R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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'help.start()' for an HTML browser interface to help.
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> x <- array(list(3484.74
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+ ,2029.6
+ ,9810.31
+ ,8593
+ ,21777
+ ,-23.6
+ ,-18
+ ,-1.1
+ ,3.5
+ ,1.98
+ ,2070.83
+ ,9691.12
+ ,8679.75
+ ,21383
+ ,-22.8
+ ,-17
+ ,-1.7
+ ,3.54
+ ,1.98
+ ,2293.41
+ ,10430.35
+ ,9374.63
+ ,21467
+ ,-18.2
+ ,-11
+ ,-0.8
+ ,3.52
+ ,1.85
+ ,2443.27
+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
+ ,-11
+ ,-1.2
+ ,3.53
+ ,1.82
+ ,2513.17
+ ,10066.24
+ ,9857.34
+ ,22680
+ ,-14.2
+ ,-12
+ ,-1
+ ,3.55
+ ,1.65
+ ,2466.92
+ ,9633.83
+ ,10238.83
+ ,24320
+ ,-8.8
+ ,-10
+ ,-0.1
+ ,3.37
+ ,1.59
+ ,2502.66
+ ,10169.02
+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,3.36
+ ,1.56)
+ ,dim=c(9
+ ,132)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_5j'
+ ,'Gem_rente_kasbon_1j')
+ ,1:132))
> y <- array(NA,dim=c(9,132),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_5j','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 = '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
> 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 Gem_rente_kasbon_1j
1 1.0 3.17 2.77
2 1.0 3.17 2.76
3 1.2 3.36 2.76
4 1.2 3.11 2.46
5 0.8 3.11 2.46
6 0.7 3.57 2.47
7 0.7 4.04 2.71
8 0.9 4.21 2.80
9 1.2 4.36 2.89
10 1.3 4.75 3.36
11 1.5 4.43 3.31
12 1.9 4.70 3.50
13 1.8 4.81 3.51
14 1.9 5.01 3.71
15 2.2 5.00 3.71
16 2.1 4.81 3.71
17 2.2 5.11 4.21
18 2.7 5.10 4.21
19 2.8 5.11 4.21
20 2.9 5.21 4.50
21 3.4 5.21 4.51
22 3.0 5.21 4.51
23 3.1 5.06 4.51
24 2.5 4.58 4.32
25 2.2 4.37 4.02
26 2.3 4.37 4.02
27 2.1 4.23 3.85
28 2.8 4.23 3.84
29 3.1 4.37 4.02
30 2.9 4.31 3.82
31 2.6 4.31 3.75
32 2.7 4.28 3.74
33 2.3 3.98 3.14
34 2.3 3.79 2.91
35 2.1 3.55 2.84
36 2.2 4.00 2.85
37 2.9 4.02 2.85
38 2.6 4.21 3.08
39 2.7 4.50 3.30
40 1.8 4.52 3.29
41 1.3 4.45 3.26
42 0.9 4.28 3.26
43 1.3 4.08 3.11
44 1.3 3.80 2.84
45 1.3 3.58 2.71
46 1.3 3.58 2.69
47 1.1 3.58 2.65
48 1.4 3.54 2.57
49 1.2 3.19 2.32
50 1.7 2.91 2.12
51 1.8 2.87 2.05
52 1.5 3.10 2.05
53 1.0 2.60 1.81
54 1.6 2.33 1.58
55 1.5 2.62 1.57
56 1.8 3.05 1.76
57 1.8 3.05 1.76
58 1.6 3.22 1.89
59 1.9 3.24 1.90
60 1.7 3.24 1.90
61 1.6 3.38 1.92
62 1.3 3.35 1.76
63 1.1 3.22 1.64
64 1.9 3.06 1.57
65 2.6 3.17 1.69
66 2.3 3.19 1.76
67 2.4 3.35 1.89
68 2.2 3.24 1.78
69 2.0 3.23 1.88
70 2.9 3.31 1.86
71 2.6 3.25 1.88
72 2.3 3.20 1.87
73 2.3 3.10 1.86
74 2.6 2.93 1.89
75 3.1 2.92 1.90
76 2.8 2.90 1.89
77 2.5 2.87 1.85
78 2.9 2.76 1.78
79 3.1 2.67 1.71
80 3.1 2.75 1.69
81 3.2 2.72 1.72
82 2.5 2.72 1.77
83 2.6 2.86 1.98
84 2.9 2.99 2.20
85 2.6 3.07 2.25
86 2.4 2.96 2.24
87 1.7 3.04 2.51
88 2.0 3.30 2.79
89 2.2 3.48 3.07
90 1.9 3.46 3.08
91 1.6 3.57 3.05
92 1.6 3.60 3.08
93 1.2 3.51 3.15
94 1.2 3.52 3.16
95 1.5 3.49 3.16
96 1.6 3.50 3.19
97 1.7 3.64 3.44
98 1.8 3.94 3.55
99 1.8 3.94 3.60
100 1.8 3.91 3.62
101 1.3 3.88 3.69
102 1.3 4.21 3.99
103 1.4 4.39 4.06
104 1.1 4.33 4.05
105 1.5 4.27 4.01
106 2.2 4.29 3.98
107 2.9 4.18 3.94
108 3.1 4.14 3.92
109 3.5 4.23 4.10
110 3.6 4.07 3.88
111 4.4 3.74 3.74
112 4.2 3.66 3.97
113 5.2 3.92 4.26
114 5.8 4.45 4.63
115 5.9 4.92 4.82
116 5.4 4.90 4.94
117 5.5 4.54 4.98
118 4.7 4.53 5.02
119 3.1 4.14 4.96
120 2.6 4.05 4.49
121 2.3 3.92 3.50
122 1.9 3.68 2.95
123 0.6 3.35 2.37
124 0.6 3.38 2.16
125 -0.4 3.44 2.08
126 -1.1 3.50 1.98
127 -1.7 3.54 1.98
128 -0.8 3.52 1.85
129 -1.2 3.53 1.82
130 -1.0 3.55 1.65
131 -0.1 3.37 1.59
132 0.3 3.36 1.56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-318.79505 0.07569 0.34091
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
0.01410 -1.85648 10.98881
Alg_consumptie_index_BE Gem_rente_kasbon_5j Gem_rente_kasbon_1j
-49.17259 -618.45480 340.99304
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-576.35 -142.03 5.18 157.99 475.00
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.188e+02 3.342e+02 -0.954 0.3421
Nikkei 7.569e-02 1.311e-02 5.776 5.90e-08 ***
DJ_Indust 3.409e-01 2.989e-02 11.406 < 2e-16 ***
Goudprijs 1.410e-02 7.205e-03 1.957 0.0526 .
Conjunct_Seizoenzuiver -1.856e+00 5.882e+00 -0.316 0.7528
Cons_vertrouw 1.099e+01 5.370e+00 2.046 0.0428 *
Alg_consumptie_index_BE -4.917e+01 2.330e+01 -2.111 0.0368 *
Gem_rente_kasbon_5j -6.185e+02 6.671e+01 -9.271 7.76e-16 ***
Gem_rente_kasbon_1j 3.410e+02 5.056e+01 6.745 5.32e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 223.5 on 123 degrees of freedom
Multiple R-squared: 0.9173, Adjusted R-squared: 0.9119
F-statistic: 170.5 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,] 0.02211404 4.422808e-02 9.778860e-01
[2,] 0.01012116 2.024232e-02 9.898788e-01
[3,] 0.05552728 1.110546e-01 9.444727e-01
[4,] 0.06076314 1.215263e-01 9.392369e-01
[5,] 0.04469262 8.938523e-02 9.553074e-01
[6,] 0.09034604 1.806921e-01 9.096540e-01
[7,] 0.08478905 1.695781e-01 9.152109e-01
[8,] 0.05851545 1.170309e-01 9.414846e-01
[9,] 0.04793917 9.587833e-02 9.520608e-01
[10,] 0.03208048 6.416096e-02 9.679195e-01
[11,] 0.01885244 3.770488e-02 9.811476e-01
[12,] 0.01076548 2.153096e-02 9.892345e-01
[13,] 0.01185813 2.371626e-02 9.881419e-01
[14,] 0.02341840 4.683681e-02 9.765816e-01
[15,] 0.02443423 4.886847e-02 9.755658e-01
[16,] 0.06664193 1.332839e-01 9.333581e-01
[17,] 0.12693376 2.538675e-01 8.730662e-01
[18,] 0.14698406 2.939681e-01 8.530159e-01
[19,] 0.13878482 2.775696e-01 8.612152e-01
[20,] 0.10833687 2.166737e-01 8.916631e-01
[21,] 0.07894484 1.578897e-01 9.210552e-01
[22,] 0.09364214 1.872843e-01 9.063579e-01
[23,] 0.08680033 1.736007e-01 9.131997e-01
[24,] 0.07590427 1.518085e-01 9.240957e-01
[25,] 0.06032562 1.206512e-01 9.396744e-01
[26,] 0.04948523 9.897046e-02 9.505148e-01
[27,] 0.06067394 1.213479e-01 9.393261e-01
[28,] 0.04674995 9.349990e-02 9.532501e-01
[29,] 0.05847462 1.169492e-01 9.415254e-01
[30,] 0.05086743 1.017349e-01 9.491326e-01
[31,] 0.06118640 1.223728e-01 9.388136e-01
[32,] 0.17267832 3.453566e-01 8.273217e-01
[33,] 0.31904929 6.380986e-01 6.809507e-01
[34,] 0.41760570 8.352114e-01 5.823943e-01
[35,] 0.55011243 8.997751e-01 4.498876e-01
[36,] 0.62939335 7.412133e-01 3.706067e-01
[37,] 0.64622076 7.075585e-01 3.537792e-01
[38,] 0.63689477 7.262105e-01 3.631052e-01
[39,] 0.58501431 8.299714e-01 4.149857e-01
[40,] 0.58918596 8.216281e-01 4.108140e-01
[41,] 0.73001125 5.399775e-01 2.699887e-01
[42,] 0.78711464 4.257707e-01 2.128854e-01
[43,] 0.89846645 2.030671e-01 1.015335e-01
[44,] 0.93679976 1.264005e-01 6.320024e-02
[45,] 0.92815686 1.436863e-01 7.184314e-02
[46,] 0.94713514 1.057297e-01 5.286486e-02
[47,] 0.95999905 8.000191e-02 4.000095e-02
[48,] 0.96610941 6.778118e-02 3.389059e-02
[49,] 0.97438744 5.122512e-02 2.561256e-02
[50,] 0.98079463 3.841073e-02 1.920537e-02
[51,] 0.98007954 3.984092e-02 1.992046e-02
[52,] 0.99014619 1.970763e-02 9.853813e-03
[53,] 0.99925317 1.493654e-03 7.468269e-04
[54,] 0.99979683 4.063467e-04 2.031733e-04
[55,] 0.99998521 2.957630e-05 1.478815e-05
[56,] 0.99999734 5.314991e-06 2.657495e-06
[57,] 0.99999931 1.372744e-06 6.863721e-07
[58,] 0.99999984 3.226657e-07 1.613328e-07
[59,] 0.99999997 6.158668e-08 3.079334e-08
[60,] 0.99999998 3.138697e-08 1.569348e-08
[61,] 0.99999998 3.195087e-08 1.597544e-08
[62,] 0.99999998 3.141806e-08 1.570903e-08
[63,] 0.99999999 2.848915e-08 1.424458e-08
[64,] 0.99999999 2.340156e-08 1.170078e-08
[65,] 0.99999999 1.896178e-08 9.480891e-09
[66,] 0.99999999 1.652630e-08 8.263151e-09
[67,] 0.99999999 2.004915e-08 1.002457e-08
[68,] 0.99999999 2.913676e-08 1.456838e-08
[69,] 0.99999999 1.417426e-08 7.087131e-09
[70,] 1.00000000 3.390683e-09 1.695341e-09
[71,] 1.00000000 7.859277e-10 3.929639e-10
[72,] 1.00000000 6.007477e-10 3.003738e-10
[73,] 1.00000000 1.581002e-09 7.905010e-10
[74,] 1.00000000 3.303769e-09 1.651885e-09
[75,] 1.00000000 8.035250e-09 4.017625e-09
[76,] 0.99999999 2.042261e-08 1.021131e-08
[77,] 1.00000000 3.590118e-09 1.795059e-09
[78,] 1.00000000 1.730826e-10 8.654129e-11
[79,] 1.00000000 2.824140e-10 1.412070e-10
[80,] 1.00000000 8.536927e-10 4.268463e-10
[81,] 1.00000000 2.512265e-09 1.256133e-09
[82,] 1.00000000 7.480090e-09 3.740045e-09
[83,] 0.99999999 1.208888e-08 6.044438e-09
[84,] 0.99999998 3.371486e-08 1.685743e-08
[85,] 0.99999996 7.322816e-08 3.661408e-08
[86,] 0.99999990 1.970632e-07 9.853160e-08
[87,] 0.99999977 4.647028e-07 2.323514e-07
[88,] 0.99999940 1.195863e-06 5.979316e-07
[89,] 0.99999868 2.633280e-06 1.316640e-06
[90,] 0.99999658 6.841783e-06 3.420892e-06
[91,] 0.99999147 1.705196e-05 8.525980e-06
[92,] 0.99997887 4.225752e-05 2.112876e-05
[93,] 0.99995325 9.350130e-05 4.675065e-05
[94,] 0.99991689 1.662291e-04 8.311454e-05
[95,] 0.99983366 3.326898e-04 1.663449e-04
[96,] 0.99981759 3.648237e-04 1.824119e-04
[97,] 0.99963966 7.206754e-04 3.603377e-04
[98,] 0.99992618 1.476343e-04 7.381713e-05
[99,] 0.99984505 3.099079e-04 1.549540e-04
[100,] 0.99968800 6.239910e-04 3.119955e-04
[101,] 0.99968784 6.243202e-04 3.121601e-04
[102,] 0.99961854 7.629214e-04 3.814607e-04
[103,] 0.99989677 2.064507e-04 1.032254e-04
[104,] 0.99961082 7.783660e-04 3.891830e-04
[105,] 0.99864263 2.714732e-03 1.357366e-03
[106,] 0.99699406 6.011873e-03 3.005936e-03
[107,] 0.98983478 2.033043e-02 1.016522e-02
[108,] 0.99785214 4.295714e-03 2.147857e-03
[109,] 0.99222162 1.555676e-02 7.778378e-03
> postscript(file="/var/www/rcomp/tmp/1oawo1291641451.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2oawo1291641451.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zjvr1291641451.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4zjvr1291641451.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5zjvr1291641451.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
445.745492 386.607210 156.545931 -223.361722 -473.257133 -89.961419
7 8 9 10 11 12
-224.917473 -88.117439 148.178477 117.825215 -37.307285 -92.241505
13 14 15 16 17 18
-278.370238 -278.564433 -313.592388 -450.874935 -147.991995 -110.601072
19 20 21 22 23 24
9.553163 -1.123523 1.873256 193.224911 134.052063 -208.690149
25 26 27 28 29 30
-198.976467 -132.350392 -38.799354 -61.091504 -239.028557 -182.437821
31 32 33 34 35 36
68.856316 171.271165 468.526225 475.002150 226.869608 293.857296
37 38 39 40 41 42
379.640414 433.350444 211.398716 394.306069 323.699011 225.113082
43 44 45 46 47 48
284.529457 154.126424 118.384943 79.601959 2.526384 53.091568
49 50 51 52 53 54
-106.175503 -128.842077 -192.808192 -41.850979 -354.542837 -576.354692
55 56 57 58 59 60
-432.275053 -207.306829 -369.735134 -243.712780 -252.836792 -368.812403
61 62 63 64 65 66
-359.263385 -265.902313 -294.453350 -325.904339 -116.990419 -293.936617
67 68 69 70 71 72
-107.935667 -56.791471 -24.121988 301.271642 206.026840 128.510958
73 74 75 76 77 78
157.062290 43.840157 65.324632 248.684078 225.736871 167.481242
79 80 81 82 83 84
164.903335 265.339931 253.534772 189.801110 84.492448 39.228065
85 86 87 88 89 90
126.805514 166.798968 161.475871 95.953923 63.991744 7.829017
91 92 93 94 95 96
167.821967 214.013324 125.360763 67.042795 79.419782 160.764947
97 98 99 100 101 102
152.958009 254.539934 287.807557 273.313689 83.150516 72.447485
103 104 105 106 107 108
63.691969 -77.040395 -89.402022 -85.152582 -25.643113 -140.627735
109 110 111 112 113 114
35.710746 -96.482461 -110.496065 -221.698996 -240.118809 -35.440942
115 116 117 118 119 120
50.396605 -76.049191 -221.852201 -98.626238 -297.307116 -343.354935
121 122 123 124 125 126
-49.932684 161.788041 109.006913 57.953662 50.415495 -41.293554
127 128 129 130 131 132
-29.330963 -81.957718 -21.962361 79.029205 -146.229147 -146.274911
> postscript(file="/var/www/rcomp/tmp/6fv201291641451.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
0 445.745492 NA
1 386.607210 445.745492
2 156.545931 386.607210
3 -223.361722 156.545931
4 -473.257133 -223.361722
5 -89.961419 -473.257133
6 -224.917473 -89.961419
7 -88.117439 -224.917473
8 148.178477 -88.117439
9 117.825215 148.178477
10 -37.307285 117.825215
11 -92.241505 -37.307285
12 -278.370238 -92.241505
13 -278.564433 -278.370238
14 -313.592388 -278.564433
15 -450.874935 -313.592388
16 -147.991995 -450.874935
17 -110.601072 -147.991995
18 9.553163 -110.601072
19 -1.123523 9.553163
20 1.873256 -1.123523
21 193.224911 1.873256
22 134.052063 193.224911
23 -208.690149 134.052063
24 -198.976467 -208.690149
25 -132.350392 -198.976467
26 -38.799354 -132.350392
27 -61.091504 -38.799354
28 -239.028557 -61.091504
29 -182.437821 -239.028557
30 68.856316 -182.437821
31 171.271165 68.856316
32 468.526225 171.271165
33 475.002150 468.526225
34 226.869608 475.002150
35 293.857296 226.869608
36 379.640414 293.857296
37 433.350444 379.640414
38 211.398716 433.350444
39 394.306069 211.398716
40 323.699011 394.306069
41 225.113082 323.699011
42 284.529457 225.113082
43 154.126424 284.529457
44 118.384943 154.126424
45 79.601959 118.384943
46 2.526384 79.601959
47 53.091568 2.526384
48 -106.175503 53.091568
49 -128.842077 -106.175503
50 -192.808192 -128.842077
51 -41.850979 -192.808192
52 -354.542837 -41.850979
53 -576.354692 -354.542837
54 -432.275053 -576.354692
55 -207.306829 -432.275053
56 -369.735134 -207.306829
57 -243.712780 -369.735134
58 -252.836792 -243.712780
59 -368.812403 -252.836792
60 -359.263385 -368.812403
61 -265.902313 -359.263385
62 -294.453350 -265.902313
63 -325.904339 -294.453350
64 -116.990419 -325.904339
65 -293.936617 -116.990419
66 -107.935667 -293.936617
67 -56.791471 -107.935667
68 -24.121988 -56.791471
69 301.271642 -24.121988
70 206.026840 301.271642
71 128.510958 206.026840
72 157.062290 128.510958
73 43.840157 157.062290
74 65.324632 43.840157
75 248.684078 65.324632
76 225.736871 248.684078
77 167.481242 225.736871
78 164.903335 167.481242
79 265.339931 164.903335
80 253.534772 265.339931
81 189.801110 253.534772
82 84.492448 189.801110
83 39.228065 84.492448
84 126.805514 39.228065
85 166.798968 126.805514
86 161.475871 166.798968
87 95.953923 161.475871
88 63.991744 95.953923
89 7.829017 63.991744
90 167.821967 7.829017
91 214.013324 167.821967
92 125.360763 214.013324
93 67.042795 125.360763
94 79.419782 67.042795
95 160.764947 79.419782
96 152.958009 160.764947
97 254.539934 152.958009
98 287.807557 254.539934
99 273.313689 287.807557
100 83.150516 273.313689
101 72.447485 83.150516
102 63.691969 72.447485
103 -77.040395 63.691969
104 -89.402022 -77.040395
105 -85.152582 -89.402022
106 -25.643113 -85.152582
107 -140.627735 -25.643113
108 35.710746 -140.627735
109 -96.482461 35.710746
110 -110.496065 -96.482461
111 -221.698996 -110.496065
112 -240.118809 -221.698996
113 -35.440942 -240.118809
114 50.396605 -35.440942
115 -76.049191 50.396605
116 -221.852201 -76.049191
117 -98.626238 -221.852201
118 -297.307116 -98.626238
119 -343.354935 -297.307116
120 -49.932684 -343.354935
121 161.788041 -49.932684
122 109.006913 161.788041
123 57.953662 109.006913
124 50.415495 57.953662
125 -41.293554 50.415495
126 -29.330963 -41.293554
127 -81.957718 -29.330963
128 -21.962361 -81.957718
129 79.029205 -21.962361
130 -146.229147 79.029205
131 -146.274911 -146.229147
132 NA -146.274911
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 386.607210 445.745492
[2,] 156.545931 386.607210
[3,] -223.361722 156.545931
[4,] -473.257133 -223.361722
[5,] -89.961419 -473.257133
[6,] -224.917473 -89.961419
[7,] -88.117439 -224.917473
[8,] 148.178477 -88.117439
[9,] 117.825215 148.178477
[10,] -37.307285 117.825215
[11,] -92.241505 -37.307285
[12,] -278.370238 -92.241505
[13,] -278.564433 -278.370238
[14,] -313.592388 -278.564433
[15,] -450.874935 -313.592388
[16,] -147.991995 -450.874935
[17,] -110.601072 -147.991995
[18,] 9.553163 -110.601072
[19,] -1.123523 9.553163
[20,] 1.873256 -1.123523
[21,] 193.224911 1.873256
[22,] 134.052063 193.224911
[23,] -208.690149 134.052063
[24,] -198.976467 -208.690149
[25,] -132.350392 -198.976467
[26,] -38.799354 -132.350392
[27,] -61.091504 -38.799354
[28,] -239.028557 -61.091504
[29,] -182.437821 -239.028557
[30,] 68.856316 -182.437821
[31,] 171.271165 68.856316
[32,] 468.526225 171.271165
[33,] 475.002150 468.526225
[34,] 226.869608 475.002150
[35,] 293.857296 226.869608
[36,] 379.640414 293.857296
[37,] 433.350444 379.640414
[38,] 211.398716 433.350444
[39,] 394.306069 211.398716
[40,] 323.699011 394.306069
[41,] 225.113082 323.699011
[42,] 284.529457 225.113082
[43,] 154.126424 284.529457
[44,] 118.384943 154.126424
[45,] 79.601959 118.384943
[46,] 2.526384 79.601959
[47,] 53.091568 2.526384
[48,] -106.175503 53.091568
[49,] -128.842077 -106.175503
[50,] -192.808192 -128.842077
[51,] -41.850979 -192.808192
[52,] -354.542837 -41.850979
[53,] -576.354692 -354.542837
[54,] -432.275053 -576.354692
[55,] -207.306829 -432.275053
[56,] -369.735134 -207.306829
[57,] -243.712780 -369.735134
[58,] -252.836792 -243.712780
[59,] -368.812403 -252.836792
[60,] -359.263385 -368.812403
[61,] -265.902313 -359.263385
[62,] -294.453350 -265.902313
[63,] -325.904339 -294.453350
[64,] -116.990419 -325.904339
[65,] -293.936617 -116.990419
[66,] -107.935667 -293.936617
[67,] -56.791471 -107.935667
[68,] -24.121988 -56.791471
[69,] 301.271642 -24.121988
[70,] 206.026840 301.271642
[71,] 128.510958 206.026840
[72,] 157.062290 128.510958
[73,] 43.840157 157.062290
[74,] 65.324632 43.840157
[75,] 248.684078 65.324632
[76,] 225.736871 248.684078
[77,] 167.481242 225.736871
[78,] 164.903335 167.481242
[79,] 265.339931 164.903335
[80,] 253.534772 265.339931
[81,] 189.801110 253.534772
[82,] 84.492448 189.801110
[83,] 39.228065 84.492448
[84,] 126.805514 39.228065
[85,] 166.798968 126.805514
[86,] 161.475871 166.798968
[87,] 95.953923 161.475871
[88,] 63.991744 95.953923
[89,] 7.829017 63.991744
[90,] 167.821967 7.829017
[91,] 214.013324 167.821967
[92,] 125.360763 214.013324
[93,] 67.042795 125.360763
[94,] 79.419782 67.042795
[95,] 160.764947 79.419782
[96,] 152.958009 160.764947
[97,] 254.539934 152.958009
[98,] 287.807557 254.539934
[99,] 273.313689 287.807557
[100,] 83.150516 273.313689
[101,] 72.447485 83.150516
[102,] 63.691969 72.447485
[103,] -77.040395 63.691969
[104,] -89.402022 -77.040395
[105,] -85.152582 -89.402022
[106,] -25.643113 -85.152582
[107,] -140.627735 -25.643113
[108,] 35.710746 -140.627735
[109,] -96.482461 35.710746
[110,] -110.496065 -96.482461
[111,] -221.698996 -110.496065
[112,] -240.118809 -221.698996
[113,] -35.440942 -240.118809
[114,] 50.396605 -35.440942
[115,] -76.049191 50.396605
[116,] -221.852201 -76.049191
[117,] -98.626238 -221.852201
[118,] -297.307116 -98.626238
[119,] -343.354935 -297.307116
[120,] -49.932684 -343.354935
[121,] 161.788041 -49.932684
[122,] 109.006913 161.788041
[123,] 57.953662 109.006913
[124,] 50.415495 57.953662
[125,] -41.293554 50.415495
[126,] -29.330963 -41.293554
[127,] -81.957718 -29.330963
[128,] -21.962361 -81.957718
[129,] 79.029205 -21.962361
[130,] -146.229147 79.029205
[131,] -146.274911 -146.229147
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 386.607210 445.745492
2 156.545931 386.607210
3 -223.361722 156.545931
4 -473.257133 -223.361722
5 -89.961419 -473.257133
6 -224.917473 -89.961419
7 -88.117439 -224.917473
8 148.178477 -88.117439
9 117.825215 148.178477
10 -37.307285 117.825215
11 -92.241505 -37.307285
12 -278.370238 -92.241505
13 -278.564433 -278.370238
14 -313.592388 -278.564433
15 -450.874935 -313.592388
16 -147.991995 -450.874935
17 -110.601072 -147.991995
18 9.553163 -110.601072
19 -1.123523 9.553163
20 1.873256 -1.123523
21 193.224911 1.873256
22 134.052063 193.224911
23 -208.690149 134.052063
24 -198.976467 -208.690149
25 -132.350392 -198.976467
26 -38.799354 -132.350392
27 -61.091504 -38.799354
28 -239.028557 -61.091504
29 -182.437821 -239.028557
30 68.856316 -182.437821
31 171.271165 68.856316
32 468.526225 171.271165
33 475.002150 468.526225
34 226.869608 475.002150
35 293.857296 226.869608
36 379.640414 293.857296
37 433.350444 379.640414
38 211.398716 433.350444
39 394.306069 211.398716
40 323.699011 394.306069
41 225.113082 323.699011
42 284.529457 225.113082
43 154.126424 284.529457
44 118.384943 154.126424
45 79.601959 118.384943
46 2.526384 79.601959
47 53.091568 2.526384
48 -106.175503 53.091568
49 -128.842077 -106.175503
50 -192.808192 -128.842077
51 -41.850979 -192.808192
52 -354.542837 -41.850979
53 -576.354692 -354.542837
54 -432.275053 -576.354692
55 -207.306829 -432.275053
56 -369.735134 -207.306829
57 -243.712780 -369.735134
58 -252.836792 -243.712780
59 -368.812403 -252.836792
60 -359.263385 -368.812403
61 -265.902313 -359.263385
62 -294.453350 -265.902313
63 -325.904339 -294.453350
64 -116.990419 -325.904339
65 -293.936617 -116.990419
66 -107.935667 -293.936617
67 -56.791471 -107.935667
68 -24.121988 -56.791471
69 301.271642 -24.121988
70 206.026840 301.271642
71 128.510958 206.026840
72 157.062290 128.510958
73 43.840157 157.062290
74 65.324632 43.840157
75 248.684078 65.324632
76 225.736871 248.684078
77 167.481242 225.736871
78 164.903335 167.481242
79 265.339931 164.903335
80 253.534772 265.339931
81 189.801110 253.534772
82 84.492448 189.801110
83 39.228065 84.492448
84 126.805514 39.228065
85 166.798968 126.805514
86 161.475871 166.798968
87 95.953923 161.475871
88 63.991744 95.953923
89 7.829017 63.991744
90 167.821967 7.829017
91 214.013324 167.821967
92 125.360763 214.013324
93 67.042795 125.360763
94 79.419782 67.042795
95 160.764947 79.419782
96 152.958009 160.764947
97 254.539934 152.958009
98 287.807557 254.539934
99 273.313689 287.807557
100 83.150516 273.313689
101 72.447485 83.150516
102 63.691969 72.447485
103 -77.040395 63.691969
104 -89.402022 -77.040395
105 -85.152582 -89.402022
106 -25.643113 -85.152582
107 -140.627735 -25.643113
108 35.710746 -140.627735
109 -96.482461 35.710746
110 -110.496065 -96.482461
111 -221.698996 -110.496065
112 -240.118809 -221.698996
113 -35.440942 -240.118809
114 50.396605 -35.440942
115 -76.049191 50.396605
116 -221.852201 -76.049191
117 -98.626238 -221.852201
118 -297.307116 -98.626238
119 -343.354935 -297.307116
120 -49.932684 -343.354935
121 161.788041 -49.932684
122 109.006913 161.788041
123 57.953662 109.006913
124 50.415495 57.953662
125 -41.293554 50.415495
126 -29.330963 -41.293554
127 -81.957718 -29.330963
128 -21.962361 -81.957718
129 79.029205 -21.962361
130 -146.229147 79.029205
131 -146.274911 -146.229147
> 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/7kkuf1291641451.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8kkuf1291641451.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9kkuf1291641451.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10vbbi1291641451.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yta61291641451.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/122cqt1291641451.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/13g4o21291641451.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/14jmm81291641451.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/1555lw1291641451.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/1685j21291641451.tab")
+ }
>
> try(system("convert tmp/1oawo1291641451.ps tmp/1oawo1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oawo1291641451.ps tmp/2oawo1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zjvr1291641451.ps tmp/3zjvr1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zjvr1291641451.ps tmp/4zjvr1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zjvr1291641451.ps tmp/5zjvr1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fv201291641451.ps tmp/6fv201291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kkuf1291641451.ps tmp/7kkuf1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kkuf1291641451.ps tmp/8kkuf1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kkuf1291641451.ps tmp/9kkuf1291641451.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vbbi1291641451.ps tmp/10vbbi1291641451.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.400 1.790 6.155