R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(3484.74
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+ ,21467
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+ ,9634.97
+ ,22052
+ ,-17.8
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+ ,3.53
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+ ,10066.24
+ ,9857.34
+ ,22680
+ ,-14.2
+ ,-12
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+ ,3.55
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+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,3.36)
+ ,dim=c(8
+ ,132)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_5j')
+ ,1:132))
> y <- array(NA,dim=c(8,132),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_5j'),1:132))
> 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 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
52 1833.42 7905.84 8334.59 9721 -16.2 -11
53 1910.43 8145.82 8623.36 9897 -14.4 -9
54 1959.67 8895.71 9098.03 9828 -17.3 -10
55 1969.60 9676.31 9154.34 9924 -15.7 -13
56 2061.41 9884.59 9284.73 10371 -12.6 -11
57 2093.48 10637.44 9492.49 10846 -9.4 -5
58 2120.88 10717.13 9682.35 10413 -8.1 -15
59 2174.56 10205.29 9762.12 10709 -5.4 -6
60 2196.72 10295.98 10124.63 10662 -4.6 -6
61 2350.44 10892.76 10540.05 10570 -4.9 -3
62 2440.25 10631.92 10601.61 10297 -4.0 -1
63 2408.64 11441.08 10323.73 10635 -3.1 -3
64 2472.81 11950.95 10418.40 10872 -1.3 -4
65 2407.60 11037.54 10092.96 10296 0.0 -6
66 2454.62 11527.72 10364.91 10383 -0.4 0
67 2448.05 11383.89 10152.09 10431 3.0 -4
68 2497.84 10989.34 10032.80 10574 0.4 -2
69 2645.64 11079.42 10204.59 10653 1.2 -2
70 2756.76 11028.93 10001.60 10805 0.6 -6
71 2849.27 10973.00 10411.75 10872 -1.3 -7
72 2921.44 11068.05 10673.38 10625 -3.2 -6
73 2981.85 11394.84 10539.51 10407 -1.8 -6
74 3080.58 11545.71 10723.78 10463 -3.6 -3
75 3106.22 11809.38 10682.06 10556 -4.2 -2
76 3119.31 11395.64 10283.19 10646 -6.9 -5
77 3061.26 11082.38 10377.18 10702 -8.0 -11
78 3097.31 11402.75 10486.64 11353 -7.5 -11
79 3161.69 11716.87 10545.38 11346 -8.2 -11
80 3257.16 12204.98 10554.27 11451 -7.6 -10
81 3277.01 12986.62 10532.54 11964 -3.7 -14
82 3295.32 13392.79 10324.31 12574 -1.7 -8
83 3363.99 14368.05 10695.25 13031 -0.7 -9
84 3494.17 15650.83 10827.81 13812 0.2 -5
85 3667.03 16102.64 10872.48 14544 0.6 -1
86 3813.06 16187.64 10971.19 14931 2.2 -2
87 3917.96 16311.54 11145.65 14886 3.3 -5
88 3895.51 17232.97 11234.68 16005 5.3 -4
89 3801.06 16397.83 11333.88 17064 5.5 -6
90 3570.12 14990.31 10997.97 15168 6.3 -2
91 3701.61 15147.55 11036.89 16050 7.7 -2
92 3862.27 15786.78 11257.35 15839 6.5 -2
93 3970.10 15934.09 11533.59 15137 5.5 -2
94 4138.52 16519.44 11963.12 14954 6.9 2
95 4199.75 16101.07 12185.15 15648 5.7 1
96 4290.89 16775.08 12377.62 15305 6.9 -8
97 4443.91 17286.32 12512.89 15579 6.1 -1
98 4502.64 17741.23 12631.48 16348 4.8 1
99 4356.98 17128.37 12268.53 15928 3.7 -1
100 4591.27 17460.53 12754.80 16171 5.8 2
101 4696.96 17611.14 13407.75 15937 6.8 2
102 4621.40 18001.37 13480.21 15713 8.5 1
103 4562.84 17974.77 13673.28 15594 7.2 -1
104 4202.52 16460.95 13239.71 15683 5.0 -2
105 4296.49 16235.39 13557.69 16438 4.7 -2
106 4435.23 16903.36 13901.28 17032 2.3 -1
107 4105.18 15543.76 13200.58 17696 2.4 -8
108 4116.68 15532.18 13406.97 17745 0.1 -4
109 3844.49 13731.31 12538.12 19394 1.9 -6
110 3720.98 13547.84 12419.57 20148 1.7 -3
111 3674.40 12602.93 12193.88 20108 2.0 -3
112 3857.62 13357.70 12656.63 18584 -1.9 -7
113 3801.06 13995.33 12812.48 18441 0.5 -9
114 3504.37 14084.60 12056.67 18391 -1.3 -11
115 3032.60 13168.91 11322.38 19178 -3.3 -13
116 3047.03 12989.35 11530.75 18079 -2.8 -11
117 2962.34 12123.53 11114.08 18483 -8.0 -9
118 2197.82 9117.03 9181.73 19644 -13.9 -17
119 2014.45 8531.45 8614.55 19195 -21.9 -22
120 1862.83 8460.94 8595.56 19650 -28.8 -25
121 1905.41 8331.49 8396.20 20830 -27.6 -20
122 1810.99 7694.78 7690.50 23595 -31.4 -24
123 1670.07 7764.58 7235.47 22937 -31.8 -24
124 1864.44 8767.96 7992.12 21814 -29.4 -22
125 2052.02 9304.43 8398.37 21928 -27.6 -19
126 2029.60 9810.31 8593.00 21777 -23.6 -18
127 2070.83 9691.12 8679.75 21383 -22.8 -17
128 2293.41 10430.35 9374.63 21467 -18.2 -11
129 2443.27 10302.87 9634.97 22052 -17.8 -11
130 2513.17 10066.24 9857.34 22680 -14.2 -12
131 2466.92 9633.83 10238.83 24320 -8.8 -10
132 2502.66 10169.02 10433.44 24977 -7.9 -15
Alg_consumptie_index_BE Gem_rente_kasbon_5j t
1 1.0 3.17 1
2 1.0 3.17 2
3 1.2 3.36 3
4 1.2 3.11 4
5 0.8 3.11 5
6 0.7 3.57 6
7 0.7 4.04 7
8 0.9 4.21 8
9 1.2 4.36 9
10 1.3 4.75 10
11 1.5 4.43 11
12 1.9 4.70 12
13 1.8 4.81 13
14 1.9 5.01 14
15 2.2 5.00 15
16 2.1 4.81 16
17 2.2 5.11 17
18 2.7 5.10 18
19 2.8 5.11 19
20 2.9 5.21 20
21 3.4 5.21 21
22 3.0 5.21 22
23 3.1 5.06 23
24 2.5 4.58 24
25 2.2 4.37 25
26 2.3 4.37 26
27 2.1 4.23 27
28 2.8 4.23 28
29 3.1 4.37 29
30 2.9 4.31 30
31 2.6 4.31 31
32 2.7 4.28 32
33 2.3 3.98 33
34 2.3 3.79 34
35 2.1 3.55 35
36 2.2 4.00 36
37 2.9 4.02 37
38 2.6 4.21 38
39 2.7 4.50 39
40 1.8 4.52 40
41 1.3 4.45 41
42 0.9 4.28 42
43 1.3 4.08 43
44 1.3 3.80 44
45 1.3 3.58 45
46 1.3 3.58 46
47 1.1 3.58 47
48 1.4 3.54 48
49 1.2 3.19 49
50 1.7 2.91 50
51 1.8 2.87 51
52 1.5 3.10 52
53 1.0 2.60 53
54 1.6 2.33 54
55 1.5 2.62 55
56 1.8 3.05 56
57 1.8 3.05 57
58 1.6 3.22 58
59 1.9 3.24 59
60 1.7 3.24 60
61 1.6 3.38 61
62 1.3 3.35 62
63 1.1 3.22 63
64 1.9 3.06 64
65 2.6 3.17 65
66 2.3 3.19 66
67 2.4 3.35 67
68 2.2 3.24 68
69 2.0 3.23 69
70 2.9 3.31 70
71 2.6 3.25 71
72 2.3 3.20 72
73 2.3 3.10 73
74 2.6 2.93 74
75 3.1 2.92 75
76 2.8 2.90 76
77 2.5 2.87 77
78 2.9 2.76 78
79 3.1 2.67 79
80 3.1 2.75 80
81 3.2 2.72 81
82 2.5 2.72 82
83 2.6 2.86 83
84 2.9 2.99 84
85 2.6 3.07 85
86 2.4 2.96 86
87 1.7 3.04 87
88 2.0 3.30 88
89 2.2 3.48 89
90 1.9 3.46 90
91 1.6 3.57 91
92 1.6 3.60 92
93 1.2 3.51 93
94 1.2 3.52 94
95 1.5 3.49 95
96 1.6 3.50 96
97 1.7 3.64 97
98 1.8 3.94 98
99 1.8 3.94 99
100 1.8 3.91 100
101 1.3 3.88 101
102 1.3 4.21 102
103 1.4 4.39 103
104 1.1 4.33 104
105 1.5 4.27 105
106 2.2 4.29 106
107 2.9 4.18 107
108 3.1 4.14 108
109 3.5 4.23 109
110 3.6 4.07 110
111 4.4 3.74 111
112 4.2 3.66 112
113 5.2 3.92 113
114 5.8 4.45 114
115 5.9 4.92 115
116 5.4 4.90 116
117 5.5 4.54 117
118 4.7 4.53 118
119 3.1 4.14 119
120 2.6 4.05 120
121 2.3 3.92 121
122 1.9 3.68 122
123 0.6 3.35 123
124 0.6 3.38 124
125 -0.4 3.44 125
126 -1.1 3.50 126
127 -1.7 3.54 127
128 -0.8 3.52 128
129 -1.2 3.53 129
130 -1.0 3.55 130
131 -0.1 3.37 131
132 0.3 3.36 132
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-1.453e+03 9.933e-02 3.616e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1.626e-02 -1.168e+01 1.436e+01
Alg_consumptie_index_BE Gem_rente_kasbon_5j t
3.509e+01 -2.687e+02 1.109e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-594.672 -160.913 9.237 169.097 717.744
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.453e+03 3.386e+02 -4.292 3.56e-05 ***
Nikkei 9.933e-02 1.744e-02 5.695 8.58e-08 ***
DJ_Indust 3.616e-01 3.876e-02 9.328 5.67e-16 ***
Goudprijs 1.626e-02 1.942e-02 0.838 0.4038
Conjunct_Seizoenzuiver -1.168e+01 7.124e+00 -1.639 0.1037
Cons_vertrouw 1.436e+01 6.637e+00 2.163 0.0325 *
Alg_consumptie_index_BE 3.509e+01 2.302e+01 1.524 0.1300
Gem_rente_kasbon_5j -2.687e+02 5.610e+01 -4.791 4.70e-06 ***
t 1.109e+00 2.561e+00 0.433 0.6657
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 261.4 on 123 degrees of freedom
Multiple R-squared: 0.8869, Adjusted R-squared: 0.8795
F-statistic: 120.5 on 8 and 123 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,] 4.765751e-02 9.531501e-02 9.523425e-01
[2,] 2.021091e-02 4.042183e-02 9.797891e-01
[3,] 3.395080e-02 6.790161e-02 9.660492e-01
[4,] 3.180803e-02 6.361606e-02 9.681920e-01
[5,] 2.085549e-02 4.171098e-02 9.791445e-01
[6,] 1.340675e-02 2.681351e-02 9.865932e-01
[7,] 7.900953e-03 1.580191e-02 9.920990e-01
[8,] 8.180634e-03 1.636127e-02 9.918194e-01
[9,] 7.192952e-03 1.438590e-02 9.928070e-01
[10,] 4.738457e-03 9.476914e-03 9.952615e-01
[11,] 4.058275e-03 8.116550e-03 9.959417e-01
[12,] 2.449289e-03 4.898577e-03 9.975507e-01
[13,] 1.274195e-03 2.548390e-03 9.987258e-01
[14,] 6.173152e-04 1.234630e-03 9.993827e-01
[15,] 2.810503e-04 5.621007e-04 9.997189e-01
[16,] 1.202762e-04 2.405523e-04 9.998797e-01
[17,] 6.267286e-05 1.253457e-04 9.999373e-01
[18,] 5.274415e-05 1.054883e-04 9.999473e-01
[19,] 3.585489e-05 7.170978e-05 9.999641e-01
[20,] 2.788127e-05 5.576254e-05 9.999721e-01
[21,] 1.452817e-05 2.905634e-05 9.999855e-01
[22,] 8.300347e-06 1.660069e-05 9.999917e-01
[23,] 6.765606e-06 1.353121e-05 9.999932e-01
[24,] 4.277707e-06 8.555414e-06 9.999957e-01
[25,] 1.931183e-06 3.862367e-06 9.999981e-01
[26,] 8.757220e-07 1.751444e-06 9.999991e-01
[27,] 6.431314e-07 1.286263e-06 9.999994e-01
[28,] 3.517266e-07 7.034532e-07 9.999996e-01
[29,] 3.196984e-06 6.393967e-06 9.999968e-01
[30,] 6.998833e-06 1.399767e-05 9.999930e-01
[31,] 3.949281e-06 7.898561e-06 9.999961e-01
[32,] 2.368218e-06 4.736436e-06 9.999976e-01
[33,] 2.439611e-06 4.879221e-06 9.999976e-01
[34,] 2.896318e-06 5.792635e-06 9.999971e-01
[35,] 9.588479e-06 1.917696e-05 9.999904e-01
[36,] 2.392200e-05 4.784399e-05 9.999761e-01
[37,] 4.636473e-05 9.272945e-05 9.999536e-01
[38,] 8.228423e-05 1.645685e-04 9.999177e-01
[39,] 8.683825e-05 1.736765e-04 9.999132e-01
[40,] 6.902421e-05 1.380484e-04 9.999310e-01
[41,] 1.563031e-04 3.126061e-04 9.998437e-01
[42,] 2.331631e-04 4.663262e-04 9.997668e-01
[43,] 2.373796e-04 4.747592e-04 9.997626e-01
[44,] 4.376236e-04 8.752472e-04 9.995624e-01
[45,] 1.013169e-03 2.026339e-03 9.989868e-01
[46,] 1.475413e-03 2.950826e-03 9.985246e-01
[47,] 6.153527e-03 1.230705e-02 9.938465e-01
[48,] 7.503703e-03 1.500741e-02 9.924963e-01
[49,] 6.361311e-03 1.272262e-02 9.936387e-01
[50,] 7.337151e-03 1.467430e-02 9.926628e-01
[51,] 8.889906e-03 1.777981e-02 9.911101e-01
[52,] 2.568405e-02 5.136809e-02 9.743160e-01
[53,] 1.613115e-01 3.226230e-01 8.386885e-01
[54,] 3.787796e-01 7.575592e-01 6.212204e-01
[55,] 6.957846e-01 6.084307e-01 3.042154e-01
[56,] 8.716830e-01 2.566339e-01 1.283170e-01
[57,] 9.562453e-01 8.750943e-02 4.375472e-02
[58,] 9.884994e-01 2.300127e-02 1.150064e-02
[59,] 9.976621e-01 4.675882e-03 2.337941e-03
[60,] 9.992918e-01 1.416375e-03 7.081876e-04
[61,] 9.998523e-01 2.953898e-04 1.476949e-04
[62,] 9.999694e-01 6.125111e-05 3.062555e-05
[63,] 9.999929e-01 1.424647e-05 7.123233e-06
[64,] 9.999981e-01 3.738629e-06 1.869314e-06
[65,] 9.999993e-01 1.404137e-06 7.020683e-07
[66,] 9.999996e-01 8.756413e-07 4.378207e-07
[67,] 9.999996e-01 8.310719e-07 4.155360e-07
[68,] 9.999995e-01 1.055564e-06 5.277819e-07
[69,] 9.999994e-01 1.274664e-06 6.373318e-07
[70,] 9.999992e-01 1.592774e-06 7.963872e-07
[71,] 9.999990e-01 2.029575e-06 1.014787e-06
[72,] 9.999988e-01 2.411036e-06 1.205518e-06
[73,] 9.999997e-01 6.282345e-07 3.141172e-07
[74,] 9.999999e-01 1.451774e-07 7.258872e-08
[75,] 1.000000e+00 9.436403e-08 4.718202e-08
[76,] 9.999999e-01 1.303557e-07 6.517787e-08
[77,] 1.000000e+00 4.841593e-08 2.420796e-08
[78,] 1.000000e+00 6.184876e-09 3.092438e-09
[79,] 1.000000e+00 1.163034e-09 5.815168e-10
[80,] 1.000000e+00 1.105082e-09 5.525408e-10
[81,] 1.000000e+00 8.929081e-10 4.464541e-10
[82,] 1.000000e+00 6.836306e-10 3.418153e-10
[83,] 1.000000e+00 1.166190e-10 5.830951e-11
[84,] 1.000000e+00 1.005654e-11 5.028268e-12
[85,] 1.000000e+00 1.075700e-11 5.378501e-12
[86,] 1.000000e+00 8.630374e-12 4.315187e-12
[87,] 1.000000e+00 2.033770e-11 1.016885e-11
[88,] 1.000000e+00 6.532109e-11 3.266054e-11
[89,] 1.000000e+00 2.004779e-10 1.002389e-10
[90,] 1.000000e+00 6.363809e-10 3.181905e-10
[91,] 1.000000e+00 1.736842e-09 8.684210e-10
[92,] 1.000000e+00 3.515088e-09 1.757544e-09
[93,] 1.000000e+00 1.262348e-08 6.311738e-09
[94,] 1.000000e+00 4.586605e-08 2.293303e-08
[95,] 9.999999e-01 1.406416e-07 7.032081e-08
[96,] 9.999998e-01 4.491830e-07 2.245915e-07
[97,] 9.999996e-01 8.203620e-07 4.101810e-07
[98,] 9.999991e-01 1.702513e-06 8.512567e-07
[99,] 9.999984e-01 3.148125e-06 1.574063e-06
[100,] 9.999980e-01 3.988281e-06 1.994141e-06
[101,] 9.999922e-01 1.554341e-05 7.771705e-06
[102,] 9.999783e-01 4.345934e-05 2.172967e-05
[103,] 9.999554e-01 8.912201e-05 4.456100e-05
[104,] 9.998166e-01 3.667225e-04 1.833613e-04
[105,] 9.993154e-01 1.369154e-03 6.845768e-04
[106,] 9.980508e-01 3.898378e-03 1.949189e-03
[107,] 9.927955e-01 1.440909e-02 7.204546e-03
[108,] 9.992415e-01 1.516914e-03 7.584571e-04
[109,] 9.947159e-01 1.056821e-02 5.284105e-03
> postscript(file="/var/www/html/rcomp/tmp/1926s1291653511.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ku6d1291653511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ku6d1291653511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ku6d1291653511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5d35g1291653511.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 = 132
Frequency = 1
1 2 3 4 5 6
717.744002 650.530102 291.431083 -92.381807 -301.358905 -53.580756
7 8 9 10 11 12
-318.436994 -209.231895 -6.438907 5.002455 -106.483285 -211.581843
13 14 15 16 17 18
-463.625754 -472.960041 -524.698147 -594.671654 -183.314429 -178.904733
19 20 21 22 23 24
-90.095052 -50.916561 -57.202250 169.357552 157.729600 -25.107741
25 26 27 28 29 30
-8.761513 57.151033 169.009960 21.525621 -190.813054 -158.312145
31 32 33 34 35 36
107.049333 234.405150 502.808372 481.209497 309.383875 195.007646
37 38 39 40 41 42
219.848687 310.730966 36.871217 306.465768 295.560556 320.729317
43 44 45 46 47 48
386.825018 264.926062 280.343837 236.664206 163.582493 182.538294
49 50 51 52 53 54
64.191008 58.851943 -37.326113 20.863324 -158.878945 -468.883432
55 56 57 58 59 60
-416.329246 -288.210358 -463.649267 -295.445040 -328.509662 -430.428297
61 62 63 64 65 66
-491.255795 -410.201742 -417.012219 -478.380038 -278.022748 -455.459993
67 68 69 70 71 72
-236.057446 -188.992839 -100.982555 125.310830 59.434271 -8.990828
73 74 75 76 77 78
59.276425 -45.932988 -75.613096 136.922722 149.733898 64.923633
79 80 81 82 83 84
36.485480 91.589047 123.594995 127.608455 16.875705 -64.614942
85 86 87 88 89 90
13.472418 118.456840 249.559486 152.426335 159.134704 176.276635
91 92 93 94 95 96
319.056968 332.871665 314.656972 233.093742 224.952365 326.403239
97 98 99 100 101 102
298.445226 288.711681 356.761411 350.552773 229.028238 213.932606
103 104 105 106 107 108
147.422261 74.766237 29.107665 -95.086872 -1.080833 -167.016549
109 110 111 112 113 114
85.817243 -81.882612 -66.701355 -104.715445 -188.227029 -91.700623
115 116 117 118 119 120
-92.739111 -129.758050 -175.147247 109.061754 124.828226 -65.586037
121 122 123 124 125 126
-40.534029 99.977546 78.511760 -75.861800 -62.271232 -130.932700
127 128 129 130 131 132
-77.144121 -251.108998 -171.956198 -115.529658 -330.159933 -364.183947
> postscript(file="/var/www/html/rcomp/tmp/6d35g1291653511.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 = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 717.744002 NA
1 650.530102 717.744002
2 291.431083 650.530102
3 -92.381807 291.431083
4 -301.358905 -92.381807
5 -53.580756 -301.358905
6 -318.436994 -53.580756
7 -209.231895 -318.436994
8 -6.438907 -209.231895
9 5.002455 -6.438907
10 -106.483285 5.002455
11 -211.581843 -106.483285
12 -463.625754 -211.581843
13 -472.960041 -463.625754
14 -524.698147 -472.960041
15 -594.671654 -524.698147
16 -183.314429 -594.671654
17 -178.904733 -183.314429
18 -90.095052 -178.904733
19 -50.916561 -90.095052
20 -57.202250 -50.916561
21 169.357552 -57.202250
22 157.729600 169.357552
23 -25.107741 157.729600
24 -8.761513 -25.107741
25 57.151033 -8.761513
26 169.009960 57.151033
27 21.525621 169.009960
28 -190.813054 21.525621
29 -158.312145 -190.813054
30 107.049333 -158.312145
31 234.405150 107.049333
32 502.808372 234.405150
33 481.209497 502.808372
34 309.383875 481.209497
35 195.007646 309.383875
36 219.848687 195.007646
37 310.730966 219.848687
38 36.871217 310.730966
39 306.465768 36.871217
40 295.560556 306.465768
41 320.729317 295.560556
42 386.825018 320.729317
43 264.926062 386.825018
44 280.343837 264.926062
45 236.664206 280.343837
46 163.582493 236.664206
47 182.538294 163.582493
48 64.191008 182.538294
49 58.851943 64.191008
50 -37.326113 58.851943
51 20.863324 -37.326113
52 -158.878945 20.863324
53 -468.883432 -158.878945
54 -416.329246 -468.883432
55 -288.210358 -416.329246
56 -463.649267 -288.210358
57 -295.445040 -463.649267
58 -328.509662 -295.445040
59 -430.428297 -328.509662
60 -491.255795 -430.428297
61 -410.201742 -491.255795
62 -417.012219 -410.201742
63 -478.380038 -417.012219
64 -278.022748 -478.380038
65 -455.459993 -278.022748
66 -236.057446 -455.459993
67 -188.992839 -236.057446
68 -100.982555 -188.992839
69 125.310830 -100.982555
70 59.434271 125.310830
71 -8.990828 59.434271
72 59.276425 -8.990828
73 -45.932988 59.276425
74 -75.613096 -45.932988
75 136.922722 -75.613096
76 149.733898 136.922722
77 64.923633 149.733898
78 36.485480 64.923633
79 91.589047 36.485480
80 123.594995 91.589047
81 127.608455 123.594995
82 16.875705 127.608455
83 -64.614942 16.875705
84 13.472418 -64.614942
85 118.456840 13.472418
86 249.559486 118.456840
87 152.426335 249.559486
88 159.134704 152.426335
89 176.276635 159.134704
90 319.056968 176.276635
91 332.871665 319.056968
92 314.656972 332.871665
93 233.093742 314.656972
94 224.952365 233.093742
95 326.403239 224.952365
96 298.445226 326.403239
97 288.711681 298.445226
98 356.761411 288.711681
99 350.552773 356.761411
100 229.028238 350.552773
101 213.932606 229.028238
102 147.422261 213.932606
103 74.766237 147.422261
104 29.107665 74.766237
105 -95.086872 29.107665
106 -1.080833 -95.086872
107 -167.016549 -1.080833
108 85.817243 -167.016549
109 -81.882612 85.817243
110 -66.701355 -81.882612
111 -104.715445 -66.701355
112 -188.227029 -104.715445
113 -91.700623 -188.227029
114 -92.739111 -91.700623
115 -129.758050 -92.739111
116 -175.147247 -129.758050
117 109.061754 -175.147247
118 124.828226 109.061754
119 -65.586037 124.828226
120 -40.534029 -65.586037
121 99.977546 -40.534029
122 78.511760 99.977546
123 -75.861800 78.511760
124 -62.271232 -75.861800
125 -130.932700 -62.271232
126 -77.144121 -130.932700
127 -251.108998 -77.144121
128 -171.956198 -251.108998
129 -115.529658 -171.956198
130 -330.159933 -115.529658
131 -364.183947 -330.159933
132 NA -364.183947
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 650.530102 717.744002
[2,] 291.431083 650.530102
[3,] -92.381807 291.431083
[4,] -301.358905 -92.381807
[5,] -53.580756 -301.358905
[6,] -318.436994 -53.580756
[7,] -209.231895 -318.436994
[8,] -6.438907 -209.231895
[9,] 5.002455 -6.438907
[10,] -106.483285 5.002455
[11,] -211.581843 -106.483285
[12,] -463.625754 -211.581843
[13,] -472.960041 -463.625754
[14,] -524.698147 -472.960041
[15,] -594.671654 -524.698147
[16,] -183.314429 -594.671654
[17,] -178.904733 -183.314429
[18,] -90.095052 -178.904733
[19,] -50.916561 -90.095052
[20,] -57.202250 -50.916561
[21,] 169.357552 -57.202250
[22,] 157.729600 169.357552
[23,] -25.107741 157.729600
[24,] -8.761513 -25.107741
[25,] 57.151033 -8.761513
[26,] 169.009960 57.151033
[27,] 21.525621 169.009960
[28,] -190.813054 21.525621
[29,] -158.312145 -190.813054
[30,] 107.049333 -158.312145
[31,] 234.405150 107.049333
[32,] 502.808372 234.405150
[33,] 481.209497 502.808372
[34,] 309.383875 481.209497
[35,] 195.007646 309.383875
[36,] 219.848687 195.007646
[37,] 310.730966 219.848687
[38,] 36.871217 310.730966
[39,] 306.465768 36.871217
[40,] 295.560556 306.465768
[41,] 320.729317 295.560556
[42,] 386.825018 320.729317
[43,] 264.926062 386.825018
[44,] 280.343837 264.926062
[45,] 236.664206 280.343837
[46,] 163.582493 236.664206
[47,] 182.538294 163.582493
[48,] 64.191008 182.538294
[49,] 58.851943 64.191008
[50,] -37.326113 58.851943
[51,] 20.863324 -37.326113
[52,] -158.878945 20.863324
[53,] -468.883432 -158.878945
[54,] -416.329246 -468.883432
[55,] -288.210358 -416.329246
[56,] -463.649267 -288.210358
[57,] -295.445040 -463.649267
[58,] -328.509662 -295.445040
[59,] -430.428297 -328.509662
[60,] -491.255795 -430.428297
[61,] -410.201742 -491.255795
[62,] -417.012219 -410.201742
[63,] -478.380038 -417.012219
[64,] -278.022748 -478.380038
[65,] -455.459993 -278.022748
[66,] -236.057446 -455.459993
[67,] -188.992839 -236.057446
[68,] -100.982555 -188.992839
[69,] 125.310830 -100.982555
[70,] 59.434271 125.310830
[71,] -8.990828 59.434271
[72,] 59.276425 -8.990828
[73,] -45.932988 59.276425
[74,] -75.613096 -45.932988
[75,] 136.922722 -75.613096
[76,] 149.733898 136.922722
[77,] 64.923633 149.733898
[78,] 36.485480 64.923633
[79,] 91.589047 36.485480
[80,] 123.594995 91.589047
[81,] 127.608455 123.594995
[82,] 16.875705 127.608455
[83,] -64.614942 16.875705
[84,] 13.472418 -64.614942
[85,] 118.456840 13.472418
[86,] 249.559486 118.456840
[87,] 152.426335 249.559486
[88,] 159.134704 152.426335
[89,] 176.276635 159.134704
[90,] 319.056968 176.276635
[91,] 332.871665 319.056968
[92,] 314.656972 332.871665
[93,] 233.093742 314.656972
[94,] 224.952365 233.093742
[95,] 326.403239 224.952365
[96,] 298.445226 326.403239
[97,] 288.711681 298.445226
[98,] 356.761411 288.711681
[99,] 350.552773 356.761411
[100,] 229.028238 350.552773
[101,] 213.932606 229.028238
[102,] 147.422261 213.932606
[103,] 74.766237 147.422261
[104,] 29.107665 74.766237
[105,] -95.086872 29.107665
[106,] -1.080833 -95.086872
[107,] -167.016549 -1.080833
[108,] 85.817243 -167.016549
[109,] -81.882612 85.817243
[110,] -66.701355 -81.882612
[111,] -104.715445 -66.701355
[112,] -188.227029 -104.715445
[113,] -91.700623 -188.227029
[114,] -92.739111 -91.700623
[115,] -129.758050 -92.739111
[116,] -175.147247 -129.758050
[117,] 109.061754 -175.147247
[118,] 124.828226 109.061754
[119,] -65.586037 124.828226
[120,] -40.534029 -65.586037
[121,] 99.977546 -40.534029
[122,] 78.511760 99.977546
[123,] -75.861800 78.511760
[124,] -62.271232 -75.861800
[125,] -130.932700 -62.271232
[126,] -77.144121 -130.932700
[127,] -251.108998 -77.144121
[128,] -171.956198 -251.108998
[129,] -115.529658 -171.956198
[130,] -330.159933 -115.529658
[131,] -364.183947 -330.159933
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 650.530102 717.744002
2 291.431083 650.530102
3 -92.381807 291.431083
4 -301.358905 -92.381807
5 -53.580756 -301.358905
6 -318.436994 -53.580756
7 -209.231895 -318.436994
8 -6.438907 -209.231895
9 5.002455 -6.438907
10 -106.483285 5.002455
11 -211.581843 -106.483285
12 -463.625754 -211.581843
13 -472.960041 -463.625754
14 -524.698147 -472.960041
15 -594.671654 -524.698147
16 -183.314429 -594.671654
17 -178.904733 -183.314429
18 -90.095052 -178.904733
19 -50.916561 -90.095052
20 -57.202250 -50.916561
21 169.357552 -57.202250
22 157.729600 169.357552
23 -25.107741 157.729600
24 -8.761513 -25.107741
25 57.151033 -8.761513
26 169.009960 57.151033
27 21.525621 169.009960
28 -190.813054 21.525621
29 -158.312145 -190.813054
30 107.049333 -158.312145
31 234.405150 107.049333
32 502.808372 234.405150
33 481.209497 502.808372
34 309.383875 481.209497
35 195.007646 309.383875
36 219.848687 195.007646
37 310.730966 219.848687
38 36.871217 310.730966
39 306.465768 36.871217
40 295.560556 306.465768
41 320.729317 295.560556
42 386.825018 320.729317
43 264.926062 386.825018
44 280.343837 264.926062
45 236.664206 280.343837
46 163.582493 236.664206
47 182.538294 163.582493
48 64.191008 182.538294
49 58.851943 64.191008
50 -37.326113 58.851943
51 20.863324 -37.326113
52 -158.878945 20.863324
53 -468.883432 -158.878945
54 -416.329246 -468.883432
55 -288.210358 -416.329246
56 -463.649267 -288.210358
57 -295.445040 -463.649267
58 -328.509662 -295.445040
59 -430.428297 -328.509662
60 -491.255795 -430.428297
61 -410.201742 -491.255795
62 -417.012219 -410.201742
63 -478.380038 -417.012219
64 -278.022748 -478.380038
65 -455.459993 -278.022748
66 -236.057446 -455.459993
67 -188.992839 -236.057446
68 -100.982555 -188.992839
69 125.310830 -100.982555
70 59.434271 125.310830
71 -8.990828 59.434271
72 59.276425 -8.990828
73 -45.932988 59.276425
74 -75.613096 -45.932988
75 136.922722 -75.613096
76 149.733898 136.922722
77 64.923633 149.733898
78 36.485480 64.923633
79 91.589047 36.485480
80 123.594995 91.589047
81 127.608455 123.594995
82 16.875705 127.608455
83 -64.614942 16.875705
84 13.472418 -64.614942
85 118.456840 13.472418
86 249.559486 118.456840
87 152.426335 249.559486
88 159.134704 152.426335
89 176.276635 159.134704
90 319.056968 176.276635
91 332.871665 319.056968
92 314.656972 332.871665
93 233.093742 314.656972
94 224.952365 233.093742
95 326.403239 224.952365
96 298.445226 326.403239
97 288.711681 298.445226
98 356.761411 288.711681
99 350.552773 356.761411
100 229.028238 350.552773
101 213.932606 229.028238
102 147.422261 213.932606
103 74.766237 147.422261
104 29.107665 74.766237
105 -95.086872 29.107665
106 -1.080833 -95.086872
107 -167.016549 -1.080833
108 85.817243 -167.016549
109 -81.882612 85.817243
110 -66.701355 -81.882612
111 -104.715445 -66.701355
112 -188.227029 -104.715445
113 -91.700623 -188.227029
114 -92.739111 -91.700623
115 -129.758050 -92.739111
116 -175.147247 -129.758050
117 109.061754 -175.147247
118 124.828226 109.061754
119 -65.586037 124.828226
120 -40.534029 -65.586037
121 99.977546 -40.534029
122 78.511760 99.977546
123 -75.861800 78.511760
124 -62.271232 -75.861800
125 -130.932700 -62.271232
126 -77.144121 -130.932700
127 -251.108998 -77.144121
128 -171.956198 -251.108998
129 -115.529658 -171.956198
130 -330.159933 -115.529658
131 -364.183947 -330.159933
> 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/7nc411291653511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nc411291653511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yllm1291653511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yllm1291653511.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11j4ka1291653511.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/12440g1291653511.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/13t5fr1291653511.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/14mxxu1291653511.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/158fd01291653511.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/16l7b91291653511.tab")
+ }
>
> try(system("convert tmp/1926s1291653511.ps tmp/1926s1291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ku6d1291653511.ps tmp/2ku6d1291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ku6d1291653511.ps tmp/3ku6d1291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ku6d1291653511.ps tmp/4ku6d1291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/5d35g1291653511.ps tmp/5d35g1291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/6d35g1291653511.ps tmp/6d35g1291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nc411291653511.ps tmp/7nc411291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nc411291653511.ps tmp/8nc411291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yllm1291653511.ps tmp/9yllm1291653511.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yllm1291653511.ps tmp/10yllm1291653511.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.780 1.730 8.647