R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2967
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+ ,2675
+ ,2683
+ ,2611
+ ,2802
+ ,2566
+ ,2888
+ ,2836
+ ,2432
+ ,2903
+ ,2619
+ ,2693
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+ ,2690
+ ,2422
+ ,2881
+ ,2585
+ ,2182
+ ,2441
+ ,2709
+ ,2480
+ ,2582
+ ,2585
+ ,2469
+ ,2659
+ ,2690
+ ,2422
+ ,2881
+ ,2585
+ ,2182
+ ,2441
+ ,2709
+ ,2480
+ ,2582
+ ,2585
+ ,2535
+ ,2659
+ ,2690
+ ,2422
+ ,2881
+ ,2585
+ ,2182
+ ,2441
+ ,2709
+ ,2480
+ ,2582
+ ,2613
+ ,2535
+ ,2659
+ ,2690
+ ,2422
+ ,2881
+ ,2585
+ ,2182
+ ,2441
+ ,2709
+ ,2480)
+ ,dim=c(11
+ ,170)
+ ,dimnames=list(c('Y'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'y5'
+ ,'y6'
+ ,'y7'
+ ,'y78'
+ ,'y9'
+ ,'y10')
+ ,1:170))
> y <- array(NA,dim=c(11,170),dimnames=list(c('Y','Y1','Y2','Y3','Y4','y5','y6','y7','y78','y9','y10'),1:170))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y Y1 Y2 Y3 Y4 y5 y6 y7 y78 y9 y10 M1 M2 M3 M4 M5 M6 M7
1 2967 2892 2798 2823 2901 2918 3059 2427 3141 2909 2938 1 0 0 0 0 0 0
2 2397 2967 2892 2798 2823 2901 2918 3059 2427 3141 2909 0 1 0 0 0 0 0
3 3458 2397 2967 2892 2798 2823 2901 2918 3059 2427 3141 0 0 1 0 0 0 0
4 3024 3458 2397 2967 2892 2798 2823 2901 2918 3059 2427 0 0 0 1 0 0 0
5 3100 3024 3458 2397 2967 2892 2798 2823 2901 2918 3059 0 0 0 0 1 0 0
6 2904 3100 3024 3458 2397 2967 2892 2798 2823 2901 2918 0 0 0 0 0 1 0
7 3056 2904 3100 3024 3458 2397 2967 2892 2798 2823 2901 0 0 0 0 0 0 1
8 2771 3056 2904 3100 3024 3458 2397 2967 2892 2798 2823 0 0 0 0 0 0 0
9 2897 2771 3056 2904 3100 3024 3458 2397 2967 2892 2798 0 0 0 0 0 0 0
10 2772 2897 2771 3056 2904 3100 3024 3458 2397 2967 2892 0 0 0 0 0 0 0
11 2857 2772 2897 2771 3056 2904 3100 3024 3458 2397 2967 0 0 0 0 0 0 0
12 3020 2857 2772 2897 2771 3056 2904 3100 3024 3458 2397 0 0 0 0 0 0 0
13 2648 3020 2857 2772 2897 2771 3056 2904 3100 3024 3458 1 0 0 0 0 0 0
14 2364 2648 3020 2857 2772 2897 2771 3056 2904 3100 3024 0 1 0 0 0 0 0
15 3194 2364 2648 3020 2857 2772 2897 2771 3056 2904 3100 0 0 1 0 0 0 0
16 3013 3194 2364 2648 3020 2857 2772 2897 2771 3056 2904 0 0 0 1 0 0 0
17 2560 3013 3194 2364 2648 3020 2857 2772 2897 2771 3056 0 0 0 0 1 0 0
18 3074 2560 3013 3194 2364 2648 3020 2857 2772 2897 2771 0 0 0 0 0 1 0
19 2746 3074 2560 3013 3194 2364 2648 3020 2857 2772 2897 0 0 0 0 0 0 1
20 2846 2746 3074 2560 3013 3194 2364 2648 3020 2857 2772 0 0 0 0 0 0 0
21 3184 2846 2746 3074 2560 3013 3194 2364 2648 3020 2857 0 0 0 0 0 0 0
22 2354 3184 2846 2746 3074 2560 3013 3194 2364 2648 3020 0 0 0 0 0 0 0
23 3080 2354 3184 2846 2746 3074 2560 3013 3194 2364 2648 0 0 0 0 0 0 0
24 2963 3080 2354 3184 2846 2746 3074 2560 3013 3194 2364 0 0 0 0 0 0 0
25 2430 2963 3080 2354 3184 2846 2746 3074 2560 3013 3194 1 0 0 0 0 0 0
26 2296 2430 2963 3080 2354 3184 2846 2746 3074 2560 3013 0 1 0 0 0 0 0
27 2416 2296 2430 2963 3080 2354 3184 2846 2746 3074 2560 0 0 1 0 0 0 0
28 2647 2416 2296 2430 2963 3080 2354 3184 2846 2746 3074 0 0 0 1 0 0 0
29 2789 2647 2416 2296 2430 2963 3080 2354 3184 2846 2746 0 0 0 0 1 0 0
30 2685 2789 2647 2416 2296 2430 2963 3080 2354 3184 2846 0 0 0 0 0 1 0
31 2666 2685 2789 2647 2416 2296 2430 2963 3080 2354 3184 0 0 0 0 0 0 1
32 2882 2666 2685 2789 2647 2416 2296 2430 2963 3080 2354 0 0 0 0 0 0 0
33 2953 2882 2666 2685 2789 2647 2416 2296 2430 2963 3080 0 0 0 0 0 0 0
34 2127 2953 2882 2666 2685 2789 2647 2416 2296 2430 2963 0 0 0 0 0 0 0
35 2563 2127 2953 2882 2666 2685 2789 2647 2416 2296 2430 0 0 0 0 0 0 0
36 3061 2563 2127 2953 2882 2666 2685 2789 2647 2416 2296 0 0 0 0 0 0 0
37 2809 3061 2563 2127 2953 2882 2666 2685 2789 2647 2416 1 0 0 0 0 0 0
38 2861 2809 3061 2563 2127 2953 2882 2666 2685 2789 2647 0 1 0 0 0 0 0
39 2781 2861 2809 3061 2563 2127 2953 2882 2666 2685 2789 0 0 1 0 0 0 0
40 2555 2781 2861 2809 3061 2563 2127 2953 2882 2666 2685 0 0 0 1 0 0 0
41 3206 2555 2781 2861 2809 3061 2563 2127 2953 2882 2666 0 0 0 0 1 0 0
42 2570 3206 2555 2781 2861 2809 3061 2563 2127 2953 2882 0 0 0 0 0 1 0
43 2410 2570 3206 2555 2781 2861 2809 3061 2563 2127 2953 0 0 0 0 0 0 1
44 3195 2410 2570 3206 2555 2781 2861 2809 3061 2563 2127 0 0 0 0 0 0 0
45 2736 3195 2410 2570 3206 2555 2781 2861 2809 3061 2563 0 0 0 0 0 0 0
46 2743 2736 3195 2410 2570 3206 2555 2781 2861 2809 3061 0 0 0 0 0 0 0
47 2934 2743 2736 3195 2410 2570 3206 2555 2781 2861 2809 0 0 0 0 0 0 0
48 2668 2934 2743 2736 3195 2410 2570 3206 2555 2781 2861 0 0 0 0 0 0 0
49 2907 2668 2934 2743 2736 3195 2410 2570 3206 2555 2781 1 0 0 0 0 0 0
50 2866 2907 2668 2934 2743 2736 3195 2410 2570 3206 2555 0 1 0 0 0 0 0
51 2983 2866 2907 2668 2934 2743 2736 3195 2410 2570 3206 0 0 1 0 0 0 0
52 2878 2983 2866 2907 2668 2934 2743 2736 3195 2410 2570 0 0 0 1 0 0 0
53 3225 2878 2983 2866 2907 2668 2934 2743 2736 3195 2410 0 0 0 0 1 0 0
54 2515 3225 2878 2983 2866 2907 2668 2934 2743 2736 3195 0 0 0 0 0 1 0
55 3193 2515 3225 2878 2983 2866 2907 2668 2934 2743 2736 0 0 0 0 0 0 1
56 2663 3193 2515 3225 2878 2983 2866 2907 2668 2934 2743 0 0 0 0 0 0 0
57 2908 2663 3193 2515 3225 2878 2983 2866 2907 2668 2934 0 0 0 0 0 0 0
58 2896 2908 2663 3193 2515 3225 2878 2983 2866 2907 2668 0 0 0 0 0 0 0
59 2853 2896 2908 2663 3193 2515 3225 2878 2983 2866 2907 0 0 0 0 0 0 0
60 3028 2853 2896 2908 2663 3193 2515 3225 2878 2983 2866 0 0 0 0 0 0 0
61 3053 3028 2853 2896 2908 2663 3193 2515 3225 2878 2983 1 0 0 0 0 0 0
62 2455 3053 3028 2853 2896 2908 2663 3193 2515 3225 2878 0 1 0 0 0 0 0
63 3401 2455 3053 3028 2853 2896 2908 2663 3193 2515 3225 0 0 1 0 0 0 0
64 2969 3401 2455 3053 3028 2853 2896 2908 2663 3193 2515 0 0 0 1 0 0 0
65 3243 2969 3401 2455 3053 3028 2853 2896 2908 2663 3193 0 0 0 0 1 0 0
66 2849 3243 2969 3401 2455 3053 3028 2853 2896 2908 2663 0 0 0 0 0 1 0
67 3296 2849 3243 2969 3401 2455 3053 3028 2853 2896 2908 0 0 0 0 0 0 1
68 3121 3296 2849 3243 2969 3401 2455 3053 3028 2853 2896 0 0 0 0 0 0 0
69 3194 3121 3296 2849 3243 2969 3401 2455 3053 3028 2853 0 0 0 0 0 0 0
70 3023 3194 3121 3296 2849 3243 2969 3401 2455 3053 3028 0 0 0 0 0 0 0
71 2984 3023 3194 3121 3296 2849 3243 2969 3401 2455 3053 0 0 0 0 0 0 0
72 3525 2984 3023 3194 3121 3296 2849 3243 2969 3401 2455 0 0 0 0 0 0 0
73 3116 3525 2984 3023 3194 3121 3296 2849 3243 2969 3401 1 0 0 0 0 0 0
74 2383 3116 3525 2984 3023 3194 3121 3296 2849 3243 2969 0 1 0 0 0 0 0
75 3294 2383 3116 3525 2984 3023 3194 3121 3296 2849 3243 0 0 1 0 0 0 0
76 2882 3294 2383 3116 3525 2984 3023 3194 3121 3296 2849 0 0 0 1 0 0 0
77 2820 2882 3294 2383 3116 3525 2984 3023 3194 3121 3296 0 0 0 0 1 0 0
78 2583 2820 2882 3294 2383 3116 3525 2984 3023 3194 3121 0 0 0 0 0 1 0
79 2803 2583 2820 2882 3294 2383 3116 3525 2984 3023 3194 0 0 0 0 0 0 1
80 2767 2803 2583 2820 2882 3294 2383 3116 3525 2984 3023 0 0 0 0 0 0 0
81 2945 2767 2803 2583 2820 2882 3294 2383 3116 3525 2984 0 0 0 0 0 0 0
82 2716 2945 2767 2803 2583 2820 2882 3294 2383 3116 3525 0 0 0 0 0 0 0
83 2644 2716 2945 2767 2803 2583 2820 2882 3294 2383 3116 0 0 0 0 0 0 0
84 2956 2644 2716 2945 2767 2803 2583 2820 2882 3294 2383 0 0 0 0 0 0 0
85 2598 2956 2644 2716 2945 2767 2803 2583 2820 2882 3294 1 0 0 0 0 0 0
86 2171 2598 2956 2644 2716 2945 2767 2803 2583 2820 2882 0 1 0 0 0 0 0
87 2994 2171 2598 2956 2644 2716 2945 2767 2803 2583 2820 0 0 1 0 0 0 0
88 2645 2994 2171 2598 2956 2644 2716 2945 2767 2803 2583 0 0 0 1 0 0 0
89 2724 2645 2994 2171 2598 2956 2644 2716 2945 2767 2803 0 0 0 0 1 0 0
90 2550 2724 2645 2994 2171 2598 2956 2644 2716 2945 2767 0 0 0 0 0 1 0
91 2707 2550 2724 2645 2994 2171 2598 2956 2644 2716 2945 0 0 0 0 0 0 1
92 2679 2707 2550 2724 2645 2994 2171 2598 2956 2644 2716 0 0 0 0 0 0 0
93 2878 2679 2707 2550 2724 2645 2994 2171 2598 2956 2644 0 0 0 0 0 0 0
94 2307 2878 2679 2707 2550 2724 2645 2994 2171 2598 2956 0 0 0 0 0 0 0
95 2496 2307 2878 2679 2707 2550 2724 2645 2994 2171 2598 0 0 0 0 0 0 0
96 2637 2496 2307 2878 2679 2707 2550 2724 2645 2994 2171 0 0 0 0 0 0 0
97 2436 2637 2496 2307 2878 2679 2707 2550 2724 2645 2994 1 0 0 0 0 0 0
98 2426 2436 2637 2496 2307 2878 2679 2707 2550 2724 2645 0 1 0 0 0 0 0
99 2607 2426 2436 2637 2496 2307 2878 2679 2707 2550 2724 0 0 1 0 0 0 0
100 2533 2607 2426 2436 2637 2496 2307 2878 2679 2707 2550 0 0 0 1 0 0 0
101 2888 2533 2607 2426 2436 2637 2496 2307 2878 2679 2707 0 0 0 0 1 0 0
102 2520 2888 2533 2607 2426 2436 2637 2496 2307 2878 2679 0 0 0 0 0 1 0
103 2229 2520 2888 2533 2607 2426 2436 2637 2496 2307 2878 0 0 0 0 0 0 1
104 2804 2229 2520 2888 2533 2607 2426 2436 2637 2496 2307 0 0 0 0 0 0 0
105 2661 2804 2229 2520 2888 2533 2607 2426 2436 2637 2496 0 0 0 0 0 0 0
106 2547 2661 2804 2229 2520 2888 2533 2607 2426 2436 2637 0 0 0 0 0 0 0
107 2509 2547 2661 2804 2229 2520 2888 2533 2607 2426 2436 0 0 0 0 0 0 0
108 2465 2509 2547 2661 2804 2229 2520 2888 2533 2607 2426 0 0 0 0 0 0 0
109 2629 2465 2509 2547 2661 2804 2229 2520 2888 2533 2607 1 0 0 0 0 0 0
110 2706 2629 2465 2509 2547 2661 2804 2229 2520 2888 2533 0 1 0 0 0 0 0
111 2666 2706 2629 2465 2509 2547 2661 2804 2229 2520 2888 0 0 1 0 0 0 0
112 2432 2666 2706 2629 2465 2509 2547 2661 2804 2229 2520 0 0 0 1 0 0 0
113 2836 2432 2666 2706 2629 2465 2509 2547 2661 2804 2229 0 0 0 0 1 0 0
114 2888 2836 2432 2666 2706 2629 2465 2509 2547 2661 2804 0 0 0 0 0 1 0
115 2566 2888 2836 2432 2666 2706 2629 2465 2509 2547 2661 0 0 0 0 0 0 1
116 2802 2566 2888 2836 2432 2666 2706 2629 2465 2509 2547 0 0 0 0 0 0 0
117 2611 2802 2566 2888 2836 2432 2666 2706 2629 2465 2509 0 0 0 0 0 0 0
118 2683 2611 2802 2566 2888 2836 2432 2666 2706 2629 2465 0 0 0 0 0 0 0
119 2675 2683 2611 2802 2566 2888 2836 2432 2666 2706 2629 0 0 0 0 0 0 0
120 2434 2675 2683 2611 2802 2566 2888 2836 2432 2666 2706 0 0 0 0 0 0 0
121 2693 2434 2675 2683 2611 2802 2566 2888 2836 2432 2666 1 0 0 0 0 0 0
122 2619 2693 2434 2675 2683 2611 2802 2566 2888 2836 2432 0 1 0 0 0 0 0
123 2903 2619 2693 2434 2675 2683 2611 2802 2566 2888 2836 0 0 1 0 0 0 0
124 2550 2903 2619 2693 2434 2675 2683 2611 2802 2566 2888 0 0 0 1 0 0 0
125 2900 2550 2903 2619 2693 2434 2675 2683 2611 2802 2566 0 0 0 0 1 0 0
126 2456 2900 2550 2903 2619 2693 2434 2675 2683 2611 2802 0 0 0 0 0 1 0
127 2912 2456 2900 2550 2903 2619 2693 2434 2675 2683 2611 0 0 0 0 0 0 1
128 2883 2912 2456 2900 2550 2903 2619 2693 2434 2675 2683 0 0 0 0 0 0 0
129 2464 2883 2912 2456 2900 2550 2903 2619 2693 2434 2675 0 0 0 0 0 0 0
130 2655 2464 2883 2912 2456 2900 2550 2903 2619 2693 2434 0 0 0 0 0 0 0
131 2447 2655 2464 2883 2912 2456 2900 2550 2903 2619 2693 0 0 0 0 0 0 0
132 2592 2447 2655 2464 2883 2912 2456 2900 2550 2903 2619 0 0 0 0 0 0 0
133 2698 2592 2447 2655 2464 2883 2912 2456 2900 2550 2903 1 0 0 0 0 0 0
134 2274 2698 2592 2447 2655 2464 2883 2912 2456 2900 2550 0 1 0 0 0 0 0
135 2901 2274 2698 2592 2447 2655 2464 2883 2912 2456 2900 0 0 1 0 0 0 0
136 2397 2901 2274 2698 2592 2447 2655 2464 2883 2912 2456 0 0 0 1 0 0 0
137 3004 2397 2901 2274 2698 2592 2447 2655 2464 2883 2912 0 0 0 0 1 0 0
138 2614 3004 2397 2901 2274 2698 2592 2447 2655 2464 2883 0 0 0 0 0 1 0
139 2882 2614 3004 2397 2901 2274 2698 2592 2447 2655 2464 0 0 0 0 0 0 1
140 2671 2882 2614 3004 2397 2901 2274 2698 2592 2447 2655 0 0 0 0 0 0 0
141 2761 2671 2882 2614 3004 2397 2901 2274 2698 2592 2447 0 0 0 0 0 0 0
142 2806 2761 2671 2882 2614 3004 2397 2901 2274 2698 2592 0 0 0 0 0 0 0
143 2414 2806 2761 2671 2882 2614 3004 2397 2901 2274 2698 0 0 0 0 0 0 0
144 2673 2414 2806 2761 2671 2882 2614 3004 2397 2901 2274 0 0 0 0 0 0 0
145 2748 2673 2414 2806 2761 2671 2882 2614 3004 2397 2901 1 0 0 0 0 0 0
146 2112 2748 2673 2414 2806 2761 2671 2882 2614 3004 2397 0 1 0 0 0 0 0
147 2903 2112 2748 2673 2414 2806 2761 2671 2882 2614 3004 0 0 1 0 0 0 0
148 2633 2903 2112 2748 2673 2414 2806 2761 2671 2882 2614 0 0 0 1 0 0 0
149 2684 2633 2903 2112 2748 2673 2414 2806 2761 2671 2882 0 0 0 0 1 0 0
150 2861 2684 2633 2903 2112 2748 2673 2414 2806 2761 2671 0 0 0 0 0 1 0
151 2504 2861 2684 2633 2903 2112 2748 2673 2414 2806 2761 0 0 0 0 0 0 1
152 2708 2504 2861 2684 2633 2903 2112 2748 2673 2414 2806 0 0 0 0 0 0 0
153 2961 2708 2504 2861 2684 2633 2903 2112 2748 2673 2414 0 0 0 0 0 0 0
154 2535 2961 2708 2504 2861 2684 2633 2903 2112 2748 2673 0 0 0 0 0 0 0
155 2688 2535 2961 2708 2504 2861 2684 2633 2903 2112 2748 0 0 0 0 0 0 0
156 2699 2688 2535 2961 2708 2504 2861 2684 2633 2903 2112 0 0 0 0 0 0 0
157 2469 2699 2688 2535 2961 2708 2504 2861 2684 2633 2903 1 0 0 0 0 0 0
158 2585 2469 2699 2688 2535 2961 2708 2504 2861 2684 2633 0 1 0 0 0 0 0
159 2582 2585 2469 2699 2688 2535 2961 2708 2504 2861 2684 0 0 1 0 0 0 0
160 2480 2582 2585 2469 2699 2688 2535 2961 2708 2504 2861 0 0 0 1 0 0 0
161 2709 2480 2582 2585 2469 2699 2688 2535 2961 2708 2504 0 0 0 0 1 0 0
162 2441 2709 2480 2582 2585 2469 2699 2688 2535 2961 2708 0 0 0 0 0 1 0
163 2182 2441 2709 2480 2582 2585 2469 2699 2688 2535 2961 0 0 0 0 0 0 1
164 2585 2182 2441 2709 2480 2582 2585 2469 2699 2688 2535 0 0 0 0 0 0 0
165 2881 2585 2182 2441 2709 2480 2582 2585 2469 2699 2688 0 0 0 0 0 0 0
166 2422 2881 2585 2182 2441 2709 2480 2582 2585 2469 2699 0 0 0 0 0 0 0
167 2690 2422 2881 2585 2182 2441 2709 2480 2582 2585 2469 0 0 0 0 0 0 0
168 2659 2690 2422 2881 2585 2182 2441 2709 2480 2582 2585 0 0 0 0 0 0 0
169 2535 2659 2690 2422 2881 2585 2182 2441 2709 2480 2582 1 0 0 0 0 0 0
170 2613 2535 2659 2690 2422 2881 2585 2182 2441 2709 2480 0 1 0 0 0 0 0
M8 M9 M10 M11 t
1 0 0 0 0 1
2 0 0 0 0 2
3 0 0 0 0 3
4 0 0 0 0 4
5 0 0 0 0 5
6 0 0 0 0 6
7 0 0 0 0 7
8 1 0 0 0 8
9 0 1 0 0 9
10 0 0 1 0 10
11 0 0 0 1 11
12 0 0 0 0 12
13 0 0 0 0 13
14 0 0 0 0 14
15 0 0 0 0 15
16 0 0 0 0 16
17 0 0 0 0 17
18 0 0 0 0 18
19 0 0 0 0 19
20 1 0 0 0 20
21 0 1 0 0 21
22 0 0 1 0 22
23 0 0 0 1 23
24 0 0 0 0 24
25 0 0 0 0 25
26 0 0 0 0 26
27 0 0 0 0 27
28 0 0 0 0 28
29 0 0 0 0 29
30 0 0 0 0 30
31 0 0 0 0 31
32 1 0 0 0 32
33 0 1 0 0 33
34 0 0 1 0 34
35 0 0 0 1 35
36 0 0 0 0 36
37 0 0 0 0 37
38 0 0 0 0 38
39 0 0 0 0 39
40 0 0 0 0 40
41 0 0 0 0 41
42 0 0 0 0 42
43 0 0 0 0 43
44 1 0 0 0 44
45 0 1 0 0 45
46 0 0 1 0 46
47 0 0 0 1 47
48 0 0 0 0 48
49 0 0 0 0 49
50 0 0 0 0 50
51 0 0 0 0 51
52 0 0 0 0 52
53 0 0 0 0 53
54 0 0 0 0 54
55 0 0 0 0 55
56 1 0 0 0 56
57 0 1 0 0 57
58 0 0 1 0 58
59 0 0 0 1 59
60 0 0 0 0 60
61 0 0 0 0 61
62 0 0 0 0 62
63 0 0 0 0 63
64 0 0 0 0 64
65 0 0 0 0 65
66 0 0 0 0 66
67 0 0 0 0 67
68 1 0 0 0 68
69 0 1 0 0 69
70 0 0 1 0 70
71 0 0 0 1 71
72 0 0 0 0 72
73 0 0 0 0 73
74 0 0 0 0 74
75 0 0 0 0 75
76 0 0 0 0 76
77 0 0 0 0 77
78 0 0 0 0 78
79 0 0 0 0 79
80 1 0 0 0 80
81 0 1 0 0 81
82 0 0 1 0 82
83 0 0 0 1 83
84 0 0 0 0 84
85 0 0 0 0 85
86 0 0 0 0 86
87 0 0 0 0 87
88 0 0 0 0 88
89 0 0 0 0 89
90 0 0 0 0 90
91 0 0 0 0 91
92 1 0 0 0 92
93 0 1 0 0 93
94 0 0 1 0 94
95 0 0 0 1 95
96 0 0 0 0 96
97 0 0 0 0 97
98 0 0 0 0 98
99 0 0 0 0 99
100 0 0 0 0 100
101 0 0 0 0 101
102 0 0 0 0 102
103 0 0 0 0 103
104 1 0 0 0 104
105 0 1 0 0 105
106 0 0 1 0 106
107 0 0 0 1 107
108 0 0 0 0 108
109 0 0 0 0 109
110 0 0 0 0 110
111 0 0 0 0 111
112 0 0 0 0 112
113 0 0 0 0 113
114 0 0 0 0 114
115 0 0 0 0 115
116 1 0 0 0 116
117 0 1 0 0 117
118 0 0 1 0 118
119 0 0 0 1 119
120 0 0 0 0 120
121 0 0 0 0 121
122 0 0 0 0 122
123 0 0 0 0 123
124 0 0 0 0 124
125 0 0 0 0 125
126 0 0 0 0 126
127 0 0 0 0 127
128 1 0 0 0 128
129 0 1 0 0 129
130 0 0 1 0 130
131 0 0 0 1 131
132 0 0 0 0 132
133 0 0 0 0 133
134 0 0 0 0 134
135 0 0 0 0 135
136 0 0 0 0 136
137 0 0 0 0 137
138 0 0 0 0 138
139 0 0 0 0 139
140 1 0 0 0 140
141 0 1 0 0 141
142 0 0 1 0 142
143 0 0 0 1 143
144 0 0 0 0 144
145 0 0 0 0 145
146 0 0 0 0 146
147 0 0 0 0 147
148 0 0 0 0 148
149 0 0 0 0 149
150 0 0 0 0 150
151 0 0 0 0 151
152 1 0 0 0 152
153 0 1 0 0 153
154 0 0 1 0 154
155 0 0 0 1 155
156 0 0 0 0 156
157 0 0 0 0 157
158 0 0 0 0 158
159 0 0 0 0 159
160 0 0 0 0 160
161 0 0 0 0 161
162 0 0 0 0 162
163 0 0 0 0 163
164 1 0 0 0 164
165 0 1 0 0 165
166 0 0 1 0 166
167 0 0 0 1 167
168 0 0 0 0 168
169 0 0 0 0 169
170 0 0 0 0 170
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y1 Y2 Y3 Y4 y5
8.900e+02 3.633e-03 1.546e-01 3.028e-01 5.185e-02 1.828e-01
y6 y7 y78 y9 y10 M1
-3.811e-02 -1.529e-01 1.603e-01 1.937e-01 -1.641e-01 -1.481e+01
M2 M3 M4 M5 M6 M7
-3.147e+02 2.368e+02 -3.395e+01 1.497e+02 -9.619e+01 6.562e+01
M8 M9 M10 M11 t
-4.414e+01 8.020e+01 -7.749e+01 -5.160e+01 -5.436e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-626.130 -112.718 8.952 105.518 440.588
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.900e+02 3.783e+02 2.353 0.019958 *
Y1 3.633e-03 8.157e-02 0.045 0.964534
Y2 1.546e-01 7.991e-02 1.934 0.055022 .
Y3 3.028e-01 7.987e-02 3.791 0.000218 ***
Y4 5.185e-02 8.293e-02 0.625 0.532773
y5 1.828e-01 8.314e-02 2.199 0.029460 *
y6 -3.811e-02 8.337e-02 -0.457 0.648301
y7 -1.529e-01 8.262e-02 -1.851 0.066201 .
y78 1.603e-01 8.050e-02 1.991 0.048358 *
y9 1.937e-01 8.085e-02 2.396 0.017830 *
y10 -1.641e-01 8.239e-02 -1.992 0.048267 *
M1 -1.481e+01 8.886e+01 -0.167 0.867868
M2 -3.147e+02 7.925e+01 -3.971 0.000112 ***
M3 2.368e+02 8.938e+01 2.649 0.008953 **
M4 -3.395e+01 8.031e+01 -0.423 0.673066
M5 1.497e+02 8.911e+01 1.680 0.095148 .
M6 -9.619e+01 8.747e+01 -1.100 0.273267
M7 6.562e+01 9.000e+01 0.729 0.467110
M8 -4.414e+01 7.961e+01 -0.554 0.580141
M9 8.020e+01 9.061e+01 0.885 0.377520
M10 -7.749e+01 8.364e+01 -0.926 0.355769
M11 -5.160e+01 9.134e+01 -0.565 0.572961
t -5.436e-01 3.767e-01 -1.443 0.151119
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 190.3 on 147 degrees of freedom
Multiple R-squared: 0.5543, Adjusted R-squared: 0.4876
F-statistic: 8.311 on 22 and 147 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,] 0.4513361 0.902672255 0.5486638724
[2,] 0.4912331 0.982466173 0.5087669137
[3,] 0.7078804 0.584239172 0.2921195862
[4,] 0.7880837 0.423832563 0.2119162815
[5,] 0.7080558 0.583888477 0.2919442385
[6,] 0.8170434 0.365913290 0.1829566452
[7,] 0.7484229 0.503154194 0.2515770969
[8,] 0.6741929 0.651614293 0.3258071465
[9,] 0.9070084 0.185983144 0.0929915718
[10,] 0.9027224 0.194555162 0.0972775808
[11,] 0.9351649 0.129670268 0.0648351339
[12,] 0.9646950 0.070610068 0.0353050342
[13,] 0.9977971 0.004405744 0.0022028719
[14,] 0.9967052 0.006589553 0.0032947764
[15,] 0.9958707 0.008258671 0.0041293356
[16,] 0.9992216 0.001556837 0.0007784184
[17,] 0.9987313 0.002537455 0.0012687274
[18,] 0.9984745 0.003050970 0.0015254852
[19,] 0.9994643 0.001071387 0.0005356934
[20,] 0.9991388 0.001722301 0.0008611503
[21,] 0.9991099 0.001780279 0.0008901397
[22,] 0.9990311 0.001937894 0.0009689469
[23,] 0.9986791 0.002641858 0.0013209288
[24,] 0.9978981 0.004203862 0.0021019310
[25,] 0.9990185 0.001963003 0.0009815017
[26,] 0.9990697 0.001860636 0.0009303179
[27,] 0.9985888 0.002822390 0.0014111952
[28,] 0.9984047 0.003190660 0.0015953298
[29,] 0.9983487 0.003302513 0.0016512567
[30,] 0.9981793 0.003641326 0.0018206629
[31,] 0.9981143 0.003771444 0.0018857221
[32,] 0.9972799 0.005440264 0.0027201320
[33,] 0.9959373 0.008125482 0.0040627408
[34,] 0.9950741 0.009851706 0.0049258530
[35,] 0.9940028 0.011994343 0.0059971717
[36,] 0.9932867 0.013426651 0.0067133256
[37,] 0.9909995 0.018000944 0.0090004722
[38,] 0.9921022 0.015795635 0.0078978173
[39,] 0.9889529 0.022094140 0.0110470700
[40,] 0.9938328 0.012334395 0.0061671974
[41,] 0.9930760 0.013847901 0.0069239505
[42,] 0.9967584 0.006483208 0.0032416042
[43,] 0.9957020 0.008596097 0.0042980485
[44,] 0.9942457 0.011508626 0.0057543129
[45,] 0.9933217 0.013356638 0.0066783192
[46,] 0.9925790 0.014841905 0.0074209524
[47,] 0.9981074 0.003785242 0.0018926210
[48,] 0.9989601 0.002079851 0.0010399255
[49,] 0.9993905 0.001218945 0.0006094724
[50,] 0.9992153 0.001569330 0.0007846648
[51,] 0.9990186 0.001962765 0.0009813824
[52,] 0.9990266 0.001946709 0.0009733547
[53,] 0.9994069 0.001186286 0.0005931429
[54,] 0.9993638 0.001272407 0.0006362036
[55,] 0.9990518 0.001896473 0.0009482365
[56,] 0.9986471 0.002705747 0.0013528736
[57,] 0.9986904 0.002619274 0.0013096370
[58,] 0.9987539 0.002492284 0.0012461419
[59,] 0.9988328 0.002334446 0.0011672231
[60,] 0.9983133 0.003373434 0.0016867171
[61,] 0.9989423 0.002115321 0.0010576604
[62,] 0.9984287 0.003142525 0.0015712626
[63,] 0.9983614 0.003277135 0.0016385677
[64,] 0.9976603 0.004679313 0.0023396564
[65,] 0.9970326 0.005934709 0.0029673547
[66,] 0.9976855 0.004628926 0.0023144632
[67,] 0.9965504 0.006899158 0.0034495790
[68,] 0.9949291 0.010141820 0.0050709098
[69,] 0.9950171 0.009965826 0.0049829129
[70,] 0.9934563 0.013087317 0.0065436587
[71,] 0.9923081 0.015383881 0.0076919403
[72,] 0.9906515 0.018696932 0.0093484658
[73,] 0.9882903 0.023419403 0.0117097013
[74,] 0.9860549 0.027890164 0.0139450820
[75,] 0.9816381 0.036723749 0.0183618747
[76,] 0.9756073 0.048785405 0.0243927027
[77,] 0.9713446 0.057310743 0.0286553716
[78,] 0.9900903 0.019819445 0.0099097226
[79,] 0.9869150 0.026169987 0.0130849933
[80,] 0.9823591 0.035281892 0.0176409459
[81,] 0.9772190 0.045562051 0.0227810257
[82,] 0.9735823 0.052835353 0.0264176765
[83,] 0.9663806 0.067238844 0.0336194222
[84,] 0.9597593 0.080481319 0.0402406596
[85,] 0.9601914 0.079617233 0.0398086164
[86,] 0.9541255 0.091748933 0.0458744667
[87,] 0.9518525 0.096294922 0.0481474609
[88,] 0.9554305 0.089139094 0.0445695470
[89,] 0.9633587 0.073282673 0.0366413366
[90,] 0.9583640 0.083271938 0.0416359688
[91,] 0.9468123 0.106375373 0.0531876864
[92,] 0.9478456 0.104308787 0.0521543935
[93,] 0.9288035 0.142393070 0.0711965351
[94,] 0.9110341 0.177931792 0.0889658958
[95,] 0.9181935 0.163612999 0.0818064996
[96,] 0.8991517 0.201696676 0.1008483382
[97,] 0.9067204 0.186559165 0.0932795823
[98,] 0.8915062 0.216987622 0.1084938112
[99,] 0.8645418 0.270916423 0.1354582116
[100,] 0.8330371 0.333925773 0.1669628863
[101,] 0.8333584 0.333283154 0.1666415770
[102,] 0.8219305 0.356139027 0.1780695133
[103,] 0.8254589 0.349082243 0.1745411213
[104,] 0.8732571 0.253485707 0.1267428534
[105,] 0.8786881 0.242623769 0.1213118844
[106,] 0.8434821 0.313035705 0.1565178525
[107,] 0.7957270 0.408546068 0.2042730342
[108,] 0.7352926 0.529414814 0.2647074070
[109,] 0.7442073 0.511585458 0.2557927290
[110,] 0.6700183 0.659963467 0.3299817333
[111,] 0.6643690 0.671262025 0.3356310124
[112,] 0.5924427 0.815114614 0.4075573070
[113,] 0.6255738 0.748852357 0.3744261783
[114,] 0.8375785 0.324842980 0.1624214902
[115,] 0.8227564 0.354487267 0.1772436337
[116,] 0.7850197 0.429960595 0.2149802976
[117,] 0.6759884 0.648023217 0.3240116086
[118,] 0.6427060 0.714588080 0.3572940398
[119,] 0.4743728 0.948745692 0.5256271539
> postscript(file="/var/www/html/rcomp/tmp/1lyso1291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2e8rr1291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3e8rr1291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4e8rr1291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e8rr1291135831.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 = 170
Frequency = 1
1 2 3 4 5
13.71252934 -99.93964442 440.58762056 116.52642014 119.42787150
6 7 8 9 10
-76.36062745 117.48651865 -255.20620542 -221.78291363 43.24781366
11 12 13 14 15
87.33510515 -58.22774724 -123.37094017 -215.22376943 78.75805567
16 17 18 19 20
297.13845216 -346.99685097 242.51165754 -72.91105027 -76.84636118
21 22 23 24 25
118.82299847 -149.80455797 210.40748496 -107.64700562 -211.59234255
26 27 28 29 30
-334.40196520 -626.12983508 83.08747532 -114.40887556 250.35136553
31 32 33 34 35
58.53379848 -20.64045560 121.92398692 -461.61803714 -144.34184722
36 37 38 39 40
334.94584699 171.60354231 378.20120238 -154.14608342 -214.45334702
41 42 43 44 45
6.89875818 -42.80860573 -230.96925054 256.05381362 -84.36381312
46 47 48 49 50
24.99405285 100.07245393 44.33054491 -28.72252765 240.83023757
51 52 53 54 55
197.70070268 7.29581999 105.74764994 -184.17974517 175.86544156
56 57 58 59 60
-215.81422974 61.46928666 -12.66997135 161.85110561 130.61154135
61 62 63 64 65
165.97344447 -77.05945087 281.86061134 75.44554709 340.69755030
66 67 68 69 70
-133.53793405 379.47835428 99.90279756 64.35820999 161.28585494
71 72 73 74 75
100.82191975 337.33338934 179.52093286 -326.98830098 -5.90396096
76 77 78 79 80
-52.27966708 -224.48425974 -315.04263691 84.13576320 -129.81908684
81 82 83 84 85
-82.22094543 218.03319714 0.33246240 -44.55652585 -99.13690984
86 87 88 89 90
-256.84199662 24.98950208 58.41125921 -103.99215408 -142.35161850
91 92 93 94 95
102.03616611 -90.26488739 24.35010357 -136.23080641 -127.40300334
96 97 98 99 100
-205.51350926 -83.81642942 99.80171047 -162.33207655 9.03170566
101 102 103 104 105
60.09691804 13.75635399 -350.31181707 72.14689228 -3.93143089
106 107 108 109 110
82.79001315 -107.62682162 -102.82956879 -60.84122027 321.81844428
111 112 113 114 115
0.05925702 -137.22976607 -88.15793651 355.93229405 -126.88166157
116 117 118 119 120
133.38428958 -140.16608039 8.87285001 -61.46001799 -138.51045563
121 122 123 124 125
52.61667939 183.70391035 73.88403502 -55.27300952 64.82454246
126 127 128 129 130
-155.19814036 121.74781599 219.19813833 -209.77912096 -81.45382690
131 132 133 134 135
-213.75962840 -77.54207907 52.99642455 50.35019177 93.50741284
136 137 138 139 140
-290.89527216 303.21207804 67.58560908 233.31514493 -31.91193082
141 142 143 144 145
-47.77233050 163.75151839 -201.28265086 -96.46866393 127.61730971
146 147 148 149 150
-253.19738116 14.64010114 80.23949911 30.60072918 165.96997470
151 152 153 154 155
-92.48379049 78.76337117 61.88460262 91.84127941 75.72414596
156 157 158 159 160
-120.75471583 -93.11302077 122.58420197 -257.47534234 22.95488318
161 162 163 164 165
-153.46602078 -46.62794671 -399.04143326 -38.94614556 337.20744668
166 167 168 169 170
46.96062021 119.32929168 104.82894862 -63.44747196 166.36260989
> postscript(file="/var/www/html/rcomp/tmp/6ozqc1291135831.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 = 170
Frequency = 1
lag(myerror, k = 1) myerror
0 13.71252934 NA
1 -99.93964442 13.71252934
2 440.58762056 -99.93964442
3 116.52642014 440.58762056
4 119.42787150 116.52642014
5 -76.36062745 119.42787150
6 117.48651865 -76.36062745
7 -255.20620542 117.48651865
8 -221.78291363 -255.20620542
9 43.24781366 -221.78291363
10 87.33510515 43.24781366
11 -58.22774724 87.33510515
12 -123.37094017 -58.22774724
13 -215.22376943 -123.37094017
14 78.75805567 -215.22376943
15 297.13845216 78.75805567
16 -346.99685097 297.13845216
17 242.51165754 -346.99685097
18 -72.91105027 242.51165754
19 -76.84636118 -72.91105027
20 118.82299847 -76.84636118
21 -149.80455797 118.82299847
22 210.40748496 -149.80455797
23 -107.64700562 210.40748496
24 -211.59234255 -107.64700562
25 -334.40196520 -211.59234255
26 -626.12983508 -334.40196520
27 83.08747532 -626.12983508
28 -114.40887556 83.08747532
29 250.35136553 -114.40887556
30 58.53379848 250.35136553
31 -20.64045560 58.53379848
32 121.92398692 -20.64045560
33 -461.61803714 121.92398692
34 -144.34184722 -461.61803714
35 334.94584699 -144.34184722
36 171.60354231 334.94584699
37 378.20120238 171.60354231
38 -154.14608342 378.20120238
39 -214.45334702 -154.14608342
40 6.89875818 -214.45334702
41 -42.80860573 6.89875818
42 -230.96925054 -42.80860573
43 256.05381362 -230.96925054
44 -84.36381312 256.05381362
45 24.99405285 -84.36381312
46 100.07245393 24.99405285
47 44.33054491 100.07245393
48 -28.72252765 44.33054491
49 240.83023757 -28.72252765
50 197.70070268 240.83023757
51 7.29581999 197.70070268
52 105.74764994 7.29581999
53 -184.17974517 105.74764994
54 175.86544156 -184.17974517
55 -215.81422974 175.86544156
56 61.46928666 -215.81422974
57 -12.66997135 61.46928666
58 161.85110561 -12.66997135
59 130.61154135 161.85110561
60 165.97344447 130.61154135
61 -77.05945087 165.97344447
62 281.86061134 -77.05945087
63 75.44554709 281.86061134
64 340.69755030 75.44554709
65 -133.53793405 340.69755030
66 379.47835428 -133.53793405
67 99.90279756 379.47835428
68 64.35820999 99.90279756
69 161.28585494 64.35820999
70 100.82191975 161.28585494
71 337.33338934 100.82191975
72 179.52093286 337.33338934
73 -326.98830098 179.52093286
74 -5.90396096 -326.98830098
75 -52.27966708 -5.90396096
76 -224.48425974 -52.27966708
77 -315.04263691 -224.48425974
78 84.13576320 -315.04263691
79 -129.81908684 84.13576320
80 -82.22094543 -129.81908684
81 218.03319714 -82.22094543
82 0.33246240 218.03319714
83 -44.55652585 0.33246240
84 -99.13690984 -44.55652585
85 -256.84199662 -99.13690984
86 24.98950208 -256.84199662
87 58.41125921 24.98950208
88 -103.99215408 58.41125921
89 -142.35161850 -103.99215408
90 102.03616611 -142.35161850
91 -90.26488739 102.03616611
92 24.35010357 -90.26488739
93 -136.23080641 24.35010357
94 -127.40300334 -136.23080641
95 -205.51350926 -127.40300334
96 -83.81642942 -205.51350926
97 99.80171047 -83.81642942
98 -162.33207655 99.80171047
99 9.03170566 -162.33207655
100 60.09691804 9.03170566
101 13.75635399 60.09691804
102 -350.31181707 13.75635399
103 72.14689228 -350.31181707
104 -3.93143089 72.14689228
105 82.79001315 -3.93143089
106 -107.62682162 82.79001315
107 -102.82956879 -107.62682162
108 -60.84122027 -102.82956879
109 321.81844428 -60.84122027
110 0.05925702 321.81844428
111 -137.22976607 0.05925702
112 -88.15793651 -137.22976607
113 355.93229405 -88.15793651
114 -126.88166157 355.93229405
115 133.38428958 -126.88166157
116 -140.16608039 133.38428958
117 8.87285001 -140.16608039
118 -61.46001799 8.87285001
119 -138.51045563 -61.46001799
120 52.61667939 -138.51045563
121 183.70391035 52.61667939
122 73.88403502 183.70391035
123 -55.27300952 73.88403502
124 64.82454246 -55.27300952
125 -155.19814036 64.82454246
126 121.74781599 -155.19814036
127 219.19813833 121.74781599
128 -209.77912096 219.19813833
129 -81.45382690 -209.77912096
130 -213.75962840 -81.45382690
131 -77.54207907 -213.75962840
132 52.99642455 -77.54207907
133 50.35019177 52.99642455
134 93.50741284 50.35019177
135 -290.89527216 93.50741284
136 303.21207804 -290.89527216
137 67.58560908 303.21207804
138 233.31514493 67.58560908
139 -31.91193082 233.31514493
140 -47.77233050 -31.91193082
141 163.75151839 -47.77233050
142 -201.28265086 163.75151839
143 -96.46866393 -201.28265086
144 127.61730971 -96.46866393
145 -253.19738116 127.61730971
146 14.64010114 -253.19738116
147 80.23949911 14.64010114
148 30.60072918 80.23949911
149 165.96997470 30.60072918
150 -92.48379049 165.96997470
151 78.76337117 -92.48379049
152 61.88460262 78.76337117
153 91.84127941 61.88460262
154 75.72414596 91.84127941
155 -120.75471583 75.72414596
156 -93.11302077 -120.75471583
157 122.58420197 -93.11302077
158 -257.47534234 122.58420197
159 22.95488318 -257.47534234
160 -153.46602078 22.95488318
161 -46.62794671 -153.46602078
162 -399.04143326 -46.62794671
163 -38.94614556 -399.04143326
164 337.20744668 -38.94614556
165 46.96062021 337.20744668
166 119.32929168 46.96062021
167 104.82894862 119.32929168
168 -63.44747196 104.82894862
169 166.36260989 -63.44747196
170 NA 166.36260989
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -99.93964442 13.71252934
[2,] 440.58762056 -99.93964442
[3,] 116.52642014 440.58762056
[4,] 119.42787150 116.52642014
[5,] -76.36062745 119.42787150
[6,] 117.48651865 -76.36062745
[7,] -255.20620542 117.48651865
[8,] -221.78291363 -255.20620542
[9,] 43.24781366 -221.78291363
[10,] 87.33510515 43.24781366
[11,] -58.22774724 87.33510515
[12,] -123.37094017 -58.22774724
[13,] -215.22376943 -123.37094017
[14,] 78.75805567 -215.22376943
[15,] 297.13845216 78.75805567
[16,] -346.99685097 297.13845216
[17,] 242.51165754 -346.99685097
[18,] -72.91105027 242.51165754
[19,] -76.84636118 -72.91105027
[20,] 118.82299847 -76.84636118
[21,] -149.80455797 118.82299847
[22,] 210.40748496 -149.80455797
[23,] -107.64700562 210.40748496
[24,] -211.59234255 -107.64700562
[25,] -334.40196520 -211.59234255
[26,] -626.12983508 -334.40196520
[27,] 83.08747532 -626.12983508
[28,] -114.40887556 83.08747532
[29,] 250.35136553 -114.40887556
[30,] 58.53379848 250.35136553
[31,] -20.64045560 58.53379848
[32,] 121.92398692 -20.64045560
[33,] -461.61803714 121.92398692
[34,] -144.34184722 -461.61803714
[35,] 334.94584699 -144.34184722
[36,] 171.60354231 334.94584699
[37,] 378.20120238 171.60354231
[38,] -154.14608342 378.20120238
[39,] -214.45334702 -154.14608342
[40,] 6.89875818 -214.45334702
[41,] -42.80860573 6.89875818
[42,] -230.96925054 -42.80860573
[43,] 256.05381362 -230.96925054
[44,] -84.36381312 256.05381362
[45,] 24.99405285 -84.36381312
[46,] 100.07245393 24.99405285
[47,] 44.33054491 100.07245393
[48,] -28.72252765 44.33054491
[49,] 240.83023757 -28.72252765
[50,] 197.70070268 240.83023757
[51,] 7.29581999 197.70070268
[52,] 105.74764994 7.29581999
[53,] -184.17974517 105.74764994
[54,] 175.86544156 -184.17974517
[55,] -215.81422974 175.86544156
[56,] 61.46928666 -215.81422974
[57,] -12.66997135 61.46928666
[58,] 161.85110561 -12.66997135
[59,] 130.61154135 161.85110561
[60,] 165.97344447 130.61154135
[61,] -77.05945087 165.97344447
[62,] 281.86061134 -77.05945087
[63,] 75.44554709 281.86061134
[64,] 340.69755030 75.44554709
[65,] -133.53793405 340.69755030
[66,] 379.47835428 -133.53793405
[67,] 99.90279756 379.47835428
[68,] 64.35820999 99.90279756
[69,] 161.28585494 64.35820999
[70,] 100.82191975 161.28585494
[71,] 337.33338934 100.82191975
[72,] 179.52093286 337.33338934
[73,] -326.98830098 179.52093286
[74,] -5.90396096 -326.98830098
[75,] -52.27966708 -5.90396096
[76,] -224.48425974 -52.27966708
[77,] -315.04263691 -224.48425974
[78,] 84.13576320 -315.04263691
[79,] -129.81908684 84.13576320
[80,] -82.22094543 -129.81908684
[81,] 218.03319714 -82.22094543
[82,] 0.33246240 218.03319714
[83,] -44.55652585 0.33246240
[84,] -99.13690984 -44.55652585
[85,] -256.84199662 -99.13690984
[86,] 24.98950208 -256.84199662
[87,] 58.41125921 24.98950208
[88,] -103.99215408 58.41125921
[89,] -142.35161850 -103.99215408
[90,] 102.03616611 -142.35161850
[91,] -90.26488739 102.03616611
[92,] 24.35010357 -90.26488739
[93,] -136.23080641 24.35010357
[94,] -127.40300334 -136.23080641
[95,] -205.51350926 -127.40300334
[96,] -83.81642942 -205.51350926
[97,] 99.80171047 -83.81642942
[98,] -162.33207655 99.80171047
[99,] 9.03170566 -162.33207655
[100,] 60.09691804 9.03170566
[101,] 13.75635399 60.09691804
[102,] -350.31181707 13.75635399
[103,] 72.14689228 -350.31181707
[104,] -3.93143089 72.14689228
[105,] 82.79001315 -3.93143089
[106,] -107.62682162 82.79001315
[107,] -102.82956879 -107.62682162
[108,] -60.84122027 -102.82956879
[109,] 321.81844428 -60.84122027
[110,] 0.05925702 321.81844428
[111,] -137.22976607 0.05925702
[112,] -88.15793651 -137.22976607
[113,] 355.93229405 -88.15793651
[114,] -126.88166157 355.93229405
[115,] 133.38428958 -126.88166157
[116,] -140.16608039 133.38428958
[117,] 8.87285001 -140.16608039
[118,] -61.46001799 8.87285001
[119,] -138.51045563 -61.46001799
[120,] 52.61667939 -138.51045563
[121,] 183.70391035 52.61667939
[122,] 73.88403502 183.70391035
[123,] -55.27300952 73.88403502
[124,] 64.82454246 -55.27300952
[125,] -155.19814036 64.82454246
[126,] 121.74781599 -155.19814036
[127,] 219.19813833 121.74781599
[128,] -209.77912096 219.19813833
[129,] -81.45382690 -209.77912096
[130,] -213.75962840 -81.45382690
[131,] -77.54207907 -213.75962840
[132,] 52.99642455 -77.54207907
[133,] 50.35019177 52.99642455
[134,] 93.50741284 50.35019177
[135,] -290.89527216 93.50741284
[136,] 303.21207804 -290.89527216
[137,] 67.58560908 303.21207804
[138,] 233.31514493 67.58560908
[139,] -31.91193082 233.31514493
[140,] -47.77233050 -31.91193082
[141,] 163.75151839 -47.77233050
[142,] -201.28265086 163.75151839
[143,] -96.46866393 -201.28265086
[144,] 127.61730971 -96.46866393
[145,] -253.19738116 127.61730971
[146,] 14.64010114 -253.19738116
[147,] 80.23949911 14.64010114
[148,] 30.60072918 80.23949911
[149,] 165.96997470 30.60072918
[150,] -92.48379049 165.96997470
[151,] 78.76337117 -92.48379049
[152,] 61.88460262 78.76337117
[153,] 91.84127941 61.88460262
[154,] 75.72414596 91.84127941
[155,] -120.75471583 75.72414596
[156,] -93.11302077 -120.75471583
[157,] 122.58420197 -93.11302077
[158,] -257.47534234 122.58420197
[159,] 22.95488318 -257.47534234
[160,] -153.46602078 22.95488318
[161,] -46.62794671 -153.46602078
[162,] -399.04143326 -46.62794671
[163,] -38.94614556 -399.04143326
[164,] 337.20744668 -38.94614556
[165,] 46.96062021 337.20744668
[166,] 119.32929168 46.96062021
[167,] 104.82894862 119.32929168
[168,] -63.44747196 104.82894862
[169,] 166.36260989 -63.44747196
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -99.93964442 13.71252934
2 440.58762056 -99.93964442
3 116.52642014 440.58762056
4 119.42787150 116.52642014
5 -76.36062745 119.42787150
6 117.48651865 -76.36062745
7 -255.20620542 117.48651865
8 -221.78291363 -255.20620542
9 43.24781366 -221.78291363
10 87.33510515 43.24781366
11 -58.22774724 87.33510515
12 -123.37094017 -58.22774724
13 -215.22376943 -123.37094017
14 78.75805567 -215.22376943
15 297.13845216 78.75805567
16 -346.99685097 297.13845216
17 242.51165754 -346.99685097
18 -72.91105027 242.51165754
19 -76.84636118 -72.91105027
20 118.82299847 -76.84636118
21 -149.80455797 118.82299847
22 210.40748496 -149.80455797
23 -107.64700562 210.40748496
24 -211.59234255 -107.64700562
25 -334.40196520 -211.59234255
26 -626.12983508 -334.40196520
27 83.08747532 -626.12983508
28 -114.40887556 83.08747532
29 250.35136553 -114.40887556
30 58.53379848 250.35136553
31 -20.64045560 58.53379848
32 121.92398692 -20.64045560
33 -461.61803714 121.92398692
34 -144.34184722 -461.61803714
35 334.94584699 -144.34184722
36 171.60354231 334.94584699
37 378.20120238 171.60354231
38 -154.14608342 378.20120238
39 -214.45334702 -154.14608342
40 6.89875818 -214.45334702
41 -42.80860573 6.89875818
42 -230.96925054 -42.80860573
43 256.05381362 -230.96925054
44 -84.36381312 256.05381362
45 24.99405285 -84.36381312
46 100.07245393 24.99405285
47 44.33054491 100.07245393
48 -28.72252765 44.33054491
49 240.83023757 -28.72252765
50 197.70070268 240.83023757
51 7.29581999 197.70070268
52 105.74764994 7.29581999
53 -184.17974517 105.74764994
54 175.86544156 -184.17974517
55 -215.81422974 175.86544156
56 61.46928666 -215.81422974
57 -12.66997135 61.46928666
58 161.85110561 -12.66997135
59 130.61154135 161.85110561
60 165.97344447 130.61154135
61 -77.05945087 165.97344447
62 281.86061134 -77.05945087
63 75.44554709 281.86061134
64 340.69755030 75.44554709
65 -133.53793405 340.69755030
66 379.47835428 -133.53793405
67 99.90279756 379.47835428
68 64.35820999 99.90279756
69 161.28585494 64.35820999
70 100.82191975 161.28585494
71 337.33338934 100.82191975
72 179.52093286 337.33338934
73 -326.98830098 179.52093286
74 -5.90396096 -326.98830098
75 -52.27966708 -5.90396096
76 -224.48425974 -52.27966708
77 -315.04263691 -224.48425974
78 84.13576320 -315.04263691
79 -129.81908684 84.13576320
80 -82.22094543 -129.81908684
81 218.03319714 -82.22094543
82 0.33246240 218.03319714
83 -44.55652585 0.33246240
84 -99.13690984 -44.55652585
85 -256.84199662 -99.13690984
86 24.98950208 -256.84199662
87 58.41125921 24.98950208
88 -103.99215408 58.41125921
89 -142.35161850 -103.99215408
90 102.03616611 -142.35161850
91 -90.26488739 102.03616611
92 24.35010357 -90.26488739
93 -136.23080641 24.35010357
94 -127.40300334 -136.23080641
95 -205.51350926 -127.40300334
96 -83.81642942 -205.51350926
97 99.80171047 -83.81642942
98 -162.33207655 99.80171047
99 9.03170566 -162.33207655
100 60.09691804 9.03170566
101 13.75635399 60.09691804
102 -350.31181707 13.75635399
103 72.14689228 -350.31181707
104 -3.93143089 72.14689228
105 82.79001315 -3.93143089
106 -107.62682162 82.79001315
107 -102.82956879 -107.62682162
108 -60.84122027 -102.82956879
109 321.81844428 -60.84122027
110 0.05925702 321.81844428
111 -137.22976607 0.05925702
112 -88.15793651 -137.22976607
113 355.93229405 -88.15793651
114 -126.88166157 355.93229405
115 133.38428958 -126.88166157
116 -140.16608039 133.38428958
117 8.87285001 -140.16608039
118 -61.46001799 8.87285001
119 -138.51045563 -61.46001799
120 52.61667939 -138.51045563
121 183.70391035 52.61667939
122 73.88403502 183.70391035
123 -55.27300952 73.88403502
124 64.82454246 -55.27300952
125 -155.19814036 64.82454246
126 121.74781599 -155.19814036
127 219.19813833 121.74781599
128 -209.77912096 219.19813833
129 -81.45382690 -209.77912096
130 -213.75962840 -81.45382690
131 -77.54207907 -213.75962840
132 52.99642455 -77.54207907
133 50.35019177 52.99642455
134 93.50741284 50.35019177
135 -290.89527216 93.50741284
136 303.21207804 -290.89527216
137 67.58560908 303.21207804
138 233.31514493 67.58560908
139 -31.91193082 233.31514493
140 -47.77233050 -31.91193082
141 163.75151839 -47.77233050
142 -201.28265086 163.75151839
143 -96.46866393 -201.28265086
144 127.61730971 -96.46866393
145 -253.19738116 127.61730971
146 14.64010114 -253.19738116
147 80.23949911 14.64010114
148 30.60072918 80.23949911
149 165.96997470 30.60072918
150 -92.48379049 165.96997470
151 78.76337117 -92.48379049
152 61.88460262 78.76337117
153 91.84127941 61.88460262
154 75.72414596 91.84127941
155 -120.75471583 75.72414596
156 -93.11302077 -120.75471583
157 122.58420197 -93.11302077
158 -257.47534234 122.58420197
159 22.95488318 -257.47534234
160 -153.46602078 22.95488318
161 -46.62794671 -153.46602078
162 -399.04143326 -46.62794671
163 -38.94614556 -399.04143326
164 337.20744668 -38.94614556
165 46.96062021 337.20744668
166 119.32929168 46.96062021
167 104.82894862 119.32929168
168 -63.44747196 104.82894862
169 166.36260989 -63.44747196
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7z8qf1291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z8qf1291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z8qf1291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10az701291135831.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11v0no1291135831.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12hi4b1291135831.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13ds221291135831.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14yb081291135831.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15jbze1291135831.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/165uxk1291135831.tab")
+ }
>
> try(system("convert tmp/1lyso1291135831.ps tmp/1lyso1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e8rr1291135831.ps tmp/2e8rr1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e8rr1291135831.ps tmp/3e8rr1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/4e8rr1291135831.ps tmp/4e8rr1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e8rr1291135831.ps tmp/5e8rr1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ozqc1291135831.ps tmp/6ozqc1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z8qf1291135831.ps tmp/7z8qf1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/8z8qf1291135831.ps tmp/8z8qf1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z8qf1291135831.ps tmp/9z8qf1291135831.png",intern=TRUE))
character(0)
> try(system("convert tmp/10az701291135831.ps tmp/10az701291135831.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.189 1.873 11.916