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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> x <- array(list(3484.74
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+ ,21467
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+ ,-11
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+ ,1.85
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+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
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+ ,10066.24
+ ,9857.34
+ ,22680
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+ ,24320
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+ ,10169.02
+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,1.56)
+ ,dim=c(8
+ ,132)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_1j')
+ ,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_1j'),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
> 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_1j t
1 1.0 2.77 1
2 1.0 2.76 2
3 1.2 2.76 3
4 1.2 2.46 4
5 0.8 2.46 5
6 0.7 2.47 6
7 0.7 2.71 7
8 0.9 2.80 8
9 1.2 2.89 9
10 1.3 3.36 10
11 1.5 3.31 11
12 1.9 3.50 12
13 1.8 3.51 13
14 1.9 3.71 14
15 2.2 3.71 15
16 2.1 3.71 16
17 2.2 4.21 17
18 2.7 4.21 18
19 2.8 4.21 19
20 2.9 4.50 20
21 3.4 4.51 21
22 3.0 4.51 22
23 3.1 4.51 23
24 2.5 4.32 24
25 2.2 4.02 25
26 2.3 4.02 26
27 2.1 3.85 27
28 2.8 3.84 28
29 3.1 4.02 29
30 2.9 3.82 30
31 2.6 3.75 31
32 2.7 3.74 32
33 2.3 3.14 33
34 2.3 2.91 34
35 2.1 2.84 35
36 2.2 2.85 36
37 2.9 2.85 37
38 2.6 3.08 38
39 2.7 3.30 39
40 1.8 3.29 40
41 1.3 3.26 41
42 0.9 3.26 42
43 1.3 3.11 43
44 1.3 2.84 44
45 1.3 2.71 45
46 1.3 2.69 46
47 1.1 2.65 47
48 1.4 2.57 48
49 1.2 2.32 49
50 1.7 2.12 50
51 1.8 2.05 51
52 1.5 2.05 52
53 1.0 1.81 53
54 1.6 1.58 54
55 1.5 1.57 55
56 1.8 1.76 56
57 1.8 1.76 57
58 1.6 1.89 58
59 1.9 1.90 59
60 1.7 1.90 60
61 1.6 1.92 61
62 1.3 1.76 62
63 1.1 1.64 63
64 1.9 1.57 64
65 2.6 1.69 65
66 2.3 1.76 66
67 2.4 1.89 67
68 2.2 1.78 68
69 2.0 1.88 69
70 2.9 1.86 70
71 2.6 1.88 71
72 2.3 1.87 72
73 2.3 1.86 73
74 2.6 1.89 74
75 3.1 1.90 75
76 2.8 1.89 76
77 2.5 1.85 77
78 2.9 1.78 78
79 3.1 1.71 79
80 3.1 1.69 80
81 3.2 1.72 81
82 2.5 1.77 82
83 2.6 1.98 83
84 2.9 2.20 84
85 2.6 2.25 85
86 2.4 2.24 86
87 1.7 2.51 87
88 2.0 2.79 88
89 2.2 3.07 89
90 1.9 3.08 90
91 1.6 3.05 91
92 1.6 3.08 92
93 1.2 3.15 93
94 1.2 3.16 94
95 1.5 3.16 95
96 1.6 3.19 96
97 1.7 3.44 97
98 1.8 3.55 98
99 1.8 3.60 99
100 1.8 3.62 100
101 1.3 3.69 101
102 1.3 3.99 102
103 1.4 4.06 103
104 1.1 4.05 104
105 1.5 4.01 105
106 2.2 3.98 106
107 2.9 3.94 107
108 3.1 3.92 108
109 3.5 4.10 109
110 3.6 3.88 110
111 4.4 3.74 111
112 4.2 3.97 112
113 5.2 4.26 113
114 5.8 4.63 114
115 5.9 4.82 115
116 5.4 4.94 116
117 5.5 4.98 117
118 4.7 5.02 118
119 3.1 4.96 119
120 2.6 4.49 120
121 2.3 3.50 121
122 1.9 2.95 122
123 0.6 2.37 123
124 0.6 2.16 124
125 -0.4 2.08 125
126 -1.1 1.98 126
127 -1.7 1.98 127
128 -0.8 1.85 128
129 -1.2 1.82 129
130 -1.0 1.65 130
131 -0.1 1.59 131
132 0.3 1.56 132
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-1.771e+03 9.560e-02 3.371e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
-3.863e-02 -5.283e+00 2.064e-01
Alg_consumptie_index_BE Gem_rente_kasbon_1j t
-2.512e+01 2.223e+01 6.580e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-635.99 -177.61 19.65 190.95 1016.03
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.771e+03 3.706e+02 -4.777 4.96e-06 ***
Nikkei 9.560e-02 1.918e-02 4.983 2.07e-06 ***
DJ_Indust 3.371e-01 4.256e-02 7.920 1.20e-12 ***
Goudprijs -3.863e-02 2.030e-02 -1.903 0.0594 .
Conjunct_Seizoenzuiver -5.283e+00 7.911e+00 -0.668 0.5056
Cons_vertrouw 2.064e-01 7.021e+00 0.029 0.9766
Alg_consumptie_index_BE -2.512e+01 2.958e+01 -0.849 0.3974
Gem_rente_kasbon_1j 2.223e+01 4.456e+01 0.499 0.6188
t 6.580e+00 2.684e+00 2.452 0.0156 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 284.4 on 123 degrees of freedom
Multiple R-squared: 0.866, Adjusted R-squared: 0.8573
F-statistic: 99.38 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,] 9.909769e-02 1.981954e-01 9.009023e-01
[2,] 4.146166e-02 8.292332e-02 9.585383e-01
[3,] 3.221304e-02 6.442608e-02 9.677870e-01
[4,] 2.552580e-02 5.105160e-02 9.744742e-01
[5,] 1.615203e-02 3.230405e-02 9.838480e-01
[6,] 9.807743e-03 1.961549e-02 9.901923e-01
[7,] 5.183151e-03 1.036630e-02 9.948168e-01
[8,] 6.419056e-03 1.283811e-02 9.935809e-01
[9,] 5.592166e-03 1.118433e-02 9.944078e-01
[10,] 3.784583e-03 7.569165e-03 9.962154e-01
[11,] 3.703212e-03 7.406423e-03 9.962968e-01
[12,] 2.179396e-03 4.358791e-03 9.978206e-01
[13,] 1.106455e-03 2.212909e-03 9.988935e-01
[14,] 5.358621e-04 1.071724e-03 9.994641e-01
[15,] 2.424292e-04 4.848584e-04 9.997576e-01
[16,] 1.032076e-04 2.064151e-04 9.998968e-01
[17,] 5.260614e-05 1.052123e-04 9.999474e-01
[18,] 4.299438e-05 8.598875e-05 9.999570e-01
[19,] 2.746910e-05 5.493820e-05 9.999725e-01
[20,] 2.184248e-05 4.368495e-05 9.999782e-01
[21,] 1.142248e-05 2.284496e-05 9.999886e-01
[22,] 6.452893e-06 1.290579e-05 9.999935e-01
[23,] 4.955962e-06 9.911924e-06 9.999950e-01
[24,] 2.984571e-06 5.969142e-06 9.999970e-01
[25,] 1.291136e-06 2.582271e-06 9.999987e-01
[26,] 5.869478e-07 1.173896e-06 9.999994e-01
[27,] 4.434899e-07 8.869797e-07 9.999996e-01
[28,] 2.382326e-07 4.764651e-07 9.999998e-01
[29,] 2.263645e-06 4.527290e-06 9.999977e-01
[30,] 3.690764e-06 7.381528e-06 9.999963e-01
[31,] 1.900823e-06 3.801647e-06 9.999981e-01
[32,] 9.282130e-07 1.856426e-06 9.999991e-01
[33,] 7.742754e-07 1.548551e-06 9.999992e-01
[34,] 7.783654e-07 1.556731e-06 9.999992e-01
[35,] 1.812131e-06 3.624262e-06 9.999982e-01
[36,] 2.735418e-06 5.470836e-06 9.999973e-01
[37,] 2.698119e-06 5.396238e-06 9.999973e-01
[38,] 3.759958e-06 7.519916e-06 9.999962e-01
[39,] 2.701966e-06 5.403931e-06 9.999973e-01
[40,] 1.759209e-06 3.518419e-06 9.999982e-01
[41,] 2.007639e-06 4.015277e-06 9.999980e-01
[42,] 2.938832e-06 5.877665e-06 9.999971e-01
[43,] 2.567031e-06 5.134062e-06 9.999974e-01
[44,] 4.483772e-06 8.967544e-06 9.999955e-01
[45,] 1.379029e-05 2.758059e-05 9.999862e-01
[46,] 2.040776e-05 4.081552e-05 9.999796e-01
[47,] 1.339388e-04 2.678776e-04 9.998661e-01
[48,] 1.610733e-04 3.221466e-04 9.998389e-01
[49,] 1.303214e-04 2.606427e-04 9.998697e-01
[50,] 1.849223e-04 3.698445e-04 9.998151e-01
[51,] 2.611078e-04 5.222156e-04 9.997389e-01
[52,] 1.555332e-03 3.110664e-03 9.984447e-01
[53,] 2.353449e-02 4.706898e-02 9.764655e-01
[54,] 8.781044e-02 1.756209e-01 9.121896e-01
[55,] 3.061825e-01 6.123649e-01 6.938175e-01
[56,] 6.044012e-01 7.911976e-01 3.955988e-01
[57,] 8.370156e-01 3.259688e-01 1.629844e-01
[58,] 9.648264e-01 7.034718e-02 3.517359e-02
[59,] 9.921604e-01 1.567914e-02 7.839572e-03
[60,] 9.984835e-01 3.032919e-03 1.516460e-03
[61,] 9.998707e-01 2.586029e-04 1.293015e-04
[62,] 9.999879e-01 2.427288e-05 1.213644e-05
[63,] 9.999987e-01 2.683933e-06 1.341967e-06
[64,] 9.999998e-01 4.476634e-07 2.238317e-07
[65,] 9.999999e-01 1.348261e-07 6.741304e-08
[66,] 1.000000e+00 7.499256e-08 3.749628e-08
[67,] 1.000000e+00 5.036382e-08 2.518191e-08
[68,] 1.000000e+00 5.238930e-08 2.619465e-08
[69,] 1.000000e+00 5.132956e-08 2.566478e-08
[70,] 1.000000e+00 2.800618e-08 1.400309e-08
[71,] 1.000000e+00 1.341974e-08 6.709869e-09
[72,] 1.000000e+00 2.012408e-08 1.006204e-08
[73,] 1.000000e+00 2.135277e-08 1.067638e-08
[74,] 1.000000e+00 1.437804e-08 7.189021e-09
[75,] 1.000000e+00 2.006505e-08 1.003253e-08
[76,] 1.000000e+00 3.419419e-08 1.709710e-08
[77,] 1.000000e+00 2.864042e-08 1.432021e-08
[78,] 1.000000e+00 5.187475e-09 2.593738e-09
[79,] 1.000000e+00 2.059632e-09 1.029816e-09
[80,] 1.000000e+00 1.478545e-09 7.392724e-10
[81,] 1.000000e+00 1.139858e-09 5.699292e-10
[82,] 1.000000e+00 1.386357e-09 6.931787e-10
[83,] 1.000000e+00 6.839178e-10 3.419589e-10
[84,] 1.000000e+00 1.660281e-10 8.301404e-11
[85,] 1.000000e+00 3.103178e-10 1.551589e-10
[86,] 1.000000e+00 6.863761e-10 3.431880e-10
[87,] 1.000000e+00 2.052331e-09 1.026166e-09
[88,] 1.000000e+00 6.841740e-09 3.420870e-09
[89,] 1.000000e+00 1.189745e-08 5.948724e-09
[90,] 1.000000e+00 1.387951e-08 6.939753e-09
[91,] 1.000000e+00 1.061962e-08 5.309808e-09
[92,] 1.000000e+00 8.673666e-09 4.336833e-09
[93,] 1.000000e+00 2.871399e-08 1.435700e-08
[94,] 1.000000e+00 9.245899e-08 4.622950e-08
[95,] 9.999999e-01 2.700411e-07 1.350205e-07
[96,] 9.999996e-01 8.416490e-07 4.208245e-07
[97,] 9.999994e-01 1.214889e-06 6.074447e-07
[98,] 9.999979e-01 4.283486e-06 2.141743e-06
[99,] 9.999971e-01 5.746797e-06 2.873398e-06
[100,] 9.999965e-01 6.960697e-06 3.480349e-06
[101,] 9.999871e-01 2.585932e-05 1.292966e-05
[102,] 9.999509e-01 9.822134e-05 4.911067e-05
[103,] 9.999740e-01 5.204011e-05 2.602006e-05
[104,] 9.998966e-01 2.068186e-04 1.034093e-04
[105,] 9.995859e-01 8.282735e-04 4.141368e-04
[106,] 9.986809e-01 2.638255e-03 1.319127e-03
[107,] 9.950925e-01 9.814907e-03 4.907454e-03
[108,] 9.986043e-01 2.791425e-03 1.395713e-03
[109,] 9.930404e-01 1.391919e-02 6.959596e-03
> postscript(file="/var/www/rcomp/tmp/17o3o1291648593.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ixkr1291648593.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ixkr1291648593.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ixkr1291648593.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5so1t1291648593.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 7
1016.02544 920.12082 534.44906 206.67482 -36.31223 -77.42644 -366.38421
8 9 10 11 12 13 14
-342.31952 -103.92768 -140.51624 -116.17300 -288.05960 -550.27164 -570.89676
15 16 17 18 19 20 21
-603.25135 -635.99490 -299.06800 -249.21942 -176.30824 -113.93261 -168.94283
22 23 24 25 26 27 28
81.68343 108.71191 14.77159 14.47588 24.53618 147.47451 27.41144
29 30 31 32 33 34 35
-173.92358 -104.86693 126.23379 187.37422 466.47589 385.84401 216.69117
36 37 38 39 40 41 42
112.81903 265.09145 338.02859 -13.46152 159.88210 140.02641 141.89984
43 44 45 46 47 48 49
219.08183 155.43784 227.92395 177.78748 75.64628 79.98255 56.52410
50 51 52 53 54 55 56
77.85306 -82.26576 -18.99993 -60.18334 -246.80823 -326.57238 -268.66376
57 58 59 60 61 62 63
-351.16333 -417.66779 -317.37479 -435.27492 -493.93129 -416.70040 -422.70782
64 65 66 67 68 69 70
-405.24974 -280.07137 -387.24432 -294.64319 -184.70773 -109.97629 94.39579
71 72 73 74 75 76 77
32.19364 -26.59235 40.32066 54.83788 75.30952 238.34386 162.92138
78 79 80 81 82 83 84
164.25885 174.84153 221.53671 210.49775 267.78363 132.58939 125.61829
85 86 87 88 89 90 91
254.56359 371.41916 380.19307 287.96117 274.50381 207.17446 338.52808
92 93 94 95 96 97 98
342.03014 292.07899 252.45221 300.46388 252.50535 306.34313 297.52104
99 100 101 102 103 104 105
303.48116 354.93888 201.67879 51.67543 -86.09765 -177.42305 -137.14150
106 107 108 109 110 111 112
-156.34556 -80.71231 -149.87060 116.05648 78.31744 214.81210 74.53353
113 114 115 116 117 118 119
-75.85422 -137.06386 -261.82288 -362.49552 -241.19675 -79.17463 -119.38760
120 121 122 123 124 125 126
-284.81479 -103.88425 183.59456 135.51172 -54.11819 -71.39357 -214.63144
127 128 129 130 131 132
-224.10100 -261.19918 -178.16150 -114.89485 -139.58089 -185.30448
> postscript(file="/var/www/rcomp/tmp/6so1t1291648593.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 1016.02544 NA
1 920.12082 1016.02544
2 534.44906 920.12082
3 206.67482 534.44906
4 -36.31223 206.67482
5 -77.42644 -36.31223
6 -366.38421 -77.42644
7 -342.31952 -366.38421
8 -103.92768 -342.31952
9 -140.51624 -103.92768
10 -116.17300 -140.51624
11 -288.05960 -116.17300
12 -550.27164 -288.05960
13 -570.89676 -550.27164
14 -603.25135 -570.89676
15 -635.99490 -603.25135
16 -299.06800 -635.99490
17 -249.21942 -299.06800
18 -176.30824 -249.21942
19 -113.93261 -176.30824
20 -168.94283 -113.93261
21 81.68343 -168.94283
22 108.71191 81.68343
23 14.77159 108.71191
24 14.47588 14.77159
25 24.53618 14.47588
26 147.47451 24.53618
27 27.41144 147.47451
28 -173.92358 27.41144
29 -104.86693 -173.92358
30 126.23379 -104.86693
31 187.37422 126.23379
32 466.47589 187.37422
33 385.84401 466.47589
34 216.69117 385.84401
35 112.81903 216.69117
36 265.09145 112.81903
37 338.02859 265.09145
38 -13.46152 338.02859
39 159.88210 -13.46152
40 140.02641 159.88210
41 141.89984 140.02641
42 219.08183 141.89984
43 155.43784 219.08183
44 227.92395 155.43784
45 177.78748 227.92395
46 75.64628 177.78748
47 79.98255 75.64628
48 56.52410 79.98255
49 77.85306 56.52410
50 -82.26576 77.85306
51 -18.99993 -82.26576
52 -60.18334 -18.99993
53 -246.80823 -60.18334
54 -326.57238 -246.80823
55 -268.66376 -326.57238
56 -351.16333 -268.66376
57 -417.66779 -351.16333
58 -317.37479 -417.66779
59 -435.27492 -317.37479
60 -493.93129 -435.27492
61 -416.70040 -493.93129
62 -422.70782 -416.70040
63 -405.24974 -422.70782
64 -280.07137 -405.24974
65 -387.24432 -280.07137
66 -294.64319 -387.24432
67 -184.70773 -294.64319
68 -109.97629 -184.70773
69 94.39579 -109.97629
70 32.19364 94.39579
71 -26.59235 32.19364
72 40.32066 -26.59235
73 54.83788 40.32066
74 75.30952 54.83788
75 238.34386 75.30952
76 162.92138 238.34386
77 164.25885 162.92138
78 174.84153 164.25885
79 221.53671 174.84153
80 210.49775 221.53671
81 267.78363 210.49775
82 132.58939 267.78363
83 125.61829 132.58939
84 254.56359 125.61829
85 371.41916 254.56359
86 380.19307 371.41916
87 287.96117 380.19307
88 274.50381 287.96117
89 207.17446 274.50381
90 338.52808 207.17446
91 342.03014 338.52808
92 292.07899 342.03014
93 252.45221 292.07899
94 300.46388 252.45221
95 252.50535 300.46388
96 306.34313 252.50535
97 297.52104 306.34313
98 303.48116 297.52104
99 354.93888 303.48116
100 201.67879 354.93888
101 51.67543 201.67879
102 -86.09765 51.67543
103 -177.42305 -86.09765
104 -137.14150 -177.42305
105 -156.34556 -137.14150
106 -80.71231 -156.34556
107 -149.87060 -80.71231
108 116.05648 -149.87060
109 78.31744 116.05648
110 214.81210 78.31744
111 74.53353 214.81210
112 -75.85422 74.53353
113 -137.06386 -75.85422
114 -261.82288 -137.06386
115 -362.49552 -261.82288
116 -241.19675 -362.49552
117 -79.17463 -241.19675
118 -119.38760 -79.17463
119 -284.81479 -119.38760
120 -103.88425 -284.81479
121 183.59456 -103.88425
122 135.51172 183.59456
123 -54.11819 135.51172
124 -71.39357 -54.11819
125 -214.63144 -71.39357
126 -224.10100 -214.63144
127 -261.19918 -224.10100
128 -178.16150 -261.19918
129 -114.89485 -178.16150
130 -139.58089 -114.89485
131 -185.30448 -139.58089
132 NA -185.30448
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 920.12082 1016.02544
[2,] 534.44906 920.12082
[3,] 206.67482 534.44906
[4,] -36.31223 206.67482
[5,] -77.42644 -36.31223
[6,] -366.38421 -77.42644
[7,] -342.31952 -366.38421
[8,] -103.92768 -342.31952
[9,] -140.51624 -103.92768
[10,] -116.17300 -140.51624
[11,] -288.05960 -116.17300
[12,] -550.27164 -288.05960
[13,] -570.89676 -550.27164
[14,] -603.25135 -570.89676
[15,] -635.99490 -603.25135
[16,] -299.06800 -635.99490
[17,] -249.21942 -299.06800
[18,] -176.30824 -249.21942
[19,] -113.93261 -176.30824
[20,] -168.94283 -113.93261
[21,] 81.68343 -168.94283
[22,] 108.71191 81.68343
[23,] 14.77159 108.71191
[24,] 14.47588 14.77159
[25,] 24.53618 14.47588
[26,] 147.47451 24.53618
[27,] 27.41144 147.47451
[28,] -173.92358 27.41144
[29,] -104.86693 -173.92358
[30,] 126.23379 -104.86693
[31,] 187.37422 126.23379
[32,] 466.47589 187.37422
[33,] 385.84401 466.47589
[34,] 216.69117 385.84401
[35,] 112.81903 216.69117
[36,] 265.09145 112.81903
[37,] 338.02859 265.09145
[38,] -13.46152 338.02859
[39,] 159.88210 -13.46152
[40,] 140.02641 159.88210
[41,] 141.89984 140.02641
[42,] 219.08183 141.89984
[43,] 155.43784 219.08183
[44,] 227.92395 155.43784
[45,] 177.78748 227.92395
[46,] 75.64628 177.78748
[47,] 79.98255 75.64628
[48,] 56.52410 79.98255
[49,] 77.85306 56.52410
[50,] -82.26576 77.85306
[51,] -18.99993 -82.26576
[52,] -60.18334 -18.99993
[53,] -246.80823 -60.18334
[54,] -326.57238 -246.80823
[55,] -268.66376 -326.57238
[56,] -351.16333 -268.66376
[57,] -417.66779 -351.16333
[58,] -317.37479 -417.66779
[59,] -435.27492 -317.37479
[60,] -493.93129 -435.27492
[61,] -416.70040 -493.93129
[62,] -422.70782 -416.70040
[63,] -405.24974 -422.70782
[64,] -280.07137 -405.24974
[65,] -387.24432 -280.07137
[66,] -294.64319 -387.24432
[67,] -184.70773 -294.64319
[68,] -109.97629 -184.70773
[69,] 94.39579 -109.97629
[70,] 32.19364 94.39579
[71,] -26.59235 32.19364
[72,] 40.32066 -26.59235
[73,] 54.83788 40.32066
[74,] 75.30952 54.83788
[75,] 238.34386 75.30952
[76,] 162.92138 238.34386
[77,] 164.25885 162.92138
[78,] 174.84153 164.25885
[79,] 221.53671 174.84153
[80,] 210.49775 221.53671
[81,] 267.78363 210.49775
[82,] 132.58939 267.78363
[83,] 125.61829 132.58939
[84,] 254.56359 125.61829
[85,] 371.41916 254.56359
[86,] 380.19307 371.41916
[87,] 287.96117 380.19307
[88,] 274.50381 287.96117
[89,] 207.17446 274.50381
[90,] 338.52808 207.17446
[91,] 342.03014 338.52808
[92,] 292.07899 342.03014
[93,] 252.45221 292.07899
[94,] 300.46388 252.45221
[95,] 252.50535 300.46388
[96,] 306.34313 252.50535
[97,] 297.52104 306.34313
[98,] 303.48116 297.52104
[99,] 354.93888 303.48116
[100,] 201.67879 354.93888
[101,] 51.67543 201.67879
[102,] -86.09765 51.67543
[103,] -177.42305 -86.09765
[104,] -137.14150 -177.42305
[105,] -156.34556 -137.14150
[106,] -80.71231 -156.34556
[107,] -149.87060 -80.71231
[108,] 116.05648 -149.87060
[109,] 78.31744 116.05648
[110,] 214.81210 78.31744
[111,] 74.53353 214.81210
[112,] -75.85422 74.53353
[113,] -137.06386 -75.85422
[114,] -261.82288 -137.06386
[115,] -362.49552 -261.82288
[116,] -241.19675 -362.49552
[117,] -79.17463 -241.19675
[118,] -119.38760 -79.17463
[119,] -284.81479 -119.38760
[120,] -103.88425 -284.81479
[121,] 183.59456 -103.88425
[122,] 135.51172 183.59456
[123,] -54.11819 135.51172
[124,] -71.39357 -54.11819
[125,] -214.63144 -71.39357
[126,] -224.10100 -214.63144
[127,] -261.19918 -224.10100
[128,] -178.16150 -261.19918
[129,] -114.89485 -178.16150
[130,] -139.58089 -114.89485
[131,] -185.30448 -139.58089
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 920.12082 1016.02544
2 534.44906 920.12082
3 206.67482 534.44906
4 -36.31223 206.67482
5 -77.42644 -36.31223
6 -366.38421 -77.42644
7 -342.31952 -366.38421
8 -103.92768 -342.31952
9 -140.51624 -103.92768
10 -116.17300 -140.51624
11 -288.05960 -116.17300
12 -550.27164 -288.05960
13 -570.89676 -550.27164
14 -603.25135 -570.89676
15 -635.99490 -603.25135
16 -299.06800 -635.99490
17 -249.21942 -299.06800
18 -176.30824 -249.21942
19 -113.93261 -176.30824
20 -168.94283 -113.93261
21 81.68343 -168.94283
22 108.71191 81.68343
23 14.77159 108.71191
24 14.47588 14.77159
25 24.53618 14.47588
26 147.47451 24.53618
27 27.41144 147.47451
28 -173.92358 27.41144
29 -104.86693 -173.92358
30 126.23379 -104.86693
31 187.37422 126.23379
32 466.47589 187.37422
33 385.84401 466.47589
34 216.69117 385.84401
35 112.81903 216.69117
36 265.09145 112.81903
37 338.02859 265.09145
38 -13.46152 338.02859
39 159.88210 -13.46152
40 140.02641 159.88210
41 141.89984 140.02641
42 219.08183 141.89984
43 155.43784 219.08183
44 227.92395 155.43784
45 177.78748 227.92395
46 75.64628 177.78748
47 79.98255 75.64628
48 56.52410 79.98255
49 77.85306 56.52410
50 -82.26576 77.85306
51 -18.99993 -82.26576
52 -60.18334 -18.99993
53 -246.80823 -60.18334
54 -326.57238 -246.80823
55 -268.66376 -326.57238
56 -351.16333 -268.66376
57 -417.66779 -351.16333
58 -317.37479 -417.66779
59 -435.27492 -317.37479
60 -493.93129 -435.27492
61 -416.70040 -493.93129
62 -422.70782 -416.70040
63 -405.24974 -422.70782
64 -280.07137 -405.24974
65 -387.24432 -280.07137
66 -294.64319 -387.24432
67 -184.70773 -294.64319
68 -109.97629 -184.70773
69 94.39579 -109.97629
70 32.19364 94.39579
71 -26.59235 32.19364
72 40.32066 -26.59235
73 54.83788 40.32066
74 75.30952 54.83788
75 238.34386 75.30952
76 162.92138 238.34386
77 164.25885 162.92138
78 174.84153 164.25885
79 221.53671 174.84153
80 210.49775 221.53671
81 267.78363 210.49775
82 132.58939 267.78363
83 125.61829 132.58939
84 254.56359 125.61829
85 371.41916 254.56359
86 380.19307 371.41916
87 287.96117 380.19307
88 274.50381 287.96117
89 207.17446 274.50381
90 338.52808 207.17446
91 342.03014 338.52808
92 292.07899 342.03014
93 252.45221 292.07899
94 300.46388 252.45221
95 252.50535 300.46388
96 306.34313 252.50535
97 297.52104 306.34313
98 303.48116 297.52104
99 354.93888 303.48116
100 201.67879 354.93888
101 51.67543 201.67879
102 -86.09765 51.67543
103 -177.42305 -86.09765
104 -137.14150 -177.42305
105 -156.34556 -137.14150
106 -80.71231 -156.34556
107 -149.87060 -80.71231
108 116.05648 -149.87060
109 78.31744 116.05648
110 214.81210 78.31744
111 74.53353 214.81210
112 -75.85422 74.53353
113 -137.06386 -75.85422
114 -261.82288 -137.06386
115 -362.49552 -261.82288
116 -241.19675 -362.49552
117 -79.17463 -241.19675
118 -119.38760 -79.17463
119 -284.81479 -119.38760
120 -103.88425 -284.81479
121 183.59456 -103.88425
122 135.51172 183.59456
123 -54.11819 135.51172
124 -71.39357 -54.11819
125 -214.63144 -71.39357
126 -224.10100 -214.63144
127 -261.19918 -224.10100
128 -178.16150 -261.19918
129 -114.89485 -178.16150
130 -139.58089 -114.89485
131 -185.30448 -139.58089
> 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/73yiw1291648593.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/83yiw1291648593.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9wpiz1291648593.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10wpiz1291648593.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zqgn1291648593.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/12lqxb1291648593.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/139ru51291648593.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/14kitq1291648593.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/15njre1291648593.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/16kt7m1291648593.tab")
+ }
>
> try(system("convert tmp/17o3o1291648593.ps tmp/17o3o1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ixkr1291648593.ps tmp/2ixkr1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ixkr1291648593.ps tmp/3ixkr1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ixkr1291648593.ps tmp/4ixkr1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/5so1t1291648593.ps tmp/5so1t1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/6so1t1291648593.ps tmp/6so1t1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/73yiw1291648593.ps tmp/73yiw1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/83yiw1291648593.ps tmp/83yiw1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wpiz1291648593.ps tmp/9wpiz1291648593.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wpiz1291648593.ps tmp/10wpiz1291648593.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.320 1.810 6.187