R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(159261
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+ ,dim=c(6
+ ,144)
+ ,dimnames=list(c('time'
+ ,'shared'
+ ,'blogs'
+ ,'reviews'
+ ,'CWcharacters'
+ ,'Cwseconds')
+ ,1:144))
> y <- array(NA,dim=c(6,144),dimnames=list(c('time','shared','blogs','reviews','CWcharacters','Cwseconds'),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 shared blogs reviews CWcharacters Cwseconds
1 159261 0 48 19 20465 23975
2 189672 1 53 20 33629 85634
3 7215 0 0 0 1423 1929
4 129098 0 51 27 25629 36294
5 230632 0 76 31 54002 72255
6 515038 1 136 36 151036 189748
7 180745 1 62 23 33287 61834
8 185559 0 83 30 31172 68167
9 154581 0 55 30 28113 38462
10 298001 1 67 26 57803 101219
11 121844 2 50 24 49830 43270
12 184039 0 77 30 52143 76183
13 100324 0 46 22 21055 31476
14 217742 4 79 28 47007 62157
15 168265 4 56 18 28735 46261
16 154647 3 54 22 59147 50063
17 142018 0 81 33 78950 64483
18 79030 5 6 15 13497 2341
19 167047 0 74 34 46154 48149
20 27997 0 13 18 53249 12743
21 73019 0 22 15 10726 18743
22 241082 0 99 30 83700 97057
23 195820 0 38 25 40400 17675
24 142001 1 59 34 33797 33106
25 145433 1 50 21 36205 53311
26 183744 0 50 21 30165 42754
27 202232 0 61 25 58534 59056
28 199532 0 87 31 44663 101621
29 354924 0 60 31 92556 118120
30 192399 0 52 20 40078 79572
31 182286 0 61 28 34711 42744
32 181590 2 60 22 31076 65931
33 133801 4 53 17 74608 38575
34 233686 0 76 25 58092 28795
35 219428 1 63 24 42009 94440
36 0 0 0 0 0 0
37 223044 0 54 28 36022 38229
38 100129 3 44 14 23333 31972
39 136733 9 36 35 53349 40071
40 249965 0 83 34 92596 132480
41 242379 2 105 22 49598 62797
42 145794 0 37 34 44093 40429
43 96404 2 25 23 84205 45545
44 195891 1 64 24 63369 57568
45 117156 2 55 26 60132 39019
46 157787 2 41 22 37403 53866
47 81293 1 23 35 24460 38345
48 237435 0 75 24 46456 50210
49 233155 1 59 31 66616 80947
50 160344 8 68 26 41554 43461
51 48188 0 12 22 22346 14812
52 161922 0 99 21 30874 37819
53 307432 0 78 27 68701 102738
54 235223 0 56 30 35728 54509
55 195583 1 67 33 29010 62956
56 146061 8 40 11 23110 55411
57 208834 0 53 26 38844 50611
58 93764 1 26 26 27084 26692
59 151985 0 67 23 35139 60056
60 190545 10 36 38 57476 25155
61 148922 6 50 31 33277 42840
62 132856 0 48 20 31141 39358
63 129561 11 46 22 61281 47241
64 112718 3 53 26 25820 49611
65 160930 0 27 26 23284 41833
66 99184 0 38 33 35378 48930
67 192535 8 71 36 74990 110600
68 138708 2 93 25 29653 52235
69 114408 0 59 24 64622 53986
70 31970 0 5 21 4157 4105
71 225558 3 53 19 29245 59331
72 137011 1 40 12 50008 47796
73 113612 2 72 30 52338 38302
74 108641 1 51 21 13310 14063
75 162203 0 81 34 92901 54414
76 100098 2 27 32 10956 9903
77 174768 1 94 28 34241 53987
78 158459 0 71 28 75043 88937
79 80934 0 20 21 21152 21928
80 84971 0 34 31 42249 29487
81 80545 0 54 26 42005 35334
82 287191 0 49 29 41152 57596
83 62974 1 26 23 14399 29750
84 134091 0 48 25 28263 41029
85 75555 0 35 22 17215 12416
86 162154 0 32 26 48140 51158
87 226638 0 55 33 62897 79935
88 115019 0 58 24 22883 26552
89 108749 7 44 24 41622 25807
90 155537 0 45 21 40715 50620
91 153133 5 49 28 65897 61467
92 165618 1 72 27 76542 65292
93 151517 0 39 25 37477 55516
94 133686 0 28 15 53216 42006
95 61342 0 24 13 40911 26273
96 245196 0 52 36 57021 90248
97 195576 0 96 24 73116 61476
98 19349 0 13 1 3895 9604
99 225371 3 38 24 46609 45108
100 152796 0 41 31 29351 47232
101 59117 0 24 4 2325 3439
102 91762 0 54 21 31747 30553
103 136769 0 68 23 32665 24751
104 114798 1 28 23 19249 34458
105 85338 1 36 12 15292 24649
106 27676 0 2 16 5842 2342
107 153535 0 91 29 33994 52739
108 122417 0 29 26 13018 6245
109 0 0 0 0 0 0
110 91529 0 46 25 98177 35381
111 107205 0 25 21 37941 19595
112 144664 0 51 23 31032 50848
113 136540 0 59 21 32683 39443
114 76656 0 36 21 34545 27023
115 3616 0 0 0 0 0
116 0 0 0 0 0 0
117 183065 0 40 23 27525 61022
118 144677 0 68 33 66856 63528
119 159104 2 28 30 28549 34835
120 113273 0 36 23 38610 37172
121 43410 0 7 1 2781 13
122 175774 1 70 29 41211 62548
123 95401 0 30 18 22698 31334
124 134837 8 69 33 41194 20839
125 60493 3 3 12 32689 5084
126 19764 1 10 2 5752 9927
127 164062 3 46 21 26757 53229
128 132696 0 34 28 22527 29877
129 155367 0 54 29 44810 37310
130 11796 0 1 2 0 0
131 10674 0 0 0 0 0
132 142261 0 39 18 100674 50067
133 6836 0 0 1 0 0
134 162563 6 48 21 57786 47708
135 5118 0 5 0 0 0
136 40248 1 8 4 5444 6012
137 0 0 0 0 0 0
138 122641 0 38 25 28470 27749
139 88837 0 21 26 61849 47555
140 7131 1 0 0 0 0
141 9056 0 0 4 2179 1336
142 76611 1 15 17 8019 11017
143 132697 0 50 21 39644 55184
144 100681 1 17 22 23494 43485
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) shared blogs reviews CWcharacters
1.260e+04 6.282e+02 7.050e+02 1.246e+03 -4.052e-02
Cwseconds
1.535e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-86729 -19639 -4683 15548 117172
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.260e+04 7.630e+03 1.651 0.101048
shared 6.282e+02 1.370e+03 0.459 0.647285
blogs 7.050e+02 1.901e+02 3.709 0.000300 ***
reviews 1.246e+03 4.506e+02 2.765 0.006463 **
CWcharacters -4.052e-02 1.917e-01 -0.211 0.832938
Cwseconds 1.535e+00 1.829e-01 8.391 5.09e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35640 on 138 degrees of freedom
Multiple R-squared: 0.7947, Adjusted R-squared: 0.7872
F-statistic: 106.8 on 5 and 138 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,] 0.2848924 5.697848e-01 7.151076e-01
[2,] 0.4279426 8.558853e-01 5.720574e-01
[3,] 0.4995284 9.990568e-01 5.004716e-01
[4,] 0.6302804 7.394391e-01 3.697196e-01
[5,] 0.5386095 9.227809e-01 4.613905e-01
[6,] 0.4709608 9.419216e-01 5.290392e-01
[7,] 0.3795332 7.590665e-01 6.204668e-01
[8,] 0.3122864 6.245727e-01 6.877136e-01
[9,] 0.5189917 9.620165e-01 4.810083e-01
[10,] 0.4970604 9.941209e-01 5.029396e-01
[11,] 0.4192105 8.384210e-01 5.807895e-01
[12,] 0.3534673 7.069346e-01 6.465327e-01
[13,] 0.2832808 5.665615e-01 7.167192e-01
[14,] 0.2491605 4.983210e-01 7.508395e-01
[15,] 0.8432620 3.134761e-01 1.567380e-01
[16,] 0.7972172 4.055657e-01 2.027828e-01
[17,] 0.7536112 4.927776e-01 2.463888e-01
[18,] 0.7837489 4.325023e-01 2.162511e-01
[19,] 0.7552275 4.895450e-01 2.447725e-01
[20,] 0.8471368 3.057264e-01 1.528632e-01
[21,] 0.9009685 1.980631e-01 9.903154e-02
[22,] 0.8749074 2.501852e-01 1.250926e-01
[23,] 0.8682300 2.635399e-01 1.317700e-01
[24,] 0.8337613 3.324773e-01 1.662387e-01
[25,] 0.8024158 3.951685e-01 1.975842e-01
[26,] 0.9556135 8.877309e-02 4.438654e-02
[27,] 0.9416290 1.167421e-01 5.837103e-02
[28,] 0.9264099 1.471802e-01 7.359010e-02
[29,] 0.9715281 5.694378e-02 2.847189e-02
[30,] 0.9621947 7.561051e-02 3.780525e-02
[31,] 0.9544899 9.102027e-02 4.551014e-02
[32,] 0.9782745 4.345094e-02 2.172547e-02
[33,] 0.9753931 4.921376e-02 2.460688e-02
[34,] 0.9665855 6.682902e-02 3.341451e-02
[35,] 0.9692943 6.141142e-02 3.070571e-02
[36,] 0.9621791 7.564183e-02 3.782091e-02
[37,] 0.9600316 7.993683e-02 3.996842e-02
[38,] 0.9478946 1.042109e-01 5.210543e-02
[39,] 0.9534591 9.308179e-02 4.654090e-02
[40,] 0.9738500 5.230003e-02 2.615002e-02
[41,] 0.9683705 6.325907e-02 3.162953e-02
[42,] 0.9584538 8.309244e-02 4.154622e-02
[43,] 0.9508364 9.832718e-02 4.916359e-02
[44,] 0.9462934 1.074132e-01 5.370662e-02
[45,] 0.9647963 7.040747e-02 3.520373e-02
[46,] 0.9833946 3.321077e-02 1.660538e-02
[47,] 0.9775151 4.496979e-02 2.248489e-02
[48,] 0.9703907 5.921850e-02 2.960925e-02
[49,] 0.9796730 4.065399e-02 2.032699e-02
[50,] 0.9736659 5.266812e-02 2.633406e-02
[51,] 0.9702982 5.940366e-02 2.970183e-02
[52,] 0.9817305 3.653893e-02 1.826947e-02
[53,] 0.9756453 4.870947e-02 2.435474e-02
[54,] 0.9681624 6.367525e-02 3.183763e-02
[55,] 0.9626524 7.469514e-02 3.734757e-02
[56,] 0.9677781 6.444372e-02 3.222186e-02
[57,] 0.9675683 6.486338e-02 3.243169e-02
[58,] 0.9791830 4.163405e-02 2.081702e-02
[59,] 0.9964495 7.101035e-03 3.550517e-03
[60,] 0.9974912 5.017564e-03 2.508782e-03
[61,] 0.9983574 3.285175e-03 1.642587e-03
[62,] 0.9978944 4.211220e-03 2.105610e-03
[63,] 0.9990800 1.839962e-03 9.199808e-04
[64,] 0.9987622 2.475673e-03 1.237836e-03
[65,] 0.9990226 1.954834e-03 9.774172e-04
[66,] 0.9986310 2.737968e-03 1.368984e-03
[67,] 0.9983996 3.200818e-03 1.600409e-03
[68,] 0.9977233 4.553449e-03 2.276725e-03
[69,] 0.9968663 6.267394e-03 3.133697e-03
[70,] 0.9990426 1.914796e-03 9.573980e-04
[71,] 0.9985661 2.867743e-03 1.433872e-03
[72,] 0.9986444 2.711226e-03 1.355613e-03
[73,] 0.9992787 1.442640e-03 7.213202e-04
[74,] 0.9999992 1.570781e-06 7.853906e-07
[75,] 0.9999997 5.251298e-07 2.625649e-07
[76,] 0.9999995 1.034266e-06 5.171331e-07
[77,] 0.9999991 1.894113e-06 9.470566e-07
[78,] 0.9999985 3.031977e-06 1.515989e-06
[79,] 0.9999977 4.680819e-06 2.340409e-06
[80,] 0.9999956 8.737267e-06 4.368633e-06
[81,] 0.9999937 1.265253e-05 6.326267e-06
[82,] 0.9999901 1.972922e-05 9.864610e-06
[83,] 0.9999909 1.824240e-05 9.121198e-06
[84,] 0.9999870 2.593904e-05 1.296952e-05
[85,] 0.9999761 4.780633e-05 2.390316e-05
[86,] 0.9999743 5.134090e-05 2.567045e-05
[87,] 0.9999623 7.532370e-05 3.766185e-05
[88,] 0.9999461 1.077618e-04 5.388092e-05
[89,] 0.9999337 1.326539e-04 6.632695e-05
[90,] 0.9998921 2.158537e-04 1.079269e-04
[91,] 0.9999994 1.161504e-06 5.807522e-07
[92,] 0.9999987 2.506439e-06 1.253219e-06
[93,] 0.9999986 2.730659e-06 1.365330e-06
[94,] 0.9999983 3.459315e-06 1.729657e-06
[95,] 0.9999977 4.612534e-06 2.306267e-06
[96,] 0.9999952 9.671195e-06 4.835598e-06
[97,] 0.9999900 1.999045e-05 9.995225e-06
[98,] 0.9999888 2.245340e-05 1.122670e-05
[99,] 0.9999847 3.059002e-05 1.529501e-05
[100,] 0.9999915 1.698940e-05 8.494699e-06
[101,] 0.9999836 3.271261e-05 1.635630e-05
[102,] 0.9999784 4.326097e-05 2.163048e-05
[103,] 0.9999739 5.217908e-05 2.608954e-05
[104,] 0.9999449 1.102910e-04 5.514549e-05
[105,] 0.9998991 2.017432e-04 1.008716e-04
[106,] 0.9998732 2.535355e-04 1.267677e-04
[107,] 0.9997498 5.003310e-04 2.501655e-04
[108,] 0.9995477 9.045173e-04 4.522587e-04
[109,] 0.9997148 5.704770e-04 2.852385e-04
[110,] 0.9999136 1.728497e-04 8.642487e-05
[111,] 0.9999608 7.839249e-05 3.919624e-05
[112,] 0.9999080 1.840377e-04 9.201885e-05
[113,] 0.9999213 1.573408e-04 7.867038e-05
[114,] 0.9998374 3.252216e-04 1.626108e-04
[115,] 0.9996113 7.774919e-04 3.887460e-04
[116,] 0.9999972 5.501965e-06 2.750982e-06
[117,] 0.9999911 1.782314e-05 8.911571e-06
[118,] 0.9999830 3.407701e-05 1.703851e-05
[119,] 0.9999818 3.646011e-05 1.823005e-05
[120,] 0.9999623 7.547905e-05 3.773952e-05
[121,] 0.9998624 2.752420e-04 1.376210e-04
[122,] 0.9994895 1.020947e-03 5.104737e-04
[123,] 0.9984224 3.155221e-03 1.577610e-03
[124,] 0.9999864 2.722553e-05 1.361277e-05
[125,] 0.9999024 1.952800e-04 9.764002e-05
[126,] 0.9996291 7.417632e-04 3.708816e-04
[127,] 0.9968517 6.296546e-03 3.148273e-03
> postscript(file="/var/www/rcomp/tmp/1mhcm1324478474.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/23w0c1324478474.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ewbg1324478474.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4sc0v1324478474.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/57gxe1324478474.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
53176.172 -15921.209 -8284.496 -7770.429 17105.251 75938.497 1585.636
8 9 10 11 12 13 14
-26307.093 7927.008 52118.218 -21564.530 -35051.197 -19578.718 18542.047
15 16 17 18 19 20 21
21400.883 233.100 -64585.826 37324.445 -12124.976 -33596.869 -2114.347
22 23 24 25 26 27 28
-24280.448 99786.598 -4634.385 -9573.288 45325.435 27200.620 -67203.284
29 30 31 32 33 34 35
83840.318 -2294.937 27588.804 -1919.567 3954.252 94509.747 -11377.063
36 37 38 39 40 41 42
-12596.272 80265.367 -10948.320 -9856.836 -63112.777 32703.880 4474.412
43 44 45 46 47 48 49
-30231.748 21842.327 -25327.523 6448.360 -49627.119 66867.891 18155.103
50 51 52 53 54 55 56
-2644.994 -22113.194 -3439.586 51285.930 63541.487 -1458.173 2414.939
57 58 59 60 61 62 63
50361.462 -10063.313 -27267.195 62650.870 -5732.769 2345.787 -19820.109
64 65 66 67 68 69 70
-46631.880 33631.491 -54996.064 -86729.200 -50841.259 -49938.427 -16452.323
71 72 73 74 75 76 77
60150.178 9294.495 -45056.802 12244.978 -29626.460 12577.248 -21099.752
78 79 80 81 82 83 84
-72557.411 -4732.289 -33774.646 -55055.359 117172.274 -42322.754 -5331.270
85 86 87 88 89 90 91
-7492.163 18024.209 13996.858 -8204.283 -7098.355 8997.301 -23719.659
92 93 94 95 96 97 98
-29131.348 -3423.178 20336.967 -23044.178 14863.789 -6009.130 -18242.476
99 100 101 102 103 104 105
86842.969 1355.178 19431.045 -30684.910 10902.255 1061.238 -5435.951
106 107 108 109 110 111 112
-9626.290 -38929.993 47917.578 -12596.272 -34981.149 22274.792 -9340.428
113 114 115 116 117 118 119
-3039.685 -27568.637 -8980.272 -12596.272 21057.458 -51785.558 35814.387
120 121 122 123 124 125 126
-8857.174 24725.106 -17277.200 -7952.192 -2871.223 22464.575 -18007.222
127 128 129 130 131 132 133
10362.517 16290.985 13108.924 -3997.536 -1922.272 6968.076 -7006.387
134 135 136 137 138 139 140
15300.464 -11003.438 7391.387 -12596.272 10661.328 -41451.611 -6093.461
141 142 143 144
-10487.113 15041.657 -24416.683 -17738.100
> postscript(file="/var/www/rcomp/tmp/65pbz1324478474.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 53176.172 NA
1 -15921.209 53176.172
2 -8284.496 -15921.209
3 -7770.429 -8284.496
4 17105.251 -7770.429
5 75938.497 17105.251
6 1585.636 75938.497
7 -26307.093 1585.636
8 7927.008 -26307.093
9 52118.218 7927.008
10 -21564.530 52118.218
11 -35051.197 -21564.530
12 -19578.718 -35051.197
13 18542.047 -19578.718
14 21400.883 18542.047
15 233.100 21400.883
16 -64585.826 233.100
17 37324.445 -64585.826
18 -12124.976 37324.445
19 -33596.869 -12124.976
20 -2114.347 -33596.869
21 -24280.448 -2114.347
22 99786.598 -24280.448
23 -4634.385 99786.598
24 -9573.288 -4634.385
25 45325.435 -9573.288
26 27200.620 45325.435
27 -67203.284 27200.620
28 83840.318 -67203.284
29 -2294.937 83840.318
30 27588.804 -2294.937
31 -1919.567 27588.804
32 3954.252 -1919.567
33 94509.747 3954.252
34 -11377.063 94509.747
35 -12596.272 -11377.063
36 80265.367 -12596.272
37 -10948.320 80265.367
38 -9856.836 -10948.320
39 -63112.777 -9856.836
40 32703.880 -63112.777
41 4474.412 32703.880
42 -30231.748 4474.412
43 21842.327 -30231.748
44 -25327.523 21842.327
45 6448.360 -25327.523
46 -49627.119 6448.360
47 66867.891 -49627.119
48 18155.103 66867.891
49 -2644.994 18155.103
50 -22113.194 -2644.994
51 -3439.586 -22113.194
52 51285.930 -3439.586
53 63541.487 51285.930
54 -1458.173 63541.487
55 2414.939 -1458.173
56 50361.462 2414.939
57 -10063.313 50361.462
58 -27267.195 -10063.313
59 62650.870 -27267.195
60 -5732.769 62650.870
61 2345.787 -5732.769
62 -19820.109 2345.787
63 -46631.880 -19820.109
64 33631.491 -46631.880
65 -54996.064 33631.491
66 -86729.200 -54996.064
67 -50841.259 -86729.200
68 -49938.427 -50841.259
69 -16452.323 -49938.427
70 60150.178 -16452.323
71 9294.495 60150.178
72 -45056.802 9294.495
73 12244.978 -45056.802
74 -29626.460 12244.978
75 12577.248 -29626.460
76 -21099.752 12577.248
77 -72557.411 -21099.752
78 -4732.289 -72557.411
79 -33774.646 -4732.289
80 -55055.359 -33774.646
81 117172.274 -55055.359
82 -42322.754 117172.274
83 -5331.270 -42322.754
84 -7492.163 -5331.270
85 18024.209 -7492.163
86 13996.858 18024.209
87 -8204.283 13996.858
88 -7098.355 -8204.283
89 8997.301 -7098.355
90 -23719.659 8997.301
91 -29131.348 -23719.659
92 -3423.178 -29131.348
93 20336.967 -3423.178
94 -23044.178 20336.967
95 14863.789 -23044.178
96 -6009.130 14863.789
97 -18242.476 -6009.130
98 86842.969 -18242.476
99 1355.178 86842.969
100 19431.045 1355.178
101 -30684.910 19431.045
102 10902.255 -30684.910
103 1061.238 10902.255
104 -5435.951 1061.238
105 -9626.290 -5435.951
106 -38929.993 -9626.290
107 47917.578 -38929.993
108 -12596.272 47917.578
109 -34981.149 -12596.272
110 22274.792 -34981.149
111 -9340.428 22274.792
112 -3039.685 -9340.428
113 -27568.637 -3039.685
114 -8980.272 -27568.637
115 -12596.272 -8980.272
116 21057.458 -12596.272
117 -51785.558 21057.458
118 35814.387 -51785.558
119 -8857.174 35814.387
120 24725.106 -8857.174
121 -17277.200 24725.106
122 -7952.192 -17277.200
123 -2871.223 -7952.192
124 22464.575 -2871.223
125 -18007.222 22464.575
126 10362.517 -18007.222
127 16290.985 10362.517
128 13108.924 16290.985
129 -3997.536 13108.924
130 -1922.272 -3997.536
131 6968.076 -1922.272
132 -7006.387 6968.076
133 15300.464 -7006.387
134 -11003.438 15300.464
135 7391.387 -11003.438
136 -12596.272 7391.387
137 10661.328 -12596.272
138 -41451.611 10661.328
139 -6093.461 -41451.611
140 -10487.113 -6093.461
141 15041.657 -10487.113
142 -24416.683 15041.657
143 -17738.100 -24416.683
144 NA -17738.100
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -15921.209 53176.172
[2,] -8284.496 -15921.209
[3,] -7770.429 -8284.496
[4,] 17105.251 -7770.429
[5,] 75938.497 17105.251
[6,] 1585.636 75938.497
[7,] -26307.093 1585.636
[8,] 7927.008 -26307.093
[9,] 52118.218 7927.008
[10,] -21564.530 52118.218
[11,] -35051.197 -21564.530
[12,] -19578.718 -35051.197
[13,] 18542.047 -19578.718
[14,] 21400.883 18542.047
[15,] 233.100 21400.883
[16,] -64585.826 233.100
[17,] 37324.445 -64585.826
[18,] -12124.976 37324.445
[19,] -33596.869 -12124.976
[20,] -2114.347 -33596.869
[21,] -24280.448 -2114.347
[22,] 99786.598 -24280.448
[23,] -4634.385 99786.598
[24,] -9573.288 -4634.385
[25,] 45325.435 -9573.288
[26,] 27200.620 45325.435
[27,] -67203.284 27200.620
[28,] 83840.318 -67203.284
[29,] -2294.937 83840.318
[30,] 27588.804 -2294.937
[31,] -1919.567 27588.804
[32,] 3954.252 -1919.567
[33,] 94509.747 3954.252
[34,] -11377.063 94509.747
[35,] -12596.272 -11377.063
[36,] 80265.367 -12596.272
[37,] -10948.320 80265.367
[38,] -9856.836 -10948.320
[39,] -63112.777 -9856.836
[40,] 32703.880 -63112.777
[41,] 4474.412 32703.880
[42,] -30231.748 4474.412
[43,] 21842.327 -30231.748
[44,] -25327.523 21842.327
[45,] 6448.360 -25327.523
[46,] -49627.119 6448.360
[47,] 66867.891 -49627.119
[48,] 18155.103 66867.891
[49,] -2644.994 18155.103
[50,] -22113.194 -2644.994
[51,] -3439.586 -22113.194
[52,] 51285.930 -3439.586
[53,] 63541.487 51285.930
[54,] -1458.173 63541.487
[55,] 2414.939 -1458.173
[56,] 50361.462 2414.939
[57,] -10063.313 50361.462
[58,] -27267.195 -10063.313
[59,] 62650.870 -27267.195
[60,] -5732.769 62650.870
[61,] 2345.787 -5732.769
[62,] -19820.109 2345.787
[63,] -46631.880 -19820.109
[64,] 33631.491 -46631.880
[65,] -54996.064 33631.491
[66,] -86729.200 -54996.064
[67,] -50841.259 -86729.200
[68,] -49938.427 -50841.259
[69,] -16452.323 -49938.427
[70,] 60150.178 -16452.323
[71,] 9294.495 60150.178
[72,] -45056.802 9294.495
[73,] 12244.978 -45056.802
[74,] -29626.460 12244.978
[75,] 12577.248 -29626.460
[76,] -21099.752 12577.248
[77,] -72557.411 -21099.752
[78,] -4732.289 -72557.411
[79,] -33774.646 -4732.289
[80,] -55055.359 -33774.646
[81,] 117172.274 -55055.359
[82,] -42322.754 117172.274
[83,] -5331.270 -42322.754
[84,] -7492.163 -5331.270
[85,] 18024.209 -7492.163
[86,] 13996.858 18024.209
[87,] -8204.283 13996.858
[88,] -7098.355 -8204.283
[89,] 8997.301 -7098.355
[90,] -23719.659 8997.301
[91,] -29131.348 -23719.659
[92,] -3423.178 -29131.348
[93,] 20336.967 -3423.178
[94,] -23044.178 20336.967
[95,] 14863.789 -23044.178
[96,] -6009.130 14863.789
[97,] -18242.476 -6009.130
[98,] 86842.969 -18242.476
[99,] 1355.178 86842.969
[100,] 19431.045 1355.178
[101,] -30684.910 19431.045
[102,] 10902.255 -30684.910
[103,] 1061.238 10902.255
[104,] -5435.951 1061.238
[105,] -9626.290 -5435.951
[106,] -38929.993 -9626.290
[107,] 47917.578 -38929.993
[108,] -12596.272 47917.578
[109,] -34981.149 -12596.272
[110,] 22274.792 -34981.149
[111,] -9340.428 22274.792
[112,] -3039.685 -9340.428
[113,] -27568.637 -3039.685
[114,] -8980.272 -27568.637
[115,] -12596.272 -8980.272
[116,] 21057.458 -12596.272
[117,] -51785.558 21057.458
[118,] 35814.387 -51785.558
[119,] -8857.174 35814.387
[120,] 24725.106 -8857.174
[121,] -17277.200 24725.106
[122,] -7952.192 -17277.200
[123,] -2871.223 -7952.192
[124,] 22464.575 -2871.223
[125,] -18007.222 22464.575
[126,] 10362.517 -18007.222
[127,] 16290.985 10362.517
[128,] 13108.924 16290.985
[129,] -3997.536 13108.924
[130,] -1922.272 -3997.536
[131,] 6968.076 -1922.272
[132,] -7006.387 6968.076
[133,] 15300.464 -7006.387
[134,] -11003.438 15300.464
[135,] 7391.387 -11003.438
[136,] -12596.272 7391.387
[137,] 10661.328 -12596.272
[138,] -41451.611 10661.328
[139,] -6093.461 -41451.611
[140,] -10487.113 -6093.461
[141,] 15041.657 -10487.113
[142,] -24416.683 15041.657
[143,] -17738.100 -24416.683
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -15921.209 53176.172
2 -8284.496 -15921.209
3 -7770.429 -8284.496
4 17105.251 -7770.429
5 75938.497 17105.251
6 1585.636 75938.497
7 -26307.093 1585.636
8 7927.008 -26307.093
9 52118.218 7927.008
10 -21564.530 52118.218
11 -35051.197 -21564.530
12 -19578.718 -35051.197
13 18542.047 -19578.718
14 21400.883 18542.047
15 233.100 21400.883
16 -64585.826 233.100
17 37324.445 -64585.826
18 -12124.976 37324.445
19 -33596.869 -12124.976
20 -2114.347 -33596.869
21 -24280.448 -2114.347
22 99786.598 -24280.448
23 -4634.385 99786.598
24 -9573.288 -4634.385
25 45325.435 -9573.288
26 27200.620 45325.435
27 -67203.284 27200.620
28 83840.318 -67203.284
29 -2294.937 83840.318
30 27588.804 -2294.937
31 -1919.567 27588.804
32 3954.252 -1919.567
33 94509.747 3954.252
34 -11377.063 94509.747
35 -12596.272 -11377.063
36 80265.367 -12596.272
37 -10948.320 80265.367
38 -9856.836 -10948.320
39 -63112.777 -9856.836
40 32703.880 -63112.777
41 4474.412 32703.880
42 -30231.748 4474.412
43 21842.327 -30231.748
44 -25327.523 21842.327
45 6448.360 -25327.523
46 -49627.119 6448.360
47 66867.891 -49627.119
48 18155.103 66867.891
49 -2644.994 18155.103
50 -22113.194 -2644.994
51 -3439.586 -22113.194
52 51285.930 -3439.586
53 63541.487 51285.930
54 -1458.173 63541.487
55 2414.939 -1458.173
56 50361.462 2414.939
57 -10063.313 50361.462
58 -27267.195 -10063.313
59 62650.870 -27267.195
60 -5732.769 62650.870
61 2345.787 -5732.769
62 -19820.109 2345.787
63 -46631.880 -19820.109
64 33631.491 -46631.880
65 -54996.064 33631.491
66 -86729.200 -54996.064
67 -50841.259 -86729.200
68 -49938.427 -50841.259
69 -16452.323 -49938.427
70 60150.178 -16452.323
71 9294.495 60150.178
72 -45056.802 9294.495
73 12244.978 -45056.802
74 -29626.460 12244.978
75 12577.248 -29626.460
76 -21099.752 12577.248
77 -72557.411 -21099.752
78 -4732.289 -72557.411
79 -33774.646 -4732.289
80 -55055.359 -33774.646
81 117172.274 -55055.359
82 -42322.754 117172.274
83 -5331.270 -42322.754
84 -7492.163 -5331.270
85 18024.209 -7492.163
86 13996.858 18024.209
87 -8204.283 13996.858
88 -7098.355 -8204.283
89 8997.301 -7098.355
90 -23719.659 8997.301
91 -29131.348 -23719.659
92 -3423.178 -29131.348
93 20336.967 -3423.178
94 -23044.178 20336.967
95 14863.789 -23044.178
96 -6009.130 14863.789
97 -18242.476 -6009.130
98 86842.969 -18242.476
99 1355.178 86842.969
100 19431.045 1355.178
101 -30684.910 19431.045
102 10902.255 -30684.910
103 1061.238 10902.255
104 -5435.951 1061.238
105 -9626.290 -5435.951
106 -38929.993 -9626.290
107 47917.578 -38929.993
108 -12596.272 47917.578
109 -34981.149 -12596.272
110 22274.792 -34981.149
111 -9340.428 22274.792
112 -3039.685 -9340.428
113 -27568.637 -3039.685
114 -8980.272 -27568.637
115 -12596.272 -8980.272
116 21057.458 -12596.272
117 -51785.558 21057.458
118 35814.387 -51785.558
119 -8857.174 35814.387
120 24725.106 -8857.174
121 -17277.200 24725.106
122 -7952.192 -17277.200
123 -2871.223 -7952.192
124 22464.575 -2871.223
125 -18007.222 22464.575
126 10362.517 -18007.222
127 16290.985 10362.517
128 13108.924 16290.985
129 -3997.536 13108.924
130 -1922.272 -3997.536
131 6968.076 -1922.272
132 -7006.387 6968.076
133 15300.464 -7006.387
134 -11003.438 15300.464
135 7391.387 -11003.438
136 -12596.272 7391.387
137 10661.328 -12596.272
138 -41451.611 10661.328
139 -6093.461 -41451.611
140 -10487.113 -6093.461
141 15041.657 -10487.113
142 -24416.683 15041.657
143 -17738.100 -24416.683
> 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/72cuc1324478474.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8gv521324478474.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9dipg1324478474.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10eqss1324478474.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11m49e1324478474.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/12gaao1324478474.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/13q72v1324478474.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/14xev01324478474.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/15rpc71324478474.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/16khay1324478475.tab")
+ }
>
> try(system("convert tmp/1mhcm1324478474.ps tmp/1mhcm1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/23w0c1324478474.ps tmp/23w0c1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ewbg1324478474.ps tmp/3ewbg1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sc0v1324478474.ps tmp/4sc0v1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/57gxe1324478474.ps tmp/57gxe1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/65pbz1324478474.ps tmp/65pbz1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/72cuc1324478474.ps tmp/72cuc1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gv521324478474.ps tmp/8gv521324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dipg1324478474.ps tmp/9dipg1324478474.png",intern=TRUE))
character(0)
> try(system("convert tmp/10eqss1324478474.ps tmp/10eqss1324478474.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.030 0.400 5.461