R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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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.
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> x <- array(list(25,15,19,0,18,3,24,2,18,3,32,12,23,3,23,0,23,12,25,15,24,0,22,10,30,20,25,20,17,2,30,3,25,16,25,4,26,2,23,4,19,0,19,0,35,15,21,9,25,1,23,15,20,5,23,4,19,15,24,4,17,12,27,2,27,4,18,2,24,4,22,8,26,30,23,6,26,6,25,7,14,4,20,17,26,5,18,0,22,3,25,4,29,15,21,0,25,8,24,10,22,4,22,0,32,6,23,11,31,10,18,0,23,0,19,0,26,0,14,0,27,0,20,0,22,7,24,4,32,12,25,6,21,12,21,10,28,9,24,0,23,16,24,2,21,0,13,0,21,1,17,10,29,14,25,12,16,12,25,12,20,5,25,0,21,4,23,3,21,0,26,3,19,0,20,12,21,12,19,15,14,0,22,8,14,6,20,14,19,5,29,10,25,16,21,4,22,0,15,8,22,12,19,6,28,4,25,20,17,0,21,13,19,0,27,0,29,0,22,0,19,10,20,6,16,16,24,6,17,0,21,4,22,9,26,17,17,12,17,3,19,8,19,3,17,0,27,10,25,3,19,0,16,8,15,0,24,4,15,13,20,12,29,16,19,20,29,20,24,14,24,12,21,15,23,9,23,4,22,8,26,0,22,13,29,0,21,21,22,0,20,1,21,16,18,12,18,2),dim=c(2,149),dimnames=list(c('Perf','Sport
'),1:149))
> y <- array(NA,dim=c(2,149),dimnames=list(c('Perf','Sport
'),1:149))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
Perf Sport\r t
1 25 15 1
2 19 0 2
3 18 3 3
4 24 2 4
5 18 3 5
6 32 12 6
7 23 3 7
8 23 0 8
9 23 12 9
10 25 15 10
11 24 0 11
12 22 10 12
13 30 20 13
14 25 20 14
15 17 2 15
16 30 3 16
17 25 16 17
18 25 4 18
19 26 2 19
20 23 4 20
21 19 0 21
22 19 0 22
23 35 15 23
24 21 9 24
25 25 1 25
26 23 15 26
27 20 5 27
28 23 4 28
29 19 15 29
30 24 4 30
31 17 12 31
32 27 2 32
33 27 4 33
34 18 2 34
35 24 4 35
36 22 8 36
37 26 30 37
38 23 6 38
39 26 6 39
40 25 7 40
41 14 4 41
42 20 17 42
43 26 5 43
44 18 0 44
45 22 3 45
46 25 4 46
47 29 15 47
48 21 0 48
49 25 8 49
50 24 10 50
51 22 4 51
52 22 0 52
53 32 6 53
54 23 11 54
55 31 10 55
56 18 0 56
57 23 0 57
58 19 0 58
59 26 0 59
60 14 0 60
61 27 0 61
62 20 0 62
63 22 7 63
64 24 4 64
65 32 12 65
66 25 6 66
67 21 12 67
68 21 10 68
69 28 9 69
70 24 0 70
71 23 16 71
72 24 2 72
73 21 0 73
74 13 0 74
75 21 1 75
76 17 10 76
77 29 14 77
78 25 12 78
79 16 12 79
80 25 12 80
81 20 5 81
82 25 0 82
83 21 4 83
84 23 3 84
85 21 0 85
86 26 3 86
87 19 0 87
88 20 12 88
89 21 12 89
90 19 15 90
91 14 0 91
92 22 8 92
93 14 6 93
94 20 14 94
95 19 5 95
96 29 10 96
97 25 16 97
98 21 4 98
99 22 0 99
100 15 8 100
101 22 12 101
102 19 6 102
103 28 4 103
104 25 20 104
105 17 0 105
106 21 13 106
107 19 0 107
108 27 0 108
109 29 0 109
110 22 0 110
111 19 10 111
112 20 6 112
113 16 16 113
114 24 6 114
115 17 0 115
116 21 4 116
117 22 9 117
118 26 17 118
119 17 12 119
120 17 3 120
121 19 8 121
122 19 3 122
123 17 0 123
124 27 10 124
125 25 3 125
126 19 0 126
127 16 8 127
128 15 0 128
129 24 4 129
130 15 13 130
131 20 12 131
132 29 16 132
133 19 20 133
134 29 20 134
135 24 14 135
136 24 12 136
137 21 15 137
138 23 9 138
139 23 4 139
140 22 8 140
141 26 0 141
142 22 13 142
143 29 0 143
144 21 21 144
145 22 0 145
146 20 1 146
147 21 16 147
148 18 12 148
149 18 2 149
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Sport\r` t
22.78253 0.15511 -0.02081
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5496 -2.5901 -0.1085 2.4059 10.3695
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.782528 0.741305 30.733 < 2e-16 ***
`Sport\r` 0.155113 0.053092 2.922 0.00404 **
t -0.020815 0.007724 -2.695 0.00787 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.044 on 146 degrees of freedom
Multiple R-squared: 0.09138, Adjusted R-squared: 0.07893
F-statistic: 7.342 on 2 and 146 DF, p-value: 0.0009159
> 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,] 0.5828007 0.83439870 0.41719935
[2,] 0.4355451 0.87109022 0.56445489
[3,] 0.2922894 0.58457886 0.70771057
[4,] 0.4936546 0.98730929 0.50634536
[5,] 0.4347225 0.86944503 0.56527748
[6,] 0.3478131 0.69562630 0.65218685
[7,] 0.3326284 0.66525671 0.66737165
[8,] 0.2595402 0.51908043 0.74045979
[9,] 0.2435765 0.48715294 0.75642353
[10,] 0.2754448 0.55088962 0.72455519
[11,] 0.5031826 0.99363489 0.49681744
[12,] 0.4406915 0.88138297 0.55930851
[13,] 0.3695489 0.73909777 0.63045112
[14,] 0.3191780 0.63835591 0.68082205
[15,] 0.2638055 0.52761093 0.73619453
[16,] 0.2683065 0.53661309 0.73169345
[17,] 0.2520613 0.50412263 0.74793868
[18,] 0.4464926 0.89298528 0.55350736
[19,] 0.4715250 0.94304994 0.52847503
[20,] 0.4234579 0.84691570 0.57654215
[21,] 0.4335277 0.86705540 0.56647230
[22,] 0.4220441 0.84408828 0.57795586
[23,] 0.3604933 0.72098657 0.63950671
[24,] 0.4746800 0.94936000 0.52532000
[25,] 0.4215996 0.84319912 0.57840044
[26,] 0.5494187 0.90116255 0.45058127
[27,] 0.5845906 0.83081875 0.41540937
[28,] 0.5883233 0.82335336 0.41167668
[29,] 0.5934575 0.81308494 0.40654247
[30,] 0.5437070 0.91258596 0.45629298
[31,] 0.4938477 0.98769534 0.50615233
[32,] 0.4476399 0.89527975 0.55236012
[33,] 0.3930958 0.78619150 0.60690425
[34,] 0.3708819 0.74176384 0.62911808
[35,] 0.3293099 0.65861979 0.67069011
[36,] 0.5043657 0.99126858 0.49563429
[37,] 0.5153045 0.96939090 0.48469545
[38,] 0.5079236 0.98415288 0.49207644
[39,] 0.4920490 0.98409801 0.50795100
[40,] 0.4415492 0.88309835 0.55845082
[41,] 0.4178289 0.83565775 0.58217113
[42,] 0.4357650 0.87152993 0.56423503
[43,] 0.3872145 0.77442898 0.61278551
[44,] 0.3493973 0.69879466 0.65060267
[45,] 0.3039315 0.60786305 0.69606847
[46,] 0.2613326 0.52266522 0.73866739
[47,] 0.2223350 0.44466995 0.77766503
[48,] 0.3986320 0.79726403 0.60136798
[49,] 0.3547909 0.70958179 0.64520910
[50,] 0.4591131 0.91822617 0.54088692
[51,] 0.4584042 0.91680848 0.54159576
[52,] 0.4134746 0.82694923 0.58652539
[53,] 0.3901608 0.78032152 0.60983924
[54,] 0.3917985 0.78359696 0.60820152
[55,] 0.5212740 0.95745202 0.47872601
[56,] 0.5527104 0.89457912 0.44728956
[57,] 0.5139464 0.97210719 0.48605359
[58,] 0.4696989 0.93939771 0.53030115
[59,] 0.4298254 0.85965082 0.57017459
[60,] 0.5786690 0.84266192 0.42133096
[61,] 0.5496674 0.90066518 0.45033259
[62,] 0.5319754 0.93604918 0.46802459
[63,] 0.5043029 0.99139420 0.49569710
[64,] 0.5343031 0.93139376 0.46569688
[65,] 0.5077614 0.98447727 0.49223864
[66,] 0.4759631 0.95192624 0.52403688
[67,] 0.4481076 0.89621519 0.55189241
[68,] 0.4047822 0.80956441 0.59521780
[69,] 0.5553087 0.88938255 0.44469127
[70,] 0.5092653 0.98146943 0.49073471
[71,] 0.5585952 0.88280968 0.44140484
[72,] 0.6151467 0.76970657 0.38485329
[73,] 0.5913358 0.81732836 0.40866418
[74,] 0.6720974 0.65580515 0.32790257
[75,] 0.6496218 0.70075634 0.35037817
[76,] 0.6120207 0.77595857 0.38797929
[77,] 0.6167714 0.76645715 0.38322858
[78,] 0.5722396 0.85552078 0.42776039
[79,] 0.5373839 0.92523215 0.46261608
[80,] 0.4908886 0.98177715 0.50911143
[81,] 0.5221108 0.95577842 0.47788921
[82,] 0.4817650 0.96352991 0.51823504
[83,] 0.4529379 0.90587579 0.54706210
[84,] 0.4138580 0.82771605 0.58614197
[85,] 0.4038531 0.80770619 0.59614690
[86,] 0.4752965 0.95059304 0.52470348
[87,] 0.4284955 0.85699093 0.57150454
[88,] 0.5364644 0.92707125 0.46353563
[89,] 0.5042291 0.99154187 0.49577094
[90,] 0.4713764 0.94275275 0.52862363
[91,] 0.5756418 0.84871643 0.42435821
[92,] 0.5520773 0.89584534 0.44792267
[93,] 0.5020469 0.99590622 0.49795311
[94,] 0.4591360 0.91827201 0.54086400
[95,] 0.5323812 0.93523763 0.46761882
[96,] 0.4817609 0.96352183 0.51823908
[97,] 0.4471248 0.89424961 0.55287519
[98,] 0.5480325 0.90393503 0.45196752
[99,] 0.5253970 0.94920608 0.47460304
[100,] 0.5112458 0.97750838 0.48875419
[101,] 0.4607047 0.92140945 0.53929528
[102,] 0.4179133 0.83582654 0.58208673
[103,] 0.4990707 0.99814143 0.50092928
[104,] 0.7075478 0.58490443 0.29245221
[105,] 0.6791321 0.64173581 0.32086790
[106,] 0.6376995 0.72460110 0.36230055
[107,] 0.5854714 0.82905718 0.41452859
[108,] 0.6326491 0.73470186 0.36735093
[109,] 0.6232521 0.75349589 0.37674795
[110,] 0.5908498 0.81830031 0.40915015
[111,] 0.5355562 0.92888754 0.46444377
[112,] 0.4829144 0.96582880 0.51708560
[113,] 0.5054688 0.98906240 0.49453120
[114,] 0.4955569 0.99111376 0.50444312
[115,] 0.4713935 0.94278701 0.52860650
[116,] 0.4240333 0.84806656 0.57596672
[117,] 0.3745400 0.74907999 0.62546000
[118,] 0.3715823 0.74316460 0.62841770
[119,] 0.4123242 0.82464849 0.58767575
[120,] 0.4201323 0.84026469 0.57986766
[121,] 0.3612266 0.72245316 0.63877342
[122,] 0.4083945 0.81678900 0.59160550
[123,] 0.6034661 0.79306776 0.39653388
[124,] 0.5394462 0.92110760 0.46055380
[125,] 0.8508229 0.29835423 0.14917712
[126,] 0.9193457 0.16130866 0.08065433
[127,] 0.9276542 0.14469150 0.07234575
[128,] 0.9689859 0.06202822 0.03101411
[129,] 0.9859834 0.02803321 0.01401660
[130,] 0.9739097 0.05218051 0.02609025
[131,] 0.9533920 0.09321591 0.04660796
[132,] 0.9421612 0.11567760 0.05783880
[133,] 0.9149571 0.17008586 0.08504293
[134,] 0.9004233 0.19915333 0.09957666
[135,] 0.9232280 0.15354398 0.07677199
[136,] 0.8668034 0.26639324 0.13319662
[137,] 0.8699497 0.26010057 0.13005028
[138,] 0.9691764 0.06164717 0.03082358
> postscript(file="/var/www/rcomp/tmp/15fu61289899365.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/25fu61289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3focr1289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4focr1289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5focr1289899365.ps",horizontal=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 = 149
Frequency = 1
1 2 3 4 5 6
-0.08840541 -3.74089865 -5.18542242 0.99050505 -5.14379311 7.48100628
7 8 9 10 11 12
-0.10216380 0.38398928 -1.45654976 0.09892647 1.44643324 -2.08388018
13 14 15 16 17 18
4.38580640 -0.59337895 -5.78053376 7.08516809 0.08951624 1.97168459
19 20 21 22 23 24
3.30272486 0.01331390 -3.34542022 -3.32460557 10.36951698 -2.67899152
25 26 27 28 29 30
2.58272559 -1.56803906 -2.99609633 0.17983113 -5.50559510 1.22146044
31 32 33 34 35 36
-6.99862737 4.57331536 4.28390440 -4.38505533 1.32553371 -1.27410287
37 38 39 40 41 42
-0.66576998 0.07775206 3.09856671 1.96426856 -8.54957837 -4.54523021
43 44 45 46 47 48
3.33693813 -3.86668318 -0.31120694 2.55449490 4.86906867 -0.78342456
49 50 51 52 53 54
1.99648764 0.70707667 -0.34143183 0.29983406 9.38997187 -0.36477752
55 56 57 58 59 60
7.81114995 -3.61690733 1.40390733 -2.57527802 4.44553664 -7.53364871
61 62 63 64 65 66
5.48716594 -1.49201940 -0.55699440 1.92915868 8.70907087 2.66056237
67 68 69 70 71 72
-2.24929982 -1.91825955 5.25766791 2.67449783 -0.78649243 2.40590152
73 74 75 76 77 78
-0.26305821 -8.24224355 -0.37654171 -5.75174232 5.64862111 1.97966137
79 80 81 82 83 84
-6.99952397 2.02129068 -1.87210501 3.92427368 -0.67536290 1.50056457
85 86 87 88 89 90
-0.01328236 4.54219387 -1.97165305 -2.81219208 -1.79137743 -4.23590120
91 92 93 94 95 96
-6.88839443 -0.10848224 -7.77744197 -2.99752978 -2.58069985 6.66455076
97 98 99 100 101 102
1.75468857 -0.36314308 1.27812280 -6.94196501 -0.54160158 -2.59011008
103 104 105 106 107 108
6.74093019 1.27993992 -3.59698928 -1.59264112 -1.55535997 6.46545469
109 110 111 112 113 114
8.48626934 1.50708400 -3.02322943 -1.38196354 -6.91227696 2.65966577
115 116 117 118 119 120
-3.38884273 0.01152069 0.25677131 3.03668350 -5.16693781 -3.75010789
121 122 123 124 125 126
-2.50485727 -1.70847858 -3.22232550 5.24736108 4.35396538 -1.15988154
127 128 129 130 131 132
-5.37996935 -5.11825223 3.28211119 -7.09308942 -1.91716196 6.48320147
133 134 135 136 137 138
-4.11643511 5.90437954 1.85587104 2.18691131 -1.25761246 1.69387904
139 140 141 142 143 144
2.49025773 0.89062116 6.15233827 0.15668643 9.19396758 -2.04258672
145 146 147 148 149
2.23559689 0.10129874 -1.20457872 -3.56331284 -1.99137011
> postscript(file="/var/www/rcomp/tmp/6qytu1289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 149
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.08840541 NA
1 -3.74089865 -0.08840541
2 -5.18542242 -3.74089865
3 0.99050505 -5.18542242
4 -5.14379311 0.99050505
5 7.48100628 -5.14379311
6 -0.10216380 7.48100628
7 0.38398928 -0.10216380
8 -1.45654976 0.38398928
9 0.09892647 -1.45654976
10 1.44643324 0.09892647
11 -2.08388018 1.44643324
12 4.38580640 -2.08388018
13 -0.59337895 4.38580640
14 -5.78053376 -0.59337895
15 7.08516809 -5.78053376
16 0.08951624 7.08516809
17 1.97168459 0.08951624
18 3.30272486 1.97168459
19 0.01331390 3.30272486
20 -3.34542022 0.01331390
21 -3.32460557 -3.34542022
22 10.36951698 -3.32460557
23 -2.67899152 10.36951698
24 2.58272559 -2.67899152
25 -1.56803906 2.58272559
26 -2.99609633 -1.56803906
27 0.17983113 -2.99609633
28 -5.50559510 0.17983113
29 1.22146044 -5.50559510
30 -6.99862737 1.22146044
31 4.57331536 -6.99862737
32 4.28390440 4.57331536
33 -4.38505533 4.28390440
34 1.32553371 -4.38505533
35 -1.27410287 1.32553371
36 -0.66576998 -1.27410287
37 0.07775206 -0.66576998
38 3.09856671 0.07775206
39 1.96426856 3.09856671
40 -8.54957837 1.96426856
41 -4.54523021 -8.54957837
42 3.33693813 -4.54523021
43 -3.86668318 3.33693813
44 -0.31120694 -3.86668318
45 2.55449490 -0.31120694
46 4.86906867 2.55449490
47 -0.78342456 4.86906867
48 1.99648764 -0.78342456
49 0.70707667 1.99648764
50 -0.34143183 0.70707667
51 0.29983406 -0.34143183
52 9.38997187 0.29983406
53 -0.36477752 9.38997187
54 7.81114995 -0.36477752
55 -3.61690733 7.81114995
56 1.40390733 -3.61690733
57 -2.57527802 1.40390733
58 4.44553664 -2.57527802
59 -7.53364871 4.44553664
60 5.48716594 -7.53364871
61 -1.49201940 5.48716594
62 -0.55699440 -1.49201940
63 1.92915868 -0.55699440
64 8.70907087 1.92915868
65 2.66056237 8.70907087
66 -2.24929982 2.66056237
67 -1.91825955 -2.24929982
68 5.25766791 -1.91825955
69 2.67449783 5.25766791
70 -0.78649243 2.67449783
71 2.40590152 -0.78649243
72 -0.26305821 2.40590152
73 -8.24224355 -0.26305821
74 -0.37654171 -8.24224355
75 -5.75174232 -0.37654171
76 5.64862111 -5.75174232
77 1.97966137 5.64862111
78 -6.99952397 1.97966137
79 2.02129068 -6.99952397
80 -1.87210501 2.02129068
81 3.92427368 -1.87210501
82 -0.67536290 3.92427368
83 1.50056457 -0.67536290
84 -0.01328236 1.50056457
85 4.54219387 -0.01328236
86 -1.97165305 4.54219387
87 -2.81219208 -1.97165305
88 -1.79137743 -2.81219208
89 -4.23590120 -1.79137743
90 -6.88839443 -4.23590120
91 -0.10848224 -6.88839443
92 -7.77744197 -0.10848224
93 -2.99752978 -7.77744197
94 -2.58069985 -2.99752978
95 6.66455076 -2.58069985
96 1.75468857 6.66455076
97 -0.36314308 1.75468857
98 1.27812280 -0.36314308
99 -6.94196501 1.27812280
100 -0.54160158 -6.94196501
101 -2.59011008 -0.54160158
102 6.74093019 -2.59011008
103 1.27993992 6.74093019
104 -3.59698928 1.27993992
105 -1.59264112 -3.59698928
106 -1.55535997 -1.59264112
107 6.46545469 -1.55535997
108 8.48626934 6.46545469
109 1.50708400 8.48626934
110 -3.02322943 1.50708400
111 -1.38196354 -3.02322943
112 -6.91227696 -1.38196354
113 2.65966577 -6.91227696
114 -3.38884273 2.65966577
115 0.01152069 -3.38884273
116 0.25677131 0.01152069
117 3.03668350 0.25677131
118 -5.16693781 3.03668350
119 -3.75010789 -5.16693781
120 -2.50485727 -3.75010789
121 -1.70847858 -2.50485727
122 -3.22232550 -1.70847858
123 5.24736108 -3.22232550
124 4.35396538 5.24736108
125 -1.15988154 4.35396538
126 -5.37996935 -1.15988154
127 -5.11825223 -5.37996935
128 3.28211119 -5.11825223
129 -7.09308942 3.28211119
130 -1.91716196 -7.09308942
131 6.48320147 -1.91716196
132 -4.11643511 6.48320147
133 5.90437954 -4.11643511
134 1.85587104 5.90437954
135 2.18691131 1.85587104
136 -1.25761246 2.18691131
137 1.69387904 -1.25761246
138 2.49025773 1.69387904
139 0.89062116 2.49025773
140 6.15233827 0.89062116
141 0.15668643 6.15233827
142 9.19396758 0.15668643
143 -2.04258672 9.19396758
144 2.23559689 -2.04258672
145 0.10129874 2.23559689
146 -1.20457872 0.10129874
147 -3.56331284 -1.20457872
148 -1.99137011 -3.56331284
149 NA -1.99137011
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.74089865 -0.08840541
[2,] -5.18542242 -3.74089865
[3,] 0.99050505 -5.18542242
[4,] -5.14379311 0.99050505
[5,] 7.48100628 -5.14379311
[6,] -0.10216380 7.48100628
[7,] 0.38398928 -0.10216380
[8,] -1.45654976 0.38398928
[9,] 0.09892647 -1.45654976
[10,] 1.44643324 0.09892647
[11,] -2.08388018 1.44643324
[12,] 4.38580640 -2.08388018
[13,] -0.59337895 4.38580640
[14,] -5.78053376 -0.59337895
[15,] 7.08516809 -5.78053376
[16,] 0.08951624 7.08516809
[17,] 1.97168459 0.08951624
[18,] 3.30272486 1.97168459
[19,] 0.01331390 3.30272486
[20,] -3.34542022 0.01331390
[21,] -3.32460557 -3.34542022
[22,] 10.36951698 -3.32460557
[23,] -2.67899152 10.36951698
[24,] 2.58272559 -2.67899152
[25,] -1.56803906 2.58272559
[26,] -2.99609633 -1.56803906
[27,] 0.17983113 -2.99609633
[28,] -5.50559510 0.17983113
[29,] 1.22146044 -5.50559510
[30,] -6.99862737 1.22146044
[31,] 4.57331536 -6.99862737
[32,] 4.28390440 4.57331536
[33,] -4.38505533 4.28390440
[34,] 1.32553371 -4.38505533
[35,] -1.27410287 1.32553371
[36,] -0.66576998 -1.27410287
[37,] 0.07775206 -0.66576998
[38,] 3.09856671 0.07775206
[39,] 1.96426856 3.09856671
[40,] -8.54957837 1.96426856
[41,] -4.54523021 -8.54957837
[42,] 3.33693813 -4.54523021
[43,] -3.86668318 3.33693813
[44,] -0.31120694 -3.86668318
[45,] 2.55449490 -0.31120694
[46,] 4.86906867 2.55449490
[47,] -0.78342456 4.86906867
[48,] 1.99648764 -0.78342456
[49,] 0.70707667 1.99648764
[50,] -0.34143183 0.70707667
[51,] 0.29983406 -0.34143183
[52,] 9.38997187 0.29983406
[53,] -0.36477752 9.38997187
[54,] 7.81114995 -0.36477752
[55,] -3.61690733 7.81114995
[56,] 1.40390733 -3.61690733
[57,] -2.57527802 1.40390733
[58,] 4.44553664 -2.57527802
[59,] -7.53364871 4.44553664
[60,] 5.48716594 -7.53364871
[61,] -1.49201940 5.48716594
[62,] -0.55699440 -1.49201940
[63,] 1.92915868 -0.55699440
[64,] 8.70907087 1.92915868
[65,] 2.66056237 8.70907087
[66,] -2.24929982 2.66056237
[67,] -1.91825955 -2.24929982
[68,] 5.25766791 -1.91825955
[69,] 2.67449783 5.25766791
[70,] -0.78649243 2.67449783
[71,] 2.40590152 -0.78649243
[72,] -0.26305821 2.40590152
[73,] -8.24224355 -0.26305821
[74,] -0.37654171 -8.24224355
[75,] -5.75174232 -0.37654171
[76,] 5.64862111 -5.75174232
[77,] 1.97966137 5.64862111
[78,] -6.99952397 1.97966137
[79,] 2.02129068 -6.99952397
[80,] -1.87210501 2.02129068
[81,] 3.92427368 -1.87210501
[82,] -0.67536290 3.92427368
[83,] 1.50056457 -0.67536290
[84,] -0.01328236 1.50056457
[85,] 4.54219387 -0.01328236
[86,] -1.97165305 4.54219387
[87,] -2.81219208 -1.97165305
[88,] -1.79137743 -2.81219208
[89,] -4.23590120 -1.79137743
[90,] -6.88839443 -4.23590120
[91,] -0.10848224 -6.88839443
[92,] -7.77744197 -0.10848224
[93,] -2.99752978 -7.77744197
[94,] -2.58069985 -2.99752978
[95,] 6.66455076 -2.58069985
[96,] 1.75468857 6.66455076
[97,] -0.36314308 1.75468857
[98,] 1.27812280 -0.36314308
[99,] -6.94196501 1.27812280
[100,] -0.54160158 -6.94196501
[101,] -2.59011008 -0.54160158
[102,] 6.74093019 -2.59011008
[103,] 1.27993992 6.74093019
[104,] -3.59698928 1.27993992
[105,] -1.59264112 -3.59698928
[106,] -1.55535997 -1.59264112
[107,] 6.46545469 -1.55535997
[108,] 8.48626934 6.46545469
[109,] 1.50708400 8.48626934
[110,] -3.02322943 1.50708400
[111,] -1.38196354 -3.02322943
[112,] -6.91227696 -1.38196354
[113,] 2.65966577 -6.91227696
[114,] -3.38884273 2.65966577
[115,] 0.01152069 -3.38884273
[116,] 0.25677131 0.01152069
[117,] 3.03668350 0.25677131
[118,] -5.16693781 3.03668350
[119,] -3.75010789 -5.16693781
[120,] -2.50485727 -3.75010789
[121,] -1.70847858 -2.50485727
[122,] -3.22232550 -1.70847858
[123,] 5.24736108 -3.22232550
[124,] 4.35396538 5.24736108
[125,] -1.15988154 4.35396538
[126,] -5.37996935 -1.15988154
[127,] -5.11825223 -5.37996935
[128,] 3.28211119 -5.11825223
[129,] -7.09308942 3.28211119
[130,] -1.91716196 -7.09308942
[131,] 6.48320147 -1.91716196
[132,] -4.11643511 6.48320147
[133,] 5.90437954 -4.11643511
[134,] 1.85587104 5.90437954
[135,] 2.18691131 1.85587104
[136,] -1.25761246 2.18691131
[137,] 1.69387904 -1.25761246
[138,] 2.49025773 1.69387904
[139,] 0.89062116 2.49025773
[140,] 6.15233827 0.89062116
[141,] 0.15668643 6.15233827
[142,] 9.19396758 0.15668643
[143,] -2.04258672 9.19396758
[144,] 2.23559689 -2.04258672
[145,] 0.10129874 2.23559689
[146,] -1.20457872 0.10129874
[147,] -3.56331284 -1.20457872
[148,] -1.99137011 -3.56331284
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.74089865 -0.08840541
2 -5.18542242 -3.74089865
3 0.99050505 -5.18542242
4 -5.14379311 0.99050505
5 7.48100628 -5.14379311
6 -0.10216380 7.48100628
7 0.38398928 -0.10216380
8 -1.45654976 0.38398928
9 0.09892647 -1.45654976
10 1.44643324 0.09892647
11 -2.08388018 1.44643324
12 4.38580640 -2.08388018
13 -0.59337895 4.38580640
14 -5.78053376 -0.59337895
15 7.08516809 -5.78053376
16 0.08951624 7.08516809
17 1.97168459 0.08951624
18 3.30272486 1.97168459
19 0.01331390 3.30272486
20 -3.34542022 0.01331390
21 -3.32460557 -3.34542022
22 10.36951698 -3.32460557
23 -2.67899152 10.36951698
24 2.58272559 -2.67899152
25 -1.56803906 2.58272559
26 -2.99609633 -1.56803906
27 0.17983113 -2.99609633
28 -5.50559510 0.17983113
29 1.22146044 -5.50559510
30 -6.99862737 1.22146044
31 4.57331536 -6.99862737
32 4.28390440 4.57331536
33 -4.38505533 4.28390440
34 1.32553371 -4.38505533
35 -1.27410287 1.32553371
36 -0.66576998 -1.27410287
37 0.07775206 -0.66576998
38 3.09856671 0.07775206
39 1.96426856 3.09856671
40 -8.54957837 1.96426856
41 -4.54523021 -8.54957837
42 3.33693813 -4.54523021
43 -3.86668318 3.33693813
44 -0.31120694 -3.86668318
45 2.55449490 -0.31120694
46 4.86906867 2.55449490
47 -0.78342456 4.86906867
48 1.99648764 -0.78342456
49 0.70707667 1.99648764
50 -0.34143183 0.70707667
51 0.29983406 -0.34143183
52 9.38997187 0.29983406
53 -0.36477752 9.38997187
54 7.81114995 -0.36477752
55 -3.61690733 7.81114995
56 1.40390733 -3.61690733
57 -2.57527802 1.40390733
58 4.44553664 -2.57527802
59 -7.53364871 4.44553664
60 5.48716594 -7.53364871
61 -1.49201940 5.48716594
62 -0.55699440 -1.49201940
63 1.92915868 -0.55699440
64 8.70907087 1.92915868
65 2.66056237 8.70907087
66 -2.24929982 2.66056237
67 -1.91825955 -2.24929982
68 5.25766791 -1.91825955
69 2.67449783 5.25766791
70 -0.78649243 2.67449783
71 2.40590152 -0.78649243
72 -0.26305821 2.40590152
73 -8.24224355 -0.26305821
74 -0.37654171 -8.24224355
75 -5.75174232 -0.37654171
76 5.64862111 -5.75174232
77 1.97966137 5.64862111
78 -6.99952397 1.97966137
79 2.02129068 -6.99952397
80 -1.87210501 2.02129068
81 3.92427368 -1.87210501
82 -0.67536290 3.92427368
83 1.50056457 -0.67536290
84 -0.01328236 1.50056457
85 4.54219387 -0.01328236
86 -1.97165305 4.54219387
87 -2.81219208 -1.97165305
88 -1.79137743 -2.81219208
89 -4.23590120 -1.79137743
90 -6.88839443 -4.23590120
91 -0.10848224 -6.88839443
92 -7.77744197 -0.10848224
93 -2.99752978 -7.77744197
94 -2.58069985 -2.99752978
95 6.66455076 -2.58069985
96 1.75468857 6.66455076
97 -0.36314308 1.75468857
98 1.27812280 -0.36314308
99 -6.94196501 1.27812280
100 -0.54160158 -6.94196501
101 -2.59011008 -0.54160158
102 6.74093019 -2.59011008
103 1.27993992 6.74093019
104 -3.59698928 1.27993992
105 -1.59264112 -3.59698928
106 -1.55535997 -1.59264112
107 6.46545469 -1.55535997
108 8.48626934 6.46545469
109 1.50708400 8.48626934
110 -3.02322943 1.50708400
111 -1.38196354 -3.02322943
112 -6.91227696 -1.38196354
113 2.65966577 -6.91227696
114 -3.38884273 2.65966577
115 0.01152069 -3.38884273
116 0.25677131 0.01152069
117 3.03668350 0.25677131
118 -5.16693781 3.03668350
119 -3.75010789 -5.16693781
120 -2.50485727 -3.75010789
121 -1.70847858 -2.50485727
122 -3.22232550 -1.70847858
123 5.24736108 -3.22232550
124 4.35396538 5.24736108
125 -1.15988154 4.35396538
126 -5.37996935 -1.15988154
127 -5.11825223 -5.37996935
128 3.28211119 -5.11825223
129 -7.09308942 3.28211119
130 -1.91716196 -7.09308942
131 6.48320147 -1.91716196
132 -4.11643511 6.48320147
133 5.90437954 -4.11643511
134 1.85587104 5.90437954
135 2.18691131 1.85587104
136 -1.25761246 2.18691131
137 1.69387904 -1.25761246
138 2.49025773 1.69387904
139 0.89062116 2.49025773
140 6.15233827 0.89062116
141 0.15668643 6.15233827
142 9.19396758 0.15668643
143 -2.04258672 9.19396758
144 2.23559689 -2.04258672
145 0.10129874 2.23559689
146 -1.20457872 0.10129874
147 -3.56331284 -1.20457872
148 -1.99137011 -3.56331284
> 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/70paf1289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/80paf1289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/90paf1289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10tgr01289899365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xzq61289899365.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/120h6c1289899365.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/13p0351289899365.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/1409l81289899365.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/15lsjw1289899365.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/16zkz51289899365.tab")
+ }
>
> try(system("convert tmp/15fu61289899365.ps tmp/15fu61289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/25fu61289899365.ps tmp/25fu61289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/3focr1289899365.ps tmp/3focr1289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/4focr1289899365.ps tmp/4focr1289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/5focr1289899365.ps tmp/5focr1289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qytu1289899365.ps tmp/6qytu1289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/70paf1289899365.ps tmp/70paf1289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/80paf1289899365.ps tmp/80paf1289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/90paf1289899365.ps tmp/90paf1289899365.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tgr01289899365.ps tmp/10tgr01289899365.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.130 2.030 7.138