R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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.
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'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(58198
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+ ,10
+ ,0
+ ,24
+ ,14831
+ ,38232
+ ,8
+ ,12
+ ,36
+ ,6585)
+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('RFC'
+ ,'Logins'
+ ,'Computations'
+ ,'Feedback'
+ ,'characters
')
+ ,1:144))
> y <- array(NA,dim=c(5,144),dimnames=list(c('RFC','Logins','Computations','Feedback','characters
'),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
RFC Logins Computations Feedback characters\r
1 58198 49 13 20 10345
2 65968 24 26 28 17607
3 7176 17 0 0 1423
4 78306 66 37 40 20050
5 127587 81 45 60 21212
6 250877 127 80 56 93979
7 65878 31 21 38 15524
8 72513 30 36 40 16182
9 72507 32 35 60 19238
10 168544 62 36 52 28909
11 66288 34 35 24 22357
12 94815 43 46 56 25560
13 45496 67 20 24 9954
14 78277 56 24 32 18490
15 66960 23 18 36 17777
16 72377 38 15 40 25268
17 61175 32 48 52 37525
18 15580 19 0 20 6023
19 71693 54 38 79 25042
20 13397 13 8 16 35713
21 38921 35 10 48 7039
22 97709 49 51 48 40841
23 47899 27 4 40 9214
24 61674 30 24 29 17446
25 77395 50 39 40 10295
26 65152 11 19 28 13206
27 85842 93 21 45 26093
28 75108 50 31 60 20744
29 182314 58 36 48 68013
30 91493 24 19 28 12840
31 56374 27 20 56 12672
32 104756 22 39 32 10872
33 50485 55 26 12 21325
34 29013 39 0 32 24542
35 90349 29 29 44 16401
36 0 0 0 0 0
37 61484 33 8 40 12821
38 65245 34 35 31 14662
39 35361 19 3 48 22190
40 106880 34 47 72 37929
41 82577 33 42 36 18009
42 53655 25 10 56 11076
43 40064 12 10 28 24981
44 58619 43 26 36 30691
45 55561 28 27 44 29164
46 31331 29 0 32 13985
47 31350 12 14 55 7588
48 93341 53 30 32 20023
49 57002 39 11 32 25524
50 60206 27 24 44 14717
51 33820 20 10 42 6832
52 49791 35 14 40 9624
53 108716 40 23 40 24300
54 87699 42 27 40 21790
55 89612 32 40 48 16493
56 62529 28 22 24 9269
57 64319 24 26 32 20105
58 25090 11 8 32 11216
59 59080 36 27 52 15569
60 19608 21 0 40 21799
61 31969 21 0 60 3772
62 29728 32 16 24 6057
63 27697 18 7 22 20828
64 42406 18 18 36 9976
65 47859 11 7 26 14055
66 55240 21 24 44 17455
67 64606 41 14 64 39553
68 61854 43 39 36 14818
69 35185 19 16 36 17065
70 12207 8 0 16 1536
71 112537 72 39 36 11938
72 43601 23 17 10 24589
73 46737 32 26 40 21332
74 40699 39 27 25 13229
75 46357 20 23 68 11331
76 17667 18 0 36 853
77 59058 27 26 32 19821
78 54106 37 19 24 34666
79 23795 13 12 35 15051
80 34323 34 23 17 27969
81 37071 28 32 36 17897
82 78258 26 19 40 6031
83 32392 15 17 40 7153
84 55020 19 25 48 13365
85 29613 25 14 40 11197
86 56879 28 11 48 25291
87 100802 105 20 68 28994
88 24612 25 14 44 10461
89 37664 21 14 28 16415
90 53398 22 22 40 8495
91 54198 20 25 28 18318
92 66038 43 35 36 25143
93 61352 28 9 40 20471
94 48096 29 16 20 14561
95 25189 21 12 22 16902
96 118291 57 20 56 12994
97 71853 25 33 52 29697
98 19349 11 13 2 3895
99 67369 51 11 52 9807
100 51588 34 11 26 10711
101 19719 13 8 3 2325
102 25497 11 22 20 19000
103 55049 36 13 32 22418
104 24912 21 6 28 7872
105 28591 19 12 36 5650
106 24716 13 2 45 3979
107 52452 16 33 40 14956
108 17850 16 5 0 3738
109 0 0 0 0 0
110 35269 11 34 28 10586
111 27554 31 12 28 18122
112 55167 12 34 32 17899
113 42982 33 30 56 10913
114 40920 38 21 13 18060
115 3058 4 0 0 0
116 0 0 0 0 0
117 96347 24 28 52 15452
118 37559 25 11 43 33996
119 62694 47 9 48 8877
120 36901 20 14 36 18708
121 43410 19 7 3 2781
122 78320 31 41 36 20854
123 37972 20 21 20 8179
124 34563 21 28 37 7139
125 39841 18 1 32 13798
126 16145 9 10 4 5619
127 45310 17 26 40 13050
128 57938 13 7 36 11297
129 48187 14 24 40 16170
130 11796 9 1 8 0
131 7627 8 0 0 0
132 62522 28 11 25 20539
133 6836 3 0 4 0
134 28834 14 17 12 10056
135 5118 3 5 0 0
136 20825 12 4 6 2418
137 0 0 0 0 0
138 34363 16 6 32 11806
139 12137 9 0 36 15924
140 7131 4 0 0 0
141 4194 11 0 0 0
142 21416 9 15 12 7084
143 19205 10 0 24 14831
144 38232 8 12 36 6585
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Logins Computations Feedback `characters\r`
-1661.605 664.679 906.084 246.974 0.614
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-37808 -9913 -807 6156 65784
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1661.6047 3174.4695 -0.523 0.601510
Logins 664.6790 100.5196 6.612 7.51e-10 ***
Computations 906.0839 139.5703 6.492 1.39e-09 ***
Feedback 246.9744 99.7154 2.477 0.014457 *
`characters\r` 0.6140 0.1607 3.820 0.000201 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16880 on 139 degrees of freedom
Multiple R-squared: 0.7776, Adjusted R-squared: 0.7712
F-statistic: 121.5 on 4 and 139 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,] 0.2447443 4.894886e-01 7.552557e-01
[2,] 0.2759590 5.519181e-01 7.240410e-01
[3,] 0.9575812 8.483751e-02 4.241876e-02
[4,] 0.9313086 1.373828e-01 6.869138e-02
[5,] 0.8925768 2.148465e-01 1.074232e-01
[6,] 0.9122556 1.754889e-01 8.774443e-02
[7,] 0.8703436 2.593127e-01 1.296564e-01
[8,] 0.8302703 3.394594e-01 1.697297e-01
[9,] 0.8541705 2.916589e-01 1.458295e-01
[10,] 0.9734616 5.307672e-02 2.653836e-02
[11,] 0.9661171 6.776579e-02 3.388289e-02
[12,] 0.9925713 1.485731e-02 7.428655e-03
[13,] 0.9967646 6.470831e-03 3.235415e-03
[14,] 0.9949155 1.016907e-02 5.084534e-03
[15,] 0.9934776 1.304480e-02 6.522402e-03
[16,] 0.9915387 1.692261e-02 8.461307e-03
[17,] 0.9878209 2.435820e-02 1.217910e-02
[18,] 0.9817884 3.642315e-02 1.821157e-02
[19,] 0.9917615 1.647708e-02 8.238541e-03
[20,] 0.9937491 1.250170e-02 6.250851e-03
[21,] 0.9916070 1.678595e-02 8.392976e-03
[22,] 0.9998205 3.590021e-04 1.795011e-04
[23,] 0.9999927 1.468322e-05 7.341609e-06
[24,] 0.9999863 2.742808e-05 1.371404e-05
[25,] 0.9999993 1.315815e-06 6.579077e-07
[26,] 0.9999996 7.934210e-07 3.967105e-07
[27,] 0.9999996 7.438296e-07 3.719148e-07
[28,] 0.9999998 3.680716e-07 1.840358e-07
[29,] 0.9999996 7.365757e-07 3.682879e-07
[30,] 0.9999996 8.481906e-07 4.240953e-07
[31,] 0.9999992 1.571403e-06 7.857013e-07
[32,] 0.9999986 2.712474e-06 1.356237e-06
[33,] 0.9999981 3.806251e-06 1.903126e-06
[34,] 0.9999968 6.383811e-06 3.191905e-06
[35,] 0.9999948 1.034452e-05 5.172262e-06
[36,] 0.9999920 1.600971e-05 8.004856e-06
[37,] 0.9999928 1.442486e-05 7.212431e-06
[38,] 0.9999916 1.674366e-05 8.371832e-06
[39,] 0.9999855 2.890953e-05 1.445476e-05
[40,] 0.9999774 4.523313e-05 2.261656e-05
[41,] 0.9999734 5.315832e-05 2.657916e-05
[42,] 0.9999558 8.837151e-05 4.418576e-05
[43,] 0.9999272 1.456914e-04 7.284570e-05
[44,] 0.9998829 2.342870e-04 1.171435e-04
[45,] 0.9998120 3.760203e-04 1.880102e-04
[46,] 0.9999864 2.728807e-05 1.364404e-05
[47,] 0.9999871 2.574045e-05 1.287022e-05
[48,] 0.9999858 2.831830e-05 1.415915e-05
[49,] 0.9999842 3.154637e-05 1.577318e-05
[50,] 0.9999795 4.109745e-05 2.054873e-05
[51,] 0.9999663 6.744006e-05 3.372003e-05
[52,] 0.9999517 9.659029e-05 4.829515e-05
[53,] 0.9999496 1.008450e-04 5.042252e-05
[54,] 0.9999274 1.452625e-04 7.263127e-05
[55,] 0.9999235 1.529778e-04 7.648891e-05
[56,] 0.9998846 2.308093e-04 1.154047e-04
[57,] 0.9998142 3.716181e-04 1.858090e-04
[58,] 0.9998720 2.559650e-04 1.279825e-04
[59,] 0.9998022 3.955582e-04 1.977791e-04
[60,] 0.9997305 5.390252e-04 2.695126e-04
[61,] 0.9997318 5.363911e-04 2.681955e-04
[62,] 0.9996288 7.423533e-04 3.711767e-04
[63,] 0.9994285 1.142967e-03 5.714833e-04
[64,] 0.9995030 9.940439e-04 4.970219e-04
[65,] 0.9993325 1.335072e-03 6.675360e-04
[66,] 0.9993300 1.339920e-03 6.699600e-04
[67,] 0.9994623 1.075371e-03 5.376856e-04
[68,] 0.9993789 1.242172e-03 6.210860e-04
[69,] 0.9992389 1.522267e-03 7.611335e-04
[70,] 0.9989149 2.170287e-03 1.085143e-03
[71,] 0.9986029 2.794239e-03 1.397120e-03
[72,] 0.9983306 3.338828e-03 1.669414e-03
[73,] 0.9987054 2.589161e-03 1.294581e-03
[74,] 0.9993163 1.367332e-03 6.836660e-04
[75,] 0.9997950 4.099534e-04 2.049767e-04
[76,] 0.9997083 5.834964e-04 2.917482e-04
[77,] 0.9995344 9.312420e-04 4.656210e-04
[78,] 0.9995652 8.695033e-04 4.347517e-04
[79,] 0.9993359 1.328146e-03 6.640728e-04
[80,] 0.9996345 7.310089e-04 3.655045e-04
[81,] 0.9998403 3.193316e-04 1.596658e-04
[82,] 0.9997405 5.190595e-04 2.595298e-04
[83,] 0.9995851 8.298963e-04 4.149481e-04
[84,] 0.9994067 1.186607e-03 5.933033e-04
[85,] 0.9993146 1.370732e-03 6.853658e-04
[86,] 0.9992337 1.532590e-03 7.662952e-04
[87,] 0.9988006 2.398809e-03 1.199405e-03
[88,] 0.9985659 2.868122e-03 1.434061e-03
[89,] 0.9998846 2.308406e-04 1.154203e-04
[90,] 0.9998152 3.695787e-04 1.847893e-04
[91,] 0.9996837 6.325291e-04 3.162646e-04
[92,] 0.9994795 1.041057e-03 5.205286e-04
[93,] 0.9992359 1.528190e-03 7.640950e-04
[94,] 0.9987671 2.465885e-03 1.232943e-03
[95,] 0.9985184 2.963226e-03 1.481613e-03
[96,] 0.9976643 4.671419e-03 2.335710e-03
[97,] 0.9966474 6.705170e-03 3.352585e-03
[98,] 0.9955136 8.972721e-03 4.486360e-03
[99,] 0.9937234 1.255318e-02 6.276592e-03
[100,] 0.9905193 1.896148e-02 9.480738e-03
[101,] 0.9858599 2.828011e-02 1.414006e-02
[102,] 0.9795033 4.099348e-02 2.049674e-02
[103,] 0.9758427 4.831469e-02 2.415734e-02
[104,] 0.9797297 4.054056e-02 2.027028e-02
[105,] 0.9716027 5.679470e-02 2.839735e-02
[106,] 0.9932748 1.345031e-02 6.725157e-03
[107,] 0.9925084 1.498319e-02 7.491593e-03
[108,] 0.9881390 2.372192e-02 1.186096e-02
[109,] 0.9815235 3.695304e-02 1.847652e-02
[110,] 0.9982068 3.586419e-03 1.793209e-03
[111,] 0.9990065 1.987031e-03 9.935155e-04
[112,] 0.9990093 1.981439e-03 9.907196e-04
[113,] 0.9988926 2.214885e-03 1.107443e-03
[114,] 0.9992224 1.555213e-03 7.776064e-04
[115,] 0.9987001 2.599802e-03 1.299901e-03
[116,] 0.9973830 5.233978e-03 2.616989e-03
[117,] 0.9993477 1.304585e-03 6.522927e-04
[118,] 0.9985612 2.877626e-03 1.438813e-03
[119,] 0.9968514 6.297234e-03 3.148617e-03
[120,] 0.9970001 5.999705e-03 2.999853e-03
[121,] 0.9998142 3.716506e-04 1.858253e-04
[122,] 0.9994490 1.101981e-03 5.509906e-04
[123,] 0.9985153 2.969495e-03 1.484747e-03
[124,] 0.9959108 8.178462e-03 4.089231e-03
[125,] 0.9991369 1.726146e-03 8.630732e-04
[126,] 0.9969612 6.077572e-03 3.038786e-03
[127,] 0.9894895 2.102106e-02 1.051053e-02
[128,] 0.9678466 6.430672e-02 3.215336e-02
[129,] 0.9202986 1.594028e-01 7.970141e-02
> postscript(file="/var/www/rcomp/tmp/1v6go1322055122.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2824d1322055122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34ij01322055122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4zk6h1322055122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/539831322055122.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
4220.0642 10393.3826 -3335.6411 -19615.7199 6793.4839 24105.2484
7 8 9 10 11 12
8990.2393 1800.7079 -5444.3971 65784.0800 -6016.7012 -3308.5145
13 14 15 16 17 18
-27536.5737 1714.7749 17218.5599 9796.3554 -37807.6655 -4024.8253
19 20 21 22 23 24
-31855.6783 -26709.7872 -7918.6215 -16339.5421 12453.6881 3775.3539
25 26 27 28 29 30
-5714.5845 27262.9518 -20473.9034 -12107.9472 59191.3614 45187.8440
31 32 33 34 35 36
356.5899 41878.9522 -24025.8781 -18219.5158 25521.6135 1661.6047
37 38 39 40 41 42
16211.6289 -4063.8950 -3803.6814 2286.5220 4300.3116 9007.7094
43 44 45 46 47 48
2435.3356 -19593.7141 -14625.8507 -2772.8693 -5892.2396 12395.0671
49 50 51 52 53 54
-800.3734 2272.3444 -1440.4950 -284.3173 38151.6654 13722.0782
55 56 57 58 59 60
11779.2694 14027.3201 6222.7465 -2598.1896 -10052.9299 -15951.9238
61 62 63 64 65 66
2537.9249 -14023.7676 -7169.7540 777.6643 20815.6326 -386.6770
67 68 69 70 71 72
-13760.7817 -18391.9954 -9648.3979 3656.4991 14783.5953 -2995.4988
73 74 75 76 77 78
-19405.8421 -22322.9297 -9866.2455 -2050.4259 -857.9183 -13252.9570
79 80 81 82 83 84
-11942.4450 -28825.5665 -28752.6880 31840.4281 -5590.8282 1339.9048
85 86 87 88 89 90
-14781.3283 2579.5641 -20045.5535 -20318.3317 -4311.7017 5408.0294
91 92 93 94 95 96
1751.6380 -16923.0715 13799.9425 2104.8249 -13791.6990 42135.5164
97 98 99 100 101 102
-4079.3671 -965.3814 6301.0183 7685.8514 3322.6646 -16691.9431
103 104 105 106 107 108
-664.4618 -4569.7430 -5609.4053 2367.7097 -5483.7869 2051.2394
109 110 111 112 113 114
1661.6047 -14602.6617 -20304.3990 -847.3223 -25004.3206 -16003.2239
115 116 117 118 119 120
2060.8887 1661.6047 34355.9705 -18856.2803 7655.8071 -7793.6891
121 122 123 124 125 126
23651.6967 531.9616 -2649.0212 -16625.3060 12257.3312 -1674.2335
127 128 129 130 131 132
-5777.6202 28788.9058 -1010.0537 4593.6147 3971.1727 16820.6395
133 134 135 136 137 138
5515.6701 -3351.2693 255.1482 7919.6553 1661.6047 4801.3311
139 140 141 142 143 144
-10851.7070 6133.8887 -1455.8642 -3808.9384 -813.6060 10768.9862
> postscript(file="/var/www/rcomp/tmp/6qll21322055122.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 4220.0642 NA
1 10393.3826 4220.0642
2 -3335.6411 10393.3826
3 -19615.7199 -3335.6411
4 6793.4839 -19615.7199
5 24105.2484 6793.4839
6 8990.2393 24105.2484
7 1800.7079 8990.2393
8 -5444.3971 1800.7079
9 65784.0800 -5444.3971
10 -6016.7012 65784.0800
11 -3308.5145 -6016.7012
12 -27536.5737 -3308.5145
13 1714.7749 -27536.5737
14 17218.5599 1714.7749
15 9796.3554 17218.5599
16 -37807.6655 9796.3554
17 -4024.8253 -37807.6655
18 -31855.6783 -4024.8253
19 -26709.7872 -31855.6783
20 -7918.6215 -26709.7872
21 -16339.5421 -7918.6215
22 12453.6881 -16339.5421
23 3775.3539 12453.6881
24 -5714.5845 3775.3539
25 27262.9518 -5714.5845
26 -20473.9034 27262.9518
27 -12107.9472 -20473.9034
28 59191.3614 -12107.9472
29 45187.8440 59191.3614
30 356.5899 45187.8440
31 41878.9522 356.5899
32 -24025.8781 41878.9522
33 -18219.5158 -24025.8781
34 25521.6135 -18219.5158
35 1661.6047 25521.6135
36 16211.6289 1661.6047
37 -4063.8950 16211.6289
38 -3803.6814 -4063.8950
39 2286.5220 -3803.6814
40 4300.3116 2286.5220
41 9007.7094 4300.3116
42 2435.3356 9007.7094
43 -19593.7141 2435.3356
44 -14625.8507 -19593.7141
45 -2772.8693 -14625.8507
46 -5892.2396 -2772.8693
47 12395.0671 -5892.2396
48 -800.3734 12395.0671
49 2272.3444 -800.3734
50 -1440.4950 2272.3444
51 -284.3173 -1440.4950
52 38151.6654 -284.3173
53 13722.0782 38151.6654
54 11779.2694 13722.0782
55 14027.3201 11779.2694
56 6222.7465 14027.3201
57 -2598.1896 6222.7465
58 -10052.9299 -2598.1896
59 -15951.9238 -10052.9299
60 2537.9249 -15951.9238
61 -14023.7676 2537.9249
62 -7169.7540 -14023.7676
63 777.6643 -7169.7540
64 20815.6326 777.6643
65 -386.6770 20815.6326
66 -13760.7817 -386.6770
67 -18391.9954 -13760.7817
68 -9648.3979 -18391.9954
69 3656.4991 -9648.3979
70 14783.5953 3656.4991
71 -2995.4988 14783.5953
72 -19405.8421 -2995.4988
73 -22322.9297 -19405.8421
74 -9866.2455 -22322.9297
75 -2050.4259 -9866.2455
76 -857.9183 -2050.4259
77 -13252.9570 -857.9183
78 -11942.4450 -13252.9570
79 -28825.5665 -11942.4450
80 -28752.6880 -28825.5665
81 31840.4281 -28752.6880
82 -5590.8282 31840.4281
83 1339.9048 -5590.8282
84 -14781.3283 1339.9048
85 2579.5641 -14781.3283
86 -20045.5535 2579.5641
87 -20318.3317 -20045.5535
88 -4311.7017 -20318.3317
89 5408.0294 -4311.7017
90 1751.6380 5408.0294
91 -16923.0715 1751.6380
92 13799.9425 -16923.0715
93 2104.8249 13799.9425
94 -13791.6990 2104.8249
95 42135.5164 -13791.6990
96 -4079.3671 42135.5164
97 -965.3814 -4079.3671
98 6301.0183 -965.3814
99 7685.8514 6301.0183
100 3322.6646 7685.8514
101 -16691.9431 3322.6646
102 -664.4618 -16691.9431
103 -4569.7430 -664.4618
104 -5609.4053 -4569.7430
105 2367.7097 -5609.4053
106 -5483.7869 2367.7097
107 2051.2394 -5483.7869
108 1661.6047 2051.2394
109 -14602.6617 1661.6047
110 -20304.3990 -14602.6617
111 -847.3223 -20304.3990
112 -25004.3206 -847.3223
113 -16003.2239 -25004.3206
114 2060.8887 -16003.2239
115 1661.6047 2060.8887
116 34355.9705 1661.6047
117 -18856.2803 34355.9705
118 7655.8071 -18856.2803
119 -7793.6891 7655.8071
120 23651.6967 -7793.6891
121 531.9616 23651.6967
122 -2649.0212 531.9616
123 -16625.3060 -2649.0212
124 12257.3312 -16625.3060
125 -1674.2335 12257.3312
126 -5777.6202 -1674.2335
127 28788.9058 -5777.6202
128 -1010.0537 28788.9058
129 4593.6147 -1010.0537
130 3971.1727 4593.6147
131 16820.6395 3971.1727
132 5515.6701 16820.6395
133 -3351.2693 5515.6701
134 255.1482 -3351.2693
135 7919.6553 255.1482
136 1661.6047 7919.6553
137 4801.3311 1661.6047
138 -10851.7070 4801.3311
139 6133.8887 -10851.7070
140 -1455.8642 6133.8887
141 -3808.9384 -1455.8642
142 -813.6060 -3808.9384
143 10768.9862 -813.6060
144 NA 10768.9862
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 10393.3826 4220.0642
[2,] -3335.6411 10393.3826
[3,] -19615.7199 -3335.6411
[4,] 6793.4839 -19615.7199
[5,] 24105.2484 6793.4839
[6,] 8990.2393 24105.2484
[7,] 1800.7079 8990.2393
[8,] -5444.3971 1800.7079
[9,] 65784.0800 -5444.3971
[10,] -6016.7012 65784.0800
[11,] -3308.5145 -6016.7012
[12,] -27536.5737 -3308.5145
[13,] 1714.7749 -27536.5737
[14,] 17218.5599 1714.7749
[15,] 9796.3554 17218.5599
[16,] -37807.6655 9796.3554
[17,] -4024.8253 -37807.6655
[18,] -31855.6783 -4024.8253
[19,] -26709.7872 -31855.6783
[20,] -7918.6215 -26709.7872
[21,] -16339.5421 -7918.6215
[22,] 12453.6881 -16339.5421
[23,] 3775.3539 12453.6881
[24,] -5714.5845 3775.3539
[25,] 27262.9518 -5714.5845
[26,] -20473.9034 27262.9518
[27,] -12107.9472 -20473.9034
[28,] 59191.3614 -12107.9472
[29,] 45187.8440 59191.3614
[30,] 356.5899 45187.8440
[31,] 41878.9522 356.5899
[32,] -24025.8781 41878.9522
[33,] -18219.5158 -24025.8781
[34,] 25521.6135 -18219.5158
[35,] 1661.6047 25521.6135
[36,] 16211.6289 1661.6047
[37,] -4063.8950 16211.6289
[38,] -3803.6814 -4063.8950
[39,] 2286.5220 -3803.6814
[40,] 4300.3116 2286.5220
[41,] 9007.7094 4300.3116
[42,] 2435.3356 9007.7094
[43,] -19593.7141 2435.3356
[44,] -14625.8507 -19593.7141
[45,] -2772.8693 -14625.8507
[46,] -5892.2396 -2772.8693
[47,] 12395.0671 -5892.2396
[48,] -800.3734 12395.0671
[49,] 2272.3444 -800.3734
[50,] -1440.4950 2272.3444
[51,] -284.3173 -1440.4950
[52,] 38151.6654 -284.3173
[53,] 13722.0782 38151.6654
[54,] 11779.2694 13722.0782
[55,] 14027.3201 11779.2694
[56,] 6222.7465 14027.3201
[57,] -2598.1896 6222.7465
[58,] -10052.9299 -2598.1896
[59,] -15951.9238 -10052.9299
[60,] 2537.9249 -15951.9238
[61,] -14023.7676 2537.9249
[62,] -7169.7540 -14023.7676
[63,] 777.6643 -7169.7540
[64,] 20815.6326 777.6643
[65,] -386.6770 20815.6326
[66,] -13760.7817 -386.6770
[67,] -18391.9954 -13760.7817
[68,] -9648.3979 -18391.9954
[69,] 3656.4991 -9648.3979
[70,] 14783.5953 3656.4991
[71,] -2995.4988 14783.5953
[72,] -19405.8421 -2995.4988
[73,] -22322.9297 -19405.8421
[74,] -9866.2455 -22322.9297
[75,] -2050.4259 -9866.2455
[76,] -857.9183 -2050.4259
[77,] -13252.9570 -857.9183
[78,] -11942.4450 -13252.9570
[79,] -28825.5665 -11942.4450
[80,] -28752.6880 -28825.5665
[81,] 31840.4281 -28752.6880
[82,] -5590.8282 31840.4281
[83,] 1339.9048 -5590.8282
[84,] -14781.3283 1339.9048
[85,] 2579.5641 -14781.3283
[86,] -20045.5535 2579.5641
[87,] -20318.3317 -20045.5535
[88,] -4311.7017 -20318.3317
[89,] 5408.0294 -4311.7017
[90,] 1751.6380 5408.0294
[91,] -16923.0715 1751.6380
[92,] 13799.9425 -16923.0715
[93,] 2104.8249 13799.9425
[94,] -13791.6990 2104.8249
[95,] 42135.5164 -13791.6990
[96,] -4079.3671 42135.5164
[97,] -965.3814 -4079.3671
[98,] 6301.0183 -965.3814
[99,] 7685.8514 6301.0183
[100,] 3322.6646 7685.8514
[101,] -16691.9431 3322.6646
[102,] -664.4618 -16691.9431
[103,] -4569.7430 -664.4618
[104,] -5609.4053 -4569.7430
[105,] 2367.7097 -5609.4053
[106,] -5483.7869 2367.7097
[107,] 2051.2394 -5483.7869
[108,] 1661.6047 2051.2394
[109,] -14602.6617 1661.6047
[110,] -20304.3990 -14602.6617
[111,] -847.3223 -20304.3990
[112,] -25004.3206 -847.3223
[113,] -16003.2239 -25004.3206
[114,] 2060.8887 -16003.2239
[115,] 1661.6047 2060.8887
[116,] 34355.9705 1661.6047
[117,] -18856.2803 34355.9705
[118,] 7655.8071 -18856.2803
[119,] -7793.6891 7655.8071
[120,] 23651.6967 -7793.6891
[121,] 531.9616 23651.6967
[122,] -2649.0212 531.9616
[123,] -16625.3060 -2649.0212
[124,] 12257.3312 -16625.3060
[125,] -1674.2335 12257.3312
[126,] -5777.6202 -1674.2335
[127,] 28788.9058 -5777.6202
[128,] -1010.0537 28788.9058
[129,] 4593.6147 -1010.0537
[130,] 3971.1727 4593.6147
[131,] 16820.6395 3971.1727
[132,] 5515.6701 16820.6395
[133,] -3351.2693 5515.6701
[134,] 255.1482 -3351.2693
[135,] 7919.6553 255.1482
[136,] 1661.6047 7919.6553
[137,] 4801.3311 1661.6047
[138,] -10851.7070 4801.3311
[139,] 6133.8887 -10851.7070
[140,] -1455.8642 6133.8887
[141,] -3808.9384 -1455.8642
[142,] -813.6060 -3808.9384
[143,] 10768.9862 -813.6060
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 10393.3826 4220.0642
2 -3335.6411 10393.3826
3 -19615.7199 -3335.6411
4 6793.4839 -19615.7199
5 24105.2484 6793.4839
6 8990.2393 24105.2484
7 1800.7079 8990.2393
8 -5444.3971 1800.7079
9 65784.0800 -5444.3971
10 -6016.7012 65784.0800
11 -3308.5145 -6016.7012
12 -27536.5737 -3308.5145
13 1714.7749 -27536.5737
14 17218.5599 1714.7749
15 9796.3554 17218.5599
16 -37807.6655 9796.3554
17 -4024.8253 -37807.6655
18 -31855.6783 -4024.8253
19 -26709.7872 -31855.6783
20 -7918.6215 -26709.7872
21 -16339.5421 -7918.6215
22 12453.6881 -16339.5421
23 3775.3539 12453.6881
24 -5714.5845 3775.3539
25 27262.9518 -5714.5845
26 -20473.9034 27262.9518
27 -12107.9472 -20473.9034
28 59191.3614 -12107.9472
29 45187.8440 59191.3614
30 356.5899 45187.8440
31 41878.9522 356.5899
32 -24025.8781 41878.9522
33 -18219.5158 -24025.8781
34 25521.6135 -18219.5158
35 1661.6047 25521.6135
36 16211.6289 1661.6047
37 -4063.8950 16211.6289
38 -3803.6814 -4063.8950
39 2286.5220 -3803.6814
40 4300.3116 2286.5220
41 9007.7094 4300.3116
42 2435.3356 9007.7094
43 -19593.7141 2435.3356
44 -14625.8507 -19593.7141
45 -2772.8693 -14625.8507
46 -5892.2396 -2772.8693
47 12395.0671 -5892.2396
48 -800.3734 12395.0671
49 2272.3444 -800.3734
50 -1440.4950 2272.3444
51 -284.3173 -1440.4950
52 38151.6654 -284.3173
53 13722.0782 38151.6654
54 11779.2694 13722.0782
55 14027.3201 11779.2694
56 6222.7465 14027.3201
57 -2598.1896 6222.7465
58 -10052.9299 -2598.1896
59 -15951.9238 -10052.9299
60 2537.9249 -15951.9238
61 -14023.7676 2537.9249
62 -7169.7540 -14023.7676
63 777.6643 -7169.7540
64 20815.6326 777.6643
65 -386.6770 20815.6326
66 -13760.7817 -386.6770
67 -18391.9954 -13760.7817
68 -9648.3979 -18391.9954
69 3656.4991 -9648.3979
70 14783.5953 3656.4991
71 -2995.4988 14783.5953
72 -19405.8421 -2995.4988
73 -22322.9297 -19405.8421
74 -9866.2455 -22322.9297
75 -2050.4259 -9866.2455
76 -857.9183 -2050.4259
77 -13252.9570 -857.9183
78 -11942.4450 -13252.9570
79 -28825.5665 -11942.4450
80 -28752.6880 -28825.5665
81 31840.4281 -28752.6880
82 -5590.8282 31840.4281
83 1339.9048 -5590.8282
84 -14781.3283 1339.9048
85 2579.5641 -14781.3283
86 -20045.5535 2579.5641
87 -20318.3317 -20045.5535
88 -4311.7017 -20318.3317
89 5408.0294 -4311.7017
90 1751.6380 5408.0294
91 -16923.0715 1751.6380
92 13799.9425 -16923.0715
93 2104.8249 13799.9425
94 -13791.6990 2104.8249
95 42135.5164 -13791.6990
96 -4079.3671 42135.5164
97 -965.3814 -4079.3671
98 6301.0183 -965.3814
99 7685.8514 6301.0183
100 3322.6646 7685.8514
101 -16691.9431 3322.6646
102 -664.4618 -16691.9431
103 -4569.7430 -664.4618
104 -5609.4053 -4569.7430
105 2367.7097 -5609.4053
106 -5483.7869 2367.7097
107 2051.2394 -5483.7869
108 1661.6047 2051.2394
109 -14602.6617 1661.6047
110 -20304.3990 -14602.6617
111 -847.3223 -20304.3990
112 -25004.3206 -847.3223
113 -16003.2239 -25004.3206
114 2060.8887 -16003.2239
115 1661.6047 2060.8887
116 34355.9705 1661.6047
117 -18856.2803 34355.9705
118 7655.8071 -18856.2803
119 -7793.6891 7655.8071
120 23651.6967 -7793.6891
121 531.9616 23651.6967
122 -2649.0212 531.9616
123 -16625.3060 -2649.0212
124 12257.3312 -16625.3060
125 -1674.2335 12257.3312
126 -5777.6202 -1674.2335
127 28788.9058 -5777.6202
128 -1010.0537 28788.9058
129 4593.6147 -1010.0537
130 3971.1727 4593.6147
131 16820.6395 3971.1727
132 5515.6701 16820.6395
133 -3351.2693 5515.6701
134 255.1482 -3351.2693
135 7919.6553 255.1482
136 1661.6047 7919.6553
137 4801.3311 1661.6047
138 -10851.7070 4801.3311
139 6133.8887 -10851.7070
140 -1455.8642 6133.8887
141 -3808.9384 -1455.8642
142 -813.6060 -3808.9384
143 10768.9862 -813.6060
> 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/7adi61322055122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/86l121322055122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9sir01322055122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10lvep1322055122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dxpd1322055122.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/12ivum1322055122.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/1352pv1322055122.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/14obxz1322055122.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/15of5v1322055122.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/1617xt1322055122.tab")
+ }
>
> try(system("convert tmp/1v6go1322055122.ps tmp/1v6go1322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/2824d1322055122.ps tmp/2824d1322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/34ij01322055122.ps tmp/34ij01322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zk6h1322055122.ps tmp/4zk6h1322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/539831322055122.ps tmp/539831322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qll21322055122.ps tmp/6qll21322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/7adi61322055122.ps tmp/7adi61322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/86l121322055122.ps tmp/86l121322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sir01322055122.ps tmp/9sir01322055122.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lvep1322055122.ps tmp/10lvep1322055122.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.590 0.300 4.942