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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+ ,9374.63
+ ,21467
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+ ,-11
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+ ,3.52
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+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
+ ,-11
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+ ,3.53
+ ,2513.17
+ ,10066.24
+ ,9857.34
+ ,22680
+ ,-14.2
+ ,-12
+ ,-1
+ ,3.55
+ ,2466.92
+ ,9633.83
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+ ,24320
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+ ,10169.02
+ ,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 = 'No 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
1 1.0 3.17
2 1.0 3.17
3 1.2 3.36
4 1.2 3.11
5 0.8 3.11
6 0.7 3.57
7 0.7 4.04
8 0.9 4.21
9 1.2 4.36
10 1.3 4.75
11 1.5 4.43
12 1.9 4.70
13 1.8 4.81
14 1.9 5.01
15 2.2 5.00
16 2.1 4.81
17 2.2 5.11
18 2.7 5.10
19 2.8 5.11
20 2.9 5.21
21 3.4 5.21
22 3.0 5.21
23 3.1 5.06
24 2.5 4.58
25 2.2 4.37
26 2.3 4.37
27 2.1 4.23
28 2.8 4.23
29 3.1 4.37
30 2.9 4.31
31 2.6 4.31
32 2.7 4.28
33 2.3 3.98
34 2.3 3.79
35 2.1 3.55
36 2.2 4.00
37 2.9 4.02
38 2.6 4.21
39 2.7 4.50
40 1.8 4.52
41 1.3 4.45
42 0.9 4.28
43 1.3 4.08
44 1.3 3.80
45 1.3 3.58
46 1.3 3.58
47 1.1 3.58
48 1.4 3.54
49 1.2 3.19
50 1.7 2.91
51 1.8 2.87
52 1.5 3.10
53 1.0 2.60
54 1.6 2.33
55 1.5 2.62
56 1.8 3.05
57 1.8 3.05
58 1.6 3.22
59 1.9 3.24
60 1.7 3.24
61 1.6 3.38
62 1.3 3.35
63 1.1 3.22
64 1.9 3.06
65 2.6 3.17
66 2.3 3.19
67 2.4 3.35
68 2.2 3.24
69 2.0 3.23
70 2.9 3.31
71 2.6 3.25
72 2.3 3.20
73 2.3 3.10
74 2.6 2.93
75 3.1 2.92
76 2.8 2.90
77 2.5 2.87
78 2.9 2.76
79 3.1 2.67
80 3.1 2.75
81 3.2 2.72
82 2.5 2.72
83 2.6 2.86
84 2.9 2.99
85 2.6 3.07
86 2.4 2.96
87 1.7 3.04
88 2.0 3.30
89 2.2 3.48
90 1.9 3.46
91 1.6 3.57
92 1.6 3.60
93 1.2 3.51
94 1.2 3.52
95 1.5 3.49
96 1.6 3.50
97 1.7 3.64
98 1.8 3.94
99 1.8 3.94
100 1.8 3.91
101 1.3 3.88
102 1.3 4.21
103 1.4 4.39
104 1.1 4.33
105 1.5 4.27
106 2.2 4.29
107 2.9 4.18
108 3.1 4.14
109 3.5 4.23
110 3.6 4.07
111 4.4 3.74
112 4.2 3.66
113 5.2 3.92
114 5.8 4.45
115 5.9 4.92
116 5.4 4.90
117 5.5 4.54
118 4.7 4.53
119 3.1 4.14
120 2.6 4.05
121 2.3 3.92
122 1.9 3.68
123 0.6 3.35
124 0.6 3.38
125 -0.4 3.44
126 -1.1 3.50
127 -1.7 3.54
128 -0.8 3.52
129 -1.2 3.53
130 -1.0 3.55
131 -0.1 3.37
132 0.3 3.36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-1.472e+03 9.544e-02 3.692e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
2.387e-02 -1.064e+01 1.341e+01
Alg_consumptie_index_BE Gem_rente_kasbon_5j
3.620e+01 -2.787e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-588.980 -164.852 9.807 168.140 701.834
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.472e+03 3.348e+02 -4.395 2.35e-05 ***
Nikkei 9.544e-02 1.489e-02 6.409 2.76e-09 ***
DJ_Indust 3.692e-01 3.450e-02 10.701 < 2e-16 ***
Goudprijs 2.387e-02 8.227e-03 2.902 0.00439 **
Conjunct_Seizoenzuiver -1.064e+01 6.686e+00 -1.591 0.11411
Cons_vertrouw 1.341e+01 6.245e+00 2.147 0.03375 *
Alg_consumptie_index_BE 3.620e+01 2.280e+01 1.588 0.11483
Gem_rente_kasbon_5j -2.787e+02 5.098e+01 -5.467 2.41e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 260.5 on 124 degrees of freedom
Multiple R-squared: 0.8867, Adjusted R-squared: 0.8803
F-statistic: 138.6 on 7 and 124 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,] 0.032399797 6.479959e-02 9.676002e-01
[2,] 0.015286326 3.057265e-02 9.847137e-01
[3,] 0.005793103 1.158621e-02 9.942069e-01
[4,] 0.014923705 2.984741e-02 9.850763e-01
[5,] 0.018304773 3.660955e-02 9.816952e-01
[6,] 0.014299780 2.859956e-02 9.857002e-01
[7,] 0.044519145 8.903829e-02 9.554809e-01
[8,] 0.039572910 7.914582e-02 9.604271e-01
[9,] 0.029343368 5.868674e-02 9.706566e-01
[10,] 0.024330821 4.866164e-02 9.756692e-01
[11,] 0.017431582 3.486316e-02 9.825684e-01
[12,] 0.010058098 2.011620e-02 9.899419e-01
[13,] 0.005554521 1.110904e-02 9.944455e-01
[14,] 0.008588467 1.717693e-02 9.914115e-01
[15,] 0.017570227 3.514045e-02 9.824298e-01
[16,] 0.019086711 3.817342e-02 9.809133e-01
[17,] 0.040983855 8.196771e-02 9.590161e-01
[18,] 0.073865638 1.477313e-01 9.261344e-01
[19,] 0.085281451 1.705629e-01 9.147185e-01
[20,] 0.079595662 1.591913e-01 9.204043e-01
[21,] 0.061833293 1.236666e-01 9.381667e-01
[22,] 0.043286341 8.657268e-02 9.567137e-01
[23,] 0.046884490 9.376898e-02 9.531155e-01
[24,] 0.039917747 7.983549e-02 9.600823e-01
[25,] 0.033405620 6.681124e-02 9.665944e-01
[26,] 0.024459360 4.891872e-02 9.755406e-01
[27,] 0.018512008 3.702402e-02 9.814880e-01
[28,] 0.019134386 3.826877e-02 9.808656e-01
[29,] 0.013817021 2.763404e-02 9.861830e-01
[30,] 0.015703907 3.140781e-02 9.842961e-01
[31,] 0.011722998 2.344600e-02 9.882770e-01
[32,] 0.012797340 2.559468e-02 9.872027e-01
[33,] 0.046238882 9.247776e-02 9.537611e-01
[34,] 0.111904818 2.238096e-01 8.880952e-01
[35,] 0.179435368 3.588707e-01 8.205646e-01
[36,] 0.298014518 5.960290e-01 7.019855e-01
[37,] 0.400206049 8.004121e-01 5.997940e-01
[38,] 0.434650696 8.693014e-01 5.653493e-01
[39,] 0.450983315 9.019666e-01 5.490167e-01
[40,] 0.414873421 8.297468e-01 5.851266e-01
[41,] 0.427696112 8.553922e-01 5.723039e-01
[42,] 0.660397133 6.792057e-01 3.396029e-01
[43,] 0.770506992 4.589860e-01 2.294930e-01
[44,] 0.883201438 2.335971e-01 1.167986e-01
[45,] 0.915391216 1.692176e-01 8.460878e-02
[46,] 0.902879799 1.942404e-01 9.712020e-02
[47,] 0.914623813 1.707524e-01 8.537619e-02
[48,] 0.927248530 1.455029e-01 7.275147e-02
[49,] 0.918582561 1.628349e-01 8.141744e-02
[50,] 0.920074722 1.598506e-01 7.992528e-02
[51,] 0.934684867 1.306303e-01 6.531513e-02
[52,] 0.931747434 1.365051e-01 6.825257e-02
[53,] 0.954696691 9.060662e-02 4.530331e-02
[54,] 0.991810971 1.637806e-02 8.189029e-03
[55,] 0.995975290 8.049420e-03 4.024710e-03
[56,] 0.999285333 1.429333e-03 7.146665e-04
[57,] 0.999818912 3.621751e-04 1.810876e-04
[58,] 0.999932644 1.347122e-04 6.735608e-05
[59,] 0.999975023 4.995423e-05 2.497712e-05
[60,] 0.999992098 1.580327e-05 7.901635e-06
[61,] 0.999995993 8.014742e-06 4.007371e-06
[62,] 0.999996772 6.456296e-06 3.228148e-06
[63,] 0.999997391 5.217105e-06 2.608552e-06
[64,] 0.999997809 4.381370e-06 2.190685e-06
[65,] 0.999998419 3.162856e-06 1.581428e-06
[66,] 0.999998661 2.677250e-06 1.338625e-06
[67,] 0.999998896 2.208648e-06 1.104324e-06
[68,] 0.999998977 2.046975e-06 1.023488e-06
[69,] 0.999998702 2.595113e-06 1.297556e-06
[70,] 0.999998495 3.010586e-06 1.505293e-06
[71,] 0.999998548 2.904873e-06 1.452437e-06
[72,] 0.999998517 2.966456e-06 1.483228e-06
[73,] 0.999998321 3.358840e-06 1.679420e-06
[74,] 0.999999552 8.960481e-07 4.480240e-07
[75,] 0.999999877 2.465761e-07 1.232880e-07
[76,] 0.999999934 1.319831e-07 6.599153e-08
[77,] 0.999999924 1.517605e-07 7.588024e-08
[78,] 0.999999968 6.318334e-08 3.159167e-08
[79,] 0.999999974 5.254889e-08 2.627445e-08
[80,] 0.999999984 3.236292e-08 1.618146e-08
[81,] 0.999999967 6.589440e-08 3.294720e-08
[82,] 0.999999932 1.350516e-07 6.752582e-08
[83,] 0.999999874 2.520925e-07 1.260462e-07
[84,] 0.999999892 2.163687e-07 1.081844e-07
[85,] 0.999999838 3.242362e-07 1.621181e-07
[86,] 0.999999695 6.096387e-07 3.048194e-07
[87,] 0.999999461 1.078367e-06 5.391835e-07
[88,] 0.999998633 2.734930e-06 1.367465e-06
[89,] 0.999996615 6.770734e-06 3.385367e-06
[90,] 0.999993309 1.338229e-05 6.691147e-06
[91,] 0.999986652 2.669610e-05 1.334805e-05
[92,] 0.999975180 4.963959e-05 2.481980e-05
[93,] 0.999958200 8.360008e-05 4.180004e-05
[94,] 0.999915599 1.688024e-04 8.440120e-05
[95,] 0.999876673 2.466547e-04 1.233274e-04
[96,] 0.999795378 4.092434e-04 2.046217e-04
[97,] 0.999784381 4.312386e-04 2.156193e-04
[98,] 0.999609742 7.805167e-04 3.902584e-04
[99,] 0.999970518 5.896489e-05 2.948245e-05
[100,] 0.999975906 4.818886e-05 2.409443e-05
[101,] 0.999951684 9.663210e-05 4.831605e-05
[102,] 0.999948156 1.036879e-04 5.184393e-05
[103,] 0.999924756 1.504883e-04 7.524414e-05
[104,] 0.999984790 3.041986e-05 1.520993e-05
[105,] 0.999936434 1.271327e-04 6.356637e-05
[106,] 0.999743598 5.128034e-04 2.564017e-04
[107,] 0.999483165 1.033671e-03 5.168355e-04
[108,] 0.997972045 4.055910e-03 2.027955e-03
[109,] 0.999485588 1.028823e-03 5.144117e-04
[110,] 0.997292038 5.415924e-03 2.707962e-03
[111,] 0.986326697 2.734661e-02 1.367330e-02
> postscript(file="/var/www/html/rcomp/tmp/1szs71291653073.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lras1291653073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lras1291653073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lras1291653073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e09d1291653073.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
701.833723 632.925149 276.764074 -115.052945 -326.781850 -76.041397
7 8 9 10 11 12
-326.713825 -215.947813 -9.849154 -1.572982 -111.511758 -218.367662
13 14 15 16 17 18
-463.036835 -466.119218 -513.930103 -588.979840 -180.511672 -174.979634
19 20 21 22 23 24
-84.639933 -46.454376 -61.244213 172.465894 158.015140 -26.488405
25 26 27 28 29 30
-9.422247 51.635456 166.698590 22.440602 -192.364629 -164.662041
31 32 33 34 35 36
104.053205 229.454726 498.055761 469.747317 290.010600 186.855071
37 38 39 40 41 42
213.807003 303.811937 33.318191 303.280603 294.448255 321.544676
43 44 45 46 47 48
396.177201 269.344269 284.030740 242.149775 164.700029 179.797486
49 50 51 52 53 54
57.945715 50.710447 -43.140485 26.466947 -159.160726 -469.493269
55 56 57 58 59 60
-415.440932 -287.158678 -461.380417 -298.799426 -330.022648 -433.469223
61 62 63 64 65 66
-488.638543 -404.885672 -411.807578 -477.905156 -276.086062 -446.587333
67 68 69 70 71 72
-231.229963 -181.059644 -94.196711 130.014793 62.182309 -2.103605
73 74 75 76 77 78
68.764927 -33.885732 -60.908435 153.552708 160.610198 70.312602
79 80 81 82 83 84
43.423436 101.795103 125.965548 133.989968 21.186477 -57.322767
85 86 87 88 89 90
22.233542 121.482768 250.796484 150.305347 145.528352 178.351639
91 92 93 94 95 96
315.825738 334.727604 322.038001 244.445796 228.487881 325.047620
97 98 99 100 101 102
305.831687 298.360100 370.317573 361.348123 237.584648 226.856220
103 104 105 106 107 108
161.938654 88.164026 33.852135 -90.887101 -9.443594 -170.692070
109 110 111 112 113 114
66.934092 -103.873072 -93.755193 -119.928634 -202.844752 -94.171921
115 116 117 118 119 120
-93.347586 -121.435844 -165.423414 113.289619 137.057284 -51.853329
121 122 123 124 125 126
-31.143497 90.498277 77.434161 -69.391562 -53.852206 -121.583361
127 128 129 130 131 132
-63.622974 -239.789569 -166.313909 -120.872897 -357.962670 -401.479329
> postscript(file="/var/www/html/rcomp/tmp/6e09d1291653073.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
0 701.833723 NA
1 632.925149 701.833723
2 276.764074 632.925149
3 -115.052945 276.764074
4 -326.781850 -115.052945
5 -76.041397 -326.781850
6 -326.713825 -76.041397
7 -215.947813 -326.713825
8 -9.849154 -215.947813
9 -1.572982 -9.849154
10 -111.511758 -1.572982
11 -218.367662 -111.511758
12 -463.036835 -218.367662
13 -466.119218 -463.036835
14 -513.930103 -466.119218
15 -588.979840 -513.930103
16 -180.511672 -588.979840
17 -174.979634 -180.511672
18 -84.639933 -174.979634
19 -46.454376 -84.639933
20 -61.244213 -46.454376
21 172.465894 -61.244213
22 158.015140 172.465894
23 -26.488405 158.015140
24 -9.422247 -26.488405
25 51.635456 -9.422247
26 166.698590 51.635456
27 22.440602 166.698590
28 -192.364629 22.440602
29 -164.662041 -192.364629
30 104.053205 -164.662041
31 229.454726 104.053205
32 498.055761 229.454726
33 469.747317 498.055761
34 290.010600 469.747317
35 186.855071 290.010600
36 213.807003 186.855071
37 303.811937 213.807003
38 33.318191 303.811937
39 303.280603 33.318191
40 294.448255 303.280603
41 321.544676 294.448255
42 396.177201 321.544676
43 269.344269 396.177201
44 284.030740 269.344269
45 242.149775 284.030740
46 164.700029 242.149775
47 179.797486 164.700029
48 57.945715 179.797486
49 50.710447 57.945715
50 -43.140485 50.710447
51 26.466947 -43.140485
52 -159.160726 26.466947
53 -469.493269 -159.160726
54 -415.440932 -469.493269
55 -287.158678 -415.440932
56 -461.380417 -287.158678
57 -298.799426 -461.380417
58 -330.022648 -298.799426
59 -433.469223 -330.022648
60 -488.638543 -433.469223
61 -404.885672 -488.638543
62 -411.807578 -404.885672
63 -477.905156 -411.807578
64 -276.086062 -477.905156
65 -446.587333 -276.086062
66 -231.229963 -446.587333
67 -181.059644 -231.229963
68 -94.196711 -181.059644
69 130.014793 -94.196711
70 62.182309 130.014793
71 -2.103605 62.182309
72 68.764927 -2.103605
73 -33.885732 68.764927
74 -60.908435 -33.885732
75 153.552708 -60.908435
76 160.610198 153.552708
77 70.312602 160.610198
78 43.423436 70.312602
79 101.795103 43.423436
80 125.965548 101.795103
81 133.989968 125.965548
82 21.186477 133.989968
83 -57.322767 21.186477
84 22.233542 -57.322767
85 121.482768 22.233542
86 250.796484 121.482768
87 150.305347 250.796484
88 145.528352 150.305347
89 178.351639 145.528352
90 315.825738 178.351639
91 334.727604 315.825738
92 322.038001 334.727604
93 244.445796 322.038001
94 228.487881 244.445796
95 325.047620 228.487881
96 305.831687 325.047620
97 298.360100 305.831687
98 370.317573 298.360100
99 361.348123 370.317573
100 237.584648 361.348123
101 226.856220 237.584648
102 161.938654 226.856220
103 88.164026 161.938654
104 33.852135 88.164026
105 -90.887101 33.852135
106 -9.443594 -90.887101
107 -170.692070 -9.443594
108 66.934092 -170.692070
109 -103.873072 66.934092
110 -93.755193 -103.873072
111 -119.928634 -93.755193
112 -202.844752 -119.928634
113 -94.171921 -202.844752
114 -93.347586 -94.171921
115 -121.435844 -93.347586
116 -165.423414 -121.435844
117 113.289619 -165.423414
118 137.057284 113.289619
119 -51.853329 137.057284
120 -31.143497 -51.853329
121 90.498277 -31.143497
122 77.434161 90.498277
123 -69.391562 77.434161
124 -53.852206 -69.391562
125 -121.583361 -53.852206
126 -63.622974 -121.583361
127 -239.789569 -63.622974
128 -166.313909 -239.789569
129 -120.872897 -166.313909
130 -357.962670 -120.872897
131 -401.479329 -357.962670
132 NA -401.479329
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 632.925149 701.833723
[2,] 276.764074 632.925149
[3,] -115.052945 276.764074
[4,] -326.781850 -115.052945
[5,] -76.041397 -326.781850
[6,] -326.713825 -76.041397
[7,] -215.947813 -326.713825
[8,] -9.849154 -215.947813
[9,] -1.572982 -9.849154
[10,] -111.511758 -1.572982
[11,] -218.367662 -111.511758
[12,] -463.036835 -218.367662
[13,] -466.119218 -463.036835
[14,] -513.930103 -466.119218
[15,] -588.979840 -513.930103
[16,] -180.511672 -588.979840
[17,] -174.979634 -180.511672
[18,] -84.639933 -174.979634
[19,] -46.454376 -84.639933
[20,] -61.244213 -46.454376
[21,] 172.465894 -61.244213
[22,] 158.015140 172.465894
[23,] -26.488405 158.015140
[24,] -9.422247 -26.488405
[25,] 51.635456 -9.422247
[26,] 166.698590 51.635456
[27,] 22.440602 166.698590
[28,] -192.364629 22.440602
[29,] -164.662041 -192.364629
[30,] 104.053205 -164.662041
[31,] 229.454726 104.053205
[32,] 498.055761 229.454726
[33,] 469.747317 498.055761
[34,] 290.010600 469.747317
[35,] 186.855071 290.010600
[36,] 213.807003 186.855071
[37,] 303.811937 213.807003
[38,] 33.318191 303.811937
[39,] 303.280603 33.318191
[40,] 294.448255 303.280603
[41,] 321.544676 294.448255
[42,] 396.177201 321.544676
[43,] 269.344269 396.177201
[44,] 284.030740 269.344269
[45,] 242.149775 284.030740
[46,] 164.700029 242.149775
[47,] 179.797486 164.700029
[48,] 57.945715 179.797486
[49,] 50.710447 57.945715
[50,] -43.140485 50.710447
[51,] 26.466947 -43.140485
[52,] -159.160726 26.466947
[53,] -469.493269 -159.160726
[54,] -415.440932 -469.493269
[55,] -287.158678 -415.440932
[56,] -461.380417 -287.158678
[57,] -298.799426 -461.380417
[58,] -330.022648 -298.799426
[59,] -433.469223 -330.022648
[60,] -488.638543 -433.469223
[61,] -404.885672 -488.638543
[62,] -411.807578 -404.885672
[63,] -477.905156 -411.807578
[64,] -276.086062 -477.905156
[65,] -446.587333 -276.086062
[66,] -231.229963 -446.587333
[67,] -181.059644 -231.229963
[68,] -94.196711 -181.059644
[69,] 130.014793 -94.196711
[70,] 62.182309 130.014793
[71,] -2.103605 62.182309
[72,] 68.764927 -2.103605
[73,] -33.885732 68.764927
[74,] -60.908435 -33.885732
[75,] 153.552708 -60.908435
[76,] 160.610198 153.552708
[77,] 70.312602 160.610198
[78,] 43.423436 70.312602
[79,] 101.795103 43.423436
[80,] 125.965548 101.795103
[81,] 133.989968 125.965548
[82,] 21.186477 133.989968
[83,] -57.322767 21.186477
[84,] 22.233542 -57.322767
[85,] 121.482768 22.233542
[86,] 250.796484 121.482768
[87,] 150.305347 250.796484
[88,] 145.528352 150.305347
[89,] 178.351639 145.528352
[90,] 315.825738 178.351639
[91,] 334.727604 315.825738
[92,] 322.038001 334.727604
[93,] 244.445796 322.038001
[94,] 228.487881 244.445796
[95,] 325.047620 228.487881
[96,] 305.831687 325.047620
[97,] 298.360100 305.831687
[98,] 370.317573 298.360100
[99,] 361.348123 370.317573
[100,] 237.584648 361.348123
[101,] 226.856220 237.584648
[102,] 161.938654 226.856220
[103,] 88.164026 161.938654
[104,] 33.852135 88.164026
[105,] -90.887101 33.852135
[106,] -9.443594 -90.887101
[107,] -170.692070 -9.443594
[108,] 66.934092 -170.692070
[109,] -103.873072 66.934092
[110,] -93.755193 -103.873072
[111,] -119.928634 -93.755193
[112,] -202.844752 -119.928634
[113,] -94.171921 -202.844752
[114,] -93.347586 -94.171921
[115,] -121.435844 -93.347586
[116,] -165.423414 -121.435844
[117,] 113.289619 -165.423414
[118,] 137.057284 113.289619
[119,] -51.853329 137.057284
[120,] -31.143497 -51.853329
[121,] 90.498277 -31.143497
[122,] 77.434161 90.498277
[123,] -69.391562 77.434161
[124,] -53.852206 -69.391562
[125,] -121.583361 -53.852206
[126,] -63.622974 -121.583361
[127,] -239.789569 -63.622974
[128,] -166.313909 -239.789569
[129,] -120.872897 -166.313909
[130,] -357.962670 -120.872897
[131,] -401.479329 -357.962670
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 632.925149 701.833723
2 276.764074 632.925149
3 -115.052945 276.764074
4 -326.781850 -115.052945
5 -76.041397 -326.781850
6 -326.713825 -76.041397
7 -215.947813 -326.713825
8 -9.849154 -215.947813
9 -1.572982 -9.849154
10 -111.511758 -1.572982
11 -218.367662 -111.511758
12 -463.036835 -218.367662
13 -466.119218 -463.036835
14 -513.930103 -466.119218
15 -588.979840 -513.930103
16 -180.511672 -588.979840
17 -174.979634 -180.511672
18 -84.639933 -174.979634
19 -46.454376 -84.639933
20 -61.244213 -46.454376
21 172.465894 -61.244213
22 158.015140 172.465894
23 -26.488405 158.015140
24 -9.422247 -26.488405
25 51.635456 -9.422247
26 166.698590 51.635456
27 22.440602 166.698590
28 -192.364629 22.440602
29 -164.662041 -192.364629
30 104.053205 -164.662041
31 229.454726 104.053205
32 498.055761 229.454726
33 469.747317 498.055761
34 290.010600 469.747317
35 186.855071 290.010600
36 213.807003 186.855071
37 303.811937 213.807003
38 33.318191 303.811937
39 303.280603 33.318191
40 294.448255 303.280603
41 321.544676 294.448255
42 396.177201 321.544676
43 269.344269 396.177201
44 284.030740 269.344269
45 242.149775 284.030740
46 164.700029 242.149775
47 179.797486 164.700029
48 57.945715 179.797486
49 50.710447 57.945715
50 -43.140485 50.710447
51 26.466947 -43.140485
52 -159.160726 26.466947
53 -469.493269 -159.160726
54 -415.440932 -469.493269
55 -287.158678 -415.440932
56 -461.380417 -287.158678
57 -298.799426 -461.380417
58 -330.022648 -298.799426
59 -433.469223 -330.022648
60 -488.638543 -433.469223
61 -404.885672 -488.638543
62 -411.807578 -404.885672
63 -477.905156 -411.807578
64 -276.086062 -477.905156
65 -446.587333 -276.086062
66 -231.229963 -446.587333
67 -181.059644 -231.229963
68 -94.196711 -181.059644
69 130.014793 -94.196711
70 62.182309 130.014793
71 -2.103605 62.182309
72 68.764927 -2.103605
73 -33.885732 68.764927
74 -60.908435 -33.885732
75 153.552708 -60.908435
76 160.610198 153.552708
77 70.312602 160.610198
78 43.423436 70.312602
79 101.795103 43.423436
80 125.965548 101.795103
81 133.989968 125.965548
82 21.186477 133.989968
83 -57.322767 21.186477
84 22.233542 -57.322767
85 121.482768 22.233542
86 250.796484 121.482768
87 150.305347 250.796484
88 145.528352 150.305347
89 178.351639 145.528352
90 315.825738 178.351639
91 334.727604 315.825738
92 322.038001 334.727604
93 244.445796 322.038001
94 228.487881 244.445796
95 325.047620 228.487881
96 305.831687 325.047620
97 298.360100 305.831687
98 370.317573 298.360100
99 361.348123 370.317573
100 237.584648 361.348123
101 226.856220 237.584648
102 161.938654 226.856220
103 88.164026 161.938654
104 33.852135 88.164026
105 -90.887101 33.852135
106 -9.443594 -90.887101
107 -170.692070 -9.443594
108 66.934092 -170.692070
109 -103.873072 66.934092
110 -93.755193 -103.873072
111 -119.928634 -93.755193
112 -202.844752 -119.928634
113 -94.171921 -202.844752
114 -93.347586 -94.171921
115 -121.435844 -93.347586
116 -165.423414 -121.435844
117 113.289619 -165.423414
118 137.057284 113.289619
119 -51.853329 137.057284
120 -31.143497 -51.853329
121 90.498277 -31.143497
122 77.434161 90.498277
123 -69.391562 77.434161
124 -53.852206 -69.391562
125 -121.583361 -53.852206
126 -63.622974 -121.583361
127 -239.789569 -63.622974
128 -166.313909 -239.789569
129 -120.872897 -166.313909
130 -357.962670 -120.872897
131 -401.479329 -357.962670
> 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/7798y1291653073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8798y1291653073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ziq11291653073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ziq11291653073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o28m1291653074.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/12gt7p1291653074.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/13nc411291653074.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/149dlp1291653074.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/15udjv1291653074.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/16xwij1291653074.tab")
+ }
>
> try(system("convert tmp/1szs71291653073.ps tmp/1szs71291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lras1291653073.ps tmp/2lras1291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lras1291653073.ps tmp/3lras1291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lras1291653073.ps tmp/4lras1291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e09d1291653073.ps tmp/5e09d1291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e09d1291653073.ps tmp/6e09d1291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/7798y1291653073.ps tmp/7798y1291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/8798y1291653073.ps tmp/8798y1291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ziq11291653073.ps tmp/9ziq11291653073.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ziq11291653073.ps tmp/10ziq11291653073.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.798 1.756 8.690