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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> x <- array(list(65
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+ ,2)
+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('geblogde_berekeningen'
+ ,'logins'
+ ,'revisions'
+ ,'LFB'
+ ,'hyperlinks'
+ ,'gedeelde_documenten')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('geblogde_berekeningen','logins','revisions','LFB','hyperlinks','gedeelde_documenten'),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 logins revisions LFB hyperlinks gedeelde_documenten
1 65 44 21387 68 127 1
2 54 48 12341 72 90 4
3 58 37 11397 37 68 9
4 75 68 25533 70 111 2
5 41 29 6630 30 51 1
6 0 17 7745 53 33 2
7 111 77 25304 74 123 0
8 1 16 1271 22 5 0
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10 60 24 13284 47 66 0
11 63 60 15628 87 99 0
12 71 72 13990 123 72 7
13 38 41 8532 69 55 6
14 76 39 13953 89 116 3
15 61 51 7210 45 71 4
16 125 100 22436 122 125 0
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18 69 97 10244 45 74 3
19 77 34 17390 53 116 0
20 95 47 9917 86 117 5
21 78 45 29625 82 98 0
22 76 54 13193 76 101 1
23 40 17 6815 51 43 3
24 81 31 11807 104 103 5
25 102 73 21472 83 107 0
26 70 85 19589 78 77 0
27 75 74 12266 59 87 4
28 93 52 18391 83 99 0
29 42 32 6711 71 46 0
30 95 32 9004 81 96 0
31 87 52 34301 93 92 3
32 44 45 8061 72 96 4
33 84 60 19463 107 96 1
34 28 23 2053 75 15 4
35 87 51 29618 84 147 1
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38 50 45 11738 51 69 2
39 30 26 11082 18 34 1
40 86 101 22648 75 98 2
41 75 53 16538 59 82 8
42 46 38 10149 63 64 5
43 52 43 19787 68 61 3
44 31 27 7740 47 45 4
45 30 49 5873 29 37 1
46 70 88 11694 69 64 2
47 20 42 7935 66 21 2
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51 70 51 15699 65 87 0
52 8 24 2694 7 7 0
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54 21 17 3597 61 21 5
55 30 57 5296 41 35 3
56 70 27 21637 70 97 1
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58 87 101 29016 71 210 5
59 112 69 27279 90 151 0
60 54 49 12889 69 57 9
61 96 82 21550 85 117 6
62 93 70 34042 47 152 6
63 49 55 8190 50 52 5
64 49 57 16163 76 83 6
65 38 37 23471 60 87 2
66 64 32 14220 35 80 0
67 62 80 12759 72 88 3
68 66 94 18142 88 83 8
69 98 48 13883 66 140 2
70 97 31 14069 58 76 5
71 56 33 11131 81 70 11
72 22 28 3007 63 26 6
73 51 43 12530 91 66 5
74 56 35 13205 50 89 1
75 94 30 13025 75 100 0
76 98 44 18778 85 98 3
77 76 55 19793 75 109 3
78 57 58 8238 70 51 6
79 75 36 11285 78 82 1
80 48 37 10490 61 65 0
81 48 29 10457 55 46 1
82 109 65 17313 60 104 0
83 27 52 9592 83 36 5
84 83 48 14282 38 123 2
85 49 25 7905 27 59 0
86 24 37 4525 62 27 0
87 43 34 21179 82 84 5
88 44 95 13724 79 61 1
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90 106 66 25961 80 125 1
91 42 46 6602 36 58 1
92 108 47 16795 88 152 2
93 27 41 5463 63 52 4
94 79 48 11299 73 85 1
95 49 48 20390 71 95 4
96 64 27 18558 76 78 0
97 75 29 26262 67 144 2
98 115 51 25267 66 149 0
99 92 88 21091 123 101 7
100 106 69 32425 65 205 7
101 73 60 24380 87 61 6
102 105 37 20460 77 145 0
103 30 101 6515 37 28 0
104 13 14 7409 64 49 4
105 69 43 12300 22 68 4
106 72 90 27127 35 142 0
107 80 27 27687 61 82 0
108 106 60 19255 80 105 0
109 28 32 15070 54 52 0
110 70 61 6291 60 56 0
111 51 39 16577 87 81 4
112 90 55 13027 75 100 0
113 12 10 238 0 11 0
114 84 47 17103 54 87 0
115 23 25 3913 30 31 4
116 57 31 5654 66 67 0
117 84 53 14354 56 150 1
118 4 16 338 0 4 0
119 56 33 8852 32 75 5
120 18 19 3988 9 39 0
121 86 71 15964 78 88 1
122 39 34 14784 90 67 7
123 16 42 2667 56 24 5
124 18 27 7164 35 58 2
125 16 34 1888 21 16 0
126 42 25 12367 78 49 1
127 75 45 20505 114 109 0
128 30 36 18330 83 124 0
129 104 45 24993 89 115 2
130 121 61 11869 83 128 0
131 106 69 31156 116 159 2
132 57 23 15234 76 75 0
133 28 27 6645 57 30 0
134 56 178 15007 91 83 4
135 81 100 16597 89 135 4
136 2 15 317 66 8 8
137 88 77 27627 82 115 0
138 41 41 8658 63 60 4
139 83 29 20493 75 99 0
140 55 44 8877 59 98 1
141 3 72 867 19 36 0
142 54 77 13259 57 93 9
143 89 49 20613 62 158 0
144 41 63 2805 78 16 3
145 94 63 20588 73 100 7
146 101 39 9812 112 49 5
147 70 46 20001 79 89 2
148 111 63 23042 84 153 1
149 0 0 0 0 0 9
150 4 10 2065 0 5 0
151 0 1 0 0 0 0
152 0 2 0 0 0 0
153 0 0 0 0 0 1
154 0 0 0 0 0 0
155 42 55 10902 48 80 2
156 97 66 11309 55 122 1
157 0 0 0 0 0 0
158 0 4 0 0 0 0
159 7 5 556 0 6 0
160 12 20 2089 13 13 0
161 0 5 2658 4 3 0
162 37 27 1419 31 18 0
163 0 2 0 0 0 0
164 39 30 10699 29 49 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logins revisions
2.3911120 0.0329154 0.0002832
LFB hyperlinks gedeelde_documenten
0.2987856 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
logins 0.0329154 0.0534787 0.615 0.5391
revisions 0.0002832 0.0002649 1.069 0.2867
LFB 0.2987856 0.0582506 5.129 8.42e-07 ***
hyperlinks 0.4578736 0.0505984 9.049 5.05e-16 ***
gedeelde_documenten -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/1l0dh1321532624.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/26v291321532624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3qa311321532624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4iewo1321532624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rfr01321532624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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/6q3k91321532624.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/75hql1321532624.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/85c2q1321532624.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/9dg7k1321532624.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/10bat11321532624.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11kw331321532624.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12b0r21321532624.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13fj361321532624.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14mngr1321532624.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/1546c91321532624.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/16jf0w1321532624.tab")
+ }
>
> try(system("convert tmp/1l0dh1321532624.ps tmp/1l0dh1321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/26v291321532624.ps tmp/26v291321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qa311321532624.ps tmp/3qa311321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/4iewo1321532624.ps tmp/4iewo1321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rfr01321532624.ps tmp/5rfr01321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q3k91321532624.ps tmp/6q3k91321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/75hql1321532624.ps tmp/75hql1321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/85c2q1321532624.ps tmp/85c2q1321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dg7k1321532624.ps tmp/9dg7k1321532624.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bat11321532624.ps tmp/10bat11321532624.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.072 0.534 5.782