R version 2.13.0 (2011-04-13)
Copyright (C) 2011 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.
Type 'q()' to quit R.
> x <- array(list(11.73
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+ ,1.5
+ ,-10.4
+ ,6.374
+ ,1946.5
+ ,1986
+ ,26.96
+ ,1.5
+ ,-10.4
+ ,6.33
+ ,1877.5
+ ,1940
+ ,27.73
+ ,1.5
+ ,-10.4
+ ,6.63
+ ,1907.5
+ ,1997.5
+ ,26.96
+ ,1.5
+ ,-10.4
+ ,6.498
+ ,1947.5
+ ,2017.5
+ ,27.33
+ ,1.5
+ ,-10.4
+ ,6.485
+ ,1937.5
+ ,2022
+ ,27.1
+ ,1.5
+ ,-10.4
+ ,6.36
+ ,1936
+ ,2016.5
+ ,26.58
+ ,1.5
+ ,-10.4)
+ ,dim=c(6
+ ,191)
+ ,dimnames=list(c('koers-nyrstar'
+ ,'Zink-prijs'
+ ,'Lood-prijs'
+ ,'NYSE-eindkoers-vorige-dag'
+ ,'Rente-op-LT-leningen-in-%'
+ ,'Conjunctuurenquete
')
+ ,1:191))
> y <- array(NA,dim=c(6,191),dimnames=list(c('koers-nyrstar','Zink-prijs','Lood-prijs','NYSE-eindkoers-vorige-dag','Rente-op-LT-leningen-in-%','Conjunctuurenquete
'),1:191))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> 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
koers-nyrstar Zink-prijs Lood-prijs NYSE-eindkoers-vorige-dag
1 11.730 2582.5 2666.0 36.98
2 11.750 2502.5 2584.0 37.79
3 11.390 2483.5 2547.5 36.66
4 11.540 2458.5 2469.0 36.80
5 9.620 2493.5 2493.5 37.02
6 9.820 2517.5 2531.0 37.00
7 9.940 2497.5 2541.5 37.00
8 9.900 2487.5 2542.0 36.50
9 9.800 2516.0 2611.5 36.33
10 9.860 2493.0 2637.5 36.22
11 10.500 2417.5 2588.5 36.00
12 10.330 2390.0 2567.5 35.59
13 10.160 2327.5 2535.5 35.11
14 9.910 2272.5 2413.0 35.17
15 9.960 2277.5 2427.5 34.49
16 10.030 2312.5 2481.5 35.27
17 9.550 2282.0 2492.5 36.55
18 9.510 2319.0 2582.5 35.81
19 9.800 2322.5 2657.0 36.02
20 10.080 2327.5 2687.5 35.33
21 10.200 2289.5 2632.5 34.59
22 10.230 2337.5 2682.5 34.44
23 10.200 2412.5 2721.5 34.88
24 10.070 2425.0 2693.5 34.59
25 10.010 2372.5 2652.5 35.08
26 10.050 2337.5 2627.5 34.48
27 9.920 2367.5 2652.5 35.38
28 10.030 2348.5 2637.5 35.30
29 10.180 2337.5 2672.5 35.51
30 10.100 2331.0 2669.0 35.17
31 10.160 2407.5 2751.0 39.60
32 10.150 2428.5 2772.5 39.60
33 10.130 2429.5 2787.5 38.98
34 10.090 2469.0 2802.5 39.81
35 10.180 2497.5 2842.5 39.52
36 10.060 2542.5 2862.5 38.70
37 9.650 2467.5 2757.5 37.59
38 9.740 2447.5 2692.0 37.85
39 9.530 2412.5 2622.5 37.84
40 9.500 2402.5 2634.0 38.90
41 9.000 2350.5 2582.5 39.01
42 9.150 2353.0 2557.5 38.32
43 9.320 2358.0 2627.5 38.70
44 9.620 2366.5 2625.0 39.08
45 9.590 2284.5 2556.0 39.03
46 9.370 2225.5 2528.0 38.76
47 9.350 2253.0 2492.5 39.29
48 9.320 2253.0 2492.5 39.39
49 9.490 2253.0 2492.5 39.75
50 9.520 2237.5 2507.5 40.05
51 9.590 2173.5 2467.5 40.40
52 9.350 2122.5 2352.5 39.99
53 9.200 2162.5 2302.5 39.66
54 9.570 2164.5 2306.5 39.57
55 9.780 2169.0 2337.5 39.98
56 9.790 2124.0 2274.5 40.64
57 9.570 2154.0 2297.5 41.55
58 9.530 2167.5 2340.5 40.68
59 9.650 2130.5 2297.5 40.60
60 9.360 2111.0 2287.5 40.89
61 9.400 2182.5 2401.5 35.73
62 9.320 2176.5 2482.0 34.50
63 9.310 2152.5 2467.5 35.16
64 9.190 2127.5 2429.5 35.82
65 9.390 2189.5 2480.5 35.76
66 9.280 2239.0 2516.5 35.24
67 9.280 2264.0 2512.5 35.36
68 9.310 2282.0 2512.5 35.60
69 9.280 2282.0 2512.5 35.52
70 9.310 2271.0 2512.5 35.67
71 9.350 2252.5 2508.5 36.41
72 9.190 2220.5 2425.5 35.49
73 9.070 2237.5 2427.5 35.61
74 8.960 2281.0 2467.5 35.80
75 8.690 2274.0 2537.5 35.50
76 8.580 2272.5 2567.5 35.27
77 8.560 2286.5 2589.5 34.55
78 8.470 2267.5 2552.5 34.70
79 8.460 2237.5 2522.5 34.24
80 8.750 2270.5 2577.5 34.50
81 8.950 2267.5 2562.5 34.61
82 9.330 2207.5 2492.5 34.04
83 9.510 2217.5 2477.5 33.57
84 9.561 2170.0 2422.5 33.34
85 9.940 2222.5 2485.5 33.32
86 9.900 2242.5 2497.5 34.02
87 9.275 2232.5 2536.5 33.67
88 9.560 2245.5 2573.5 32.50
89 9.779 2257.5 2561.5 32.27
90 9.746 2270.5 2579.5 32.65
91 9.991 2302.5 2632.5 33.24
92 9.980 2363.5 2645.5 33.92
93 10.195 2377.5 2667.5 34.27
94 10.310 2392.5 2681.0 34.72
95 10.250 2411.0 2687.5 34.67
96 9.871 2391.0 2682.5 34.36
97 10.060 2400.0 2722.5 35.40
98 9.894 2357.5 2700.5 35.44
99 9.590 2302.5 2664.0 33.64
100 9.640 2342.5 2704.0 33.19
101 9.890 2387.5 2771.0 33.85
102 9.530 2351.0 2674.5 33.81
103 9.388 2369.0 2692.5 34.35
104 9.160 2417.5 2715.5 34.07
105 9.418 2483.5 2761.0 34.23
106 9.570 2462.5 2722.0 34.52
107 9.857 2458.5 2737.5 35.00
108 9.877 2468.5 2682.5 35.06
109 9.760 2471.0 2677.5 35.18
110 9.760 2512.0 2717.5 35.28
111 9.695 2515.0 2702.5 34.24
112 9.475 2512.5 2692.5 34.29
113 9.262 2493.5 2633.5 33.46
114 9.097 2477.5 2617.5 33.00
115 8.550 2437.5 2571.0 31.40
116 8.160 2380.5 2532.5 31.24
117 7.532 2337.5 2497.5 29.20
118 7.325 2267.5 2399.5 28.00
119 6.749 2051.0 2287.5 25.38
120 7.130 2130.5 2284.5 28.22
121 6.995 2112.5 2297.5 26.87
122 7.346 2171.5 2332.5 28.37
123 7.730 2205.5 2397.5 28.16
124 7.837 2182.5 2382.5 28.86
125 7.514 2175.5 2377.5 26.54
126 7.580 2209.0 2397.5 27.16
127 6.830 2162.0 2312.5 25.31
128 6.617 2201.0 2312.5 25.25
129 6.715 2157.5 2282.5 24.94
130 6.630 2182.5 2307.5 26.23
131 6.891 2192.5 2342.5 27.28
132 7.002 2202.5 2387.5 26.68
133 7.090 2247.5 2450.5 27.15
134 7.360 2247.5 2450.5 27.93
135 7.477 2277.5 2511.5 27.45
136 7.826 2290.0 2572.5 27.28
137 7.790 2242.5 2537.5 26.88
138 7.578 2187.5 2480.5 25.88
139 7.204 2172.5 2427.5 25.09
140 7.198 2173.0 2387.5 27.23
141 7.685 2237.5 2427.5 26.50
142 7.795 2241.0 2435.5 25.67
143 7.460 2197.5 2442.5 25.42
144 7.274 2174.0 2417.5 26.28
145 7.330 2217.5 2402.5 27.66
146 7.655 2155.0 2333.5 28.16
147 7.767 2194.0 2392.5 27.73
148 7.840 2187.5 2382.5 26.28
149 7.424 2107.5 2312.5 26.39
150 7.540 2095.0 2326.5 25.70
151 7.351 2067.5 2236.5 24.33
152 6.735 2031.0 2132.5 24.72
153 6.777 1957.5 2027.5 25.30
154 6.679 1867.5 1902.5 25.61
155 7.340 1995.5 2015.0 24.01
156 6.978 1960.0 2005.0 24.45
157 6.920 1926.5 2012.5 23.75
158 6.628 1872.5 1992.5 21.98
159 6.385 1877.5 1936.5 23.51
160 5.984 1876.0 1912.5 24.25
161 6.268 1831.0 1872.5 24.45
162 6.596 1862.5 1932.5 24.37
163 6.395 1892.5 1952.5 25.87
164 6.715 1947.5 1991.5 26.46
165 6.804 1908.5 1972.5 27.71
166 6.929 1967.5 2032.5 26.99
167 6.846 1912.5 2000.5 27.50
168 6.992 1922.5 2028.5 26.89
169 6.774 1897.5 1992.5 27.19
170 6.750 1869.5 1932.5 26.53
171 6.485 1851.0 1902.5 27.03
172 6.270 1762.5 1807.5 26.78
173 6.470 1803.5 1867.5 27.49
174 6.780 1873.5 1987.5 26.49
175 6.710 1868.5 1997.5 26.05
176 6.141 1862.5 1943.5 27.51
177 6.720 1919.0 1987.5 28.00
178 6.680 1947.0 2023.5 26.57
179 6.371 1962.5 2077.5 24.76
180 6.097 1922.5 2007.5 25.53
181 6.270 1942.5 2022.5 27.08
182 6.447 1946.0 2032.5 26.66
183 6.370 1951.5 2038.5 26.63
184 6.446 1937.5 2012.5 27.61
185 6.540 1986.5 2032.5 26.72
186 6.374 1946.5 1986.0 26.96
187 6.330 1877.5 1940.0 27.73
188 6.630 1907.5 1997.5 26.96
189 6.498 1947.5 2017.5 27.33
190 6.485 1937.5 2022.0 27.10
191 6.360 1936.0 2016.5 26.58
Rente-op-LT-leningen-in-% Conjunctuurenquete\r M1 M2 M3 M4 M5 M6 M7 M8 M9
1 1.00 4.5 1 0 0 0 0 0 0 0 0
2 1.00 4.5 0 1 0 0 0 0 0 0 0
3 1.00 4.5 0 0 1 0 0 0 0 0 0
4 1.00 4.5 0 0 0 1 0 0 0 0 0
5 1.00 4.5 0 0 0 0 1 0 0 0 0
6 1.00 4.5 0 0 0 0 0 1 0 0 0
7 1.00 5.8 0 0 0 0 0 0 1 0 0
8 1.00 5.8 0 0 0 0 0 0 0 1 0
9 1.00 5.8 0 0 0 0 0 0 0 0 1
10 1.00 5.8 0 0 0 0 0 0 0 0 0
11 1.00 5.8 0 0 0 0 0 0 0 0 0
12 1.00 5.8 0 0 0 0 0 0 0 0 0
13 1.00 5.8 1 0 0 0 0 0 0 0 0
14 1.00 5.8 0 1 0 0 0 0 0 0 0
15 1.00 5.8 0 0 1 0 0 0 0 0 0
16 1.00 5.8 0 0 0 1 0 0 0 0 0
17 1.00 5.8 0 0 0 0 1 0 0 0 0
18 1.00 5.8 0 0 0 0 0 1 0 0 0
19 1.00 5.8 0 0 0 0 0 0 1 0 0
20 1.00 5.8 0 0 0 0 0 0 0 1 0
21 1.00 5.8 0 0 0 0 0 0 0 0 1
22 1.00 5.8 0 0 0 0 0 0 0 0 0
23 1.00 5.8 0 0 0 0 0 0 0 0 0
24 1.00 5.8 0 0 0 0 0 0 0 0 0
25 1.00 5.8 1 0 0 0 0 0 0 0 0
26 1.00 5.8 0 1 0 0 0 0 0 0 0
27 1.00 5.8 0 0 1 0 0 0 0 0 0
28 1.00 5.8 0 0 0 1 0 0 0 0 0
29 1.00 5.8 0 0 0 0 1 0 0 0 0
30 1.00 6.2 0 0 0 0 0 1 0 0 0
31 1.00 6.2 0 0 0 0 0 0 1 0 0
32 1.00 6.2 0 0 0 0 0 0 0 1 0
33 1.00 6.2 0 0 0 0 0 0 0 0 1
34 1.00 6.2 0 0 0 0 0 0 0 0 0
35 1.00 6.2 0 0 0 0 0 0 0 0 0
36 1.00 6.2 0 0 0 0 0 0 0 0 0
37 1.25 6.2 1 0 0 0 0 0 0 0 0
38 1.25 6.2 0 1 0 0 0 0 0 0 0
39 1.25 6.2 0 0 1 0 0 0 0 0 0
40 1.25 6.2 0 0 0 1 0 0 0 0 0
41 1.25 6.2 0 0 0 0 1 0 0 0 0
42 1.25 6.2 0 0 0 0 0 1 0 0 0
43 1.25 6.2 0 0 0 0 0 0 1 0 0
44 1.25 6.2 0 0 0 0 0 0 0 1 0
45 1.25 6.2 0 0 0 0 0 0 0 0 1
46 1.25 6.2 0 0 0 0 0 0 0 0 0
47 1.25 6.2 0 0 0 0 0 0 0 0 0
48 1.25 6.2 0 0 0 0 0 0 0 0 0
49 1.25 2.8 1 0 0 0 0 0 0 0 0
50 1.25 2.8 0 1 0 0 0 0 0 0 0
51 1.25 2.8 0 0 1 0 0 0 0 0 0
52 1.25 2.8 0 0 0 1 0 0 0 0 0
53 1.25 2.8 0 0 0 0 1 0 0 0 0
54 1.25 2.8 0 0 0 0 0 1 0 0 0
55 1.25 2.8 0 0 0 0 0 0 1 0 0
56 1.25 2.8 0 0 0 0 0 0 0 1 0
57 1.25 2.8 0 0 0 0 0 0 0 0 1
58 1.25 2.8 0 0 0 0 0 0 0 0 0
59 1.25 2.8 0 0 0 0 0 0 0 0 0
60 1.25 2.8 0 0 0 0 0 0 0 0 0
61 1.25 2.8 1 0 0 0 0 0 0 0 0
62 1.25 2.8 0 1 0 0 0 0 0 0 0
63 1.25 2.8 0 0 1 0 0 0 0 0 0
64 1.25 2.8 0 0 0 1 0 0 0 0 0
65 1.25 2.8 0 0 0 0 1 0 0 0 0
66 1.25 2.8 0 0 0 0 0 1 0 0 0
67 1.25 2.8 0 0 0 0 0 0 1 0 0
68 1.25 2.8 0 0 0 0 0 0 0 1 0
69 1.25 2.8 0 0 0 0 0 0 0 0 1
70 1.25 2.8 0 0 0 0 0 0 0 0 0
71 1.25 -0.5 0 0 0 0 0 0 0 0 0
72 1.25 -0.5 0 0 0 0 0 0 0 0 0
73 1.25 -0.5 1 0 0 0 0 0 0 0 0
74 1.25 -0.5 0 1 0 0 0 0 0 0 0
75 1.25 -0.5 0 0 1 0 0 0 0 0 0
76 1.25 -0.5 0 0 0 1 0 0 0 0 0
77 1.25 -0.5 0 0 0 0 1 0 0 0 0
78 1.25 -0.5 0 0 0 0 0 1 0 0 0
79 1.25 -0.5 0 0 0 0 0 0 1 0 0
80 1.25 -0.5 0 0 0 0 0 0 0 1 0
81 1.25 -0.5 0 0 0 0 0 0 0 0 1
82 1.25 -0.5 0 0 0 0 0 0 0 0 0
83 1.25 -0.5 0 0 0 0 0 0 0 0 0
84 1.25 -0.5 0 0 0 0 0 0 0 0 0
85 1.25 -0.5 1 0 0 0 0 0 0 0 0
86 1.25 -0.5 0 1 0 0 0 0 0 0 0
87 1.25 -0.5 0 0 1 0 0 0 0 0 0
88 1.25 -0.5 0 0 0 1 0 0 0 0 0
89 1.25 -0.5 0 0 0 0 1 0 0 0 0
90 1.25 -0.5 0 0 0 0 0 1 0 0 0
91 1.25 -0.5 0 0 0 0 0 0 1 0 0
92 1.25 -0.5 0 0 0 0 0 0 0 1 0
93 1.25 -1.1 0 0 0 0 0 0 0 0 1
94 1.25 -1.1 0 0 0 0 0 0 0 0 0
95 1.25 -1.1 0 0 0 0 0 0 0 0 0
96 1.25 -1.1 0 0 0 0 0 0 0 0 0
97 1.25 -1.1 1 0 0 0 0 0 0 0 0
98 1.25 -1.1 0 1 0 0 0 0 0 0 0
99 1.25 -1.1 0 0 1 0 0 0 0 0 0
100 1.50 -1.1 0 0 0 1 0 0 0 0 0
101 1.50 -1.1 0 0 0 0 1 0 0 0 0
102 1.50 -1.1 0 0 0 0 0 1 0 0 0
103 1.50 -1.1 0 0 0 0 0 0 1 0 0
104 1.50 -1.1 0 0 0 0 0 0 0 1 0
105 1.50 -1.1 0 0 0 0 0 0 0 0 1
106 1.50 -1.1 0 0 0 0 0 0 0 0 0
107 1.50 -1.1 0 0 0 0 0 0 0 0 0
108 1.50 -1.1 0 0 0 0 0 0 0 0 0
109 1.50 -1.1 1 0 0 0 0 0 0 0 0
110 1.50 -1.1 0 1 0 0 0 0 0 0 0
111 1.50 -1.1 0 0 1 0 0 0 0 0 0
112 1.50 -1.1 0 0 0 1 0 0 0 0 0
113 1.50 -1.1 0 0 0 0 1 0 0 0 0
114 1.50 -2.5 0 0 0 0 0 1 0 0 0
115 1.50 -2.5 0 0 0 0 0 0 1 0 0
116 1.50 -2.5 0 0 0 0 0 0 0 1 0
117 1.50 -2.5 0 0 0 0 0 0 0 0 1
118 1.50 -2.5 0 0 0 0 0 0 0 0 0
119 1.50 -2.5 0 0 0 0 0 0 0 0 0
120 1.50 -2.5 0 0 0 0 0 0 0 0 0
121 1.50 -2.5 1 0 0 0 0 0 0 0 0
122 1.50 -2.5 0 1 0 0 0 0 0 0 0
123 1.50 -2.5 0 0 1 0 0 0 0 0 0
124 1.50 -2.5 0 0 0 1 0 0 0 0 0
125 1.50 -2.5 0 0 0 0 1 0 0 0 0
126 1.50 -2.5 0 0 0 0 0 1 0 0 0
127 1.50 -2.5 0 0 0 0 0 0 1 0 0
128 1.50 -2.5 0 0 0 0 0 0 0 1 0
129 1.50 -2.5 0 0 0 0 0 0 0 0 1
130 1.50 -2.5 0 0 0 0 0 0 0 0 0
131 1.50 -2.5 0 0 0 0 0 0 0 0 0
132 1.50 -2.5 0 0 0 0 0 0 0 0 0
133 1.50 -2.5 1 0 0 0 0 0 0 0 0
134 1.50 -2.5 0 1 0 0 0 0 0 0 0
135 1.50 -2.5 0 0 1 0 0 0 0 0 0
136 1.50 -2.5 0 0 0 1 0 0 0 0 0
137 1.50 -7.8 0 0 0 0 1 0 0 0 0
138 1.50 -7.8 0 0 0 0 0 1 0 0 0
139 1.50 -7.8 0 0 0 0 0 0 1 0 0
140 1.50 -7.8 0 0 0 0 0 0 0 1 0
141 1.50 -7.8 0 0 0 0 0 0 0 0 1
142 1.50 -7.8 0 0 0 0 0 0 0 0 0
143 1.50 -7.8 0 0 0 0 0 0 0 0 0
144 1.50 -7.8 0 0 0 0 0 0 0 0 0
145 1.50 -7.8 1 0 0 0 0 0 0 0 0
146 1.50 -7.8 0 1 0 0 0 0 0 0 0
147 1.50 -7.8 0 0 1 0 0 0 0 0 0
148 1.50 -7.8 0 0 0 1 0 0 0 0 0
149 1.50 -7.8 0 0 0 0 1 0 0 0 0
150 1.50 -7.8 0 0 0 0 0 1 0 0 0
151 1.50 -7.8 0 0 0 0 0 0 1 0 0
152 1.50 -7.8 0 0 0 0 0 0 0 1 0
153 1.50 -7.8 0 0 0 0 0 0 0 0 1
154 1.50 -7.8 0 0 0 0 0 0 0 0 0
155 1.50 -7.8 0 0 0 0 0 0 0 0 0
156 1.50 -7.8 0 0 0 0 0 0 0 0 0
157 1.50 -7.8 1 0 0 0 0 0 0 0 0
158 1.50 -7.8 0 1 0 0 0 0 0 0 0
159 1.50 -9.4 0 0 1 0 0 0 0 0 0
160 1.50 -9.4 0 0 0 1 0 0 0 0 0
161 1.50 -9.4 0 0 0 0 1 0 0 0 0
162 1.50 -9.4 0 0 0 0 0 1 0 0 0
163 1.50 -9.4 0 0 0 0 0 0 1 0 0
164 1.50 -9.4 0 0 0 0 0 0 0 1 0
165 1.50 -9.4 0 0 0 0 0 0 0 0 1
166 1.50 -9.4 0 0 0 0 0 0 0 0 0
167 1.50 -9.4 0 0 0 0 0 0 0 0 0
168 1.50 -9.4 0 0 0 0 0 0 0 0 0
169 1.50 -9.4 1 0 0 0 0 0 0 0 0
170 1.50 -9.4 0 1 0 0 0 0 0 0 0
171 1.50 -9.4 0 0 1 0 0 0 0 0 0
172 1.50 -9.4 0 0 0 1 0 0 0 0 0
173 1.50 -9.4 0 0 0 0 1 0 0 0 0
174 1.50 -9.4 0 0 0 0 0 1 0 0 0
175 1.50 -9.4 0 0 0 0 0 0 1 0 0
176 1.50 -9.4 0 0 0 0 0 0 0 1 0
177 1.50 -9.4 0 0 0 0 0 0 0 0 1
178 1.50 -9.4 0 0 0 0 0 0 0 0 0
179 1.50 -9.4 0 0 0 0 0 0 0 0 0
180 1.50 -10.4 0 0 0 0 0 0 0 0 0
181 1.50 -10.4 1 0 0 0 0 0 0 0 0
182 1.50 -10.4 0 1 0 0 0 0 0 0 0
183 1.50 -10.4 0 0 1 0 0 0 0 0 0
184 1.50 -10.4 0 0 0 1 0 0 0 0 0
185 1.50 -10.4 0 0 0 0 1 0 0 0 0
186 1.50 -10.4 0 0 0 0 0 1 0 0 0
187 1.50 -10.4 0 0 0 0 0 0 1 0 0
188 1.50 -10.4 0 0 0 0 0 0 0 1 0
189 1.50 -10.4 0 0 0 0 0 0 0 0 1
190 1.50 -10.4 0 0 0 0 0 0 0 0 0
191 1.50 -10.4 0 0 0 0 0 0 0 0 0
M10 M11
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 1 0
11 0 1
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 0 0
18 0 0
19 0 0
20 0 0
21 0 0
22 1 0
23 0 1
24 0 0
25 0 0
26 0 0
27 0 0
28 0 0
29 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 1 0
35 0 1
36 0 0
37 0 0
38 0 0
39 0 0
40 0 0
41 0 0
42 0 0
43 0 0
44 0 0
45 0 0
46 1 0
47 0 1
48 0 0
49 0 0
50 0 0
51 0 0
52 0 0
53 0 0
54 0 0
55 0 0
56 0 0
57 0 0
58 1 0
59 0 1
60 0 0
61 0 0
62 0 0
63 0 0
64 0 0
65 0 0
66 0 0
67 0 0
68 0 0
69 0 0
70 1 0
71 0 1
72 0 0
73 0 0
74 0 0
75 0 0
76 0 0
77 0 0
78 0 0
79 0 0
80 0 0
81 0 0
82 1 0
83 0 1
84 0 0
85 0 0
86 0 0
87 0 0
88 0 0
89 0 0
90 0 0
91 0 0
92 0 0
93 0 0
94 1 0
95 0 1
96 0 0
97 0 0
98 0 0
99 0 0
100 0 0
101 0 0
102 0 0
103 0 0
104 0 0
105 0 0
106 1 0
107 0 1
108 0 0
109 0 0
110 0 0
111 0 0
112 0 0
113 0 0
114 0 0
115 0 0
116 0 0
117 0 0
118 1 0
119 0 1
120 0 0
121 0 0
122 0 0
123 0 0
124 0 0
125 0 0
126 0 0
127 0 0
128 0 0
129 0 0
130 1 0
131 0 1
132 0 0
133 0 0
134 0 0
135 0 0
136 0 0
137 0 0
138 0 0
139 0 0
140 0 0
141 0 0
142 1 0
143 0 1
144 0 0
145 0 0
146 0 0
147 0 0
148 0 0
149 0 0
150 0 0
151 0 0
152 0 0
153 0 0
154 1 0
155 0 1
156 0 0
157 0 0
158 0 0
159 0 0
160 0 0
161 0 0
162 0 0
163 0 0
164 0 0
165 0 0
166 1 0
167 0 1
168 0 0
169 0 0
170 0 0
171 0 0
172 0 0
173 0 0
174 0 0
175 0 0
176 0 0
177 0 0
178 1 0
179 0 1
180 0 0
181 0 0
182 0 0
183 0 0
184 0 0
185 0 0
186 0 0
187 0 0
188 0 0
189 0 0
190 1 0
191 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Zink-prijs`
0.4703110 0.0017139
`Lood-prijs` `NYSE-eindkoers-vorige-dag`
0.0014134 0.1287340
`Rente-op-LT-leningen-in-%` `Conjunctuurenquete\r`
-2.5055190 -0.0458396
M1 M2
0.0981553 0.1603869
M3 M4
0.0669641 0.1296845
M5 M6
-0.0312837 -0.0006878
M7 M8
-0.0885562 -0.1702354
M9 M10
-0.1031389 -0.0433015
M11
0.0406707
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.00604 -0.31196 0.02599 0.27211 1.21109
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4703110 0.8098605 0.581 0.562173
`Zink-prijs` 0.0017139 0.0005462 3.138 0.002000 **
`Lood-prijs` 0.0014134 0.0004145 3.410 0.000808 ***
`NYSE-eindkoers-vorige-dag` 0.1287340 0.0131438 9.794 < 2e-16 ***
`Rente-op-LT-leningen-in-%` -2.5055190 0.3255761 -7.696 1.01e-12 ***
`Conjunctuurenquete\r` -0.0458396 0.0170663 -2.686 0.007932 **
M1 0.0981553 0.1609376 0.610 0.542726
M2 0.1603869 0.1608528 0.997 0.320099
M3 0.0669641 0.1608634 0.416 0.677719
M4 0.1296845 0.1609279 0.806 0.421427
M5 -0.0312837 0.1608680 -0.194 0.846036
M6 -0.0006878 0.1608918 -0.004 0.996594
M7 -0.0885562 0.1609162 -0.550 0.582802
M8 -0.1702354 0.1609271 -1.058 0.291594
M9 -0.1031389 0.1609332 -0.641 0.522444
M10 -0.0433015 0.1608611 -0.269 0.788106
M11 0.0406707 0.1609211 0.253 0.800770
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4475 on 174 degrees of freedom
Multiple R-squared: 0.9124, Adjusted R-squared: 0.9043
F-statistic: 113.3 on 16 and 174 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,] 0.31542662 6.308532e-01 6.845734e-01
[2,] 0.37478209 7.495642e-01 6.252179e-01
[3,] 0.42997366 8.599473e-01 5.700263e-01
[4,] 0.30383995 6.076799e-01 6.961600e-01
[5,] 0.21031412 4.206282e-01 7.896859e-01
[6,] 0.18231292 3.646258e-01 8.176871e-01
[7,] 0.14048593 2.809719e-01 8.595141e-01
[8,] 0.13782886 2.756577e-01 8.621711e-01
[9,] 0.09884505 1.976901e-01 9.011550e-01
[10,] 0.46454081 9.290816e-01 5.354592e-01
[11,] 0.73883823 5.223235e-01 2.611618e-01
[12,] 0.73112479 5.377504e-01 2.688752e-01
[13,] 0.69591636 6.081673e-01 3.040836e-01
[14,] 0.64538839 7.092232e-01 3.546116e-01
[15,] 0.61128051 7.774390e-01 3.887195e-01
[16,] 0.60343101 7.931380e-01 3.965690e-01
[17,] 0.58846646 8.230671e-01 4.115335e-01
[18,] 0.52877387 9.424523e-01 4.712261e-01
[19,] 0.46760785 9.352157e-01 5.323921e-01
[20,] 0.40301492 8.060298e-01 5.969851e-01
[21,] 0.35692598 7.138520e-01 6.430740e-01
[22,] 0.33786206 6.757241e-01 6.621379e-01
[23,] 0.30954711 6.190942e-01 6.904529e-01
[24,] 0.27610032 5.522006e-01 7.238997e-01
[25,] 0.23009552 4.601910e-01 7.699045e-01
[26,] 0.18645266 3.729053e-01 8.135473e-01
[27,] 0.15887269 3.177454e-01 8.411273e-01
[28,] 0.14573247 2.914649e-01 8.542675e-01
[29,] 0.12585080 2.517016e-01 8.741492e-01
[30,] 0.66040680 6.791864e-01 3.395932e-01
[31,] 0.71640961 5.671808e-01 2.835904e-01
[32,] 0.67809452 6.438110e-01 3.219055e-01
[33,] 0.64933264 7.013347e-01 3.506674e-01
[34,] 0.60863789 7.827242e-01 3.913621e-01
[35,] 0.58829597 8.234081e-01 4.117040e-01
[36,] 0.56508945 8.698211e-01 4.349105e-01
[37,] 0.54354188 9.129162e-01 4.564581e-01
[38,] 0.49220862 9.844172e-01 5.077914e-01
[39,] 0.44316441 8.863288e-01 5.568356e-01
[40,] 0.39280590 7.856118e-01 6.071941e-01
[41,] 0.35275259 7.055052e-01 6.472474e-01
[42,] 0.33236219 6.647244e-01 6.676378e-01
[43,] 0.30673710 6.134742e-01 6.932629e-01
[44,] 0.27044863 5.408973e-01 7.295514e-01
[45,] 0.24004727 4.800945e-01 7.599527e-01
[46,] 0.22783062 4.556612e-01 7.721694e-01
[47,] 0.19324604 3.864921e-01 8.067540e-01
[48,] 0.16329422 3.265884e-01 8.367058e-01
[49,] 0.14128212 2.825642e-01 8.587179e-01
[50,] 0.11956358 2.391272e-01 8.804364e-01
[51,] 0.09833256 1.966651e-01 9.016674e-01
[52,] 0.09968407 1.993681e-01 9.003159e-01
[53,] 0.08850971 1.770194e-01 9.114903e-01
[54,] 0.11050508 2.210102e-01 8.894949e-01
[55,] 0.16010762 3.202152e-01 8.398924e-01
[56,] 0.24511702 4.902340e-01 7.548830e-01
[57,] 0.38174082 7.634816e-01 6.182592e-01
[58,] 0.49402284 9.880457e-01 5.059772e-01
[59,] 0.66640505 6.671899e-01 3.335950e-01
[60,] 0.78570001 4.286000e-01 2.143000e-01
[61,] 0.82736963 3.452607e-01 1.726304e-01
[62,] 0.85868957 2.826209e-01 1.413104e-01
[63,] 0.86064400 2.787120e-01 1.393560e-01
[64,] 0.85539439 2.892112e-01 1.446056e-01
[65,] 0.86962288 2.607542e-01 1.303771e-01
[66,] 0.90557153 1.888569e-01 9.442847e-02
[67,] 0.91494258 1.701148e-01 8.505742e-02
[68,] 0.90420684 1.915863e-01 9.579316e-02
[69,] 0.90259950 1.948010e-01 9.740050e-02
[70,] 0.94389721 1.122056e-01 5.610279e-02
[71,] 0.95837647 8.324705e-02 4.162353e-02
[72,] 0.97243883 5.512234e-02 2.756117e-02
[73,] 0.97632323 4.735354e-02 2.367677e-02
[74,] 0.98265353 3.469294e-02 1.734647e-02
[75,] 0.98677549 2.644903e-02 1.322451e-02
[76,] 0.98679618 2.640764e-02 1.320382e-02
[77,] 0.98345052 3.309895e-02 1.654948e-02
[78,] 0.97929801 4.140399e-02 2.070199e-02
[79,] 0.97333826 5.332349e-02 2.666174e-02
[80,] 0.96569676 6.860648e-02 3.430324e-02
[81,] 0.97232210 5.535580e-02 2.767790e-02
[82,] 0.98436237 3.127526e-02 1.563763e-02
[83,] 0.98451130 3.097740e-02 1.548870e-02
[84,] 0.98101961 3.796077e-02 1.898039e-02
[85,] 0.97631714 4.736572e-02 2.368286e-02
[86,] 0.97020488 5.959024e-02 2.979512e-02
[87,] 0.96569079 6.861842e-02 3.430921e-02
[88,] 0.96824267 6.351465e-02 3.175733e-02
[89,] 0.97730613 4.538775e-02 2.269387e-02
[90,] 0.98487990 3.024019e-02 1.512010e-02
[91,] 0.98629301 2.741398e-02 1.370699e-02
[92,] 0.99131295 1.737409e-02 8.687046e-03
[93,] 0.99325219 1.349563e-02 6.747814e-03
[94,] 0.99508523 9.829545e-03 4.914772e-03
[95,] 0.99646511 7.069775e-03 3.534888e-03
[96,] 0.99828801 3.423979e-03 1.711989e-03
[97,] 0.99941591 1.168186e-03 5.840929e-04
[98,] 0.99970209 5.958169e-04 2.979085e-04
[99,] 0.99978888 4.222326e-04 2.111163e-04
[100,] 0.99985080 2.984079e-04 1.492040e-04
[101,] 0.99982286 3.542872e-04 1.771436e-04
[102,] 0.99979428 4.114450e-04 2.057225e-04
[103,] 0.99973726 5.254838e-04 2.627419e-04
[104,] 0.99972990 5.401978e-04 2.700989e-04
[105,] 0.99981391 3.721784e-04 1.860892e-04
[106,] 0.99975160 4.968062e-04 2.484031e-04
[107,] 0.99969992 6.001544e-04 3.000772e-04
[108,] 0.99955094 8.981155e-04 4.490578e-04
[109,] 0.99948193 1.036150e-03 5.180748e-04
[110,] 0.99949434 1.011327e-03 5.056633e-04
[111,] 0.99976694 4.661119e-04 2.330559e-04
[112,] 0.99980205 3.959041e-04 1.979521e-04
[113,] 0.99982800 3.439923e-04 1.719961e-04
[114,] 0.99992434 1.513134e-04 7.565670e-05
[115,] 0.99996921 6.158077e-05 3.079038e-05
[116,] 0.99999611 7.776387e-06 3.888193e-06
[117,] 0.99999995 1.056777e-07 5.283883e-08
[118,] 0.99999989 2.102784e-07 1.051392e-07
[119,] 0.99999977 4.606429e-07 2.303214e-07
[120,] 0.99999968 6.346059e-07 3.173029e-07
[121,] 0.99999961 7.755187e-07 3.877594e-07
[122,] 0.99999913 1.733815e-06 8.669075e-07
[123,] 0.99999852 2.968882e-06 1.484441e-06
[124,] 0.99999702 5.961893e-06 2.980947e-06
[125,] 0.99999718 5.643693e-06 2.821846e-06
[126,] 0.99999879 2.417584e-06 1.208792e-06
[127,] 0.99999808 3.845485e-06 1.922743e-06
[128,] 0.99999601 7.983478e-06 3.991739e-06
[129,] 0.99999525 9.506537e-06 4.753268e-06
[130,] 0.99998948 2.104382e-05 1.052191e-05
[131,] 0.99997718 4.564863e-05 2.282431e-05
[132,] 0.99996031 7.937108e-05 3.968554e-05
[133,] 0.99996078 7.843367e-05 3.921683e-05
[134,] 0.99996308 7.383949e-05 3.691974e-05
[135,] 0.99995687 8.626869e-05 4.313435e-05
[136,] 0.99997124 5.751274e-05 2.875637e-05
[137,] 0.99993174 1.365201e-04 6.826006e-05
[138,] 0.99984813 3.037401e-04 1.518701e-04
[139,] 0.99971716 5.656825e-04 2.828412e-04
[140,] 0.99954478 9.104306e-04 4.552153e-04
[141,] 0.99925159 1.496829e-03 7.484145e-04
[142,] 0.99830074 3.398512e-03 1.699256e-03
[143,] 0.99768616 4.627679e-03 2.313840e-03
[144,] 0.99517282 9.654354e-03 4.827177e-03
[145,] 0.99221769 1.556463e-02 7.782314e-03
[146,] 0.98401125 3.197749e-02 1.598875e-02
[147,] 0.97114647 5.770705e-02 2.885353e-02
[148,] 0.94771601 1.045680e-01 5.228399e-02
[149,] 0.94751207 1.049759e-01 5.248793e-02
[150,] 0.93038973 1.392205e-01 6.961027e-02
[151,] 0.90090238 1.981952e-01 9.909762e-02
[152,] 0.84022413 3.195517e-01 1.597759e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1vw9b1321989417.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/wessaorg/rcomp/tmp/2qyjt1321989417.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3cpgr1321989417.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4q2qg1321989417.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/59kug1321989417.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 191
Frequency = 1
1 2 3 4 5 6
0.918483315 1.024987737 0.988033266 1.211089835 -0.670878106 -0.593035396
7 8 9 10 11 12
-0.306138571 -0.183660242 -0.475949595 -0.458955370 0.324049477 0.324314646
13 14 15 16 17 18
0.270298737 0.217749466 0.419647558 0.190204501 -0.256880780 -0.422834028
19 20 21 22 23 24
-0.183297244 0.215529987 0.506561895 0.343097584 -0.011182185 -0.045026803
25 26 27 28 29 30
-0.118332939 0.031997224 -0.207192450 -0.095849114 0.157468396 0.125065141
31 32 33 34 35 36
-0.544370118 -0.539070920 -0.569267299 -0.864853634 -0.926875135 -1.006035676
37 38 39 40 41 42
-0.467967037 -0.346813462 -0.303885369 -0.532179126 -0.723459005 -0.484177908
43 44 45 46 47 48
-0.382736407 -0.061010744 0.086393506 -0.017990810 -0.187148047 -0.189350738
49 50 51 52 53 54
-0.319704803 -0.385192405 -0.100601308 -0.100590532 -0.045025333 0.296883471
55 56 57 58 59 60
0.490442794 0.663327150 0.175157669 0.103404879 0.273921792 0.034814547
61 62 63 64 65 66
0.357255251 0.269870565 0.329956661 0.158828527 0.349175810 0.139801477
67 68 69 70 71 72
0.175028212 0.224961175 0.138163425 0.107868735 -0.145276613 0.025986684
73 74 75 76 77 78
-0.239579672 -0.567361169 -0.792259464 -0.975202424 -0.796635193 -0.851681174
79 80 81 82 83 84
-0.620776213 -0.416863685 -0.271778119 0.323534783 0.484129773 0.764556456
85 86 87 88 89 90
0.868951941 0.625367894 0.100863604 0.399185386 0.805156582 0.644919909
91 92 93 94 95 96
0.772080192 0.632298659 0.652552135 0.604995121 0.426565499 0.169488576
97 98 99 100 101 102
0.054488537 -0.074957134 0.092040194 0.638537994 0.792718367 0.606222662
103 104 105 106 107 108
0.426283360 0.200376028 0.193255395 0.339199728 0.465382904 0.578928105
109 110 111 112 113 114
0.351106993 0.149196281 0.327561926 0.056823633 0.227595961 0.077079139
115 116 117 118 119 120
-0.041798994 -0.177414556 -0.486727121 -0.340597478 -0.133927945 -0.209875817
121 122 123 124 125 126
-0.256764524 -0.311685837 0.042627397 0.057413748 0.213109097 0.083014718
127 128 129 130 131 132
-0.140266491 -0.330704988 -0.142937514 -0.532024188 -0.556775301 -0.408606406
133 134 135 136 137 138
-0.645436428 -0.538080526 -0.403499919 -0.202977002 -0.138585870 -0.077619566
139 140 141 142 143 144
-0.161432334 -0.305564661 0.041232565 0.180938570 -0.141189836 -0.321618806
145 146 147 148 149 150
-0.594780163 -0.191735548 -0.081189826 0.141028477 0.107885529 0.283752063
151 152 153 154 155 156
0.533324725 0.158348925 0.332965357 0.466146404 0.870762475 0.567767349
157 158 159 160 161 162
0.548540545 0.542986371 0.193684221 -0.328806732 0.224075916 0.392986828
163 164 165 166 167 168
0.007069378 0.183408642 0.138091050 0.110018279 0.016884633 0.225368798
169 170 171 172 173 174
-0.035676856 0.095849393 -0.065985637 -0.025569623 0.088923458 0.207480492
175 176 177 178 179 180
0.276427243 -0.312237960 -0.022438766 -0.037058038 -0.299910968 -0.510710915
181 182 183 184 185 186
-0.690882895 -0.542178849 -0.539800854 -0.591937547 -0.334644828 -0.427857831
187 188 189 190 191
-0.299839533 0.048277190 -0.295274582 -0.327724566 -0.459410524
> postscript(file="/var/wessaorg/rcomp/tmp/6b4r61321989417.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 = 191
Frequency = 1
lag(myerror, k = 1) myerror
0 0.918483315 NA
1 1.024987737 0.918483315
2 0.988033266 1.024987737
3 1.211089835 0.988033266
4 -0.670878106 1.211089835
5 -0.593035396 -0.670878106
6 -0.306138571 -0.593035396
7 -0.183660242 -0.306138571
8 -0.475949595 -0.183660242
9 -0.458955370 -0.475949595
10 0.324049477 -0.458955370
11 0.324314646 0.324049477
12 0.270298737 0.324314646
13 0.217749466 0.270298737
14 0.419647558 0.217749466
15 0.190204501 0.419647558
16 -0.256880780 0.190204501
17 -0.422834028 -0.256880780
18 -0.183297244 -0.422834028
19 0.215529987 -0.183297244
20 0.506561895 0.215529987
21 0.343097584 0.506561895
22 -0.011182185 0.343097584
23 -0.045026803 -0.011182185
24 -0.118332939 -0.045026803
25 0.031997224 -0.118332939
26 -0.207192450 0.031997224
27 -0.095849114 -0.207192450
28 0.157468396 -0.095849114
29 0.125065141 0.157468396
30 -0.544370118 0.125065141
31 -0.539070920 -0.544370118
32 -0.569267299 -0.539070920
33 -0.864853634 -0.569267299
34 -0.926875135 -0.864853634
35 -1.006035676 -0.926875135
36 -0.467967037 -1.006035676
37 -0.346813462 -0.467967037
38 -0.303885369 -0.346813462
39 -0.532179126 -0.303885369
40 -0.723459005 -0.532179126
41 -0.484177908 -0.723459005
42 -0.382736407 -0.484177908
43 -0.061010744 -0.382736407
44 0.086393506 -0.061010744
45 -0.017990810 0.086393506
46 -0.187148047 -0.017990810
47 -0.189350738 -0.187148047
48 -0.319704803 -0.189350738
49 -0.385192405 -0.319704803
50 -0.100601308 -0.385192405
51 -0.100590532 -0.100601308
52 -0.045025333 -0.100590532
53 0.296883471 -0.045025333
54 0.490442794 0.296883471
55 0.663327150 0.490442794
56 0.175157669 0.663327150
57 0.103404879 0.175157669
58 0.273921792 0.103404879
59 0.034814547 0.273921792
60 0.357255251 0.034814547
61 0.269870565 0.357255251
62 0.329956661 0.269870565
63 0.158828527 0.329956661
64 0.349175810 0.158828527
65 0.139801477 0.349175810
66 0.175028212 0.139801477
67 0.224961175 0.175028212
68 0.138163425 0.224961175
69 0.107868735 0.138163425
70 -0.145276613 0.107868735
71 0.025986684 -0.145276613
72 -0.239579672 0.025986684
73 -0.567361169 -0.239579672
74 -0.792259464 -0.567361169
75 -0.975202424 -0.792259464
76 -0.796635193 -0.975202424
77 -0.851681174 -0.796635193
78 -0.620776213 -0.851681174
79 -0.416863685 -0.620776213
80 -0.271778119 -0.416863685
81 0.323534783 -0.271778119
82 0.484129773 0.323534783
83 0.764556456 0.484129773
84 0.868951941 0.764556456
85 0.625367894 0.868951941
86 0.100863604 0.625367894
87 0.399185386 0.100863604
88 0.805156582 0.399185386
89 0.644919909 0.805156582
90 0.772080192 0.644919909
91 0.632298659 0.772080192
92 0.652552135 0.632298659
93 0.604995121 0.652552135
94 0.426565499 0.604995121
95 0.169488576 0.426565499
96 0.054488537 0.169488576
97 -0.074957134 0.054488537
98 0.092040194 -0.074957134
99 0.638537994 0.092040194
100 0.792718367 0.638537994
101 0.606222662 0.792718367
102 0.426283360 0.606222662
103 0.200376028 0.426283360
104 0.193255395 0.200376028
105 0.339199728 0.193255395
106 0.465382904 0.339199728
107 0.578928105 0.465382904
108 0.351106993 0.578928105
109 0.149196281 0.351106993
110 0.327561926 0.149196281
111 0.056823633 0.327561926
112 0.227595961 0.056823633
113 0.077079139 0.227595961
114 -0.041798994 0.077079139
115 -0.177414556 -0.041798994
116 -0.486727121 -0.177414556
117 -0.340597478 -0.486727121
118 -0.133927945 -0.340597478
119 -0.209875817 -0.133927945
120 -0.256764524 -0.209875817
121 -0.311685837 -0.256764524
122 0.042627397 -0.311685837
123 0.057413748 0.042627397
124 0.213109097 0.057413748
125 0.083014718 0.213109097
126 -0.140266491 0.083014718
127 -0.330704988 -0.140266491
128 -0.142937514 -0.330704988
129 -0.532024188 -0.142937514
130 -0.556775301 -0.532024188
131 -0.408606406 -0.556775301
132 -0.645436428 -0.408606406
133 -0.538080526 -0.645436428
134 -0.403499919 -0.538080526
135 -0.202977002 -0.403499919
136 -0.138585870 -0.202977002
137 -0.077619566 -0.138585870
138 -0.161432334 -0.077619566
139 -0.305564661 -0.161432334
140 0.041232565 -0.305564661
141 0.180938570 0.041232565
142 -0.141189836 0.180938570
143 -0.321618806 -0.141189836
144 -0.594780163 -0.321618806
145 -0.191735548 -0.594780163
146 -0.081189826 -0.191735548
147 0.141028477 -0.081189826
148 0.107885529 0.141028477
149 0.283752063 0.107885529
150 0.533324725 0.283752063
151 0.158348925 0.533324725
152 0.332965357 0.158348925
153 0.466146404 0.332965357
154 0.870762475 0.466146404
155 0.567767349 0.870762475
156 0.548540545 0.567767349
157 0.542986371 0.548540545
158 0.193684221 0.542986371
159 -0.328806732 0.193684221
160 0.224075916 -0.328806732
161 0.392986828 0.224075916
162 0.007069378 0.392986828
163 0.183408642 0.007069378
164 0.138091050 0.183408642
165 0.110018279 0.138091050
166 0.016884633 0.110018279
167 0.225368798 0.016884633
168 -0.035676856 0.225368798
169 0.095849393 -0.035676856
170 -0.065985637 0.095849393
171 -0.025569623 -0.065985637
172 0.088923458 -0.025569623
173 0.207480492 0.088923458
174 0.276427243 0.207480492
175 -0.312237960 0.276427243
176 -0.022438766 -0.312237960
177 -0.037058038 -0.022438766
178 -0.299910968 -0.037058038
179 -0.510710915 -0.299910968
180 -0.690882895 -0.510710915
181 -0.542178849 -0.690882895
182 -0.539800854 -0.542178849
183 -0.591937547 -0.539800854
184 -0.334644828 -0.591937547
185 -0.427857831 -0.334644828
186 -0.299839533 -0.427857831
187 0.048277190 -0.299839533
188 -0.295274582 0.048277190
189 -0.327724566 -0.295274582
190 -0.459410524 -0.327724566
191 NA -0.459410524
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.024987737 0.918483315
[2,] 0.988033266 1.024987737
[3,] 1.211089835 0.988033266
[4,] -0.670878106 1.211089835
[5,] -0.593035396 -0.670878106
[6,] -0.306138571 -0.593035396
[7,] -0.183660242 -0.306138571
[8,] -0.475949595 -0.183660242
[9,] -0.458955370 -0.475949595
[10,] 0.324049477 -0.458955370
[11,] 0.324314646 0.324049477
[12,] 0.270298737 0.324314646
[13,] 0.217749466 0.270298737
[14,] 0.419647558 0.217749466
[15,] 0.190204501 0.419647558
[16,] -0.256880780 0.190204501
[17,] -0.422834028 -0.256880780
[18,] -0.183297244 -0.422834028
[19,] 0.215529987 -0.183297244
[20,] 0.506561895 0.215529987
[21,] 0.343097584 0.506561895
[22,] -0.011182185 0.343097584
[23,] -0.045026803 -0.011182185
[24,] -0.118332939 -0.045026803
[25,] 0.031997224 -0.118332939
[26,] -0.207192450 0.031997224
[27,] -0.095849114 -0.207192450
[28,] 0.157468396 -0.095849114
[29,] 0.125065141 0.157468396
[30,] -0.544370118 0.125065141
[31,] -0.539070920 -0.544370118
[32,] -0.569267299 -0.539070920
[33,] -0.864853634 -0.569267299
[34,] -0.926875135 -0.864853634
[35,] -1.006035676 -0.926875135
[36,] -0.467967037 -1.006035676
[37,] -0.346813462 -0.467967037
[38,] -0.303885369 -0.346813462
[39,] -0.532179126 -0.303885369
[40,] -0.723459005 -0.532179126
[41,] -0.484177908 -0.723459005
[42,] -0.382736407 -0.484177908
[43,] -0.061010744 -0.382736407
[44,] 0.086393506 -0.061010744
[45,] -0.017990810 0.086393506
[46,] -0.187148047 -0.017990810
[47,] -0.189350738 -0.187148047
[48,] -0.319704803 -0.189350738
[49,] -0.385192405 -0.319704803
[50,] -0.100601308 -0.385192405
[51,] -0.100590532 -0.100601308
[52,] -0.045025333 -0.100590532
[53,] 0.296883471 -0.045025333
[54,] 0.490442794 0.296883471
[55,] 0.663327150 0.490442794
[56,] 0.175157669 0.663327150
[57,] 0.103404879 0.175157669
[58,] 0.273921792 0.103404879
[59,] 0.034814547 0.273921792
[60,] 0.357255251 0.034814547
[61,] 0.269870565 0.357255251
[62,] 0.329956661 0.269870565
[63,] 0.158828527 0.329956661
[64,] 0.349175810 0.158828527
[65,] 0.139801477 0.349175810
[66,] 0.175028212 0.139801477
[67,] 0.224961175 0.175028212
[68,] 0.138163425 0.224961175
[69,] 0.107868735 0.138163425
[70,] -0.145276613 0.107868735
[71,] 0.025986684 -0.145276613
[72,] -0.239579672 0.025986684
[73,] -0.567361169 -0.239579672
[74,] -0.792259464 -0.567361169
[75,] -0.975202424 -0.792259464
[76,] -0.796635193 -0.975202424
[77,] -0.851681174 -0.796635193
[78,] -0.620776213 -0.851681174
[79,] -0.416863685 -0.620776213
[80,] -0.271778119 -0.416863685
[81,] 0.323534783 -0.271778119
[82,] 0.484129773 0.323534783
[83,] 0.764556456 0.484129773
[84,] 0.868951941 0.764556456
[85,] 0.625367894 0.868951941
[86,] 0.100863604 0.625367894
[87,] 0.399185386 0.100863604
[88,] 0.805156582 0.399185386
[89,] 0.644919909 0.805156582
[90,] 0.772080192 0.644919909
[91,] 0.632298659 0.772080192
[92,] 0.652552135 0.632298659
[93,] 0.604995121 0.652552135
[94,] 0.426565499 0.604995121
[95,] 0.169488576 0.426565499
[96,] 0.054488537 0.169488576
[97,] -0.074957134 0.054488537
[98,] 0.092040194 -0.074957134
[99,] 0.638537994 0.092040194
[100,] 0.792718367 0.638537994
[101,] 0.606222662 0.792718367
[102,] 0.426283360 0.606222662
[103,] 0.200376028 0.426283360
[104,] 0.193255395 0.200376028
[105,] 0.339199728 0.193255395
[106,] 0.465382904 0.339199728
[107,] 0.578928105 0.465382904
[108,] 0.351106993 0.578928105
[109,] 0.149196281 0.351106993
[110,] 0.327561926 0.149196281
[111,] 0.056823633 0.327561926
[112,] 0.227595961 0.056823633
[113,] 0.077079139 0.227595961
[114,] -0.041798994 0.077079139
[115,] -0.177414556 -0.041798994
[116,] -0.486727121 -0.177414556
[117,] -0.340597478 -0.486727121
[118,] -0.133927945 -0.340597478
[119,] -0.209875817 -0.133927945
[120,] -0.256764524 -0.209875817
[121,] -0.311685837 -0.256764524
[122,] 0.042627397 -0.311685837
[123,] 0.057413748 0.042627397
[124,] 0.213109097 0.057413748
[125,] 0.083014718 0.213109097
[126,] -0.140266491 0.083014718
[127,] -0.330704988 -0.140266491
[128,] -0.142937514 -0.330704988
[129,] -0.532024188 -0.142937514
[130,] -0.556775301 -0.532024188
[131,] -0.408606406 -0.556775301
[132,] -0.645436428 -0.408606406
[133,] -0.538080526 -0.645436428
[134,] -0.403499919 -0.538080526
[135,] -0.202977002 -0.403499919
[136,] -0.138585870 -0.202977002
[137,] -0.077619566 -0.138585870
[138,] -0.161432334 -0.077619566
[139,] -0.305564661 -0.161432334
[140,] 0.041232565 -0.305564661
[141,] 0.180938570 0.041232565
[142,] -0.141189836 0.180938570
[143,] -0.321618806 -0.141189836
[144,] -0.594780163 -0.321618806
[145,] -0.191735548 -0.594780163
[146,] -0.081189826 -0.191735548
[147,] 0.141028477 -0.081189826
[148,] 0.107885529 0.141028477
[149,] 0.283752063 0.107885529
[150,] 0.533324725 0.283752063
[151,] 0.158348925 0.533324725
[152,] 0.332965357 0.158348925
[153,] 0.466146404 0.332965357
[154,] 0.870762475 0.466146404
[155,] 0.567767349 0.870762475
[156,] 0.548540545 0.567767349
[157,] 0.542986371 0.548540545
[158,] 0.193684221 0.542986371
[159,] -0.328806732 0.193684221
[160,] 0.224075916 -0.328806732
[161,] 0.392986828 0.224075916
[162,] 0.007069378 0.392986828
[163,] 0.183408642 0.007069378
[164,] 0.138091050 0.183408642
[165,] 0.110018279 0.138091050
[166,] 0.016884633 0.110018279
[167,] 0.225368798 0.016884633
[168,] -0.035676856 0.225368798
[169,] 0.095849393 -0.035676856
[170,] -0.065985637 0.095849393
[171,] -0.025569623 -0.065985637
[172,] 0.088923458 -0.025569623
[173,] 0.207480492 0.088923458
[174,] 0.276427243 0.207480492
[175,] -0.312237960 0.276427243
[176,] -0.022438766 -0.312237960
[177,] -0.037058038 -0.022438766
[178,] -0.299910968 -0.037058038
[179,] -0.510710915 -0.299910968
[180,] -0.690882895 -0.510710915
[181,] -0.542178849 -0.690882895
[182,] -0.539800854 -0.542178849
[183,] -0.591937547 -0.539800854
[184,] -0.334644828 -0.591937547
[185,] -0.427857831 -0.334644828
[186,] -0.299839533 -0.427857831
[187,] 0.048277190 -0.299839533
[188,] -0.295274582 0.048277190
[189,] -0.327724566 -0.295274582
[190,] -0.459410524 -0.327724566
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.024987737 0.918483315
2 0.988033266 1.024987737
3 1.211089835 0.988033266
4 -0.670878106 1.211089835
5 -0.593035396 -0.670878106
6 -0.306138571 -0.593035396
7 -0.183660242 -0.306138571
8 -0.475949595 -0.183660242
9 -0.458955370 -0.475949595
10 0.324049477 -0.458955370
11 0.324314646 0.324049477
12 0.270298737 0.324314646
13 0.217749466 0.270298737
14 0.419647558 0.217749466
15 0.190204501 0.419647558
16 -0.256880780 0.190204501
17 -0.422834028 -0.256880780
18 -0.183297244 -0.422834028
19 0.215529987 -0.183297244
20 0.506561895 0.215529987
21 0.343097584 0.506561895
22 -0.011182185 0.343097584
23 -0.045026803 -0.011182185
24 -0.118332939 -0.045026803
25 0.031997224 -0.118332939
26 -0.207192450 0.031997224
27 -0.095849114 -0.207192450
28 0.157468396 -0.095849114
29 0.125065141 0.157468396
30 -0.544370118 0.125065141
31 -0.539070920 -0.544370118
32 -0.569267299 -0.539070920
33 -0.864853634 -0.569267299
34 -0.926875135 -0.864853634
35 -1.006035676 -0.926875135
36 -0.467967037 -1.006035676
37 -0.346813462 -0.467967037
38 -0.303885369 -0.346813462
39 -0.532179126 -0.303885369
40 -0.723459005 -0.532179126
41 -0.484177908 -0.723459005
42 -0.382736407 -0.484177908
43 -0.061010744 -0.382736407
44 0.086393506 -0.061010744
45 -0.017990810 0.086393506
46 -0.187148047 -0.017990810
47 -0.189350738 -0.187148047
48 -0.319704803 -0.189350738
49 -0.385192405 -0.319704803
50 -0.100601308 -0.385192405
51 -0.100590532 -0.100601308
52 -0.045025333 -0.100590532
53 0.296883471 -0.045025333
54 0.490442794 0.296883471
55 0.663327150 0.490442794
56 0.175157669 0.663327150
57 0.103404879 0.175157669
58 0.273921792 0.103404879
59 0.034814547 0.273921792
60 0.357255251 0.034814547
61 0.269870565 0.357255251
62 0.329956661 0.269870565
63 0.158828527 0.329956661
64 0.349175810 0.158828527
65 0.139801477 0.349175810
66 0.175028212 0.139801477
67 0.224961175 0.175028212
68 0.138163425 0.224961175
69 0.107868735 0.138163425
70 -0.145276613 0.107868735
71 0.025986684 -0.145276613
72 -0.239579672 0.025986684
73 -0.567361169 -0.239579672
74 -0.792259464 -0.567361169
75 -0.975202424 -0.792259464
76 -0.796635193 -0.975202424
77 -0.851681174 -0.796635193
78 -0.620776213 -0.851681174
79 -0.416863685 -0.620776213
80 -0.271778119 -0.416863685
81 0.323534783 -0.271778119
82 0.484129773 0.323534783
83 0.764556456 0.484129773
84 0.868951941 0.764556456
85 0.625367894 0.868951941
86 0.100863604 0.625367894
87 0.399185386 0.100863604
88 0.805156582 0.399185386
89 0.644919909 0.805156582
90 0.772080192 0.644919909
91 0.632298659 0.772080192
92 0.652552135 0.632298659
93 0.604995121 0.652552135
94 0.426565499 0.604995121
95 0.169488576 0.426565499
96 0.054488537 0.169488576
97 -0.074957134 0.054488537
98 0.092040194 -0.074957134
99 0.638537994 0.092040194
100 0.792718367 0.638537994
101 0.606222662 0.792718367
102 0.426283360 0.606222662
103 0.200376028 0.426283360
104 0.193255395 0.200376028
105 0.339199728 0.193255395
106 0.465382904 0.339199728
107 0.578928105 0.465382904
108 0.351106993 0.578928105
109 0.149196281 0.351106993
110 0.327561926 0.149196281
111 0.056823633 0.327561926
112 0.227595961 0.056823633
113 0.077079139 0.227595961
114 -0.041798994 0.077079139
115 -0.177414556 -0.041798994
116 -0.486727121 -0.177414556
117 -0.340597478 -0.486727121
118 -0.133927945 -0.340597478
119 -0.209875817 -0.133927945
120 -0.256764524 -0.209875817
121 -0.311685837 -0.256764524
122 0.042627397 -0.311685837
123 0.057413748 0.042627397
124 0.213109097 0.057413748
125 0.083014718 0.213109097
126 -0.140266491 0.083014718
127 -0.330704988 -0.140266491
128 -0.142937514 -0.330704988
129 -0.532024188 -0.142937514
130 -0.556775301 -0.532024188
131 -0.408606406 -0.556775301
132 -0.645436428 -0.408606406
133 -0.538080526 -0.645436428
134 -0.403499919 -0.538080526
135 -0.202977002 -0.403499919
136 -0.138585870 -0.202977002
137 -0.077619566 -0.138585870
138 -0.161432334 -0.077619566
139 -0.305564661 -0.161432334
140 0.041232565 -0.305564661
141 0.180938570 0.041232565
142 -0.141189836 0.180938570
143 -0.321618806 -0.141189836
144 -0.594780163 -0.321618806
145 -0.191735548 -0.594780163
146 -0.081189826 -0.191735548
147 0.141028477 -0.081189826
148 0.107885529 0.141028477
149 0.283752063 0.107885529
150 0.533324725 0.283752063
151 0.158348925 0.533324725
152 0.332965357 0.158348925
153 0.466146404 0.332965357
154 0.870762475 0.466146404
155 0.567767349 0.870762475
156 0.548540545 0.567767349
157 0.542986371 0.548540545
158 0.193684221 0.542986371
159 -0.328806732 0.193684221
160 0.224075916 -0.328806732
161 0.392986828 0.224075916
162 0.007069378 0.392986828
163 0.183408642 0.007069378
164 0.138091050 0.183408642
165 0.110018279 0.138091050
166 0.016884633 0.110018279
167 0.225368798 0.016884633
168 -0.035676856 0.225368798
169 0.095849393 -0.035676856
170 -0.065985637 0.095849393
171 -0.025569623 -0.065985637
172 0.088923458 -0.025569623
173 0.207480492 0.088923458
174 0.276427243 0.207480492
175 -0.312237960 0.276427243
176 -0.022438766 -0.312237960
177 -0.037058038 -0.022438766
178 -0.299910968 -0.037058038
179 -0.510710915 -0.299910968
180 -0.690882895 -0.510710915
181 -0.542178849 -0.690882895
182 -0.539800854 -0.542178849
183 -0.591937547 -0.539800854
184 -0.334644828 -0.591937547
185 -0.427857831 -0.334644828
186 -0.299839533 -0.427857831
187 0.048277190 -0.299839533
188 -0.295274582 0.048277190
189 -0.327724566 -0.295274582
190 -0.459410524 -0.327724566
> 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/wessaorg/rcomp/tmp/7a10n1321989417.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/wessaorg/rcomp/tmp/8lz6g1321989417.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/wessaorg/rcomp/tmp/9vfsm1321989417.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/wessaorg/rcomp/tmp/10ecfq1321989417.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/111wir1321989417.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12z9881321989417.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13u2p51321989417.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/146gch1321989418.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/wessaorg/rcomp/tmp/15ic431321989418.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/wessaorg/rcomp/tmp/16cbj71321989418.tab")
+ }
>
> try(system("convert tmp/1vw9b1321989417.ps tmp/1vw9b1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qyjt1321989417.ps tmp/2qyjt1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cpgr1321989417.ps tmp/3cpgr1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q2qg1321989417.ps tmp/4q2qg1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/59kug1321989417.ps tmp/59kug1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b4r61321989417.ps tmp/6b4r61321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a10n1321989417.ps tmp/7a10n1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lz6g1321989417.ps tmp/8lz6g1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vfsm1321989417.ps tmp/9vfsm1321989417.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ecfq1321989417.ps tmp/10ecfq1321989417.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.190 0.570 6.912