.37 3032.60
19 3.6 4.07 3674.40 3857.62 3801.06 3504.37
20 3.5 4.23 3720.98 3674.40 3857.62 3801.06
21 3.1 4.14 3844.49 3720.98 3674.40 3857.62
22 2.9 4.18 4116.68 3844.49 3720.98 3674.40
23 2.2 4.29 4105.18 4116.68 3844.49 3720.98
24 1.5 4.27 4435.23 4105.18 4116.68 3844.49
25 1.1 4.33 4296.49 4435.23 4105.18 4116.68
26 1.4 4.39 4202.52 4296.49 4435.23 4105.18
27 1.3 4.21 4562.84 4202.52 4296.49 4435.23
28 1.3 3.88 4621.40 4562.84 4202.52 4296.49
29 1.8 3.91 4696.96 4621.40 4562.84 4202.52
30 1.8 3.94 4591.27 4696.96 4621.40 4562.84
31 1.8 3.94 4356.98 4591.27 4696.96 4621.40
32 1.7 3.64 4502.64 4356.98 4591.27 4696.96
33 1.6 3.50 4443.91 4502.64 4356.98 4591.27
34 1.5 3.49 4290.89 4443.91 4502.64 4356.98
35 1.2 3.52 4199.75 4290.89 4443.91 4502.64
36 1.2 3.51 4138.52 4199.75 4290.89 4443.91
37 1.6 3.60 3970.10 4138.52 4199.75 4290.89
38 1.6 3.57 3862.27 3970.10 4138.52 4199.75
39 1.9 3.46 3701.61 3862.27 3970.10 4138.52
40 2.2 3.48 3570.12 3701.61 3862.27 3970.10
41 2.0 3.30 3801.06 3570.12 3701.61 3862.27
42 1.7 3.04 3895.51 3801.06 3570.12 3701.61
43 2.4 2.96 3917.96 3895.51 3801.06 3570.12
44 2.6 3.07 3813.06 3917.96 3895.51 3801.06
45 2.9 2.99 3667.03 3813.06 3917.96 3895.51
46 2.6 2.86 3494.17 3667.03 3813.06 3917.96
47 2.5 2.72 3363.99 3494.17 3667.03 3813.06
48 3.2 2.72 3295.32 3363.99 3494.17 3667.03
49 3.1 2.75 3277.01 3295.32 3363.99 3494.17
50 3.1 2.67 3257.16 3277.01 3295.32 3363.99
51 2.9 2.76 3161.69 3257.16 3277.01 3295.32
52 2.5 2.87 3097.31 3161.69 3257.16 3277.01
53 2.8 2.90 3061.26 3097.31 3161.69 3257.16
54 3.1 2.92 3119.31 3061.26 3097.31 3161.69
55 2.6 2.93 3106.22 3119.31 3061.26 3097.31
56 2.3 3.10 3080.58 3106.22 3119.31 3061.26
57 2.3 3.20 2981.85 3080.58 3106.22 3119.31
58 2.6 3.25 2921.44 2981.85 3080.58 3106.22
59 2.9 3.31 2849.27 2921.44 2981.85 3080.58
60 2.0 3.23 2756.76 2849.27 2921.44 2981.85
61 2.2 3.24 2645.64 2756.76 2849.27 2921.44
62 2.4 3.35 2497.84 2645.64 2756.76 2849.27
63 2.3 3.19 2448.05 2497.84 2645.64 2756.76
64 2.6 3.17 2454.62 2448.05 2497.84 2645.64
65 1.9 3.06 2407.60 2454.62 2448.05 2497.84
66 1.1 3.22 2472.81 2407.60 2454.62 2448.05
67 1.3 3.35 2408.64 2472.81 2407.60 2454.62
68 1.6 3.38 2440.25 2408.64 2472.81 2407.60
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1 0 0 0 0 0 0 0 0 0 0 1
2 0 1 0 0 0 0 0 0 0 0 0 2
3 0 0 1 0 0 0 0 0 0 0 0 3
4 0 0 0 1 0 0 0 0 0 0 0 4
5 0 0 0 0 1 0 0 0 0 0 0 5
6 0 0 0 0 0 1 0 0 0 0 0 6
7 0 0 0 0 0 0 1 0 0 0 0 7
8 0 0 0 0 0 0 0 1 0 0 0 8
9 0 0 0 0 0 0 0 0 1 0 0 9
10 0 0 0 0 0 0 0 0 0 1 0 10
11 0 0 0 0 0 0 0 0 0 0 1 11
12 0 0 0 0 0 0 0 0 0 0 0 12
13 1 0 0 0 0 0 0 0 0 0 0 13
14 0 1 0 0 0 0 0 0 0 0 0 14
15 0 0 1 0 0 0 0 0 0 0 0 15
16 0 0 0 1 0 0 0 0 0 0 0 16
17 0 0 0 0 1 0 0 0 0 0 0 17
18 0 0 0 0 0 1 0 0 0 0 0 18
19 0 0 0 0 0 0 1 0 0 0 0 19
20 0 0 0 0 0 0 0 1 0 0 0 20
21 0 0 0 0 0 0 0 0 1 0 0 21
22 0 0 0 0 0 0 0 0 0 1 0 22
23 0 0 0 0 0 0 0 0 0 0 1 23
24 0 0 0 0 0 0 0 0 0 0 0 24
25 1 0 0 0 0 0 0 0 0 0 0 25
26 0 1 0 0 0 0 0 0 0 0 0 26
27 0 0 1 0 0 0 0 0 0 0 0 27
28 0 0 0 1 0 0 0 0 0 0 0 28
29 0 0 0 0 1 0 0 0 0 0 0 29
30 0 0 0 0 0 1 0 0 0 0 0 30
31 0 0 0 0 0 0 1 0 0 0 0 31
32 0 0 0 0 0 0 0 1 0 0 0 32
33 0 0 0 0 0 0 0 0 1 0 0 33
34 0 0 0 0 0 0 0 0 0 1 0 34
35 0 0 0 0 0 0 0 0 0 0 1 35
36 0 0 0 0 0 0 0 0 0 0 0 36
37 1 0 0 0 0 0 0 0 0 0 0 37
38 0 1 0 0 0 0 0 0 0 0 0 38
39 0 0 1 0 0 0 0 0 0 0 0 39
40 0 0 0 1 0 0 0 0 0 0 0 40
41 0 0 0 0 1 0 0 0 0 0 0 41
42 0 0 0 0 0 1 0 0 0 0 0 42
43 0 0 0 0 0 0 1 0 0 0 0 43
44 0 0 0 0 0 0 0 1 0 0 0 44
45 0 0 0 0 0 0 0 0 1 0 0 45
46 0 0 0 0 0 0 0 0 0 1 0 46
47 0 0 0 0 0 0 0 0 0 0 1 47
48 0 0 0 0 0 0 0 0 0 0 0 48
49 1 0 0 0 0 0 0 0 0 0 0 49
50 0 1 0 0 0 0 0 0 0 0 0 50
51 0 0 1 0 0 0 0 0 0 0 0 51
52 0 0 0 1 0 0 0 0 0 0 0 52
53 0 0 0 0 1 0 0 0 0 0 0 53
54 0 0 0 0 0 1 0 0 0 0 0 54
55 0 0 0 0 0 0 1 0 0 0 0 55
56 0 0 0 0 0 0 0 1 0 0 0 56
57 0 0 0 0 0 0 0 0 1 0 0 57
58 0 0 0 0 0 0 0 0 0 1 0 58
59 0 0 0 0 0 0 0 0 0 0 1 59
60 0 0 0 0 0 0 0 0 0 0 0 60
61 1 0 0 0 0 0 0 0 0 0 0 61
62 0 1 0 0 0 0 0 0 0 0 0 62
63 0 0 1 0 0 0 0 0 0 0 0 63
64 0 0 0 1 0 0 0 0 0 0 0 64
65 0 0 0 0 1 0 0 0 0 0 0 65
66 0 0 0 0 0 1 0 0 0 0 0 66
67 0 0 0 0 0 0 1 0 0 0 0 67
68 0 0 0 0 0 0 0 1 0 0 0 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-4.372e+02 8.225e-02 1.576e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
-3.563e-02 -4.971e+00 -5.058e-01
Alg_consumptie_index_BE Gem_rente_kasbon_5j Y1
5.582e+01 -3.284e+01 2.735e-01
Y2 Y3 Y4
-3.351e-03 1.190e-01 1.418e-01
M1 M2 M3
-5.372e+01 -4.017e+01 -2.681e+01
M4 M5 M6
5.333e+01 5.998e+01 4.995e+01
M7 M8 M9
7.631e+01 -6.061e+00 -7.705e+01
M10 M11 t
-1.806e+01 1.763e+01 -9.752e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-147.45 -44.70 3.25 41.09 163.07
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.372e+02 5.575e+02 -0.784 0.4372
Nikkei 8.225e-02 1.240e-02 6.630 3.99e-08 ***
DJ_Indust 1.576e-01 2.436e-02 6.470 6.87e-08 ***
Goudprijs -3.563e-02 1.501e-02 -2.374 0.0220 *
Conjunct_Seizoenzuiver -4.971e+00 5.318e+00 -0.935 0.3551
Cons_vertrouw -5.058e-01 5.081e+00 -0.100 0.9212
Alg_consumptie_index_BE 5.582e+01 1.196e+01 4.669 2.86e-05 ***
Gem_rente_kasbon_5j -3.284e+01 4.463e+01 -0.736 0.4657
Y1 2.735e-01 1.059e-01 2.582 0.0132 *
Y2 -3.351e-03 1.092e-01 -0.031 0.9757
Y3 1.190e-01 1.084e-01 1.097 0.2784
Y4 1.418e-01 7.886e-02 1.798 0.0790 .
M1 -5.372e+01 5.038e+01 -1.066 0.2921
M2 -4.017e+01 5.143e+01 -0.781 0.4390
M3 -2.681e+01 5.017e+01 -0.534 0.5958
M4 5.333e+01 5.158e+01 1.034 0.3069
M5 5.998e+01 5.641e+01 1.063 0.2934
M6 4.995e+01 5.976e+01 0.836 0.4077
M7 7.631e+01 6.106e+01 1.250 0.2180
M8 -6.061e+00 5.575e+01 -0.109 0.9139
M9 -7.705e+01 5.490e+01 -1.403 0.1675
M10 -1.806e+01 5.320e+01 -0.340 0.7358
M11 1.763e+01 5.206e+01 0.339 0.7364
t -9.752e+00 3.709e+00 -2.629 0.0117 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 81.02 on 44 degrees of freedom
Multiple R-squared: 0.994, Adjusted R-squared: 0.9909
F-statistic: 319.1 on 23 and 44 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,] 0.5790317 0.8419365884 0.4209682942
[2,] 0.6408512 0.7182976842 0.3591488421
[3,] 0.5475411 0.9049178408 0.4524589204
[4,] 0.7867525 0.4264950566 0.2132475283
[5,] 0.7887203 0.4225593547 0.2112796774
[6,] 0.8983987 0.2032025560 0.1016012780
[7,] 0.8293635 0.3412729965 0.1706364983
[8,] 0.9411475 0.1177049914 0.0588524957
[9,] 0.9201308 0.1597384311 0.0798692155
[10,] 0.8630241 0.2739517571 0.1369758786
[11,] 0.7714304 0.4571392590 0.2285696295
[12,] 0.7271688 0.5456624636 0.2728312318
[13,] 0.9997655 0.0004690728 0.0002345364
[14,] 0.9991798 0.0016404533 0.0008202267
[15,] 0.9950382 0.0099235240 0.0049617620
> postscript(file="/var/www/html/rcomp/tmp/1ceao1291584636.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/255991291584636.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/355991291584636.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/455991291584636.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fw8c1291584636.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 = 68
Frequency = 1
1 2 3 4 5 6
-20.6802764 -14.4032640 -18.4833785 -11.9368033 -119.1190411 22.0301437
7 8 9 10 11 12
99.7743894 10.5908405 -31.9670002 79.0285743 7.8407925 137.5752282
13 14 15 16 17 18
-43.8107613 -99.4759269 37.9593605 -67.2760347 20.9866940 -8.8106317
19 20 21 22 23 24
-88.5924031 14.5550432 15.2445814 -47.3689698 39.2929012 -74.4987868
25 26 27 28 29 30
-72.7101719 72.0533929 37.4671786 91.4092726 30.5569849 -128.5087378
31 32 33 34 35 36
-39.1922898 -14.6779142 27.4851252 46.4971230 -29.8651198 -14.8638646
37 38 39 40 41 42
85.4542156 69.9530816 -32.5206728 76.9346631 59.7623649 163.0672072
43 44 45 46 47 48
20.5492930 -54.8114934 -128.6685029 -147.4469688 -55.8639729 -60.3742124
49 50 51 52 53 54
59.8145100 24.2291044 47.1462596 6.8268121 71.5869385 -17.7843299
55 56 57 58 59 60
-0.3271783 67.4605790 117.9057964 69.2902413 38.5953990 12.1616356
61 62 63 64 65 66
-8.0675160 -52.3563880 -71.5687474 -95.9579099 -63.7739413 -29.9936515
67 68
7.7881887 -23.1170551
> postscript(file="/var/www/html/rcomp/tmp/6fw8c1291584636.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -20.6802764 NA
1 -14.4032640 -20.6802764
2 -18.4833785 -14.4032640
3 -11.9368033 -18.4833785
4 -119.1190411 -11.9368033
5 22.0301437 -119.1190411
6 99.7743894 22.0301437
7 10.5908405 99.7743894
8 -31.9670002 10.5908405
9 79.0285743 -31.9670002
10 7.8407925 79.0285743
11 137.5752282 7.8407925
12 -43.8107613 137.5752282
13 -99.4759269 -43.8107613
14 37.9593605 -99.4759269
15 -67.2760347 37.9593605
16 20.9866940 -67.2760347
17 -8.8106317 20.9866940
18 -88.5924031 -8.8106317
19 14.5550432 -88.5924031
20 15.2445814 14.5550432
21 -47.3689698 15.2445814
22 39.2929012 -47.3689698
23 -74.4987868 39.2929012
24 -72.7101719 -74.4987868
25 72.0533929 -72.7101719
26 37.4671786 72.0533929
27 91.4092726 37.4671786
28 30.5569849 91.4092726
29 -128.5087378 30.5569849
30 -39.1922898 -128.5087378
31 -14.6779142 -39.1922898
32 27.4851252 -14.6779142
33 46.4971230 27.4851252
34 -29.8651198 46.4971230
35 -14.8638646 -29.8651198
36 85.4542156 -14.8638646
37 69.9530816 85.4542156
38 -32.5206728 69.9530816
39 76.9346631 -32.5206728
40 59.7623649 76.9346631
41 163.0672072 59.7623649
42 20.5492930 163.0672072
43 -54.8114934 20.5492930
44 -128.6685029 -54.8114934
45 -147.4469688 -128.6685029
46 -55.8639729 -147.4469688
47 -60.3742124 -55.8639729
48 59.8145100 -60.3742124
49 24.2291044 59.8145100
50 47.1462596 24.2291044
51 6.8268121 47.1462596
52 71.5869385 6.8268121
53 -17.7843299 71.5869385
54 -0.3271783 -17.7843299
55 67.4605790 -0.3271783
56 117.9057964 67.4605790
57 69.2902413 117.9057964
58 38.5953990 69.2902413
59 12.1616356 38.5953990
60 -8.0675160 12.1616356
61 -52.3563880 -8.0675160
62 -71.5687474 -52.3563880
63 -95.9579099 -71.5687474
64 -63.7739413 -95.9579099
65 -29.9936515 -63.7739413
66 7.7881887 -29.9936515
67 -23.1170551 7.7881887
68 NA -23.1170551
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -14.4032640 -20.6802764
[2,] -18.4833785 -14.4032640
[3,] -11.9368033 -18.4833785
[4,] -119.1190411 -11.9368033
[5,] 22.0301437 -119.1190411
[6,] 99.7743894 22.0301437
[7,] 10.5908405 99.7743894
[8,] -31.9670002 10.5908405
[9,] 79.0285743 -31.9670002
[10,] 7.8407925 79.0285743
[11,] 137.5752282 7.8407925
[12,] -43.8107613 137.5752282
[13,] -99.4759269 -43.8107613
[14,] 37.9593605 -99.4759269
[15,] -67.2760347 37.9593605
[16,] 20.9866940 -67.2760347
[17,] -8.8106317 20.9866940
[18,] -88.5924031 -8.8106317
[19,] 14.5550432 -88.5924031
[20,] 15.2445814 14.5550432
[21,] -47.3689698 15.2445814
[22,] 39.2929012 -47.3689698
[23,] -74.4987868 39.2929012
[24,] -72.7101719 -74.4987868
[25,] 72.0533929 -72.7101719
[26,] 37.4671786 72.0533929
[27,] 91.4092726 37.4671786
[28,] 30.5569849 91.4092726
[29,] -128.5087378 30.5569849
[30,] -39.1922898 -128.5087378
[31,] -14.6779142 -39.1922898
[32,] 27.4851252 -14.6779142
[33,] 46.4971230 27.4851252
[34,] -29.8651198 46.4971230
[35,] -14.8638646 -29.8651198
[36,] 85.4542156 -14.8638646
[37,] 69.9530816 85.4542156
[38,] -32.5206728 69.9530816
[39,] 76.9346631 -32.5206728
[40,] 59.7623649 76.9346631
[41,] 163.0672072 59.7623649
[42,] 20.5492930 163.0672072
[43,] -54.8114934 20.5492930
[44,] -128.6685029 -54.8114934
[45,] -147.4469688 -128.6685029
[46,] -55.8639729 -147.4469688
[47,] -60.3742124 -55.8639729
[48,] 59.8145100 -60.3742124
[49,] 24.2291044 59.8145100
[50,] 47.1462596 24.2291044
[51,] 6.8268121 47.1462596
[52,] 71.5869385 6.8268121
[53,] -17.7843299 71.5869385
[54,] -0.3271783 -17.7843299
[55,] 67.4605790 -0.3271783
[56,] 117.9057964 67.4605790
[57,] 69.2902413 117.9057964
[58,] 38.5953990 69.2902413
[59,] 12.1616356 38.5953990
[60,] -8.0675160 12.1616356
[61,] -52.3563880 -8.0675160
[62,] -71.5687474 -52.3563880
[63,] -95.9579099 -71.5687474
[64,] -63.7739413 -95.9579099
[65,] -29.9936515 -63.7739413
[66,] 7.7881887 -29.9936515
[67,] -23.1170551 7.7881887
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -14.4032640 -20.6802764
2 -18.4833785 -14.4032640
3 -11.9368033 -18.4833785
4 -119.1190411 -11.9368033
5 22.0301437 -119.1190411
6 99.7743894 22.0301437
7 10.5908405 99.7743894
8 -31.9670002 10.5908405
9 79.0285743 -31.9670002
10 7.8407925 79.0285743
11 137.5752282 7.8407925
12 -43.8107613 137.5752282
13 -99.4759269 -43.8107613
14 37.9593605 -99.4759269
15 -67.2760347 37.9593605
16 20.9866940 -67.2760347
17 -8.8106317 20.9866940
18 -88.5924031 -8.8106317
19 14.5550432 -88.5924031
20 15.2445814 14.5550432
21 -47.3689698 15.2445814
22 39.2929012 -47.3689698
23 -74.4987868 39.2929012
24 -72.7101719 -74.4987868
25 72.0533929 -72.7101719
26 37.4671786 72.0533929
27 91.4092726 37.4671786
28 30.5569849 91.4092726
29 -128.5087378 30.5569849
30 -39.1922898 -128.5087378
31 -14.6779142 -39.1922898
32 27.4851252 -14.6779142
33 46.4971230 27.4851252
34 -29.8651198 46.4971230
35 -14.8638646 -29.8651198
36 85.4542156 -14.8638646
37 69.9530816 85.4542156
38 -32.5206728 69.9530816
39 76.9346631 -32.5206728
40 59.7623649 76.9346631
41 163.0672072 59.7623649
42 20.5492930 163.0672072
43 -54.8114934 20.5492930
44 -128.6685029 -54.8114934
45 -147.4469688 -128.6685029
46 -55.8639729 -147.4469688
47 -60.3742124 -55.8639729
48 59.8145100 -60.3742124
49 24.2291044 59.8145100
50 47.1462596 24.2291044
51 6.8268121 47.1462596
52 71.5869385 6.8268121
53 -17.7843299 71.5869385
54 -0.3271783 -17.7843299
55 67.4605790 -0.3271783
56 117.9057964 67.4605790
57 69.2902413 117.9057964
58 38.5953990 69.2902413
59 12.1616356 38.5953990
60 -8.0675160 12.1616356
61 -52.3563880 -8.0675160
62 -71.5687474 -52.3563880
63 -95.9579099 -71.5687474
64 -63.7739413 -95.9579099
65 -29.9936515 -63.7739413
66 7.7881887 -29.9936515
67 -23.1170551 7.7881887
> 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/78nqf1291584636.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88nqf1291584636.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jx701291584636.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jx701291584636.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mx561291584636.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/1207lw1291584636.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/137qi81291584636.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/14zzhb1291584636.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/15liyh1291584636.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/16zrwq1291584636.tab")
+ }
>
> try(system("convert tmp/1ceao1291584636.ps tmp/1ceao1291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/255991291584636.ps tmp/255991291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/355991291584636.ps tmp/355991291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/455991291584636.ps tmp/455991291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fw8c1291584636.ps tmp/5fw8c1291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fw8c1291584636.ps tmp/6fw8c1291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/78nqf1291584636.ps tmp/78nqf1291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/88nqf1291584636.ps tmp/88nqf1291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jx701291584636.ps tmp/9jx701291584636.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jx701291584636.ps tmp/10jx701291584636.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.615 1.640 6.016