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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(159261
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+ ,51
+ ,100681
+ ,1147
+ ,414
+ ,19)
+ ,dim=c(4
+ ,144)
+ ,dimnames=list(c('time_spent_seconds'
+ ,'page_views'
+ ,'number_course_compenium_views'
+ ,'number_logins
')
+ ,1:144))
> y <- array(NA,dim=c(4,144),dimnames=list(c('time_spent_seconds','page_views','number_course_compenium_views','number_logins
'),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
time_spent_seconds page_views number_course_compenium_views number_logins\r
1 159261 1801 586 91
2 189672 1717 520 59
3 7215 192 72 18
4 129098 2295 645 95
5 230632 3450 1163 136
6 515038 6861 1945 263
7 180745 1795 585 56
8 185559 1681 470 59
9 154581 1897 612 44
10 298001 2974 992 96
11 121844 1946 634 75
12 184039 2148 677 69
13 100324 1832 665 98
14 220269 3183 1079 119
15 168265 1476 413 58
16 154647 1567 469 88
17 142018 1756 431 57
18 79030 1247 361 61
19 167047 2779 877 87
20 27997 726 221 24
21 73019 1048 366 59
22 241082 2805 846 100
23 195820 1760 642 72
24 142001 2266 689 54
25 145433 1848 576 86
26 183744 1665 610 32
27 202357 2084 673 163
28 199532 1440 361 93
29 354924 2741 907 118
30 192399 2112 882 44
31 182286 1684 490 44
32 181590 1616 548 45
33 133801 2227 723 105
34 233686 3088 918 123
35 219428 2389 787 53
36 0 1 0 1
37 223044 2099 983 63
38 100129 1669 539 51
39 145864 2137 515 49
40 249965 2153 795 64
41 242379 2390 753 71
42 145794 1701 635 59
43 96404 983 361 32
44 195891 2161 804 78
45 117156 1276 394 50
46 157787 1190 320 95
47 81293 745 212 32
48 237435 2330 772 101
49 233155 2289 740 89
50 160344 2639 938 59
51 48188 658 205 28
52 161922 1917 492 69
53 307432 2557 818 74
54 235223 2026 680 79
55 195583 1911 691 59
56 146061 1716 534 56
57 208834 1852 487 67
58 93764 981 301 24
59 151985 1177 421 66
60 193222 2833 947 96
61 148922 1688 492 60
62 132856 2097 790 80
63 129561 1331 362 61
64 112718 1244 430 37
65 160930 1256 416 35
66 99184 1294 409 41
67 192535 2303 498 70
68 138708 2897 887 65
69 114408 1103 267 38
70 31970 340 101 15
71 225558 2791 1000 112
72 139220 1338 416 72
73 113612 1441 480 68
74 108641 1623 454 71
75 162203 2650 671 67
76 100098 1499 413 44
77 174768 2302 677 60
78 158459 2540 820 97
79 80934 1000 316 30
80 84971 1234 395 71
81 80545 927 217 68
82 287191 2176 818 64
83 62974 957 292 28
84 134091 1551 513 40
85 75555 1014 345 46
86 162154 1771 557 54
87 226638 2613 645 227
88 115367 1205 284 112
89 108749 1337 424 62
90 155537 1524 614 52
91 153133 1829 672 41
92 165618 2229 649 78
93 151517 1233 415 57
94 133686 1365 505 58
95 61342 950 387 40
96 245196 2319 730 117
97 195576 1857 563 70
98 19349 223 67 12
99 225371 2390 812 105
100 153213 1985 811 78
101 59117 700 281 29
102 91762 1062 338 24
103 136769 1311 413 54
104 114798 1157 298 61
105 85338 823 223 40
106 27676 596 194 22
107 153535 1545 371 48
108 122417 1130 268 37
109 0 0 0 0
110 91529 1082 332 32
111 107205 1135 371 67
112 144664 1367 465 45
113 146445 1506 447 63
114 76656 870 295 60
115 3616 78 14 5
116 0 0 0 0
117 183088 1130 388 44
118 144677 1582 564 84
119 159104 2034 562 98
120 113273 919 288 38
121 43410 778 292 19
122 175774 1752 530 73
123 95401 957 256 42
124 134837 2098 602 55
125 60493 731 174 40
126 19764 285 75 12
127 164062 1834 565 56
128 132696 1148 377 33
129 155367 1646 544 54
130 11796 256 79 9
131 10674 98 33 9
132 142261 1404 479 57
133 6836 41 11 3
134 162563 1824 626 63
135 5118 42 6 3
136 40248 528 183 16
137 0 0 0 0
138 122641 1073 334 47
139 88837 1305 269 38
140 7131 81 27 4
141 9056 261 99 14
142 76611 934 260 24
143 132697 1180 290 51
144 100681 1147 414 19
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) page_views
13965.97 41.53
number_course_compenium_views `number_logins\r`
87.63 264.05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-90451 -18458 -2680 21231 116495
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13965.97 5912.47 2.362 0.01955 *
page_views 41.53 14.60 2.844 0.00512 **
number_course_compenium_views 87.63 39.90 2.196 0.02973 *
`number_logins\r` 264.05 139.38 1.894 0.06024 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 34570 on 140 degrees of freedom
Multiple R-squared: 0.8042, Adjusted R-squared: 0.8
F-statistic: 191.7 on 3 and 140 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,] 0.8515334 2.969331e-01 1.484666e-01
[2,] 0.7962132 4.075736e-01 2.037868e-01
[3,] 0.7971976 4.056049e-01 2.028024e-01
[4,] 0.8386788 3.226424e-01 1.613212e-01
[5,] 0.8449080 3.101839e-01 1.550920e-01
[6,] 0.7775601 4.448798e-01 2.224399e-01
[7,] 0.7324774 5.350452e-01 2.675226e-01
[8,] 0.7000365 5.999269e-01 2.999635e-01
[9,] 0.7060099 5.879802e-01 2.939901e-01
[10,] 0.7402041 5.195918e-01 2.597959e-01
[11,] 0.7298821 5.402357e-01 2.701179e-01
[12,] 0.7025329 5.949342e-01 2.974671e-01
[13,] 0.8340037 3.319926e-01 1.659963e-01
[14,] 0.8629758 2.740483e-01 1.370242e-01
[15,] 0.8267141 3.465717e-01 1.732859e-01
[16,] 0.7893545 4.212910e-01 2.106455e-01
[17,] 0.8572202 2.855595e-01 1.427798e-01
[18,] 0.8915379 2.169243e-01 1.084621e-01
[19,] 0.8610681 2.778639e-01 1.389319e-01
[20,] 0.8624429 2.751143e-01 1.375571e-01
[21,] 0.8832901 2.334198e-01 1.167099e-01
[22,] 0.9451170 1.097660e-01 5.488302e-02
[23,] 0.9990579 1.884191e-03 9.420955e-04
[24,] 0.9985520 2.896027e-03 1.448014e-03
[25,] 0.9986693 2.661314e-03 1.330657e-03
[26,] 0.9987133 2.573458e-03 1.286729e-03
[27,] 0.9994235 1.153003e-03 5.765015e-04
[28,] 0.9992138 1.572318e-03 7.861589e-04
[29,] 0.9989175 2.165061e-03 1.082530e-03
[30,] 0.9985125 2.974963e-03 1.487481e-03
[31,] 0.9980664 3.867265e-03 1.933632e-03
[32,] 0.9984506 3.098826e-03 1.549413e-03
[33,] 0.9978516 4.296718e-03 2.148359e-03
[34,] 0.9988983 2.203487e-03 1.101743e-03
[35,] 0.9990602 1.879687e-03 9.398436e-04
[36,] 0.9986094 2.781125e-03 1.390562e-03
[37,] 0.9978975 4.205034e-03 2.102517e-03
[38,] 0.9968641 6.271859e-03 3.135929e-03
[39,] 0.9954063 9.187350e-03 4.593675e-03
[40,] 0.9961357 7.728671e-03 3.864335e-03
[41,] 0.9945064 1.098720e-02 5.493598e-03
[42,] 0.9940975 1.180503e-02 5.902514e-03
[43,] 0.9939984 1.200327e-02 6.001634e-03
[44,] 0.9971604 5.679286e-03 2.839643e-03
[45,] 0.9963266 7.346874e-03 3.673437e-03
[46,] 0.9947485 1.050301e-02 5.251503e-03
[47,] 0.9995641 8.717601e-04 4.358800e-04
[48,] 0.9997907 4.186480e-04 2.093240e-04
[49,] 0.9997490 5.020634e-04 2.510317e-04
[50,] 0.9996073 7.853036e-04 3.926518e-04
[51,] 0.9998202 3.595641e-04 1.797820e-04
[52,] 0.9997217 5.565765e-04 2.782882e-04
[53,] 0.9997233 5.534023e-04 2.767011e-04
[54,] 0.9997992 4.015743e-04 2.007872e-04
[55,] 0.9996885 6.229727e-04 3.114863e-04
[56,] 0.9998722 2.555329e-04 1.277664e-04
[57,] 0.9998082 3.835875e-04 1.917938e-04
[58,] 0.9996989 6.022464e-04 3.011232e-04
[59,] 0.9998206 3.587891e-04 1.793945e-04
[60,] 0.9997447 5.105257e-04 2.552628e-04
[61,] 0.9996903 6.193940e-04 3.096970e-04
[62,] 0.9999885 2.302387e-05 1.151193e-05
[63,] 0.9999849 3.021866e-05 1.510933e-05
[64,] 0.9999758 4.843494e-05 2.421747e-05
[65,] 0.9999735 5.305247e-05 2.652623e-05
[66,] 0.9999596 8.089558e-05 4.044779e-05
[67,] 0.9999467 1.065149e-04 5.325744e-05
[68,] 0.9999461 1.077213e-04 5.386067e-05
[69,] 0.9999669 6.620575e-05 3.310288e-05
[70,] 0.9999624 7.525351e-05 3.762676e-05
[71,] 0.9999512 9.760166e-05 4.880083e-05
[72,] 0.9999957 8.559374e-06 4.279687e-06
[73,] 0.9999929 1.412543e-05 7.062715e-06
[74,] 0.9999937 1.257046e-05 6.285230e-06
[75,] 0.9999891 2.178602e-05 1.089301e-05
[76,] 0.9999999 2.202145e-07 1.101073e-07
[77,] 0.9999999 2.686615e-07 1.343307e-07
[78,] 0.9999997 5.258036e-07 2.629018e-07
[79,] 0.9999996 7.259961e-07 3.629980e-07
[80,] 0.9999993 1.405624e-06 7.028122e-07
[81,] 0.9999994 1.223652e-06 6.118262e-07
[82,] 0.9999991 1.746985e-06 8.734923e-07
[83,] 0.9999988 2.473307e-06 1.236653e-06
[84,] 0.9999981 3.851052e-06 1.925526e-06
[85,] 0.9999963 7.336495e-06 3.668248e-06
[86,] 0.9999975 4.917150e-06 2.458575e-06
[87,] 0.9999981 3.831203e-06 1.915601e-06
[88,] 0.9999964 7.273298e-06 3.636649e-06
[89,] 0.9999966 6.825599e-06 3.412800e-06
[90,] 0.9999957 8.551184e-06 4.275592e-06
[91,] 0.9999955 9.098777e-06 4.549389e-06
[92,] 0.9999915 1.697276e-05 8.486379e-06
[93,] 0.9999841 3.185996e-05 1.592998e-05
[94,] 0.9999901 1.971393e-05 9.856967e-06
[95,] 0.9999835 3.298808e-05 1.649404e-05
[96,] 0.9999686 6.270742e-05 3.135371e-05
[97,] 0.9999482 1.035850e-04 5.179250e-05
[98,] 0.9999064 1.871326e-04 9.356631e-05
[99,] 0.9998369 3.262245e-04 1.631122e-04
[100,] 0.9998490 3.019603e-04 1.509801e-04
[101,] 0.9998307 3.386115e-04 1.693057e-04
[102,] 0.9998547 2.905516e-04 1.452758e-04
[103,] 0.9997379 5.242296e-04 2.621148e-04
[104,] 0.9995309 9.382927e-04 4.691463e-04
[105,] 0.9992286 1.542809e-03 7.714045e-04
[106,] 0.9988565 2.287090e-03 1.143545e-03
[107,] 0.9981201 3.759836e-03 1.879918e-03
[108,] 0.9976753 4.649373e-03 2.324686e-03
[109,] 0.9961860 7.627905e-03 3.813953e-03
[110,] 0.9937484 1.250323e-02 6.251616e-03
[111,] 0.9998784 2.431934e-04 1.215967e-04
[112,] 0.9998729 2.541885e-04 1.270943e-04
[113,] 0.9999822 3.555437e-05 1.777719e-05
[114,] 0.9999809 3.828000e-05 1.914000e-05
[115,] 0.9999833 3.331163e-05 1.665581e-05
[116,] 0.9999582 8.359961e-05 4.179981e-05
[117,] 0.9998986 2.028393e-04 1.014197e-04
[118,] 0.9999844 3.126626e-05 1.563313e-05
[119,] 0.9999726 5.481423e-05 2.740711e-05
[120,] 0.9999269 1.462769e-04 7.313846e-05
[121,] 0.9998019 3.962604e-04 1.981302e-04
[122,] 0.9999555 8.903982e-05 4.451991e-05
[123,] 0.9998567 2.866489e-04 1.433245e-04
[124,] 0.9996576 6.847014e-04 3.423507e-04
[125,] 0.9989691 2.061707e-03 1.030854e-03
[126,] 0.9970077 5.984550e-03 2.992275e-03
[127,] 0.9920126 1.597481e-02 7.987407e-03
[128,] 0.9932401 1.351977e-02 6.759887e-03
[129,] 0.9835567 3.288662e-02 1.644331e-02
[130,] 0.9589008 8.219833e-02 4.109917e-02
[131,] 0.9189557 1.620885e-01 8.104426e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1pr2t1324642855.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/21x9i1324642855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wbbd1324642855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4g3qi1324642855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/57ygz1324642855.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
-4873.9771 43258.5144 -25786.3423 -61777.2093 -64425.1014 -23726.3073
7 8 9 10 11 12
26188.5434 45022.0654 -3408.5652 48257.2888 -48293.7768 3329.1233
13 14 15 16 17 18
-73869.5882 -51850.7571 41499.9440 11274.1963 2312.4006 -34460.9200
19 20 21 22 23 24
-62145.0096 -41820.6269 -32118.3603 10094.2842 33497.1003 -40699.7613
25 26 27 28 29 30
-18457.1146 38732.2667 -165.3551 69577.0265 116495.4586 1820.6289
31 32 33 34 35 36
43832.5814 40613.5983 -63726.2879 -21436.1794 23295.6382 -14271.5442
37 38 39 40 41 42
19137.6415 -43843.8709 -14911.6403 60027.1090 44431.7254 -10032.8095
43 44 45 46 47 48
1533.3128 1135.5173 2473.5063 41278.2952 9362.6801 32392.7669
49 50 51 52 53 54
35788.1563 -60986.6002 -18459.9547 7016.5799 96061.6661 56675.8713
55 56 57 58 59 60
26128.3651 -745.6661 57594.0147 6346.7096 34822.6853 -46723.1195
61 62 63 64 65 66
5902.4210 -58543.0794 12494.2850 -357.7955 49110.8494 -15183.9986
67 68 69 70 71 72
20810.7983 -90451.2825 21207.3799 -8926.3458 -21512.3525 14225.9058
73 74 75 76 77 78
-20211.5267 -31253.9161 -38298.9061 -23925.4546 -9960.3954 -58453.8005
79 80 81 82 83 84
-10171.0640 -33600.0851 -8886.9509 94282.4697 -23714.1783 201.1563
85 86 87 88 89 90
-22897.5590 11575.9699 -12296.9340 -3098.6635 -14264.1330 10748.8760
91 92 93 94 95 96
-6498.6151 -18378.1981 34931.4843 3468.1130 -36549.1785 40066.3219
97 98 99 100 101 102
36676.1685 -12917.1807 13275.7247 -34847.3792 -16199.1543 -2261.2776
103 104 105 106 107 108
17911.8899 10565.2435 7092.3185 -33849.1041 30225.7274 28271.6019
109 110 111 112 113 114
-13965.9690 -4911.3938 -4095.6587 21301.0048 14134.4196 -15131.9210
115 116 117 118 119 120
-16136.0794 -13965.9690 76578.3570 -6587.5779 -14451.6425 25872.8346
121 122 123 124 125 126
-33468.6244 23334.0950 8170.8977 -33527.4597 -9638.3213 -15777.8367
127 128 129 130 131 132
9638.6891 29307.3958 11118.9102 -22099.9715 -12629.8096 12966.1031
133 134 135 136 137 138
-10588.6304 1361.0182 -11909.9937 -15905.0990 -13965.9690 22438.3300
139 140 141 142 143 144
-12927.0853 -13620.8294 -28120.4976 -5261.6485 30850.7066 -2211.7870
> postscript(file="/var/wessaorg/rcomp/tmp/62n6c1324642855.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 -4873.9771 NA
1 43258.5144 -4873.9771
2 -25786.3423 43258.5144
3 -61777.2093 -25786.3423
4 -64425.1014 -61777.2093
5 -23726.3073 -64425.1014
6 26188.5434 -23726.3073
7 45022.0654 26188.5434
8 -3408.5652 45022.0654
9 48257.2888 -3408.5652
10 -48293.7768 48257.2888
11 3329.1233 -48293.7768
12 -73869.5882 3329.1233
13 -51850.7571 -73869.5882
14 41499.9440 -51850.7571
15 11274.1963 41499.9440
16 2312.4006 11274.1963
17 -34460.9200 2312.4006
18 -62145.0096 -34460.9200
19 -41820.6269 -62145.0096
20 -32118.3603 -41820.6269
21 10094.2842 -32118.3603
22 33497.1003 10094.2842
23 -40699.7613 33497.1003
24 -18457.1146 -40699.7613
25 38732.2667 -18457.1146
26 -165.3551 38732.2667
27 69577.0265 -165.3551
28 116495.4586 69577.0265
29 1820.6289 116495.4586
30 43832.5814 1820.6289
31 40613.5983 43832.5814
32 -63726.2879 40613.5983
33 -21436.1794 -63726.2879
34 23295.6382 -21436.1794
35 -14271.5442 23295.6382
36 19137.6415 -14271.5442
37 -43843.8709 19137.6415
38 -14911.6403 -43843.8709
39 60027.1090 -14911.6403
40 44431.7254 60027.1090
41 -10032.8095 44431.7254
42 1533.3128 -10032.8095
43 1135.5173 1533.3128
44 2473.5063 1135.5173
45 41278.2952 2473.5063
46 9362.6801 41278.2952
47 32392.7669 9362.6801
48 35788.1563 32392.7669
49 -60986.6002 35788.1563
50 -18459.9547 -60986.6002
51 7016.5799 -18459.9547
52 96061.6661 7016.5799
53 56675.8713 96061.6661
54 26128.3651 56675.8713
55 -745.6661 26128.3651
56 57594.0147 -745.6661
57 6346.7096 57594.0147
58 34822.6853 6346.7096
59 -46723.1195 34822.6853
60 5902.4210 -46723.1195
61 -58543.0794 5902.4210
62 12494.2850 -58543.0794
63 -357.7955 12494.2850
64 49110.8494 -357.7955
65 -15183.9986 49110.8494
66 20810.7983 -15183.9986
67 -90451.2825 20810.7983
68 21207.3799 -90451.2825
69 -8926.3458 21207.3799
70 -21512.3525 -8926.3458
71 14225.9058 -21512.3525
72 -20211.5267 14225.9058
73 -31253.9161 -20211.5267
74 -38298.9061 -31253.9161
75 -23925.4546 -38298.9061
76 -9960.3954 -23925.4546
77 -58453.8005 -9960.3954
78 -10171.0640 -58453.8005
79 -33600.0851 -10171.0640
80 -8886.9509 -33600.0851
81 94282.4697 -8886.9509
82 -23714.1783 94282.4697
83 201.1563 -23714.1783
84 -22897.5590 201.1563
85 11575.9699 -22897.5590
86 -12296.9340 11575.9699
87 -3098.6635 -12296.9340
88 -14264.1330 -3098.6635
89 10748.8760 -14264.1330
90 -6498.6151 10748.8760
91 -18378.1981 -6498.6151
92 34931.4843 -18378.1981
93 3468.1130 34931.4843
94 -36549.1785 3468.1130
95 40066.3219 -36549.1785
96 36676.1685 40066.3219
97 -12917.1807 36676.1685
98 13275.7247 -12917.1807
99 -34847.3792 13275.7247
100 -16199.1543 -34847.3792
101 -2261.2776 -16199.1543
102 17911.8899 -2261.2776
103 10565.2435 17911.8899
104 7092.3185 10565.2435
105 -33849.1041 7092.3185
106 30225.7274 -33849.1041
107 28271.6019 30225.7274
108 -13965.9690 28271.6019
109 -4911.3938 -13965.9690
110 -4095.6587 -4911.3938
111 21301.0048 -4095.6587
112 14134.4196 21301.0048
113 -15131.9210 14134.4196
114 -16136.0794 -15131.9210
115 -13965.9690 -16136.0794
116 76578.3570 -13965.9690
117 -6587.5779 76578.3570
118 -14451.6425 -6587.5779
119 25872.8346 -14451.6425
120 -33468.6244 25872.8346
121 23334.0950 -33468.6244
122 8170.8977 23334.0950
123 -33527.4597 8170.8977
124 -9638.3213 -33527.4597
125 -15777.8367 -9638.3213
126 9638.6891 -15777.8367
127 29307.3958 9638.6891
128 11118.9102 29307.3958
129 -22099.9715 11118.9102
130 -12629.8096 -22099.9715
131 12966.1031 -12629.8096
132 -10588.6304 12966.1031
133 1361.0182 -10588.6304
134 -11909.9937 1361.0182
135 -15905.0990 -11909.9937
136 -13965.9690 -15905.0990
137 22438.3300 -13965.9690
138 -12927.0853 22438.3300
139 -13620.8294 -12927.0853
140 -28120.4976 -13620.8294
141 -5261.6485 -28120.4976
142 30850.7066 -5261.6485
143 -2211.7870 30850.7066
144 NA -2211.7870
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 43258.5144 -4873.9771
[2,] -25786.3423 43258.5144
[3,] -61777.2093 -25786.3423
[4,] -64425.1014 -61777.2093
[5,] -23726.3073 -64425.1014
[6,] 26188.5434 -23726.3073
[7,] 45022.0654 26188.5434
[8,] -3408.5652 45022.0654
[9,] 48257.2888 -3408.5652
[10,] -48293.7768 48257.2888
[11,] 3329.1233 -48293.7768
[12,] -73869.5882 3329.1233
[13,] -51850.7571 -73869.5882
[14,] 41499.9440 -51850.7571
[15,] 11274.1963 41499.9440
[16,] 2312.4006 11274.1963
[17,] -34460.9200 2312.4006
[18,] -62145.0096 -34460.9200
[19,] -41820.6269 -62145.0096
[20,] -32118.3603 -41820.6269
[21,] 10094.2842 -32118.3603
[22,] 33497.1003 10094.2842
[23,] -40699.7613 33497.1003
[24,] -18457.1146 -40699.7613
[25,] 38732.2667 -18457.1146
[26,] -165.3551 38732.2667
[27,] 69577.0265 -165.3551
[28,] 116495.4586 69577.0265
[29,] 1820.6289 116495.4586
[30,] 43832.5814 1820.6289
[31,] 40613.5983 43832.5814
[32,] -63726.2879 40613.5983
[33,] -21436.1794 -63726.2879
[34,] 23295.6382 -21436.1794
[35,] -14271.5442 23295.6382
[36,] 19137.6415 -14271.5442
[37,] -43843.8709 19137.6415
[38,] -14911.6403 -43843.8709
[39,] 60027.1090 -14911.6403
[40,] 44431.7254 60027.1090
[41,] -10032.8095 44431.7254
[42,] 1533.3128 -10032.8095
[43,] 1135.5173 1533.3128
[44,] 2473.5063 1135.5173
[45,] 41278.2952 2473.5063
[46,] 9362.6801 41278.2952
[47,] 32392.7669 9362.6801
[48,] 35788.1563 32392.7669
[49,] -60986.6002 35788.1563
[50,] -18459.9547 -60986.6002
[51,] 7016.5799 -18459.9547
[52,] 96061.6661 7016.5799
[53,] 56675.8713 96061.6661
[54,] 26128.3651 56675.8713
[55,] -745.6661 26128.3651
[56,] 57594.0147 -745.6661
[57,] 6346.7096 57594.0147
[58,] 34822.6853 6346.7096
[59,] -46723.1195 34822.6853
[60,] 5902.4210 -46723.1195
[61,] -58543.0794 5902.4210
[62,] 12494.2850 -58543.0794
[63,] -357.7955 12494.2850
[64,] 49110.8494 -357.7955
[65,] -15183.9986 49110.8494
[66,] 20810.7983 -15183.9986
[67,] -90451.2825 20810.7983
[68,] 21207.3799 -90451.2825
[69,] -8926.3458 21207.3799
[70,] -21512.3525 -8926.3458
[71,] 14225.9058 -21512.3525
[72,] -20211.5267 14225.9058
[73,] -31253.9161 -20211.5267
[74,] -38298.9061 -31253.9161
[75,] -23925.4546 -38298.9061
[76,] -9960.3954 -23925.4546
[77,] -58453.8005 -9960.3954
[78,] -10171.0640 -58453.8005
[79,] -33600.0851 -10171.0640
[80,] -8886.9509 -33600.0851
[81,] 94282.4697 -8886.9509
[82,] -23714.1783 94282.4697
[83,] 201.1563 -23714.1783
[84,] -22897.5590 201.1563
[85,] 11575.9699 -22897.5590
[86,] -12296.9340 11575.9699
[87,] -3098.6635 -12296.9340
[88,] -14264.1330 -3098.6635
[89,] 10748.8760 -14264.1330
[90,] -6498.6151 10748.8760
[91,] -18378.1981 -6498.6151
[92,] 34931.4843 -18378.1981
[93,] 3468.1130 34931.4843
[94,] -36549.1785 3468.1130
[95,] 40066.3219 -36549.1785
[96,] 36676.1685 40066.3219
[97,] -12917.1807 36676.1685
[98,] 13275.7247 -12917.1807
[99,] -34847.3792 13275.7247
[100,] -16199.1543 -34847.3792
[101,] -2261.2776 -16199.1543
[102,] 17911.8899 -2261.2776
[103,] 10565.2435 17911.8899
[104,] 7092.3185 10565.2435
[105,] -33849.1041 7092.3185
[106,] 30225.7274 -33849.1041
[107,] 28271.6019 30225.7274
[108,] -13965.9690 28271.6019
[109,] -4911.3938 -13965.9690
[110,] -4095.6587 -4911.3938
[111,] 21301.0048 -4095.6587
[112,] 14134.4196 21301.0048
[113,] -15131.9210 14134.4196
[114,] -16136.0794 -15131.9210
[115,] -13965.9690 -16136.0794
[116,] 76578.3570 -13965.9690
[117,] -6587.5779 76578.3570
[118,] -14451.6425 -6587.5779
[119,] 25872.8346 -14451.6425
[120,] -33468.6244 25872.8346
[121,] 23334.0950 -33468.6244
[122,] 8170.8977 23334.0950
[123,] -33527.4597 8170.8977
[124,] -9638.3213 -33527.4597
[125,] -15777.8367 -9638.3213
[126,] 9638.6891 -15777.8367
[127,] 29307.3958 9638.6891
[128,] 11118.9102 29307.3958
[129,] -22099.9715 11118.9102
[130,] -12629.8096 -22099.9715
[131,] 12966.1031 -12629.8096
[132,] -10588.6304 12966.1031
[133,] 1361.0182 -10588.6304
[134,] -11909.9937 1361.0182
[135,] -15905.0990 -11909.9937
[136,] -13965.9690 -15905.0990
[137,] 22438.3300 -13965.9690
[138,] -12927.0853 22438.3300
[139,] -13620.8294 -12927.0853
[140,] -28120.4976 -13620.8294
[141,] -5261.6485 -28120.4976
[142,] 30850.7066 -5261.6485
[143,] -2211.7870 30850.7066
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 43258.5144 -4873.9771
2 -25786.3423 43258.5144
3 -61777.2093 -25786.3423
4 -64425.1014 -61777.2093
5 -23726.3073 -64425.1014
6 26188.5434 -23726.3073
7 45022.0654 26188.5434
8 -3408.5652 45022.0654
9 48257.2888 -3408.5652
10 -48293.7768 48257.2888
11 3329.1233 -48293.7768
12 -73869.5882 3329.1233
13 -51850.7571 -73869.5882
14 41499.9440 -51850.7571
15 11274.1963 41499.9440
16 2312.4006 11274.1963
17 -34460.9200 2312.4006
18 -62145.0096 -34460.9200
19 -41820.6269 -62145.0096
20 -32118.3603 -41820.6269
21 10094.2842 -32118.3603
22 33497.1003 10094.2842
23 -40699.7613 33497.1003
24 -18457.1146 -40699.7613
25 38732.2667 -18457.1146
26 -165.3551 38732.2667
27 69577.0265 -165.3551
28 116495.4586 69577.0265
29 1820.6289 116495.4586
30 43832.5814 1820.6289
31 40613.5983 43832.5814
32 -63726.2879 40613.5983
33 -21436.1794 -63726.2879
34 23295.6382 -21436.1794
35 -14271.5442 23295.6382
36 19137.6415 -14271.5442
37 -43843.8709 19137.6415
38 -14911.6403 -43843.8709
39 60027.1090 -14911.6403
40 44431.7254 60027.1090
41 -10032.8095 44431.7254
42 1533.3128 -10032.8095
43 1135.5173 1533.3128
44 2473.5063 1135.5173
45 41278.2952 2473.5063
46 9362.6801 41278.2952
47 32392.7669 9362.6801
48 35788.1563 32392.7669
49 -60986.6002 35788.1563
50 -18459.9547 -60986.6002
51 7016.5799 -18459.9547
52 96061.6661 7016.5799
53 56675.8713 96061.6661
54 26128.3651 56675.8713
55 -745.6661 26128.3651
56 57594.0147 -745.6661
57 6346.7096 57594.0147
58 34822.6853 6346.7096
59 -46723.1195 34822.6853
60 5902.4210 -46723.1195
61 -58543.0794 5902.4210
62 12494.2850 -58543.0794
63 -357.7955 12494.2850
64 49110.8494 -357.7955
65 -15183.9986 49110.8494
66 20810.7983 -15183.9986
67 -90451.2825 20810.7983
68 21207.3799 -90451.2825
69 -8926.3458 21207.3799
70 -21512.3525 -8926.3458
71 14225.9058 -21512.3525
72 -20211.5267 14225.9058
73 -31253.9161 -20211.5267
74 -38298.9061 -31253.9161
75 -23925.4546 -38298.9061
76 -9960.3954 -23925.4546
77 -58453.8005 -9960.3954
78 -10171.0640 -58453.8005
79 -33600.0851 -10171.0640
80 -8886.9509 -33600.0851
81 94282.4697 -8886.9509
82 -23714.1783 94282.4697
83 201.1563 -23714.1783
84 -22897.5590 201.1563
85 11575.9699 -22897.5590
86 -12296.9340 11575.9699
87 -3098.6635 -12296.9340
88 -14264.1330 -3098.6635
89 10748.8760 -14264.1330
90 -6498.6151 10748.8760
91 -18378.1981 -6498.6151
92 34931.4843 -18378.1981
93 3468.1130 34931.4843
94 -36549.1785 3468.1130
95 40066.3219 -36549.1785
96 36676.1685 40066.3219
97 -12917.1807 36676.1685
98 13275.7247 -12917.1807
99 -34847.3792 13275.7247
100 -16199.1543 -34847.3792
101 -2261.2776 -16199.1543
102 17911.8899 -2261.2776
103 10565.2435 17911.8899
104 7092.3185 10565.2435
105 -33849.1041 7092.3185
106 30225.7274 -33849.1041
107 28271.6019 30225.7274
108 -13965.9690 28271.6019
109 -4911.3938 -13965.9690
110 -4095.6587 -4911.3938
111 21301.0048 -4095.6587
112 14134.4196 21301.0048
113 -15131.9210 14134.4196
114 -16136.0794 -15131.9210
115 -13965.9690 -16136.0794
116 76578.3570 -13965.9690
117 -6587.5779 76578.3570
118 -14451.6425 -6587.5779
119 25872.8346 -14451.6425
120 -33468.6244 25872.8346
121 23334.0950 -33468.6244
122 8170.8977 23334.0950
123 -33527.4597 8170.8977
124 -9638.3213 -33527.4597
125 -15777.8367 -9638.3213
126 9638.6891 -15777.8367
127 29307.3958 9638.6891
128 11118.9102 29307.3958
129 -22099.9715 11118.9102
130 -12629.8096 -22099.9715
131 12966.1031 -12629.8096
132 -10588.6304 12966.1031
133 1361.0182 -10588.6304
134 -11909.9937 1361.0182
135 -15905.0990 -11909.9937
136 -13965.9690 -15905.0990
137 22438.3300 -13965.9690
138 -12927.0853 22438.3300
139 -13620.8294 -12927.0853
140 -28120.4976 -13620.8294
141 -5261.6485 -28120.4976
142 30850.7066 -5261.6485
143 -2211.7870 30850.7066
> 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/7i0e01324642855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89r5y1324642855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9j8sk1324642855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1052321324642855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11eegu1324642855.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/12h6do1324642855.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/134to71324642855.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/14huj01324642855.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/15vy041324642855.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/16q5391324642855.tab")
+ }
>
> try(system("convert tmp/1pr2t1324642855.ps tmp/1pr2t1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/21x9i1324642855.ps tmp/21x9i1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wbbd1324642855.ps tmp/3wbbd1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g3qi1324642855.ps tmp/4g3qi1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/57ygz1324642855.ps tmp/57ygz1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/62n6c1324642855.ps tmp/62n6c1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/7i0e01324642855.ps tmp/7i0e01324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/89r5y1324642855.ps tmp/89r5y1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/9j8sk1324642855.ps tmp/9j8sk1324642855.png",intern=TRUE))
character(0)
> try(system("convert tmp/1052321324642855.ps tmp/1052321324642855.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.344 0.615 5.010