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(1801
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+ ,dim=c(7
+ ,144)
+ ,dimnames=list(c('page_views'
+ ,'time_spent_seconds'
+ ,'number_logins'
+ ,'number_course_compenium_views'
+ ,'number_compendium_views'
+ ,'number_compediums_shared'
+ ,'number_feedbackmessage_PR')
+ ,1:144))
> y <- array(NA,dim=c(7,144),dimnames=list(c('page_views','time_spent_seconds','number_logins','number_course_compenium_views','number_compendium_views','number_compediums_shared','number_feedbackmessage_PR'),1:144))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
page_views time_spent_seconds number_logins number_course_compenium_views
1 1801 159261 91 586
2 1717 189672 59 520
3 192 7215 18 72
4 2295 129098 95 645
5 3450 230632 136 1163
6 6861 515038 263 1945
7 1795 180745 56 585
8 1681 185559 59 470
9 1897 154581 44 612
10 2974 298001 96 992
11 1946 121844 75 634
12 2148 184039 69 677
13 1832 100324 98 665
14 3183 220269 119 1079
15 1476 168265 58 413
16 1567 154647 88 469
17 1756 142018 57 431
18 1247 79030 61 361
19 2779 167047 87 877
20 726 27997 24 221
21 1048 73019 59 366
22 2805 241082 100 846
23 1760 195820 72 642
24 2266 142001 54 689
25 1848 145433 86 576
26 1665 183744 32 610
27 2084 202357 163 673
28 1440 199532 93 361
29 2741 354924 118 907
30 2112 192399 44 882
31 1684 182286 44 490
32 1616 181590 45 548
33 2227 133801 105 723
34 3088 233686 123 918
35 2389 219428 53 787
36 1 0 1 0
37 2099 223044 63 983
38 1669 100129 51 539
39 2137 145864 49 515
40 2153 249965 64 795
41 2390 242379 71 753
42 1701 145794 59 635
43 983 96404 32 361
44 2161 195891 78 804
45 1276 117156 50 394
46 1190 157787 95 320
47 745 81293 32 212
48 2330 237435 101 772
49 2289 233155 89 740
50 2639 160344 59 938
51 658 48188 28 205
52 1917 161922 69 492
53 2557 307432 74 818
54 2026 235223 79 680
55 1911 195583 59 691
56 1716 146061 56 534
57 1852 208834 67 487
58 981 93764 24 301
59 1177 151985 66 421
60 2833 193222 96 947
61 1688 148922 60 492
62 2097 132856 80 790
63 1331 129561 61 362
64 1244 112718 37 430
65 1256 160930 35 416
66 1294 99184 41 409
67 2303 192535 70 498
68 2897 138708 65 887
69 1103 114408 38 267
70 340 31970 15 101
71 2791 225558 112 1000
72 1338 139220 72 416
73 1441 113612 68 480
74 1623 108641 71 454
75 2650 162203 67 671
76 1499 100098 44 413
77 2302 174768 60 677
78 2540 158459 97 820
79 1000 80934 30 316
80 1234 84971 71 395
81 927 80545 68 217
82 2176 287191 64 818
83 957 62974 28 292
84 1551 134091 40 513
85 1014 75555 46 345
86 1771 162154 54 557
87 2613 226638 227 645
88 1205 115367 112 284
89 1337 108749 62 424
90 1524 155537 52 614
91 1829 153133 41 672
92 2229 165618 78 649
93 1233 151517 57 415
94 1365 133686 58 505
95 950 61342 40 387
96 2319 245196 117 730
97 1857 195576 70 563
98 223 19349 12 67
99 2390 225371 105 812
100 1985 153213 78 811
101 700 59117 29 281
102 1062 91762 24 338
103 1311 136769 54 413
104 1157 114798 61 298
105 823 85338 40 223
106 596 27676 22 194
107 1545 153535 48 371
108 1130 122417 37 268
109 0 0 0 0
110 1082 91529 32 332
111 1135 107205 67 371
112 1367 144664 45 465
113 1506 146445 63 447
114 870 76656 60 295
115 78 3616 5 14
116 0 0 0 0
117 1130 183088 44 388
118 1582 144677 84 564
119 2034 159104 98 562
120 919 113273 38 288
121 778 43410 19 292
122 1752 175774 73 530
123 957 95401 42 256
124 2098 134837 55 602
125 731 60493 40 174
126 285 19764 12 75
127 1834 164062 56 565
128 1148 132696 33 377
129 1646 155367 54 544
130 256 11796 9 79
131 98 10674 9 33
132 1404 142261 57 479
133 41 6836 3 11
134 1824 162563 63 626
135 42 5118 3 6
136 528 40248 16 183
137 0 0 0 0
138 1073 122641 47 334
139 1305 88837 38 269
140 81 7131 4 27
141 261 9056 14 99
142 934 76611 24 260
143 1180 132697 51 290
144 1147 100681 19 414
number_compendium_views number_compediums_shared number_feedbackmessage_PR
1 111 0 74
2 76 1 80
3 1 0 0
4 155 0 84
5 125 0 124
6 278 1 140
7 89 1 88
8 59 0 115
9 87 0 109
10 129 1 104
11 158 2 63
12 120 0 118
13 87 0 71
14 264 4 112
15 51 4 63
16 85 3 86
17 96 0 132
18 72 5 54
19 147 0 134
20 49 0 57
21 40 0 59
22 99 0 113
23 127 0 96
24 164 1 96
25 41 1 78
26 160 0 80
27 92 0 93
28 59 0 109
29 89 0 115
30 90 0 79
31 76 0 103
32 116 2 71
33 92 4 66
34 344 0 100
35 84 1 96
36 0 0 0
37 61 0 109
38 138 3 51
39 270 9 119
40 64 0 136
41 96 2 84
42 62 0 136
43 35 2 84
44 59 1 92
45 56 2 103
46 40 2 82
47 49 1 106
48 121 0 96
49 113 1 124
50 172 8 97
51 37 0 82
52 51 0 79
53 89 0 97
54 73 0 107
55 49 1 126
56 74 8 40
57 58 0 96
58 72 1 100
59 32 0 91
60 59 10 136
61 70 6 124
62 85 0 79
63 87 11 74
64 48 3 96
65 56 0 97
66 41 0 122
67 86 8 144
68 152 2 90
69 48 0 93
70 40 0 78
71 135 3 72
72 83 1 45
73 62 2 120
74 91 1 59
75 91 0 133
76 82 2 117
77 112 1 123
78 69 0 110
79 78 0 75
80 105 0 114
81 49 0 94
82 60 0 116
83 49 1 86
84 132 0 90
85 49 0 87
86 71 0 99
87 100 0 132
88 74 0 96
89 49 7 91
90 72 0 77
91 59 5 104
92 90 1 97
93 68 0 94
94 81 0 60
95 33 0 46
96 166 0 135
97 94 0 90
98 15 0 2
99 104 3 96
100 61 0 109
101 11 0 15
102 45 0 68
103 84 0 88
104 66 1 84
105 27 1 46
106 59 0 59
107 127 0 116
108 48 0 29
109 0 0 0
110 58 0 91
111 57 0 76
112 59 0 83
113 76 1 84
114 71 0 65
115 5 0 0
116 0 0 0
117 70 0 84
118 76 0 114
119 122 2 124
120 56 0 92
121 63 0 3
122 92 1 109
123 54 0 74
124 64 8 121
125 29 3 48
126 19 1 8
127 64 3 80
128 79 0 107
129 97 0 116
130 22 0 8
131 7 0 0
132 37 0 56
133 5 0 4
134 48 6 70
135 1 0 0
136 34 1 14
137 0 0 0
138 49 0 91
139 44 0 89
140 0 1 0
141 18 0 12
142 48 1 60
143 54 0 80
144 50 1 88
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_spent_seconds
-57.486955 0.001307
number_logins number_course_compenium_views
3.688767 1.941027
number_compendium_views number_compediums_shared
1.924615 18.608580
number_feedbackmessage_PR
1.091017
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-511.72 -99.98 -8.22 68.91 793.63
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -57.486955 35.926502 -1.600 0.11187
time_spent_seconds 0.001307 0.000438 2.984 0.00337 **
number_logins 3.688767 0.637222 5.789 4.62e-08 ***
number_course_compenium_views 1.941027 0.122191 15.885 < 2e-16 ***
number_compendium_views 1.924615 0.392627 4.902 2.64e-06 ***
number_compediums_shared 18.608580 6.680595 2.785 0.00610 **
number_feedbackmessage_PR 1.091017 0.559025 1.952 0.05302 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 171.9 on 137 degrees of freedom
Multiple R-squared: 0.9636, Adjusted R-squared: 0.962
F-statistic: 604.3 on 6 and 137 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,] 0.9277846 1.444307e-01 7.221536e-02
[2,] 0.8627197 2.745605e-01 1.372803e-01
[3,] 0.7788007 4.423986e-01 2.211993e-01
[4,] 0.7434471 5.131057e-01 2.565529e-01
[5,] 0.6611103 6.777795e-01 3.388897e-01
[6,] 0.7132958 5.734084e-01 2.867042e-01
[7,] 0.6578037 6.843926e-01 3.421963e-01
[8,] 0.7230608 5.538783e-01 2.769392e-01
[9,] 0.7810693 4.378615e-01 2.189307e-01
[10,] 0.7893558 4.212884e-01 2.106442e-01
[11,] 0.7828545 4.342911e-01 2.171455e-01
[12,] 0.7387272 5.225457e-01 2.612728e-01
[13,] 0.7044521 5.910957e-01 2.955479e-01
[14,] 0.8875074 2.249853e-01 1.124926e-01
[15,] 0.9254894 1.490212e-01 7.451062e-02
[16,] 0.9021182 1.957635e-01 9.788176e-02
[17,] 0.8915199 2.169602e-01 1.084801e-01
[18,] 0.9763820 4.723593e-02 2.361797e-02
[19,] 0.9658880 6.822404e-02 3.411202e-02
[20,] 0.9800581 3.988387e-02 1.994193e-02
[21,] 0.9889361 2.212786e-02 1.106393e-02
[22,] 0.9870421 2.591585e-02 1.295793e-02
[23,] 0.9824200 3.516002e-02 1.758001e-02
[24,] 0.9752586 4.948282e-02 2.474141e-02
[25,] 0.9670272 6.594565e-02 3.297282e-02
[26,] 0.9619151 7.616984e-02 3.808492e-02
[27,] 0.9630877 7.382457e-02 3.691229e-02
[28,] 0.9985539 2.892146e-03 1.446073e-03
[29,] 0.9978303 4.339465e-03 2.169733e-03
[30,] 0.9968791 6.241878e-03 3.120939e-03
[31,] 0.9967004 6.599235e-03 3.299617e-03
[32,] 0.9957579 8.484183e-03 4.242092e-03
[33,] 0.9950808 9.838323e-03 4.919162e-03
[34,] 0.9934711 1.305773e-02 6.528863e-03
[35,] 0.9916180 1.676397e-02 8.381983e-03
[36,] 0.9882220 2.355610e-02 1.177805e-02
[37,] 0.9863415 2.731699e-02 1.365850e-02
[38,] 0.9822138 3.557242e-02 1.778621e-02
[39,] 0.9789686 4.206285e-02 2.103142e-02
[40,] 0.9733890 5.322202e-02 2.661101e-02
[41,] 0.9690083 6.198342e-02 3.099171e-02
[42,] 0.9603098 7.938039e-02 3.969020e-02
[43,] 0.9882603 2.347941e-02 1.173970e-02
[44,] 0.9858517 2.829665e-02 1.414832e-02
[45,] 0.9816542 3.669160e-02 1.834580e-02
[46,] 0.9765347 4.693054e-02 2.346527e-02
[47,] 0.9689698 6.206048e-02 3.103024e-02
[48,] 0.9782432 4.351354e-02 2.175677e-02
[49,] 0.9724480 5.510401e-02 2.755200e-02
[50,] 0.9715810 5.683801e-02 2.841900e-02
[51,] 0.9627021 7.459572e-02 3.729786e-02
[52,] 0.9523901 9.521971e-02 4.760985e-02
[53,] 0.9416077 1.167845e-01 5.839226e-02
[54,] 0.9476919 1.046162e-01 5.230810e-02
[55,] 0.9387879 1.224241e-01 6.121206e-02
[56,] 0.9228762 1.542477e-01 7.712384e-02
[57,] 0.9083034 1.833933e-01 9.169663e-02
[58,] 0.9640666 7.186682e-02 3.593341e-02
[59,] 0.9909578 1.808441e-02 9.042206e-03
[60,] 0.9903746 1.925082e-02 9.625410e-03
[61,] 0.9884976 2.300484e-02 1.150242e-02
[62,] 0.9869812 2.603756e-02 1.301878e-02
[63,] 0.9824858 3.502848e-02 1.751424e-02
[64,] 0.9813821 3.723586e-02 1.861793e-02
[65,] 0.9816856 3.662877e-02 1.831438e-02
[66,] 0.9999456 1.087003e-04 5.435015e-05
[67,] 0.9999306 1.388256e-04 6.941280e-05
[68,] 0.9999773 4.532313e-05 2.266157e-05
[69,] 0.9999970 5.934580e-06 2.967290e-06
[70,] 0.9999945 1.090373e-05 5.451866e-06
[71,] 0.9999948 1.033165e-05 5.165823e-06
[72,] 0.9999907 1.868127e-05 9.340637e-06
[73,] 0.9999900 1.996888e-05 9.984440e-06
[74,] 0.9999831 3.380261e-05 1.690131e-05
[75,] 0.9999703 5.940197e-05 2.970099e-05
[76,] 0.9999497 1.006424e-04 5.032121e-05
[77,] 0.9999476 1.047597e-04 5.237985e-05
[78,] 0.9999552 8.962747e-05 4.481373e-05
[79,] 0.9999264 1.471936e-04 7.359678e-05
[80,] 0.9999592 8.166306e-05 4.083153e-05
[81,] 0.9999568 8.635136e-05 4.317568e-05
[82,] 0.9999537 9.250830e-05 4.625415e-05
[83,] 0.9999973 5.494531e-06 2.747265e-06
[84,] 0.9999974 5.124704e-06 2.562352e-06
[85,] 0.9999959 8.242398e-06 4.121199e-06
[86,] 0.9999919 1.612254e-05 8.061270e-06
[87,] 0.9999931 1.375991e-05 6.879957e-06
[88,] 0.9999896 2.081457e-05 1.040729e-05
[89,] 0.9999818 3.638335e-05 1.819167e-05
[90,] 0.9999715 5.691906e-05 2.845953e-05
[91,] 0.9999601 7.972408e-05 3.986204e-05
[92,] 0.9999280 1.439899e-04 7.199495e-05
[93,] 0.9999009 1.982838e-04 9.914191e-05
[94,] 0.9998332 3.336818e-04 1.668409e-04
[95,] 0.9997009 5.982096e-04 2.991048e-04
[96,] 0.9994975 1.005063e-03 5.025313e-04
[97,] 0.9991626 1.674869e-03 8.374343e-04
[98,] 0.9988177 2.364618e-03 1.182309e-03
[99,] 0.9997757 4.485192e-04 2.242596e-04
[100,] 0.9995971 8.057071e-04 4.028536e-04
[101,] 0.9992679 1.464216e-03 7.321078e-04
[102,] 0.9988795 2.240901e-03 1.120451e-03
[103,] 0.9980151 3.969718e-03 1.984859e-03
[104,] 0.9968651 6.269811e-03 3.134906e-03
[105,] 0.9975770 4.846000e-03 2.423000e-03
[106,] 0.9958801 8.239718e-03 4.119859e-03
[107,] 0.9931020 1.379601e-02 6.898005e-03
[108,] 0.9936246 1.275075e-02 6.375374e-03
[109,] 0.9980555 3.889095e-03 1.944548e-03
[110,] 0.9970076 5.984818e-03 2.992409e-03
[111,] 0.9967080 6.584085e-03 3.292043e-03
[112,] 0.9961826 7.634743e-03 3.817372e-03
[113,] 0.9930478 1.390440e-02 6.952201e-03
[114,] 0.9876501 2.469974e-02 1.234987e-02
[115,] 0.9800458 3.990836e-02 1.995418e-02
[116,] 0.9787678 4.246447e-02 2.123223e-02
[117,] 0.9622312 7.553765e-02 3.776883e-02
[118,] 0.9758720 4.825603e-02 2.412802e-02
[119,] 0.9612199 7.756015e-02 3.878007e-02
[120,] 0.9486622 1.026755e-01 5.133775e-02
[121,] 0.9060233 1.879533e-01 9.397666e-02
[122,] 0.8383873 3.232254e-01 1.616127e-01
[123,] 0.9217432 1.565135e-01 7.825675e-02
[124,] 0.8410673 3.178654e-01 1.589327e-01
[125,] 0.7680220 4.639560e-01 2.319780e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1w5vv1324638944.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/28nho1324638944.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mgyp1324638944.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/40uzc1324638944.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/53izq1324638944.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 = 144
Frequency = 1
1 2 3 4 5 6
-117.112566 47.503378 31.982482 191.433692 71.161723 793.630149
7 8 9 10 11 12
-11.679949 127.070774 115.913477 -17.892344 -73.040241 36.702212
13 14 15 16 17 18
-138.796018 -285.404855 56.692293 -125.794785 252.286998 -15.040167
19 20 21 22 23 24
165.882244 72.910743 -59.337467 222.648624 -299.292759 162.384789
25 26 27 28 29 30
97.561516 -214.905072 -309.049957 -39.488321 -157.849397 -215.625342
31 32 33 34 35 36
131.232444 -131.415606 -4.544933 -166.629773 151.645303 54.798188
37 38 39 40 41 42
-511.717301 -15.760533 6.548146 -166.903018 93.639708 -149.921424
43 44 45 46 47 48
-100.463212 -118.335187 -26.178576 -133.925979 -61.842490 -131.432718
49 50 51 52 53 54
-94.221954 -137.092241 -9.352294 369.041420 74.905662 -92.434533
55 56 57 58 59 60
-96.358533 4.613314 227.802789 -23.100123 -185.618832 -2.294131
61 62 63 64 65 66
-7.087019 -97.416289 -161.353032 -69.876810 -46.987559 64.746683
67 68 69 70 71 72
412.557033 383.821019 158.712212 -57.748622 -194.629097 -86.942945
73 74 75 76 77 78
-119.968595 137.274388 625.708421 139.050625 227.349138 188.159757
79 80 81 82 83 84
-4.246358 -174.616503 10.334992 -207.672471 55.388906 -62.273077
85 86 87 88 89 90
-55.805834 91.590370 -51.457787 -99.820204 -123.167230 -227.946181
91 92 93 94 95 96
-89.288275 224.964716 -156.721407 -167.727558 -85.098150 -259.228798
97 98 99 100 101 102
28.803142 49.837612 -171.171658 -255.941343 -9.702309 94.183706
103 104 105 106 107 108
-68.749014 23.756613 67.812880 -18.312719 133.691019 246.819381
109 110 111 112 113 114
57.486955 46.510813 -107.490636 -37.229748 15.565578 -174.175100
115 116 117 118 119 120
75.520503 57.486955 -193.553801 -224.810295 23.917087 -78.872108
121 122 123 124 125 126
17.371226 -32.821917 53.327248 203.852846 60.140684 62.913958
127 128 129 130 131 132
107.567254 -90.191846 -67.894730 60.462976 30.813736 3.269757
133 134 135 136 137 138
43.149352 -58.819685 68.161991 19.345432 57.486955 -45.036449
139 140 141 142 143 144
402.307289 43.397230 15.113458 121.727906 121.851220 -11.597299
> postscript(file="/var/wessaorg/rcomp/tmp/6utk71324638944.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 -117.112566 NA
1 47.503378 -117.112566
2 31.982482 47.503378
3 191.433692 31.982482
4 71.161723 191.433692
5 793.630149 71.161723
6 -11.679949 793.630149
7 127.070774 -11.679949
8 115.913477 127.070774
9 -17.892344 115.913477
10 -73.040241 -17.892344
11 36.702212 -73.040241
12 -138.796018 36.702212
13 -285.404855 -138.796018
14 56.692293 -285.404855
15 -125.794785 56.692293
16 252.286998 -125.794785
17 -15.040167 252.286998
18 165.882244 -15.040167
19 72.910743 165.882244
20 -59.337467 72.910743
21 222.648624 -59.337467
22 -299.292759 222.648624
23 162.384789 -299.292759
24 97.561516 162.384789
25 -214.905072 97.561516
26 -309.049957 -214.905072
27 -39.488321 -309.049957
28 -157.849397 -39.488321
29 -215.625342 -157.849397
30 131.232444 -215.625342
31 -131.415606 131.232444
32 -4.544933 -131.415606
33 -166.629773 -4.544933
34 151.645303 -166.629773
35 54.798188 151.645303
36 -511.717301 54.798188
37 -15.760533 -511.717301
38 6.548146 -15.760533
39 -166.903018 6.548146
40 93.639708 -166.903018
41 -149.921424 93.639708
42 -100.463212 -149.921424
43 -118.335187 -100.463212
44 -26.178576 -118.335187
45 -133.925979 -26.178576
46 -61.842490 -133.925979
47 -131.432718 -61.842490
48 -94.221954 -131.432718
49 -137.092241 -94.221954
50 -9.352294 -137.092241
51 369.041420 -9.352294
52 74.905662 369.041420
53 -92.434533 74.905662
54 -96.358533 -92.434533
55 4.613314 -96.358533
56 227.802789 4.613314
57 -23.100123 227.802789
58 -185.618832 -23.100123
59 -2.294131 -185.618832
60 -7.087019 -2.294131
61 -97.416289 -7.087019
62 -161.353032 -97.416289
63 -69.876810 -161.353032
64 -46.987559 -69.876810
65 64.746683 -46.987559
66 412.557033 64.746683
67 383.821019 412.557033
68 158.712212 383.821019
69 -57.748622 158.712212
70 -194.629097 -57.748622
71 -86.942945 -194.629097
72 -119.968595 -86.942945
73 137.274388 -119.968595
74 625.708421 137.274388
75 139.050625 625.708421
76 227.349138 139.050625
77 188.159757 227.349138
78 -4.246358 188.159757
79 -174.616503 -4.246358
80 10.334992 -174.616503
81 -207.672471 10.334992
82 55.388906 -207.672471
83 -62.273077 55.388906
84 -55.805834 -62.273077
85 91.590370 -55.805834
86 -51.457787 91.590370
87 -99.820204 -51.457787
88 -123.167230 -99.820204
89 -227.946181 -123.167230
90 -89.288275 -227.946181
91 224.964716 -89.288275
92 -156.721407 224.964716
93 -167.727558 -156.721407
94 -85.098150 -167.727558
95 -259.228798 -85.098150
96 28.803142 -259.228798
97 49.837612 28.803142
98 -171.171658 49.837612
99 -255.941343 -171.171658
100 -9.702309 -255.941343
101 94.183706 -9.702309
102 -68.749014 94.183706
103 23.756613 -68.749014
104 67.812880 23.756613
105 -18.312719 67.812880
106 133.691019 -18.312719
107 246.819381 133.691019
108 57.486955 246.819381
109 46.510813 57.486955
110 -107.490636 46.510813
111 -37.229748 -107.490636
112 15.565578 -37.229748
113 -174.175100 15.565578
114 75.520503 -174.175100
115 57.486955 75.520503
116 -193.553801 57.486955
117 -224.810295 -193.553801
118 23.917087 -224.810295
119 -78.872108 23.917087
120 17.371226 -78.872108
121 -32.821917 17.371226
122 53.327248 -32.821917
123 203.852846 53.327248
124 60.140684 203.852846
125 62.913958 60.140684
126 107.567254 62.913958
127 -90.191846 107.567254
128 -67.894730 -90.191846
129 60.462976 -67.894730
130 30.813736 60.462976
131 3.269757 30.813736
132 43.149352 3.269757
133 -58.819685 43.149352
134 68.161991 -58.819685
135 19.345432 68.161991
136 57.486955 19.345432
137 -45.036449 57.486955
138 402.307289 -45.036449
139 43.397230 402.307289
140 15.113458 43.397230
141 121.727906 15.113458
142 121.851220 121.727906
143 -11.597299 121.851220
144 NA -11.597299
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 47.503378 -117.112566
[2,] 31.982482 47.503378
[3,] 191.433692 31.982482
[4,] 71.161723 191.433692
[5,] 793.630149 71.161723
[6,] -11.679949 793.630149
[7,] 127.070774 -11.679949
[8,] 115.913477 127.070774
[9,] -17.892344 115.913477
[10,] -73.040241 -17.892344
[11,] 36.702212 -73.040241
[12,] -138.796018 36.702212
[13,] -285.404855 -138.796018
[14,] 56.692293 -285.404855
[15,] -125.794785 56.692293
[16,] 252.286998 -125.794785
[17,] -15.040167 252.286998
[18,] 165.882244 -15.040167
[19,] 72.910743 165.882244
[20,] -59.337467 72.910743
[21,] 222.648624 -59.337467
[22,] -299.292759 222.648624
[23,] 162.384789 -299.292759
[24,] 97.561516 162.384789
[25,] -214.905072 97.561516
[26,] -309.049957 -214.905072
[27,] -39.488321 -309.049957
[28,] -157.849397 -39.488321
[29,] -215.625342 -157.849397
[30,] 131.232444 -215.625342
[31,] -131.415606 131.232444
[32,] -4.544933 -131.415606
[33,] -166.629773 -4.544933
[34,] 151.645303 -166.629773
[35,] 54.798188 151.645303
[36,] -511.717301 54.798188
[37,] -15.760533 -511.717301
[38,] 6.548146 -15.760533
[39,] -166.903018 6.548146
[40,] 93.639708 -166.903018
[41,] -149.921424 93.639708
[42,] -100.463212 -149.921424
[43,] -118.335187 -100.463212
[44,] -26.178576 -118.335187
[45,] -133.925979 -26.178576
[46,] -61.842490 -133.925979
[47,] -131.432718 -61.842490
[48,] -94.221954 -131.432718
[49,] -137.092241 -94.221954
[50,] -9.352294 -137.092241
[51,] 369.041420 -9.352294
[52,] 74.905662 369.041420
[53,] -92.434533 74.905662
[54,] -96.358533 -92.434533
[55,] 4.613314 -96.358533
[56,] 227.802789 4.613314
[57,] -23.100123 227.802789
[58,] -185.618832 -23.100123
[59,] -2.294131 -185.618832
[60,] -7.087019 -2.294131
[61,] -97.416289 -7.087019
[62,] -161.353032 -97.416289
[63,] -69.876810 -161.353032
[64,] -46.987559 -69.876810
[65,] 64.746683 -46.987559
[66,] 412.557033 64.746683
[67,] 383.821019 412.557033
[68,] 158.712212 383.821019
[69,] -57.748622 158.712212
[70,] -194.629097 -57.748622
[71,] -86.942945 -194.629097
[72,] -119.968595 -86.942945
[73,] 137.274388 -119.968595
[74,] 625.708421 137.274388
[75,] 139.050625 625.708421
[76,] 227.349138 139.050625
[77,] 188.159757 227.349138
[78,] -4.246358 188.159757
[79,] -174.616503 -4.246358
[80,] 10.334992 -174.616503
[81,] -207.672471 10.334992
[82,] 55.388906 -207.672471
[83,] -62.273077 55.388906
[84,] -55.805834 -62.273077
[85,] 91.590370 -55.805834
[86,] -51.457787 91.590370
[87,] -99.820204 -51.457787
[88,] -123.167230 -99.820204
[89,] -227.946181 -123.167230
[90,] -89.288275 -227.946181
[91,] 224.964716 -89.288275
[92,] -156.721407 224.964716
[93,] -167.727558 -156.721407
[94,] -85.098150 -167.727558
[95,] -259.228798 -85.098150
[96,] 28.803142 -259.228798
[97,] 49.837612 28.803142
[98,] -171.171658 49.837612
[99,] -255.941343 -171.171658
[100,] -9.702309 -255.941343
[101,] 94.183706 -9.702309
[102,] -68.749014 94.183706
[103,] 23.756613 -68.749014
[104,] 67.812880 23.756613
[105,] -18.312719 67.812880
[106,] 133.691019 -18.312719
[107,] 246.819381 133.691019
[108,] 57.486955 246.819381
[109,] 46.510813 57.486955
[110,] -107.490636 46.510813
[111,] -37.229748 -107.490636
[112,] 15.565578 -37.229748
[113,] -174.175100 15.565578
[114,] 75.520503 -174.175100
[115,] 57.486955 75.520503
[116,] -193.553801 57.486955
[117,] -224.810295 -193.553801
[118,] 23.917087 -224.810295
[119,] -78.872108 23.917087
[120,] 17.371226 -78.872108
[121,] -32.821917 17.371226
[122,] 53.327248 -32.821917
[123,] 203.852846 53.327248
[124,] 60.140684 203.852846
[125,] 62.913958 60.140684
[126,] 107.567254 62.913958
[127,] -90.191846 107.567254
[128,] -67.894730 -90.191846
[129,] 60.462976 -67.894730
[130,] 30.813736 60.462976
[131,] 3.269757 30.813736
[132,] 43.149352 3.269757
[133,] -58.819685 43.149352
[134,] 68.161991 -58.819685
[135,] 19.345432 68.161991
[136,] 57.486955 19.345432
[137,] -45.036449 57.486955
[138,] 402.307289 -45.036449
[139,] 43.397230 402.307289
[140,] 15.113458 43.397230
[141,] 121.727906 15.113458
[142,] 121.851220 121.727906
[143,] -11.597299 121.851220
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 47.503378 -117.112566
2 31.982482 47.503378
3 191.433692 31.982482
4 71.161723 191.433692
5 793.630149 71.161723
6 -11.679949 793.630149
7 127.070774 -11.679949
8 115.913477 127.070774
9 -17.892344 115.913477
10 -73.040241 -17.892344
11 36.702212 -73.040241
12 -138.796018 36.702212
13 -285.404855 -138.796018
14 56.692293 -285.404855
15 -125.794785 56.692293
16 252.286998 -125.794785
17 -15.040167 252.286998
18 165.882244 -15.040167
19 72.910743 165.882244
20 -59.337467 72.910743
21 222.648624 -59.337467
22 -299.292759 222.648624
23 162.384789 -299.292759
24 97.561516 162.384789
25 -214.905072 97.561516
26 -309.049957 -214.905072
27 -39.488321 -309.049957
28 -157.849397 -39.488321
29 -215.625342 -157.849397
30 131.232444 -215.625342
31 -131.415606 131.232444
32 -4.544933 -131.415606
33 -166.629773 -4.544933
34 151.645303 -166.629773
35 54.798188 151.645303
36 -511.717301 54.798188
37 -15.760533 -511.717301
38 6.548146 -15.760533
39 -166.903018 6.548146
40 93.639708 -166.903018
41 -149.921424 93.639708
42 -100.463212 -149.921424
43 -118.335187 -100.463212
44 -26.178576 -118.335187
45 -133.925979 -26.178576
46 -61.842490 -133.925979
47 -131.432718 -61.842490
48 -94.221954 -131.432718
49 -137.092241 -94.221954
50 -9.352294 -137.092241
51 369.041420 -9.352294
52 74.905662 369.041420
53 -92.434533 74.905662
54 -96.358533 -92.434533
55 4.613314 -96.358533
56 227.802789 4.613314
57 -23.100123 227.802789
58 -185.618832 -23.100123
59 -2.294131 -185.618832
60 -7.087019 -2.294131
61 -97.416289 -7.087019
62 -161.353032 -97.416289
63 -69.876810 -161.353032
64 -46.987559 -69.876810
65 64.746683 -46.987559
66 412.557033 64.746683
67 383.821019 412.557033
68 158.712212 383.821019
69 -57.748622 158.712212
70 -194.629097 -57.748622
71 -86.942945 -194.629097
72 -119.968595 -86.942945
73 137.274388 -119.968595
74 625.708421 137.274388
75 139.050625 625.708421
76 227.349138 139.050625
77 188.159757 227.349138
78 -4.246358 188.159757
79 -174.616503 -4.246358
80 10.334992 -174.616503
81 -207.672471 10.334992
82 55.388906 -207.672471
83 -62.273077 55.388906
84 -55.805834 -62.273077
85 91.590370 -55.805834
86 -51.457787 91.590370
87 -99.820204 -51.457787
88 -123.167230 -99.820204
89 -227.946181 -123.167230
90 -89.288275 -227.946181
91 224.964716 -89.288275
92 -156.721407 224.964716
93 -167.727558 -156.721407
94 -85.098150 -167.727558
95 -259.228798 -85.098150
96 28.803142 -259.228798
97 49.837612 28.803142
98 -171.171658 49.837612
99 -255.941343 -171.171658
100 -9.702309 -255.941343
101 94.183706 -9.702309
102 -68.749014 94.183706
103 23.756613 -68.749014
104 67.812880 23.756613
105 -18.312719 67.812880
106 133.691019 -18.312719
107 246.819381 133.691019
108 57.486955 246.819381
109 46.510813 57.486955
110 -107.490636 46.510813
111 -37.229748 -107.490636
112 15.565578 -37.229748
113 -174.175100 15.565578
114 75.520503 -174.175100
115 57.486955 75.520503
116 -193.553801 57.486955
117 -224.810295 -193.553801
118 23.917087 -224.810295
119 -78.872108 23.917087
120 17.371226 -78.872108
121 -32.821917 17.371226
122 53.327248 -32.821917
123 203.852846 53.327248
124 60.140684 203.852846
125 62.913958 60.140684
126 107.567254 62.913958
127 -90.191846 107.567254
128 -67.894730 -90.191846
129 60.462976 -67.894730
130 30.813736 60.462976
131 3.269757 30.813736
132 43.149352 3.269757
133 -58.819685 43.149352
134 68.161991 -58.819685
135 19.345432 68.161991
136 57.486955 19.345432
137 -45.036449 57.486955
138 402.307289 -45.036449
139 43.397230 402.307289
140 15.113458 43.397230
141 121.727906 15.113458
142 121.851220 121.727906
143 -11.597299 121.851220
> 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/79i5a1324638944.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/865n21324638944.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ajxi1324638944.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/107f6t1324638944.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11v5iv1324638944.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/120i831324638944.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/1307tv1324638944.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/141e2b1324638944.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/15w8l31324638944.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/16d96n1324638945.tab")
+ }
>
> try(system("convert tmp/1w5vv1324638944.ps tmp/1w5vv1324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/28nho1324638944.ps tmp/28nho1324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mgyp1324638944.ps tmp/3mgyp1324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/40uzc1324638944.ps tmp/40uzc1324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/53izq1324638944.ps tmp/53izq1324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/6utk71324638944.ps tmp/6utk71324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/79i5a1324638944.ps tmp/79i5a1324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/865n21324638944.ps tmp/865n21324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ajxi1324638944.ps tmp/9ajxi1324638944.png",intern=TRUE))
character(0)
> try(system("convert tmp/107f6t1324638944.ps tmp/107f6t1324638944.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.756 0.735 5.577