R version 2.12.1 (2010-12-16)
Copyright (C) 2010 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(1
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+ ,6)
+ ,dim=c(8
+ ,144)
+ ,dimnames=list(c('PlaatsHoF'
+ ,'Pageviews'
+ ,'TimeRFC'
+ ,'BloggedComp'
+ ,'PeerReviews'
+ ,'CharComp'
+ ,'SecComp'
+ ,'HypComp')
+ ,1:144))
> y <- array(NA,dim=c(8,144),dimnames=list(c('PlaatsHoF','Pageviews','TimeRFC','BloggedComp','PeerReviews','CharComp','SecComp','HypComp'),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
PlaatsHoF Pageviews TimeRFC BloggedComp PeerReviews CharComp SecComp
1 1 812 58085 13 20 10345 10823
2 2 537 65968 26 28 17607 44480
3 3 186 7176 0 0 1423 1929
4 4 1405 78306 37 40 20050 30032
5 5 1859 123860 45 48 21212 27669
6 6 3347 226694 80 40 93979 114967
7 7 735 58032 21 35 15524 29951
8 8 609 72513 36 40 16182 38824
9 9 1100 65784 35 40 19238 26517
10 10 1743 164794 36 52 28909 63570
11 11 833 66288 35 24 22357 27131
12 12 1143 85319 46 44 25560 41061
13 13 888 45400 20 24 9954 18810
14 14 1460 78191 24 32 18490 27582
15 15 638 61175 18 28 17777 37026
16 16 854 72377 15 40 25268 24252
17 17 724 49850 48 40 37525 32579
18 18 323 15580 0 20 6023 0
19 19 1467 65240 37 67 25042 29666
20 20 412 13397 8 16 35713 7533
21 21 580 35385 10 44 7039 11892
22 22 1177 90047 51 36 40841 51557
23 23 595 47802 4 40 9214 5737
24 24 1103 61598 24 29 17446 11203
25 25 1037 73756 38 32 10295 28714
26 26 611 65152 19 28 13206 24268
27 27 1111 78226 20 41 26093 30749
28 28 542 66026 31 40 20744 46643
29 29 1341 170050 36 44 68013 64530
30 30 1169 91493 19 28 12840 35346
31 31 752 56374 20 56 12672 5766
32 32 889 85227 34 26 10872 29217
33 33 1009 50281 26 12 21325 15912
34 34 577 29008 0 32 24542 3728
35 35 1015 84775 29 36 16401 37494
36 36 0 0 0 0 0 0
37 37 562 55273 8 32 12821 13214
38 38 1115 62498 35 31 14662 19576
39 39 1015 35361 3 48 22190 13632
40 40 976 89502 41 72 37929 67378
41 41 940 73972 42 24 18009 29387
42 42 680 53655 10 56 11076 15936
43 43 404 40064 10 28 24981 18156
44 44 938 58480 26 36 30691 23750
45 45 580 48473 27 44 29164 15559
46 46 396 30737 0 32 13985 21713
47 47 256 27044 13 32 7588 12023
48 48 932 92011 30 32 20023 23588
49 49 734 56303 11 32 25524 28661
50 50 998 52792 24 36 14717 16874
51 51 425 33820 10 42 6832 11804
52 52 631 44121 14 28 9624 12949
53 53 903 103438 23 36 24300 38340
54 54 804 82720 27 32 21790 36573
55 55 915 89612 40 48 16493 40068
56 56 753 60722 22 20 9269 25561
57 57 674 64096 26 32 20105 31287
58 58 382 25090 8 32 11216 8383
59 59 550 57096 27 52 15569 29178
60 60 509 19608 0 40 21799 1237
61 61 423 28665 0 56 3772 10241
62 62 696 28400 16 24 6057 8219
63 63 460 27697 7 22 20828 9348
64 64 475 42406 18 36 9976 25242
65 65 373 47859 7 26 14055 24267
66 66 754 54987 24 44 17455 25902
67 67 936 58198 14 44 39553 51849
68 68 1501 61854 39 36 14818 29065
69 69 499 35185 16 36 17065 22417
70 70 80 12207 0 16 1536 1714
71 71 1517 108584 39 32 11938 29085
72 72 552 43273 17 10 24589 22118
73 73 517 39695 24 40 21332 14803
74 74 917 40699 27 25 13229 13243
75 75 691 38999 22 48 11331 13985
76 76 459 17667 0 36 853 657
77 77 683 59058 26 32 19821 26171
78 78 887 54106 19 24 34666 34867
79 79 410 23795 12 35 15051 12297
80 80 590 33465 23 17 27969 17487
81 81 439 36937 32 36 17897 13461
82 82 621 77075 19 40 6031 15192
83 83 537 32346 17 40 7153 16584
84 84 699 48592 25 36 13365 22892
85 85 477 26642 14 32 11197 7081
86 86 813 51086 11 40 25291 21623
87 87 1171 95985 20 60 28994 41992
88 88 400 24612 14 44 10461 11301
89 89 352 30113 14 28 16415 15230
90 90 639 53398 22 40 8495 14667
91 91 773 54198 25 28 18318 23795
92 92 1050 65255 35 36 25143 28055
93 93 489 59960 9 36 20471 29162
94 94 573 48096 16 20 14561 14962
95 95 334 17371 12 22 16902 8749
96 96 1229 115424 20 52 12994 37310
97 97 701 69334 33 48 29697 31551
98 98 222 19349 13 2 3895 9604
99 99 810 63594 11 44 9807 13937
100 100 739 49547 11 22 10711 16850
101 101 231 13066 8 3 2325 3439
102 102 425 25497 22 20 19000 16638
103 103 578 55049 13 32 22418 12847
104 104 305 24912 6 28 7872 13462
105 105 323 22431 12 24 5650 8086
106 106 463 24716 2 45 3979 2255
107 107 519 52452 33 40 14956 25918
108 108 294 17850 5 0 3738 3255
109 109 0 0 0 0 0 0
110 110 565 35269 34 28 10586 16138
111 111 462 27554 12 28 18122 5941
112 112 630 55167 34 32 17899 27123
113 113 498 36708 30 32 10913 19148
114 114 403 40920 21 13 18060 15214
115 115 38 3058 0 0 0 0
116 116 0 0 0 0 0 0
117 117 559 86153 28 40 15452 34998
118 118 592 37545 11 43 33996 18998
119 119 799 54332 9 32 8877 10651
120 120 406 33277 14 32 18708 13465
121 121 778 43410 7 3 2781 13
122 122 706 69693 41 28 20854 32505
123 123 367 31897 21 16 8179 15769
124 124 639 34563 28 37 7139 5936
125 125 481 39830 1 32 13798 4174
126 126 214 16145 10 4 5619 9876
127 127 538 38139 26 28 13050 17678
128 128 451 49667 7 36 11297 14633
129 129 629 48133 24 40 16170 13380
130 130 256 11796 1 8 0 0
131 131 80 7627 0 0 0 0
132 132 555 60315 11 21 20539 5652
133 133 41 6836 0 4 0 0
134 134 497 28834 17 12 10056 3636
135 135 42 5118 5 0 0 0
136 136 339 20825 4 6 2418 1695
137 137 0 0 0 0 0 0
138 138 375 32626 6 32 11806 8778
139 139 203 11747 0 36 15924 4148
140 140 81 7131 0 0 0 0
141 141 61 4194 0 0 0 0
142 142 313 21416 15 12 7084 10404
143 143 227 18648 0 24 14831 20794
144 144 462 38232 12 36 6585 11200
HypComp
1 13
2 27
3 0
4 37
5 39
6 99
7 21
8 33
9 36
10 44
11 33
12 47
13 19
14 41
15 22
16 17
17 46
18 0
19 31
20 20
21 10
22 55
23 6
24 17
25 33
26 33
27 32
28 37
29 44
30 22
31 15
32 18
33 25
34 7
35 35
36 0
37 14
38 31
39 9
40 59
41 62
42 12
43 23
44 31
45 57
46 23
47 14
48 31
49 17
50 24
51 11
52 16
53 32
54 36
55 37
56 25
57 30
58 10
59 16
60 3
61 0
62 17
63 9
64 22
65 5
66 23
67 16
68 53
69 23
70 0
71 51
72 25
73 51
74 46
75 16
76 0
77 25
78 34
79 14
80 32
81 24
82 16
83 19
84 27
85 24
86 12
87 43
88 13
89 19
90 24
91 27
92 26
93 14
94 26
95 15
96 30
97 33
98 14
99 11
100 12
101 8
102 22
103 12
104 6
105 10
106 1
107 31
108 5
109 0
110 35
111 15
112 36
113 27
114 36
115 0
116 0
117 29
118 19
119 16
120 15
121 1
122 36
123 22
124 16
125 1
126 10
127 31
128 22
129 22
130 0
131 0
132 10
133 0
134 9
135 0
136 0
137 0
138 7
139 2
140 0
141 0
142 16
143 25
144 6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pageviews TimeRFC BloggedComp PeerReviews CharComp
1.113e+02 -4.865e-02 2.188e-04 1.082e-01 -3.590e-01 1.432e-04
SecComp HypComp
-6.857e-04 1.053e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-99.692 -22.874 -0.999 29.433 68.578
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.113e+02 7.151e+00 15.563 < 2e-16 ***
Pageviews -4.865e-02 1.627e-02 -2.990 0.00331 **
TimeRFC 2.188e-04 2.581e-04 0.848 0.39799
BloggedComp 1.082e-01 5.026e-01 0.215 0.82987
PeerReviews -3.590e-01 2.408e-01 -1.491 0.13832
CharComp 1.432e-04 4.199e-04 0.341 0.73356
SecComp -6.857e-04 4.781e-04 -1.434 0.15379
HypComp 1.053e-01 4.523e-01 0.233 0.81623
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35.7 on 136 degrees of freedom
Multiple R-squared: 0.3034, Adjusted R-squared: 0.2676
F-statistic: 8.464 on 7 and 136 DF, p-value: 1.413e-08
> 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,] 2.234229e-03 4.468459e-03 9.977658e-01
[2,] 2.196111e-04 4.392222e-04 9.997804e-01
[3,] 2.911317e-04 5.822634e-04 9.997089e-01
[4,] 4.108186e-05 8.216373e-05 9.999589e-01
[5,] 2.084415e-05 4.168830e-05 9.999792e-01
[6,] 3.820635e-06 7.641271e-06 9.999962e-01
[7,] 6.073125e-07 1.214625e-06 9.999994e-01
[8,] 2.175077e-07 4.350154e-07 9.999998e-01
[9,] 3.183727e-08 6.367454e-08 1.000000e+00
[10,] 7.364958e-09 1.472992e-08 1.000000e+00
[11,] 1.651535e-09 3.303070e-09 1.000000e+00
[12,] 2.608458e-09 5.216915e-09 1.000000e+00
[13,] 8.345122e-10 1.669024e-09 1.000000e+00
[14,] 4.633688e-09 9.267377e-09 1.000000e+00
[15,] 3.846291e-08 7.692582e-08 1.000000e+00
[16,] 2.852892e-08 5.705785e-08 1.000000e+00
[17,] 1.218637e-08 2.437274e-08 1.000000e+00
[18,] 1.008840e-08 2.017680e-08 1.000000e+00
[19,] 3.424419e-09 6.848839e-09 1.000000e+00
[20,] 2.133015e-08 4.266031e-08 1.000000e+00
[21,] 1.081222e-08 2.162445e-08 1.000000e+00
[22,] 6.416396e-08 1.283279e-07 9.999999e-01
[23,] 1.795926e-07 3.591851e-07 9.999998e-01
[24,] 1.433260e-07 2.866521e-07 9.999999e-01
[25,] 2.640280e-07 5.280560e-07 9.999997e-01
[26,] 8.786845e-07 1.757369e-06 9.999991e-01
[27,] 1.032968e-06 2.065935e-06 9.999990e-01
[28,] 2.247448e-06 4.494896e-06 9.999978e-01
[29,] 2.476318e-06 4.952636e-06 9.999975e-01
[30,] 2.453010e-06 4.906021e-06 9.999975e-01
[31,] 2.582512e-06 5.165024e-06 9.999974e-01
[32,] 2.463890e-06 4.927780e-06 9.999975e-01
[33,] 2.620689e-06 5.241378e-06 9.999974e-01
[34,] 3.640131e-06 7.280261e-06 9.999964e-01
[35,] 2.576845e-06 5.153690e-06 9.999974e-01
[36,] 2.218884e-06 4.437768e-06 9.999978e-01
[37,] 5.478215e-06 1.095643e-05 9.999945e-01
[38,] 1.495700e-05 2.991399e-05 9.999850e-01
[39,] 3.127421e-05 6.254842e-05 9.999687e-01
[40,] 7.802694e-05 1.560539e-04 9.999220e-01
[41,] 1.328570e-04 2.657140e-04 9.998671e-01
[42,] 3.294976e-04 6.589952e-04 9.996705e-01
[43,] 5.660339e-04 1.132068e-03 9.994340e-01
[44,] 1.034119e-03 2.068239e-03 9.989659e-01
[45,] 1.978107e-03 3.956214e-03 9.980219e-01
[46,] 5.167711e-03 1.033542e-02 9.948323e-01
[47,] 9.523360e-03 1.904672e-02 9.904766e-01
[48,] 1.678692e-02 3.357383e-02 9.832131e-01
[49,] 2.666540e-02 5.333080e-02 9.733346e-01
[50,] 3.700590e-02 7.401180e-02 9.629941e-01
[51,] 4.244519e-02 8.489039e-02 9.575548e-01
[52,] 7.747383e-02 1.549477e-01 9.225262e-01
[53,] 1.366140e-01 2.732281e-01 8.633860e-01
[54,] 1.760532e-01 3.521064e-01 8.239468e-01
[55,] 2.800216e-01 5.600433e-01 7.199784e-01
[56,] 3.410315e-01 6.820630e-01 6.589685e-01
[57,] 3.569498e-01 7.138996e-01 6.430502e-01
[58,] 4.601143e-01 9.202287e-01 5.398857e-01
[59,] 5.060041e-01 9.879918e-01 4.939959e-01
[60,] 7.159624e-01 5.680752e-01 2.840376e-01
[61,] 7.863448e-01 4.273104e-01 2.136552e-01
[62,] 8.680341e-01 2.639319e-01 1.319659e-01
[63,] 8.719590e-01 2.560820e-01 1.280410e-01
[64,] 8.758563e-01 2.482873e-01 1.241437e-01
[65,] 9.005306e-01 1.989388e-01 9.946942e-02
[66,] 9.336106e-01 1.327787e-01 6.638937e-02
[67,] 9.576305e-01 8.473909e-02 4.236954e-02
[68,] 9.624365e-01 7.512709e-02 3.756355e-02
[69,] 9.748045e-01 5.039095e-02 2.519548e-02
[70,] 9.834549e-01 3.309011e-02 1.654505e-02
[71,] 9.914565e-01 1.708694e-02 8.543471e-03
[72,] 9.975103e-01 4.979486e-03 2.489743e-03
[73,] 9.980794e-01 3.841255e-03 1.920628e-03
[74,] 9.985420e-01 2.915980e-03 1.457990e-03
[75,] 9.992547e-01 1.490597e-03 7.452985e-04
[76,] 9.994037e-01 1.192548e-03 5.962739e-04
[77,] 9.993194e-01 1.361257e-03 6.806286e-04
[78,] 9.996377e-01 7.245134e-04 3.622567e-04
[79,] 9.998322e-01 3.356038e-04 1.678019e-04
[80,] 9.999335e-01 1.330770e-04 6.653850e-05
[81,] 9.999475e-01 1.049941e-04 5.249706e-05
[82,] 9.999576e-01 8.488288e-05 4.244144e-05
[83,] 9.999688e-01 6.236696e-05 3.118348e-05
[84,] 9.999830e-01 3.403886e-05 1.701943e-05
[85,] 9.999921e-01 1.578530e-05 7.892650e-06
[86,] 9.999923e-01 1.533025e-05 7.665127e-06
[87,] 9.999932e-01 1.365730e-05 6.828650e-06
[88,] 9.999971e-01 5.826182e-06 2.913091e-06
[89,] 9.999979e-01 4.138821e-06 2.069410e-06
[90,] 9.999985e-01 3.026766e-06 1.513383e-06
[91,] 9.999993e-01 1.334657e-06 6.673287e-07
[92,] 9.999994e-01 1.112316e-06 5.561581e-07
[93,] 9.999997e-01 5.785315e-07 2.892657e-07
[94,] 9.999999e-01 2.807769e-07 1.403885e-07
[95,] 9.999999e-01 1.726488e-07 8.632438e-08
[96,] 1.000000e+00 4.564866e-08 2.282433e-08
[97,] 1.000000e+00 4.165068e-08 2.082534e-08
[98,] 1.000000e+00 2.369917e-08 1.184959e-08
[99,] 1.000000e+00 5.113372e-09 2.556686e-09
[100,] 1.000000e+00 9.497375e-09 4.748687e-09
[101,] 1.000000e+00 8.284874e-09 4.142437e-09
[102,] 1.000000e+00 1.536670e-08 7.683351e-09
[103,] 1.000000e+00 1.643011e-08 8.215057e-09
[104,] 1.000000e+00 3.338803e-08 1.669401e-08
[105,] 1.000000e+00 1.135512e-08 5.677558e-09
[106,] 1.000000e+00 1.161589e-09 5.807946e-10
[107,] 1.000000e+00 1.815272e-09 9.076359e-10
[108,] 1.000000e+00 3.324413e-09 1.662207e-09
[109,] 1.000000e+00 8.156397e-09 4.078198e-09
[110,] 1.000000e+00 4.507349e-09 2.253674e-09
[111,] 1.000000e+00 1.544385e-08 7.721925e-09
[112,] 1.000000e+00 4.969591e-08 2.484796e-08
[113,] 1.000000e+00 6.260029e-08 3.130015e-08
[114,] 9.999999e-01 2.839957e-07 1.419978e-07
[115,] 9.999998e-01 3.502443e-07 1.751221e-07
[116,] 1.000000e+00 8.102133e-09 4.051066e-09
[117,] 1.000000e+00 3.699896e-08 1.849948e-08
[118,] 9.999999e-01 2.420169e-07 1.210084e-07
[119,] 9.999990e-01 2.096942e-06 1.048471e-06
[120,] 9.999917e-01 1.669685e-05 8.348427e-06
[121,] 9.999679e-01 6.425706e-05 3.212853e-05
[122,] 9.997956e-01 4.087346e-04 2.043673e-04
[123,] 9.998436e-01 3.127182e-04 1.563591e-04
> postscript(file="/var/www/rcomp/tmp/1u38y1321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2tzr01321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3259t1321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4vld21321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5l1s61321890385.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 7
-73.152643 -65.227490 -99.692322 -31.892029 -18.763966 68.578013 -54.836084
8 9 10 11 12 13 14
-58.236338 -40.961777 -2.966963 -57.515552 -31.990248 -49.100701 -22.534512
15 16 17 18 19 20 21
-50.008319 -45.623334 -48.689813 -74.667092 -1.656933 -71.355734 -49.007937
22 23 24 25 26 27 28
-20.618875 -53.892547 -35.901578 -29.864527 -50.551500 -20.825809 -35.248887
29 30 31 32 33 34 35
-12.488902 -16.362974 -37.540370 -32.453794 -33.487803 -45.773920 -16.000934
36 37 38 39 40 41 42
-75.289470 -42.670331 -17.321550 -8.519159 12.573449 -25.633436 -20.848304
43 44 45 46 47 48 49
-41.982617 -15.718062 -35.315903 -30.798762 -41.987999 -19.798666 -14.397122
50 51 52 53 54 55 56
-7.027431 -27.060006 -23.893330 -7.119253 -9.544471 2.735212 -13.578169
57 58 59 60 61 62 63
-11.436592 -26.484001 -6.186544 -19.046662 -9.395571 -11.781506 -22.351994
64 65 66 67 68 69 70
-8.921161 -15.938976 5.397792 30.993756 37.126742 -4.014946 -33.368710
71 72 73 74 75 76 77
29.881067 -11.143033 -8.336188 6.810624 9.927267 -3.571903 7.163008
78 79 80 81 82 83 84
19.944013 -1.481150 -1.680965 -3.413746 4.229767 11.624463 17.241646
85 86 87 88 89 90 91
1.784304 26.194199 51.163963 9.948461 2.874226 16.406010 23.652286
92 93 94 95 96 97 98
39.547236 19.919846 10.946483 4.756492 56.310563 32.209111 -2.859224
99 100 101 102 103 104 105
34.800946 29.284065 -0.516851 16.976679 22.200289 18.972018 15.516045
106 107 108 109 110 111 112
28.637891 32.636590 7.736077 -2.289470 30.716381 24.809502 39.339973
113 114 115 116 117 118 119
34.870623 19.810590 4.890042 4.710530 44.114951 47.699024 49.553871
120 121 122 123 124 125 126
36.127698 37.885121 50.932131 33.380986 47.851091 40.556460 26.856162
127 128 129 130 131 132 133
47.764812 46.049501 54.083920 31.347485 21.933531 40.742590 23.645513
134 135 136 137 138 139 140
43.153208 24.092847 39.182778 25.710530 52.245233 48.293768 31.090706
141 142 143 144
31.760379 48.370567 56.790614 64.551732
> postscript(file="/var/www/rcomp/tmp/6jtcw1321890385.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 -73.152643 NA
1 -65.227490 -73.152643
2 -99.692322 -65.227490
3 -31.892029 -99.692322
4 -18.763966 -31.892029
5 68.578013 -18.763966
6 -54.836084 68.578013
7 -58.236338 -54.836084
8 -40.961777 -58.236338
9 -2.966963 -40.961777
10 -57.515552 -2.966963
11 -31.990248 -57.515552
12 -49.100701 -31.990248
13 -22.534512 -49.100701
14 -50.008319 -22.534512
15 -45.623334 -50.008319
16 -48.689813 -45.623334
17 -74.667092 -48.689813
18 -1.656933 -74.667092
19 -71.355734 -1.656933
20 -49.007937 -71.355734
21 -20.618875 -49.007937
22 -53.892547 -20.618875
23 -35.901578 -53.892547
24 -29.864527 -35.901578
25 -50.551500 -29.864527
26 -20.825809 -50.551500
27 -35.248887 -20.825809
28 -12.488902 -35.248887
29 -16.362974 -12.488902
30 -37.540370 -16.362974
31 -32.453794 -37.540370
32 -33.487803 -32.453794
33 -45.773920 -33.487803
34 -16.000934 -45.773920
35 -75.289470 -16.000934
36 -42.670331 -75.289470
37 -17.321550 -42.670331
38 -8.519159 -17.321550
39 12.573449 -8.519159
40 -25.633436 12.573449
41 -20.848304 -25.633436
42 -41.982617 -20.848304
43 -15.718062 -41.982617
44 -35.315903 -15.718062
45 -30.798762 -35.315903
46 -41.987999 -30.798762
47 -19.798666 -41.987999
48 -14.397122 -19.798666
49 -7.027431 -14.397122
50 -27.060006 -7.027431
51 -23.893330 -27.060006
52 -7.119253 -23.893330
53 -9.544471 -7.119253
54 2.735212 -9.544471
55 -13.578169 2.735212
56 -11.436592 -13.578169
57 -26.484001 -11.436592
58 -6.186544 -26.484001
59 -19.046662 -6.186544
60 -9.395571 -19.046662
61 -11.781506 -9.395571
62 -22.351994 -11.781506
63 -8.921161 -22.351994
64 -15.938976 -8.921161
65 5.397792 -15.938976
66 30.993756 5.397792
67 37.126742 30.993756
68 -4.014946 37.126742
69 -33.368710 -4.014946
70 29.881067 -33.368710
71 -11.143033 29.881067
72 -8.336188 -11.143033
73 6.810624 -8.336188
74 9.927267 6.810624
75 -3.571903 9.927267
76 7.163008 -3.571903
77 19.944013 7.163008
78 -1.481150 19.944013
79 -1.680965 -1.481150
80 -3.413746 -1.680965
81 4.229767 -3.413746
82 11.624463 4.229767
83 17.241646 11.624463
84 1.784304 17.241646
85 26.194199 1.784304
86 51.163963 26.194199
87 9.948461 51.163963
88 2.874226 9.948461
89 16.406010 2.874226
90 23.652286 16.406010
91 39.547236 23.652286
92 19.919846 39.547236
93 10.946483 19.919846
94 4.756492 10.946483
95 56.310563 4.756492
96 32.209111 56.310563
97 -2.859224 32.209111
98 34.800946 -2.859224
99 29.284065 34.800946
100 -0.516851 29.284065
101 16.976679 -0.516851
102 22.200289 16.976679
103 18.972018 22.200289
104 15.516045 18.972018
105 28.637891 15.516045
106 32.636590 28.637891
107 7.736077 32.636590
108 -2.289470 7.736077
109 30.716381 -2.289470
110 24.809502 30.716381
111 39.339973 24.809502
112 34.870623 39.339973
113 19.810590 34.870623
114 4.890042 19.810590
115 4.710530 4.890042
116 44.114951 4.710530
117 47.699024 44.114951
118 49.553871 47.699024
119 36.127698 49.553871
120 37.885121 36.127698
121 50.932131 37.885121
122 33.380986 50.932131
123 47.851091 33.380986
124 40.556460 47.851091
125 26.856162 40.556460
126 47.764812 26.856162
127 46.049501 47.764812
128 54.083920 46.049501
129 31.347485 54.083920
130 21.933531 31.347485
131 40.742590 21.933531
132 23.645513 40.742590
133 43.153208 23.645513
134 24.092847 43.153208
135 39.182778 24.092847
136 25.710530 39.182778
137 52.245233 25.710530
138 48.293768 52.245233
139 31.090706 48.293768
140 31.760379 31.090706
141 48.370567 31.760379
142 56.790614 48.370567
143 64.551732 56.790614
144 NA 64.551732
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -65.227490 -73.152643
[2,] -99.692322 -65.227490
[3,] -31.892029 -99.692322
[4,] -18.763966 -31.892029
[5,] 68.578013 -18.763966
[6,] -54.836084 68.578013
[7,] -58.236338 -54.836084
[8,] -40.961777 -58.236338
[9,] -2.966963 -40.961777
[10,] -57.515552 -2.966963
[11,] -31.990248 -57.515552
[12,] -49.100701 -31.990248
[13,] -22.534512 -49.100701
[14,] -50.008319 -22.534512
[15,] -45.623334 -50.008319
[16,] -48.689813 -45.623334
[17,] -74.667092 -48.689813
[18,] -1.656933 -74.667092
[19,] -71.355734 -1.656933
[20,] -49.007937 -71.355734
[21,] -20.618875 -49.007937
[22,] -53.892547 -20.618875
[23,] -35.901578 -53.892547
[24,] -29.864527 -35.901578
[25,] -50.551500 -29.864527
[26,] -20.825809 -50.551500
[27,] -35.248887 -20.825809
[28,] -12.488902 -35.248887
[29,] -16.362974 -12.488902
[30,] -37.540370 -16.362974
[31,] -32.453794 -37.540370
[32,] -33.487803 -32.453794
[33,] -45.773920 -33.487803
[34,] -16.000934 -45.773920
[35,] -75.289470 -16.000934
[36,] -42.670331 -75.289470
[37,] -17.321550 -42.670331
[38,] -8.519159 -17.321550
[39,] 12.573449 -8.519159
[40,] -25.633436 12.573449
[41,] -20.848304 -25.633436
[42,] -41.982617 -20.848304
[43,] -15.718062 -41.982617
[44,] -35.315903 -15.718062
[45,] -30.798762 -35.315903
[46,] -41.987999 -30.798762
[47,] -19.798666 -41.987999
[48,] -14.397122 -19.798666
[49,] -7.027431 -14.397122
[50,] -27.060006 -7.027431
[51,] -23.893330 -27.060006
[52,] -7.119253 -23.893330
[53,] -9.544471 -7.119253
[54,] 2.735212 -9.544471
[55,] -13.578169 2.735212
[56,] -11.436592 -13.578169
[57,] -26.484001 -11.436592
[58,] -6.186544 -26.484001
[59,] -19.046662 -6.186544
[60,] -9.395571 -19.046662
[61,] -11.781506 -9.395571
[62,] -22.351994 -11.781506
[63,] -8.921161 -22.351994
[64,] -15.938976 -8.921161
[65,] 5.397792 -15.938976
[66,] 30.993756 5.397792
[67,] 37.126742 30.993756
[68,] -4.014946 37.126742
[69,] -33.368710 -4.014946
[70,] 29.881067 -33.368710
[71,] -11.143033 29.881067
[72,] -8.336188 -11.143033
[73,] 6.810624 -8.336188
[74,] 9.927267 6.810624
[75,] -3.571903 9.927267
[76,] 7.163008 -3.571903
[77,] 19.944013 7.163008
[78,] -1.481150 19.944013
[79,] -1.680965 -1.481150
[80,] -3.413746 -1.680965
[81,] 4.229767 -3.413746
[82,] 11.624463 4.229767
[83,] 17.241646 11.624463
[84,] 1.784304 17.241646
[85,] 26.194199 1.784304
[86,] 51.163963 26.194199
[87,] 9.948461 51.163963
[88,] 2.874226 9.948461
[89,] 16.406010 2.874226
[90,] 23.652286 16.406010
[91,] 39.547236 23.652286
[92,] 19.919846 39.547236
[93,] 10.946483 19.919846
[94,] 4.756492 10.946483
[95,] 56.310563 4.756492
[96,] 32.209111 56.310563
[97,] -2.859224 32.209111
[98,] 34.800946 -2.859224
[99,] 29.284065 34.800946
[100,] -0.516851 29.284065
[101,] 16.976679 -0.516851
[102,] 22.200289 16.976679
[103,] 18.972018 22.200289
[104,] 15.516045 18.972018
[105,] 28.637891 15.516045
[106,] 32.636590 28.637891
[107,] 7.736077 32.636590
[108,] -2.289470 7.736077
[109,] 30.716381 -2.289470
[110,] 24.809502 30.716381
[111,] 39.339973 24.809502
[112,] 34.870623 39.339973
[113,] 19.810590 34.870623
[114,] 4.890042 19.810590
[115,] 4.710530 4.890042
[116,] 44.114951 4.710530
[117,] 47.699024 44.114951
[118,] 49.553871 47.699024
[119,] 36.127698 49.553871
[120,] 37.885121 36.127698
[121,] 50.932131 37.885121
[122,] 33.380986 50.932131
[123,] 47.851091 33.380986
[124,] 40.556460 47.851091
[125,] 26.856162 40.556460
[126,] 47.764812 26.856162
[127,] 46.049501 47.764812
[128,] 54.083920 46.049501
[129,] 31.347485 54.083920
[130,] 21.933531 31.347485
[131,] 40.742590 21.933531
[132,] 23.645513 40.742590
[133,] 43.153208 23.645513
[134,] 24.092847 43.153208
[135,] 39.182778 24.092847
[136,] 25.710530 39.182778
[137,] 52.245233 25.710530
[138,] 48.293768 52.245233
[139,] 31.090706 48.293768
[140,] 31.760379 31.090706
[141,] 48.370567 31.760379
[142,] 56.790614 48.370567
[143,] 64.551732 56.790614
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -65.227490 -73.152643
2 -99.692322 -65.227490
3 -31.892029 -99.692322
4 -18.763966 -31.892029
5 68.578013 -18.763966
6 -54.836084 68.578013
7 -58.236338 -54.836084
8 -40.961777 -58.236338
9 -2.966963 -40.961777
10 -57.515552 -2.966963
11 -31.990248 -57.515552
12 -49.100701 -31.990248
13 -22.534512 -49.100701
14 -50.008319 -22.534512
15 -45.623334 -50.008319
16 -48.689813 -45.623334
17 -74.667092 -48.689813
18 -1.656933 -74.667092
19 -71.355734 -1.656933
20 -49.007937 -71.355734
21 -20.618875 -49.007937
22 -53.892547 -20.618875
23 -35.901578 -53.892547
24 -29.864527 -35.901578
25 -50.551500 -29.864527
26 -20.825809 -50.551500
27 -35.248887 -20.825809
28 -12.488902 -35.248887
29 -16.362974 -12.488902
30 -37.540370 -16.362974
31 -32.453794 -37.540370
32 -33.487803 -32.453794
33 -45.773920 -33.487803
34 -16.000934 -45.773920
35 -75.289470 -16.000934
36 -42.670331 -75.289470
37 -17.321550 -42.670331
38 -8.519159 -17.321550
39 12.573449 -8.519159
40 -25.633436 12.573449
41 -20.848304 -25.633436
42 -41.982617 -20.848304
43 -15.718062 -41.982617
44 -35.315903 -15.718062
45 -30.798762 -35.315903
46 -41.987999 -30.798762
47 -19.798666 -41.987999
48 -14.397122 -19.798666
49 -7.027431 -14.397122
50 -27.060006 -7.027431
51 -23.893330 -27.060006
52 -7.119253 -23.893330
53 -9.544471 -7.119253
54 2.735212 -9.544471
55 -13.578169 2.735212
56 -11.436592 -13.578169
57 -26.484001 -11.436592
58 -6.186544 -26.484001
59 -19.046662 -6.186544
60 -9.395571 -19.046662
61 -11.781506 -9.395571
62 -22.351994 -11.781506
63 -8.921161 -22.351994
64 -15.938976 -8.921161
65 5.397792 -15.938976
66 30.993756 5.397792
67 37.126742 30.993756
68 -4.014946 37.126742
69 -33.368710 -4.014946
70 29.881067 -33.368710
71 -11.143033 29.881067
72 -8.336188 -11.143033
73 6.810624 -8.336188
74 9.927267 6.810624
75 -3.571903 9.927267
76 7.163008 -3.571903
77 19.944013 7.163008
78 -1.481150 19.944013
79 -1.680965 -1.481150
80 -3.413746 -1.680965
81 4.229767 -3.413746
82 11.624463 4.229767
83 17.241646 11.624463
84 1.784304 17.241646
85 26.194199 1.784304
86 51.163963 26.194199
87 9.948461 51.163963
88 2.874226 9.948461
89 16.406010 2.874226
90 23.652286 16.406010
91 39.547236 23.652286
92 19.919846 39.547236
93 10.946483 19.919846
94 4.756492 10.946483
95 56.310563 4.756492
96 32.209111 56.310563
97 -2.859224 32.209111
98 34.800946 -2.859224
99 29.284065 34.800946
100 -0.516851 29.284065
101 16.976679 -0.516851
102 22.200289 16.976679
103 18.972018 22.200289
104 15.516045 18.972018
105 28.637891 15.516045
106 32.636590 28.637891
107 7.736077 32.636590
108 -2.289470 7.736077
109 30.716381 -2.289470
110 24.809502 30.716381
111 39.339973 24.809502
112 34.870623 39.339973
113 19.810590 34.870623
114 4.890042 19.810590
115 4.710530 4.890042
116 44.114951 4.710530
117 47.699024 44.114951
118 49.553871 47.699024
119 36.127698 49.553871
120 37.885121 36.127698
121 50.932131 37.885121
122 33.380986 50.932131
123 47.851091 33.380986
124 40.556460 47.851091
125 26.856162 40.556460
126 47.764812 26.856162
127 46.049501 47.764812
128 54.083920 46.049501
129 31.347485 54.083920
130 21.933531 31.347485
131 40.742590 21.933531
132 23.645513 40.742590
133 43.153208 23.645513
134 24.092847 43.153208
135 39.182778 24.092847
136 25.710530 39.182778
137 52.245233 25.710530
138 48.293768 52.245233
139 31.090706 48.293768
140 31.760379 31.090706
141 48.370567 31.760379
142 56.790614 48.370567
143 64.551732 56.790614
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/775ck1321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/83kix1321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9nqwg1321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10qnhs1321890385.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11tqmo1321890385.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12czrx1321890385.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/131k4k1321890385.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14eggm1321890385.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15mt6p1321890386.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16dom11321890386.tab")
+ }
>
> try(system("convert tmp/1u38y1321890385.ps tmp/1u38y1321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tzr01321890385.ps tmp/2tzr01321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/3259t1321890385.ps tmp/3259t1321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vld21321890385.ps tmp/4vld21321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/5l1s61321890385.ps tmp/5l1s61321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jtcw1321890385.ps tmp/6jtcw1321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/775ck1321890385.ps tmp/775ck1321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/83kix1321890385.ps tmp/83kix1321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nqwg1321890385.ps tmp/9nqwg1321890385.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qnhs1321890385.ps tmp/10qnhs1321890385.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.364 0.648 7.016