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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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(65
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+ ,2)
+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('Geblogde_berekeningen'
+ ,'Aantal_Logins'
+ ,'Aantal_revisions'
+ ,'Aantal_lange_feedback'
+ ,'Aantal_hyperlinks'
+ ,'Aantal_gedeelde_compendiums')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('Geblogde_berekeningen','Aantal_Logins','Aantal_revisions','Aantal_lange_feedback','Aantal_hyperlinks','Aantal_gedeelde_compendiums'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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
Geblogde_berekeningen Aantal_Logins Aantal_revisions Aantal_lange_feedback
1 65 44 21387 68
2 54 48 12341 72
3 58 37 11397 37
4 75 68 25533 70
5 41 29 6630 30
6 0 17 7745 53
7 111 77 25304 74
8 1 16 1271 22
9 36 35 18035 68
10 60 24 13284 47
11 63 60 15628 87
12 71 72 13990 123
13 38 41 8532 69
14 76 39 13953 89
15 61 51 7210 45
16 125 100 22436 122
17 84 39 20238 75
18 69 97 10244 45
19 77 34 17390 53
20 95 47 9917 86
21 78 45 29625 82
22 76 54 13193 76
23 40 17 6815 51
24 81 31 11807 104
25 102 73 21472 83
26 70 85 19589 78
27 75 74 12266 59
28 93 52 18391 83
29 42 32 6711 71
30 95 32 9004 81
31 87 52 34301 93
32 44 45 8061 72
33 84 60 19463 107
34 28 23 2053 75
35 87 51 29618 84
36 71 37 3963 69
37 68 79 17609 90
38 50 45 11738 51
39 30 26 11082 18
40 86 101 22648 75
41 75 53 16538 59
42 46 38 10149 63
43 52 43 19787 68
44 31 27 7740 47
45 30 49 5873 29
46 70 88 11694 69
47 20 42 7935 66
48 84 51 15093 106
49 81 63 14533 73
50 79 38 15834 87
51 70 51 15699 65
52 8 24 2694 7
53 67 186 13834 111
54 21 17 3597 61
55 30 57 5296 41
56 70 27 21637 70
57 87 54 18081 112
58 87 101 29016 71
59 112 69 27279 90
60 54 49 12889 69
61 96 82 21550 85
62 93 70 34042 47
63 49 55 8190 50
64 49 57 16163 76
65 38 37 23471 60
66 64 32 14220 35
67 62 80 12759 72
68 66 94 18142 88
69 98 48 13883 66
70 97 31 14069 58
71 56 33 11131 81
72 22 28 3007 63
73 51 43 12530 91
74 56 35 13205 50
75 94 30 13025 75
76 98 44 18778 85
77 76 55 19793 75
78 57 58 8238 70
79 75 36 11285 78
80 48 37 10490 61
81 48 29 10457 55
82 109 65 17313 60
83 27 52 9592 83
84 83 48 14282 38
85 49 25 7905 27
86 24 37 4525 62
87 43 34 21179 82
88 44 95 13724 79
89 49 52 18446 59
90 106 66 25961 80
91 42 46 6602 36
92 108 47 16795 88
93 27 41 5463 63
94 79 48 11299 73
95 49 48 20390 71
96 64 27 18558 76
97 75 29 26262 67
98 115 51 25267 66
99 92 88 21091 123
100 106 69 32425 65
101 73 60 24380 87
102 105 37 20460 77
103 30 101 6515 37
104 13 14 7409 64
105 69 43 12300 22
106 72 90 27127 35
107 80 27 27687 61
108 106 60 19255 80
109 28 32 15070 54
110 70 61 6291 60
111 51 39 16577 87
112 90 55 13027 75
113 12 10 238 0
114 84 47 17103 54
115 23 25 3913 30
116 57 31 5654 66
117 84 53 14354 56
118 4 16 338 0
119 56 33 8852 32
120 18 19 3988 9
121 86 71 15964 78
122 39 34 14784 90
123 16 42 2667 56
124 18 27 7164 35
125 16 34 1888 21
126 42 25 12367 78
127 75 45 20505 114
128 30 36 18330 83
129 104 45 24993 89
130 121 61 11869 83
131 106 69 31156 116
132 57 23 15234 76
133 28 27 6645 57
134 56 178 15007 91
135 81 100 16597 89
136 2 15 317 66
137 88 77 27627 82
138 41 41 8658 63
139 83 29 20493 75
140 55 44 8877 59
141 3 72 867 19
142 54 77 13259 57
143 89 49 20613 62
144 41 63 2805 78
145 94 63 20588 73
146 101 39 9812 112
147 70 46 20001 79
148 111 63 23042 84
149 0 0 0 0
150 4 10 2065 0
151 0 1 0 0
152 0 2 0 0
153 0 0 0 0
154 0 0 0 0
155 42 55 10902 48
156 97 66 11309 55
157 0 0 0 0
158 0 4 0 0
159 7 5 556 0
160 12 20 2089 13
161 0 5 2658 4
162 37 27 1419 31
163 0 2 0 0
164 39 30 10699 29
Aantal_hyperlinks Aantal_gedeelde_compendiums
1 127 1
2 90 4
3 68 9
4 111 2
5 51 1
6 33 2
7 123 0
8 5 0
9 63 5
10 66 0
11 99 0
12 72 7
13 55 6
14 116 3
15 71 4
16 125 0
17 123 4
18 74 3
19 116 0
20 117 5
21 98 0
22 101 1
23 43 3
24 103 5
25 107 0
26 77 0
27 87 4
28 99 0
29 46 0
30 96 0
31 92 3
32 96 4
33 96 1
34 15 4
35 147 1
36 56 0
37 81 0
38 69 2
39 34 1
40 98 2
41 82 8
42 64 5
43 61 3
44 45 4
45 37 1
46 64 2
47 21 2
48 104 0
49 126 6
50 104 3
51 87 0
52 7 0
53 130 6
54 21 5
55 35 3
56 97 1
57 103 5
58 210 5
59 151 0
60 57 9
61 117 6
62 152 6
63 52 5
64 83 6
65 87 2
66 80 0
67 88 3
68 83 8
69 140 2
70 76 5
71 70 11
72 26 6
73 66 5
74 89 1
75 100 0
76 98 3
77 109 3
78 51 6
79 82 1
80 65 0
81 46 1
82 104 0
83 36 5
84 123 2
85 59 0
86 27 0
87 84 5
88 61 1
89 46 0
90 125 1
91 58 1
92 152 2
93 52 4
94 85 1
95 95 4
96 78 0
97 144 2
98 149 0
99 101 7
100 205 7
101 61 6
102 145 0
103 28 0
104 49 4
105 68 4
106 142 0
107 82 0
108 105 0
109 52 0
110 56 0
111 81 4
112 100 0
113 11 0
114 87 0
115 31 4
116 67 0
117 150 1
118 4 0
119 75 5
120 39 0
121 88 1
122 67 7
123 24 5
124 58 2
125 16 0
126 49 1
127 109 0
128 124 0
129 115 2
130 128 0
131 159 2
132 75 0
133 30 0
134 83 4
135 135 4
136 8 8
137 115 0
138 60 4
139 99 0
140 98 1
141 36 0
142 93 9
143 158 0
144 16 3
145 100 7
146 49 5
147 89 2
148 153 1
149 0 9
150 5 0
151 0 0
152 0 0
153 0 1
154 0 0
155 80 2
156 122 1
157 0 0
158 0 0
159 6 0
160 13 0
161 3 0
162 18 0
163 0 0
164 49 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Aantal_Logins
2.3911120 0.0329154
Aantal_revisions Aantal_lange_feedback
0.0002832 0.2987856
Aantal_hyperlinks Aantal_gedeelde_compendiums
0.4578736 -1.2103276
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-60.343 -7.766 -0.503 9.118 44.698
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.3911120 2.9815315 0.802 0.4238
Aantal_Logins 0.0329154 0.0534787 0.615 0.5391
Aantal_revisions 0.0002832 0.0002649 1.069 0.2867
Aantal_lange_feedback 0.2987856 0.0582506 5.129 8.42e-07 ***
Aantal_hyperlinks 0.4578736 0.0505984 9.049 5.05e-16 ***
Aantal_gedeelde_compendiums -1.2103276 0.4781978 -2.531 0.0124 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15.16 on 158 degrees of freedom
Multiple R-squared: 0.7858, Adjusted R-squared: 0.779
F-statistic: 115.9 on 5 and 158 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,] 0.330575001 0.661150001 0.6694249994
[2,] 0.687669824 0.624660352 0.3123301761
[3,] 0.568837976 0.862324048 0.4311620239
[4,] 0.619800375 0.760399251 0.3801996255
[5,] 0.508390696 0.983218608 0.4916093038
[6,] 0.485806277 0.971612553 0.5141937234
[7,] 0.388972939 0.777945878 0.6110270609
[8,] 0.362790451 0.725580903 0.6372095486
[9,] 0.328900751 0.657801501 0.6710992494
[10,] 0.396613474 0.793226947 0.6033865263
[11,] 0.321748062 0.643496125 0.6782519376
[12,] 0.295061497 0.590122995 0.7049385027
[13,] 0.273418171 0.546836343 0.7265818287
[14,] 0.210737486 0.421474973 0.7892625136
[15,] 0.220612354 0.441224708 0.7793876458
[16,] 0.191080953 0.382161907 0.8089190465
[17,] 0.191763254 0.383526507 0.8082367463
[18,] 0.150509252 0.301018505 0.8494907477
[19,] 0.116592482 0.233184964 0.8834075182
[20,] 0.129524598 0.259049195 0.8704754024
[21,] 0.099355393 0.198710787 0.9006446067
[22,] 0.149268930 0.298537859 0.8507310705
[23,] 0.161953112 0.323906224 0.8380468882
[24,] 0.268317259 0.536634518 0.7316827412
[25,] 0.219246817 0.438493633 0.7807531833
[26,] 0.191721996 0.383443992 0.8082780039
[27,] 0.204779374 0.409558748 0.7952206261
[28,] 0.243753794 0.487507588 0.7562462059
[29,] 0.228725560 0.457451120 0.7712744399
[30,] 0.188435646 0.376871292 0.8115643540
[31,] 0.158141158 0.316282317 0.8418588416
[32,] 0.129963584 0.259927168 0.8700364158
[33,] 0.143738636 0.287477272 0.8562613640
[34,] 0.115747654 0.231495309 0.8842523457
[35,] 0.090855992 0.181711984 0.9091440082
[36,] 0.071223815 0.142447629 0.9287761853
[37,] 0.055838790 0.111677580 0.9441612100
[38,] 0.045495347 0.090990694 0.9545046529
[39,] 0.040635241 0.081270482 0.9593647592
[40,] 0.030400975 0.060801951 0.9695990245
[41,] 0.024830941 0.049661881 0.9751690594
[42,] 0.018308379 0.036616757 0.9816916214
[43,] 0.013186414 0.026372827 0.9868135863
[44,] 0.009321977 0.018643954 0.9906780228
[45,] 0.097034721 0.194069441 0.9029652794
[46,] 0.077804941 0.155609883 0.9221950585
[47,] 0.060971425 0.121942851 0.9390285747
[48,] 0.048170591 0.096341182 0.9518294088
[49,] 0.037841076 0.075682151 0.9621589244
[50,] 0.139035811 0.278071621 0.8609641893
[51,] 0.116215922 0.232431843 0.8837840784
[52,] 0.104566683 0.209133366 0.8954333168
[53,] 0.100703711 0.201407422 0.8992962888
[54,] 0.081567402 0.163134805 0.9184325977
[55,] 0.070464576 0.140929152 0.9295354241
[56,] 0.067856215 0.135712431 0.9321437847
[57,] 0.113663477 0.227326954 0.8863365228
[58,] 0.100629502 0.201259005 0.8993704977
[59,] 0.083098199 0.166196397 0.9169018014
[60,] 0.066352795 0.132705589 0.9336472053
[61,] 0.058029214 0.116058428 0.9419707858
[62,] 0.230441569 0.460883138 0.7695584312
[63,] 0.202008924 0.404017849 0.7979910757
[64,] 0.176974929 0.353949859 0.8230250707
[65,] 0.155392209 0.310784417 0.8446077914
[66,] 0.133056010 0.266112020 0.8669439899
[67,] 0.145922565 0.291845129 0.8540774354
[68,] 0.177398381 0.354796761 0.8226016193
[69,] 0.149573769 0.299147538 0.8504262312
[70,] 0.143172389 0.286344777 0.8568276114
[71,] 0.125852750 0.251705500 0.8741472500
[72,] 0.108018018 0.216036035 0.8919819823
[73,] 0.090086114 0.180172228 0.9099138862
[74,] 0.184801699 0.369603397 0.8151983014
[75,] 0.182338463 0.364676926 0.8176615370
[76,] 0.163392446 0.326784892 0.8366075542
[77,] 0.142928734 0.285857468 0.8570712659
[78,] 0.133691384 0.267382768 0.8663086162
[79,] 0.166624008 0.333248016 0.8333759918
[80,] 0.167864297 0.335728595 0.8321357027
[81,] 0.140749324 0.281498648 0.8592506761
[82,] 0.136527037 0.273054074 0.8634729628
[83,] 0.113320488 0.226640976 0.8866795122
[84,] 0.096641803 0.193283607 0.9033581966
[85,] 0.098258671 0.196517342 0.9017413292
[86,] 0.091296022 0.182592044 0.9087039780
[87,] 0.107110959 0.214221918 0.8928890410
[88,] 0.087929991 0.175859982 0.9120700090
[89,] 0.099024690 0.198049380 0.9009753102
[90,] 0.100333814 0.200667628 0.8996661860
[91,] 0.085094281 0.170188562 0.9149057188
[92,] 0.078556956 0.157113912 0.9214430439
[93,] 0.076491432 0.152982863 0.9235085684
[94,] 0.064080629 0.128161257 0.9359193715
[95,] 0.050823410 0.101646819 0.9491765904
[96,] 0.087493471 0.174986943 0.9125065287
[97,] 0.142819881 0.285639763 0.8571801187
[98,] 0.141912714 0.283825429 0.8580872856
[99,] 0.135888776 0.271777552 0.8641112238
[100,] 0.183808089 0.367616177 0.8161919114
[101,] 0.197927921 0.395855842 0.8020720788
[102,] 0.225176277 0.450352554 0.7748237232
[103,] 0.225067060 0.450134119 0.7749329404
[104,] 0.225470335 0.450940670 0.7745296648
[105,] 0.194155364 0.388310727 0.8058446363
[106,] 0.224793458 0.449586916 0.7752065419
[107,] 0.188940394 0.377880788 0.8110596058
[108,] 0.158666817 0.317333633 0.8413331834
[109,] 0.135081825 0.270163650 0.8649181750
[110,] 0.110331653 0.220663306 0.8896683469
[111,] 0.102179321 0.204358643 0.8978206785
[112,] 0.083757701 0.167515403 0.9162422985
[113,] 0.086667160 0.173334321 0.9133328397
[114,] 0.100912999 0.201825997 0.8990870014
[115,] 0.091634090 0.183268180 0.9083659102
[116,] 0.109207624 0.218415248 0.8907923762
[117,] 0.086723631 0.173447262 0.9132763689
[118,] 0.074319110 0.148638221 0.9256808896
[119,] 0.084071821 0.168143642 0.9159281790
[120,] 0.802709553 0.394580894 0.1972904472
[121,] 0.794399503 0.411200995 0.2056004974
[122,] 0.903578742 0.192842517 0.0964212584
[123,] 0.917177204 0.165645593 0.0828227963
[124,] 0.922073144 0.155853712 0.0779268559
[125,] 0.920200332 0.159599335 0.0797996676
[126,] 0.899900508 0.200198983 0.1000994917
[127,] 0.885222690 0.229554620 0.1147773102
[128,] 0.985748360 0.028503280 0.0142516402
[129,] 0.977758229 0.044483542 0.0222417709
[130,] 0.987266258 0.025467483 0.0127337416
[131,] 0.980811909 0.038376182 0.0191880911
[132,] 0.992552280 0.014895440 0.0074477199
[133,] 0.990547149 0.018905702 0.0094528512
[134,] 0.990742128 0.018515744 0.0092578721
[135,] 0.994742609 0.010514783 0.0052573914
[136,] 0.996228117 0.007543767 0.0037718834
[137,] 0.999333020 0.001333959 0.0006669797
[138,] 0.998953345 0.002093311 0.0010466553
[139,] 0.997521562 0.004956877 0.0024784385
[140,] 0.994541437 0.010917126 0.0054585628
[141,] 0.994623766 0.010752467 0.0053762336
[142,] 0.992540102 0.014919796 0.0074598981
[143,] 0.982996541 0.034006918 0.0170034591
[144,] 0.961289180 0.077421641 0.0387108204
[145,] 0.934410411 0.131179177 0.0655895887
[146,] 0.869758726 0.260482547 0.1302412737
[147,] 0.990852112 0.018295776 0.0091478882
> postscript(file="/var/wessaorg/rcomp/tmp/17iiq1321532206.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/2t0tn1321532206.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3sapf1321532206.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4rf861321532206.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5msfz1321532206.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 164
Frequency = 1
1 2 3 4 5 6
-22.15310543 -11.34582801 19.86592987 -6.17846433 4.67196931 -33.66882275
7 8 9 10 11 12
20.47986740 -11.14034927 -15.76237779 8.79438553 -17.11562914 1.03185824
13 14 15 16 17 18
-6.69414484 -7.70049472 13.77530443 19.27755878 0.70784015 16.81803545
19 20 21 22 23 24
-0.38395824 15.03829212 -3.63396557 0.35266302 3.82371050 2.06179201
25 26 27 28 29 30
17.33363689 0.70204286 14.07744400 13.56037095 -5.62088391 20.84821529
31 32 33 34 35 36
6.90299368 -22.78225226 -0.59344196 -0.16527623 -16.65252635 20.01159167
37 38 39 40 41 42
-5.95666149 -1.60712791 3.87921337 9.01077120 20.68954083 -2.59180031
43 44 45 46 47 48
-2.02676544 -4.27767560 -0.06294488 13.90119728 -12.93524427 -3.63417551
49 50 51 52 53 54
0.17809860 0.89178694 2.22826892 -1.24062154 -30.85789164 -4.75895152
55 56 57 58 59 60
-0.41188960 -3.52570457 3.13769233 -38.24832905 3.58285177 10.52389038
61 62 63 64 65 66
13.10096753 2.28656604 9.78211087 -13.29380674 -27.59731736 9.44118873
67 68 69 70 71 72
-4.81207702 0.76310978 8.69586398 43.52793087 6.43126387 -5.63054814
73 74 75 76 77 78
-7.71240619 -5.76243396 18.73654126 22.20538354 -2.49288218 13.36226101
79 80 81 82 83 84
8.58750262 -6.56739479 5.40791375 34.02045404 -15.05012735 9.73271710
85 86 87 88 89 90
8.46559856 -11.77772961 -23.41816892 -15.72865745 0.98295069 14.15774538
91 92 93 94 95 96
0.12250786 5.83635182 -16.07934282 12.30885991 -20.61584062 -2.95719205
97 98 99 100 101 102
-19.31468409 15.83171713 6.21591469 -12.65770784 15.06701843 6.19870063
103 104 105 106 107 108
-1.43610710 -28.66688714 28.84285843 -16.51125725 13.10781729 24.20148285
109 110 111 112 113 114
-19.65599110 20.25141569 -15.61012099 13.91308938 4.17572387 19.24896729
115 116 117 118 119 120
0.36152486 1.58994976 -8.40330560 -0.84497277 12.16582280 -6.69202456
121 122 123 124 125 126
14.36315559 -17.79291989 -10.19815936 -21.90214109 -1.64538183 -9.24701461
127 128 129 130 131 132
-18.64898269 -60.34254786 16.22309476 29.83278992 -12.52585078 -7.51057201
133 134 135 136 137 138
-7.92864008 -16.85163894 -12.94637180 -14.67483167 -1.90528843 -6.64713720
139 140 141 142 143 144
6.11243600 -12.64294097 -24.16692762 -3.40054642 -11.71018644 8.74058696
145 146 147 148 149 150
24.57839934 44.69833165 -1.50354546 6.06753199 8.50183683 -1.59443025
151 152 153 154 155 156
-2.42402738 -2.45694280 -1.18078432 -2.39111196 -13.83978478 18.15036707
157 158 159 160 161 162
-2.39111196 -2.52277364 1.53961328 -1.47758253 -5.87718239 15.81424119
163 164
-2.45694280 3.91159714
> postscript(file="/var/wessaorg/rcomp/tmp/6kp8o1321532206.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 -22.15310543 NA
1 -11.34582801 -22.15310543
2 19.86592987 -11.34582801
3 -6.17846433 19.86592987
4 4.67196931 -6.17846433
5 -33.66882275 4.67196931
6 20.47986740 -33.66882275
7 -11.14034927 20.47986740
8 -15.76237779 -11.14034927
9 8.79438553 -15.76237779
10 -17.11562914 8.79438553
11 1.03185824 -17.11562914
12 -6.69414484 1.03185824
13 -7.70049472 -6.69414484
14 13.77530443 -7.70049472
15 19.27755878 13.77530443
16 0.70784015 19.27755878
17 16.81803545 0.70784015
18 -0.38395824 16.81803545
19 15.03829212 -0.38395824
20 -3.63396557 15.03829212
21 0.35266302 -3.63396557
22 3.82371050 0.35266302
23 2.06179201 3.82371050
24 17.33363689 2.06179201
25 0.70204286 17.33363689
26 14.07744400 0.70204286
27 13.56037095 14.07744400
28 -5.62088391 13.56037095
29 20.84821529 -5.62088391
30 6.90299368 20.84821529
31 -22.78225226 6.90299368
32 -0.59344196 -22.78225226
33 -0.16527623 -0.59344196
34 -16.65252635 -0.16527623
35 20.01159167 -16.65252635
36 -5.95666149 20.01159167
37 -1.60712791 -5.95666149
38 3.87921337 -1.60712791
39 9.01077120 3.87921337
40 20.68954083 9.01077120
41 -2.59180031 20.68954083
42 -2.02676544 -2.59180031
43 -4.27767560 -2.02676544
44 -0.06294488 -4.27767560
45 13.90119728 -0.06294488
46 -12.93524427 13.90119728
47 -3.63417551 -12.93524427
48 0.17809860 -3.63417551
49 0.89178694 0.17809860
50 2.22826892 0.89178694
51 -1.24062154 2.22826892
52 -30.85789164 -1.24062154
53 -4.75895152 -30.85789164
54 -0.41188960 -4.75895152
55 -3.52570457 -0.41188960
56 3.13769233 -3.52570457
57 -38.24832905 3.13769233
58 3.58285177 -38.24832905
59 10.52389038 3.58285177
60 13.10096753 10.52389038
61 2.28656604 13.10096753
62 9.78211087 2.28656604
63 -13.29380674 9.78211087
64 -27.59731736 -13.29380674
65 9.44118873 -27.59731736
66 -4.81207702 9.44118873
67 0.76310978 -4.81207702
68 8.69586398 0.76310978
69 43.52793087 8.69586398
70 6.43126387 43.52793087
71 -5.63054814 6.43126387
72 -7.71240619 -5.63054814
73 -5.76243396 -7.71240619
74 18.73654126 -5.76243396
75 22.20538354 18.73654126
76 -2.49288218 22.20538354
77 13.36226101 -2.49288218
78 8.58750262 13.36226101
79 -6.56739479 8.58750262
80 5.40791375 -6.56739479
81 34.02045404 5.40791375
82 -15.05012735 34.02045404
83 9.73271710 -15.05012735
84 8.46559856 9.73271710
85 -11.77772961 8.46559856
86 -23.41816892 -11.77772961
87 -15.72865745 -23.41816892
88 0.98295069 -15.72865745
89 14.15774538 0.98295069
90 0.12250786 14.15774538
91 5.83635182 0.12250786
92 -16.07934282 5.83635182
93 12.30885991 -16.07934282
94 -20.61584062 12.30885991
95 -2.95719205 -20.61584062
96 -19.31468409 -2.95719205
97 15.83171713 -19.31468409
98 6.21591469 15.83171713
99 -12.65770784 6.21591469
100 15.06701843 -12.65770784
101 6.19870063 15.06701843
102 -1.43610710 6.19870063
103 -28.66688714 -1.43610710
104 28.84285843 -28.66688714
105 -16.51125725 28.84285843
106 13.10781729 -16.51125725
107 24.20148285 13.10781729
108 -19.65599110 24.20148285
109 20.25141569 -19.65599110
110 -15.61012099 20.25141569
111 13.91308938 -15.61012099
112 4.17572387 13.91308938
113 19.24896729 4.17572387
114 0.36152486 19.24896729
115 1.58994976 0.36152486
116 -8.40330560 1.58994976
117 -0.84497277 -8.40330560
118 12.16582280 -0.84497277
119 -6.69202456 12.16582280
120 14.36315559 -6.69202456
121 -17.79291989 14.36315559
122 -10.19815936 -17.79291989
123 -21.90214109 -10.19815936
124 -1.64538183 -21.90214109
125 -9.24701461 -1.64538183
126 -18.64898269 -9.24701461
127 -60.34254786 -18.64898269
128 16.22309476 -60.34254786
129 29.83278992 16.22309476
130 -12.52585078 29.83278992
131 -7.51057201 -12.52585078
132 -7.92864008 -7.51057201
133 -16.85163894 -7.92864008
134 -12.94637180 -16.85163894
135 -14.67483167 -12.94637180
136 -1.90528843 -14.67483167
137 -6.64713720 -1.90528843
138 6.11243600 -6.64713720
139 -12.64294097 6.11243600
140 -24.16692762 -12.64294097
141 -3.40054642 -24.16692762
142 -11.71018644 -3.40054642
143 8.74058696 -11.71018644
144 24.57839934 8.74058696
145 44.69833165 24.57839934
146 -1.50354546 44.69833165
147 6.06753199 -1.50354546
148 8.50183683 6.06753199
149 -1.59443025 8.50183683
150 -2.42402738 -1.59443025
151 -2.45694280 -2.42402738
152 -1.18078432 -2.45694280
153 -2.39111196 -1.18078432
154 -13.83978478 -2.39111196
155 18.15036707 -13.83978478
156 -2.39111196 18.15036707
157 -2.52277364 -2.39111196
158 1.53961328 -2.52277364
159 -1.47758253 1.53961328
160 -5.87718239 -1.47758253
161 15.81424119 -5.87718239
162 -2.45694280 15.81424119
163 3.91159714 -2.45694280
164 NA 3.91159714
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -11.34582801 -22.15310543
[2,] 19.86592987 -11.34582801
[3,] -6.17846433 19.86592987
[4,] 4.67196931 -6.17846433
[5,] -33.66882275 4.67196931
[6,] 20.47986740 -33.66882275
[7,] -11.14034927 20.47986740
[8,] -15.76237779 -11.14034927
[9,] 8.79438553 -15.76237779
[10,] -17.11562914 8.79438553
[11,] 1.03185824 -17.11562914
[12,] -6.69414484 1.03185824
[13,] -7.70049472 -6.69414484
[14,] 13.77530443 -7.70049472
[15,] 19.27755878 13.77530443
[16,] 0.70784015 19.27755878
[17,] 16.81803545 0.70784015
[18,] -0.38395824 16.81803545
[19,] 15.03829212 -0.38395824
[20,] -3.63396557 15.03829212
[21,] 0.35266302 -3.63396557
[22,] 3.82371050 0.35266302
[23,] 2.06179201 3.82371050
[24,] 17.33363689 2.06179201
[25,] 0.70204286 17.33363689
[26,] 14.07744400 0.70204286
[27,] 13.56037095 14.07744400
[28,] -5.62088391 13.56037095
[29,] 20.84821529 -5.62088391
[30,] 6.90299368 20.84821529
[31,] -22.78225226 6.90299368
[32,] -0.59344196 -22.78225226
[33,] -0.16527623 -0.59344196
[34,] -16.65252635 -0.16527623
[35,] 20.01159167 -16.65252635
[36,] -5.95666149 20.01159167
[37,] -1.60712791 -5.95666149
[38,] 3.87921337 -1.60712791
[39,] 9.01077120 3.87921337
[40,] 20.68954083 9.01077120
[41,] -2.59180031 20.68954083
[42,] -2.02676544 -2.59180031
[43,] -4.27767560 -2.02676544
[44,] -0.06294488 -4.27767560
[45,] 13.90119728 -0.06294488
[46,] -12.93524427 13.90119728
[47,] -3.63417551 -12.93524427
[48,] 0.17809860 -3.63417551
[49,] 0.89178694 0.17809860
[50,] 2.22826892 0.89178694
[51,] -1.24062154 2.22826892
[52,] -30.85789164 -1.24062154
[53,] -4.75895152 -30.85789164
[54,] -0.41188960 -4.75895152
[55,] -3.52570457 -0.41188960
[56,] 3.13769233 -3.52570457
[57,] -38.24832905 3.13769233
[58,] 3.58285177 -38.24832905
[59,] 10.52389038 3.58285177
[60,] 13.10096753 10.52389038
[61,] 2.28656604 13.10096753
[62,] 9.78211087 2.28656604
[63,] -13.29380674 9.78211087
[64,] -27.59731736 -13.29380674
[65,] 9.44118873 -27.59731736
[66,] -4.81207702 9.44118873
[67,] 0.76310978 -4.81207702
[68,] 8.69586398 0.76310978
[69,] 43.52793087 8.69586398
[70,] 6.43126387 43.52793087
[71,] -5.63054814 6.43126387
[72,] -7.71240619 -5.63054814
[73,] -5.76243396 -7.71240619
[74,] 18.73654126 -5.76243396
[75,] 22.20538354 18.73654126
[76,] -2.49288218 22.20538354
[77,] 13.36226101 -2.49288218
[78,] 8.58750262 13.36226101
[79,] -6.56739479 8.58750262
[80,] 5.40791375 -6.56739479
[81,] 34.02045404 5.40791375
[82,] -15.05012735 34.02045404
[83,] 9.73271710 -15.05012735
[84,] 8.46559856 9.73271710
[85,] -11.77772961 8.46559856
[86,] -23.41816892 -11.77772961
[87,] -15.72865745 -23.41816892
[88,] 0.98295069 -15.72865745
[89,] 14.15774538 0.98295069
[90,] 0.12250786 14.15774538
[91,] 5.83635182 0.12250786
[92,] -16.07934282 5.83635182
[93,] 12.30885991 -16.07934282
[94,] -20.61584062 12.30885991
[95,] -2.95719205 -20.61584062
[96,] -19.31468409 -2.95719205
[97,] 15.83171713 -19.31468409
[98,] 6.21591469 15.83171713
[99,] -12.65770784 6.21591469
[100,] 15.06701843 -12.65770784
[101,] 6.19870063 15.06701843
[102,] -1.43610710 6.19870063
[103,] -28.66688714 -1.43610710
[104,] 28.84285843 -28.66688714
[105,] -16.51125725 28.84285843
[106,] 13.10781729 -16.51125725
[107,] 24.20148285 13.10781729
[108,] -19.65599110 24.20148285
[109,] 20.25141569 -19.65599110
[110,] -15.61012099 20.25141569
[111,] 13.91308938 -15.61012099
[112,] 4.17572387 13.91308938
[113,] 19.24896729 4.17572387
[114,] 0.36152486 19.24896729
[115,] 1.58994976 0.36152486
[116,] -8.40330560 1.58994976
[117,] -0.84497277 -8.40330560
[118,] 12.16582280 -0.84497277
[119,] -6.69202456 12.16582280
[120,] 14.36315559 -6.69202456
[121,] -17.79291989 14.36315559
[122,] -10.19815936 -17.79291989
[123,] -21.90214109 -10.19815936
[124,] -1.64538183 -21.90214109
[125,] -9.24701461 -1.64538183
[126,] -18.64898269 -9.24701461
[127,] -60.34254786 -18.64898269
[128,] 16.22309476 -60.34254786
[129,] 29.83278992 16.22309476
[130,] -12.52585078 29.83278992
[131,] -7.51057201 -12.52585078
[132,] -7.92864008 -7.51057201
[133,] -16.85163894 -7.92864008
[134,] -12.94637180 -16.85163894
[135,] -14.67483167 -12.94637180
[136,] -1.90528843 -14.67483167
[137,] -6.64713720 -1.90528843
[138,] 6.11243600 -6.64713720
[139,] -12.64294097 6.11243600
[140,] -24.16692762 -12.64294097
[141,] -3.40054642 -24.16692762
[142,] -11.71018644 -3.40054642
[143,] 8.74058696 -11.71018644
[144,] 24.57839934 8.74058696
[145,] 44.69833165 24.57839934
[146,] -1.50354546 44.69833165
[147,] 6.06753199 -1.50354546
[148,] 8.50183683 6.06753199
[149,] -1.59443025 8.50183683
[150,] -2.42402738 -1.59443025
[151,] -2.45694280 -2.42402738
[152,] -1.18078432 -2.45694280
[153,] -2.39111196 -1.18078432
[154,] -13.83978478 -2.39111196
[155,] 18.15036707 -13.83978478
[156,] -2.39111196 18.15036707
[157,] -2.52277364 -2.39111196
[158,] 1.53961328 -2.52277364
[159,] -1.47758253 1.53961328
[160,] -5.87718239 -1.47758253
[161,] 15.81424119 -5.87718239
[162,] -2.45694280 15.81424119
[163,] 3.91159714 -2.45694280
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -11.34582801 -22.15310543
2 19.86592987 -11.34582801
3 -6.17846433 19.86592987
4 4.67196931 -6.17846433
5 -33.66882275 4.67196931
6 20.47986740 -33.66882275
7 -11.14034927 20.47986740
8 -15.76237779 -11.14034927
9 8.79438553 -15.76237779
10 -17.11562914 8.79438553
11 1.03185824 -17.11562914
12 -6.69414484 1.03185824
13 -7.70049472 -6.69414484
14 13.77530443 -7.70049472
15 19.27755878 13.77530443
16 0.70784015 19.27755878
17 16.81803545 0.70784015
18 -0.38395824 16.81803545
19 15.03829212 -0.38395824
20 -3.63396557 15.03829212
21 0.35266302 -3.63396557
22 3.82371050 0.35266302
23 2.06179201 3.82371050
24 17.33363689 2.06179201
25 0.70204286 17.33363689
26 14.07744400 0.70204286
27 13.56037095 14.07744400
28 -5.62088391 13.56037095
29 20.84821529 -5.62088391
30 6.90299368 20.84821529
31 -22.78225226 6.90299368
32 -0.59344196 -22.78225226
33 -0.16527623 -0.59344196
34 -16.65252635 -0.16527623
35 20.01159167 -16.65252635
36 -5.95666149 20.01159167
37 -1.60712791 -5.95666149
38 3.87921337 -1.60712791
39 9.01077120 3.87921337
40 20.68954083 9.01077120
41 -2.59180031 20.68954083
42 -2.02676544 -2.59180031
43 -4.27767560 -2.02676544
44 -0.06294488 -4.27767560
45 13.90119728 -0.06294488
46 -12.93524427 13.90119728
47 -3.63417551 -12.93524427
48 0.17809860 -3.63417551
49 0.89178694 0.17809860
50 2.22826892 0.89178694
51 -1.24062154 2.22826892
52 -30.85789164 -1.24062154
53 -4.75895152 -30.85789164
54 -0.41188960 -4.75895152
55 -3.52570457 -0.41188960
56 3.13769233 -3.52570457
57 -38.24832905 3.13769233
58 3.58285177 -38.24832905
59 10.52389038 3.58285177
60 13.10096753 10.52389038
61 2.28656604 13.10096753
62 9.78211087 2.28656604
63 -13.29380674 9.78211087
64 -27.59731736 -13.29380674
65 9.44118873 -27.59731736
66 -4.81207702 9.44118873
67 0.76310978 -4.81207702
68 8.69586398 0.76310978
69 43.52793087 8.69586398
70 6.43126387 43.52793087
71 -5.63054814 6.43126387
72 -7.71240619 -5.63054814
73 -5.76243396 -7.71240619
74 18.73654126 -5.76243396
75 22.20538354 18.73654126
76 -2.49288218 22.20538354
77 13.36226101 -2.49288218
78 8.58750262 13.36226101
79 -6.56739479 8.58750262
80 5.40791375 -6.56739479
81 34.02045404 5.40791375
82 -15.05012735 34.02045404
83 9.73271710 -15.05012735
84 8.46559856 9.73271710
85 -11.77772961 8.46559856
86 -23.41816892 -11.77772961
87 -15.72865745 -23.41816892
88 0.98295069 -15.72865745
89 14.15774538 0.98295069
90 0.12250786 14.15774538
91 5.83635182 0.12250786
92 -16.07934282 5.83635182
93 12.30885991 -16.07934282
94 -20.61584062 12.30885991
95 -2.95719205 -20.61584062
96 -19.31468409 -2.95719205
97 15.83171713 -19.31468409
98 6.21591469 15.83171713
99 -12.65770784 6.21591469
100 15.06701843 -12.65770784
101 6.19870063 15.06701843
102 -1.43610710 6.19870063
103 -28.66688714 -1.43610710
104 28.84285843 -28.66688714
105 -16.51125725 28.84285843
106 13.10781729 -16.51125725
107 24.20148285 13.10781729
108 -19.65599110 24.20148285
109 20.25141569 -19.65599110
110 -15.61012099 20.25141569
111 13.91308938 -15.61012099
112 4.17572387 13.91308938
113 19.24896729 4.17572387
114 0.36152486 19.24896729
115 1.58994976 0.36152486
116 -8.40330560 1.58994976
117 -0.84497277 -8.40330560
118 12.16582280 -0.84497277
119 -6.69202456 12.16582280
120 14.36315559 -6.69202456
121 -17.79291989 14.36315559
122 -10.19815936 -17.79291989
123 -21.90214109 -10.19815936
124 -1.64538183 -21.90214109
125 -9.24701461 -1.64538183
126 -18.64898269 -9.24701461
127 -60.34254786 -18.64898269
128 16.22309476 -60.34254786
129 29.83278992 16.22309476
130 -12.52585078 29.83278992
131 -7.51057201 -12.52585078
132 -7.92864008 -7.51057201
133 -16.85163894 -7.92864008
134 -12.94637180 -16.85163894
135 -14.67483167 -12.94637180
136 -1.90528843 -14.67483167
137 -6.64713720 -1.90528843
138 6.11243600 -6.64713720
139 -12.64294097 6.11243600
140 -24.16692762 -12.64294097
141 -3.40054642 -24.16692762
142 -11.71018644 -3.40054642
143 8.74058696 -11.71018644
144 24.57839934 8.74058696
145 44.69833165 24.57839934
146 -1.50354546 44.69833165
147 6.06753199 -1.50354546
148 8.50183683 6.06753199
149 -1.59443025 8.50183683
150 -2.42402738 -1.59443025
151 -2.45694280 -2.42402738
152 -1.18078432 -2.45694280
153 -2.39111196 -1.18078432
154 -13.83978478 -2.39111196
155 18.15036707 -13.83978478
156 -2.39111196 18.15036707
157 -2.52277364 -2.39111196
158 1.53961328 -2.52277364
159 -1.47758253 1.53961328
160 -5.87718239 -1.47758253
161 15.81424119 -5.87718239
162 -2.45694280 15.81424119
163 3.91159714 -2.45694280
> 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/7se6n1321532206.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/8gsso1321532206.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/9abe41321532206.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/10c7r41321532206.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11hhj81321532206.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12wsfj1321532206.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13lvi51321532206.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14lrtd1321532206.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/15601m1321532206.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/16sua31321532206.tab")
+ }
>
> try(system("convert tmp/17iiq1321532206.ps tmp/17iiq1321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t0tn1321532206.ps tmp/2t0tn1321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sapf1321532206.ps tmp/3sapf1321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rf861321532206.ps tmp/4rf861321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/5msfz1321532206.ps tmp/5msfz1321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kp8o1321532206.ps tmp/6kp8o1321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/7se6n1321532206.ps tmp/7se6n1321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gsso1321532206.ps tmp/8gsso1321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/9abe41321532206.ps tmp/9abe41321532206.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c7r41321532206.ps tmp/10c7r41321532206.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.093 0.533 5.711