R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(20465
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+ ,20
+ ,28)
+ ,dim=c(4
+ ,144)
+ ,dimnames=list(c('A'
+ ,'B'
+ ,'C'
+ ,'D')
+ ,1:144))
> y <- array(NA,dim=c(4,144),dimnames=list(c('A','B','C','D'),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 = '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
A B C D t
1 20465 162687 95 39 1
2 33629 201906 63 46 2
3 1423 7215 18 0 3
4 25629 146367 97 54 4
5 54002 257045 139 93 5
6 151036 524450 266 198 6
7 33287 188294 59 42 7
8 31172 195674 60 59 8
9 28113 177020 45 49 9
10 57803 330194 99 83 10
11 49830 121844 75 49 11
12 52143 203938 72 83 12
13 21055 113213 106 39 13
14 47007 220751 120 93 14
15 28735 173259 65 31 15
16 59147 156326 88 29 16
17 78950 145178 58 104 17
18 13497 89171 61 2 18
19 46154 172624 88 46 19
20 53249 39790 27 27 20
21 10726 87927 62 16 21
22 83700 241285 103 108 22
23 40400 198587 74 36 23
24 33797 146946 57 33 24
25 36205 159763 89 46 25
26 30165 207078 34 65 26
27 58534 212394 166 80 27
28 44663 201536 95 81 28
29 92556 394662 121 69 29
30 40078 217892 46 69 30
31 34711 182286 45 37 31
32 31076 188748 48 45 32
33 74608 137978 107 62 33
34 58092 255929 131 33 34
35 42009 236489 55 77 35
36 0 0 1 0 36
37 36022 230761 65 34 37
38 23333 132807 54 44 38
39 53349 158599 51 43 39
40 92596 253254 68 117 40
41 49598 269329 72 125 41
42 44093 161273 61 49 42
43 84205 107181 33 76 43
44 63369 213097 81 81 44
45 60132 139667 51 111 45
46 37403 171101 99 61 46
47 24460 81407 33 56 47
48 46456 247596 106 54 48
49 66616 239807 90 47 49
50 41554 172743 60 55 50
51 22346 48188 28 14 51
52 30874 169355 71 44 52
53 68701 325322 77 115 53
54 35728 241518 80 57 54
55 29010 195583 60 48 55
56 23110 159913 57 40 56
57 38844 223936 71 51 57
58 27084 101694 26 32 58
59 35139 157258 68 36 59
60 57476 202536 100 47 60
61 33277 173505 65 51 61
62 31141 150518 84 37 62
63 61281 141491 64 52 63
64 25820 125612 39 42 64
65 23284 166049 36 11 65
66 35378 124197 43 47 66
67 74990 195043 71 59 67
68 29653 138708 66 82 68
69 64622 116552 40 49 69
70 4157 31970 15 6 70
71 29245 258158 115 83 71
72 50008 151194 79 56 72
73 52338 135926 68 114 73
74 13310 119629 73 46 74
75 92901 171518 71 46 75
76 10956 108949 45 2 76
77 34241 183471 60 51 77
78 75043 159966 98 96 78
79 21152 93786 34 20 79
80 42249 84971 72 57 80
81 42005 88882 76 49 81
82 41152 304603 65 51 82
83 14399 75101 30 40 83
84 28263 145043 41 40 84
85 17215 95827 48 36 85
86 48140 173924 59 64 86
87 62897 241957 238 117 87
88 22883 115367 115 40 88
89 41622 118689 65 46 89
90 40715 164078 53 61 90
91 65897 158931 42 59 91
92 76542 184139 83 94 92
93 37477 152856 58 36 93
94 53216 146159 61 51 94
95 40911 62535 43 39 95
96 57021 245196 117 62 96
97 73116 199841 71 79 97
98 3895 19349 12 14 98
99 46609 247280 109 45 99
100 29351 160833 85 43 100
101 2325 72128 30 8 101
102 31747 104253 26 41 102
103 32665 151090 57 25 103
104 19249 146461 67 22 104
105 15292 87448 42 18 105
106 5842 27676 22 3 106
107 33994 170326 52 54 107
108 13018 132148 38 6 108
109 0 0 0 0 109
110 98177 95778 34 50 110
111 37941 109001 68 33 111
112 31032 158833 46 54 112
113 32683 150013 66 63 113
114 34545 89887 63 56 114
115 0 3616 5 0 115
116 0 0 0 0 116
117 27525 199005 45 49 117
118 66856 160930 93 90 118
119 28549 177948 102 51 119
120 38610 136061 40 29 120
121 2781 43410 19 1 121
122 41211 184277 75 68 122
123 22698 109873 45 29 123
124 41194 151030 59 27 124
125 32689 60493 40 4 125
126 5752 19764 12 10 126
127 26757 177559 56 47 127
128 22527 140281 35 44 128
129 44810 164249 54 53 129
130 0 11796 9 0 130
131 0 10674 9 0 131
132 100674 151322 59 40 132
133 0 6836 3 0 133
134 57786 174712 68 57 134
135 0 5118 3 0 135
136 5444 40248 16 6 136
137 0 0 0 0 137
138 28470 127628 51 24 138
139 61849 88837 38 34 139
140 0 7131 4 0 140
141 2179 9056 15 10 141
142 8019 88589 29 16 142
143 39644 144470 53 93 143
144 23494 111408 20 28 144
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) B C D t
2.210e+03 6.785e-02 5.783e+01 4.326e+02 2.418e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-34750 -9809 -4103 6303 64291
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.210e+03 4.792e+03 0.461 0.6454
B 6.785e-02 2.931e-02 2.315 0.0221 *
C 5.783e+01 5.571e+01 1.038 0.3011
D 4.326e+02 6.580e+01 6.574 9.16e-10 ***
t 2.418e+01 3.603e+01 0.671 0.5032
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15960 on 139 degrees of freedom
Multiple R-squared: 0.6072, Adjusted R-squared: 0.5959
F-statistic: 53.72 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,] 0.29848390 0.59696780 0.7015161
[2,] 0.16074957 0.32149913 0.8392504
[3,] 0.07881346 0.15762693 0.9211865
[4,] 0.14248459 0.28496919 0.8575154
[5,] 0.08826945 0.17653890 0.9117305
[6,] 0.07351016 0.14702031 0.9264898
[7,] 0.09262713 0.18525425 0.9073729
[8,] 0.06513343 0.13026686 0.9348666
[9,] 0.21635859 0.43271719 0.7836414
[10,] 0.24899031 0.49798063 0.7510097
[11,] 0.18765095 0.37530190 0.8123491
[12,] 0.13433141 0.26866282 0.8656686
[13,] 0.22590430 0.45180860 0.7740957
[14,] 0.25557574 0.51115147 0.7444243
[15,] 0.20233402 0.40466805 0.7976660
[16,] 0.15417474 0.30834948 0.8458253
[17,] 0.11934007 0.23868015 0.8806599
[18,] 0.10678180 0.21356359 0.8932182
[19,] 0.16244962 0.32489924 0.8375504
[20,] 0.14889733 0.29779466 0.8511027
[21,] 0.16816890 0.33633780 0.8318311
[22,] 0.24765575 0.49531151 0.7523442
[23,] 0.24673005 0.49346010 0.7532699
[24,] 0.20091274 0.40182547 0.7990873
[25,] 0.17775449 0.35550897 0.8222455
[26,] 0.22153232 0.44306464 0.7784677
[27,] 0.19127331 0.38254663 0.8087267
[28,] 0.20177951 0.40355902 0.7982205
[29,] 0.17198672 0.34397345 0.8280133
[30,] 0.14048598 0.28097197 0.8595140
[31,] 0.13982731 0.27965462 0.8601727
[32,] 0.13566062 0.27132125 0.8643394
[33,] 0.13001757 0.26003514 0.8699824
[34,] 0.25939014 0.51878027 0.7406099
[35,] 0.21665643 0.43331285 0.7833436
[36,] 0.40468590 0.80937180 0.5953141
[37,] 0.35607957 0.71215913 0.6439204
[38,] 0.32412409 0.64824817 0.6758759
[39,] 0.33781302 0.67562604 0.6621870
[40,] 0.33105958 0.66211916 0.6689404
[41,] 0.29778605 0.59557210 0.7022140
[42,] 0.30678311 0.61356621 0.6932169
[43,] 0.26757662 0.53515325 0.7324234
[44,] 0.23036695 0.46073391 0.7696330
[45,] 0.21468651 0.42937302 0.7853135
[46,] 0.20110105 0.40220210 0.7988990
[47,] 0.20295030 0.40590060 0.7970497
[48,] 0.19542349 0.39084697 0.8045765
[49,] 0.18731607 0.37463213 0.8126839
[50,] 0.16189596 0.32379191 0.8381040
[51,] 0.13271017 0.26542033 0.8672898
[52,] 0.10745807 0.21491614 0.8925419
[53,] 0.09707642 0.19415284 0.9029236
[54,] 0.08479903 0.16959806 0.9152010
[55,] 0.06977167 0.13954335 0.9302283
[56,] 0.07917702 0.15835404 0.9208230
[57,] 0.06720831 0.13441663 0.9327917
[58,] 0.05249570 0.10499141 0.9475043
[59,] 0.04055600 0.08111200 0.9594440
[60,] 0.06290180 0.12580359 0.9370982
[61,] 0.09265408 0.18530816 0.9073459
[62,] 0.13778351 0.27556702 0.8622165
[63,] 0.11844880 0.23689760 0.8815512
[64,] 0.25477338 0.50954677 0.7452266
[65,] 0.22143544 0.44287088 0.7785646
[66,] 0.22752004 0.45504008 0.7724800
[67,] 0.27591460 0.55182920 0.7240854
[68,] 0.71634391 0.56731219 0.2836561
[69,] 0.67943788 0.64112423 0.3205621
[70,] 0.64859953 0.70280094 0.3514005
[71,] 0.62043601 0.75912798 0.3795640
[72,] 0.57361725 0.85276549 0.4263827
[73,] 0.52681647 0.94636707 0.4731835
[74,] 0.48708049 0.97416099 0.5129195
[75,] 0.46555370 0.93110741 0.5344463
[76,] 0.46039772 0.92079544 0.5396023
[77,] 0.42288878 0.84577755 0.5771112
[78,] 0.40642660 0.81285320 0.5935734
[79,] 0.36204359 0.72408718 0.6379564
[80,] 0.38667815 0.77335631 0.6133218
[81,] 0.37396781 0.74793563 0.6260322
[82,] 0.33027028 0.66054055 0.6697297
[83,] 0.29546927 0.59093854 0.7045307
[84,] 0.32323345 0.64646690 0.6767666
[85,] 0.29823459 0.59646917 0.7017654
[86,] 0.25679399 0.51358799 0.7432060
[87,] 0.23752365 0.47504731 0.7624763
[88,] 0.21923895 0.43847789 0.7807611
[89,] 0.18294819 0.36589638 0.8170518
[90,] 0.18193290 0.36386581 0.8180671
[91,] 0.15656935 0.31313871 0.8434306
[92,] 0.12734799 0.25469598 0.8726520
[93,] 0.11277633 0.22555266 0.8872237
[94,] 0.10076851 0.20153701 0.8992315
[95,] 0.07983550 0.15967101 0.9201645
[96,] 0.06239752 0.12479504 0.9376025
[97,] 0.05269256 0.10538512 0.9473074
[98,] 0.04126142 0.08252284 0.9587386
[99,] 0.03097262 0.06194525 0.9690274
[100,] 0.02453650 0.04907299 0.9754635
[101,] 0.01866250 0.03732499 0.9813375
[102,] 0.01347016 0.02694032 0.9865298
[103,] 0.52748225 0.94503549 0.4725177
[104,] 0.47261227 0.94522453 0.5273877
[105,] 0.42028609 0.84057219 0.5797139
[106,] 0.38090825 0.76181651 0.6190917
[107,] 0.32467420 0.64934840 0.6753258
[108,] 0.27595111 0.55190222 0.7240489
[109,] 0.23872212 0.47744424 0.7612779
[110,] 0.20757708 0.41515416 0.7924229
[111,] 0.19461716 0.38923433 0.8053828
[112,] 0.38440696 0.76881392 0.6155930
[113,] 0.37261121 0.74522242 0.6273888
[114,] 0.30990380 0.61980760 0.6900962
[115,] 0.31795449 0.63590897 0.6820455
[116,] 0.28320734 0.56641468 0.7167927
[117,] 0.24442706 0.48885413 0.7555729
[118,] 0.20171231 0.40342462 0.7982877
[119,] 0.15164873 0.30329745 0.8483513
[120,] 0.19926977 0.39853953 0.8007302
[121,] 0.16968963 0.33937926 0.8303104
[122,] 0.17086456 0.34172912 0.8291354
[123,] 0.14146315 0.28292630 0.8585369
[124,] 0.12476882 0.24953763 0.8752312
[125,] 0.59004327 0.81991346 0.4099567
[126,] 0.47721682 0.95443365 0.5227832
[127,] 0.35425136 0.70850271 0.6457486
[128,] 0.24170800 0.48341600 0.7582920
[129,] 0.17342666 0.34685332 0.8265733
> postscript(file="/var/wessaorg/rcomp/tmp/1n4dh1324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2g37e1324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/38fdm1324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4q2am1324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mmz21324566506.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
-15170.56482 -5869.09125 -2390.02015 -15575.99010 -14035.02751 12069.00771
7 8 9 10 11 12
-3446.91042 -13498.22898 -10122.75084 -8679.08094 13554.55066 -4260.12341
13 14 15 16 17 18
-12150.22586 -17686.53976 -2761.04751 28310.71465 18138.32436 409.14923
19 20 21 22 23 24
6785.80701 34615.09742 -8463.73295 11914.43817 4308.81882 3466.03855
25 26 27 28 29 30
-2493.54246 -16806.10608 -2943.62102 -12428.96422 26024.26485 -10147.70880
31 32 33 34 35 36
776.74670 -6954.85867 29232.10172 15845.92648 -13580.31871 -3138.45407
37 38 39 40 41 42
-1205.03777 -10961.96553 17886.01024 17693.97771 -30110.66020 5202.38187
43 44 45 46 47 48
38900.04239 5915.33450 -3606.03605 -9639.35104 -10541.71866 -3201.44425
49 50 51 52 53 54
21416.01738 -845.83256 7958.20044 -7222.21800 -11060.24176 -13456.35538
55 56 57 58 59 60
-12032.39459 -11902.50193 -6104.19534 1226.23339 1328.24456 13960.43856
61 62 63 64 65 66
-7999.40944 -3642.81808 21753.51645 -6883.00674 1396.31088 328.49800
67 68 69 70 71 72
28299.74456 -22899.18122 29326.98765 -5377.65514 -34750.05065 7006.89925
73 74 75 76 77 78
-14104.07668 -22925.28718 53236.71461 -3951.03404 -7809.34069 12900.34511
79 80 81 82 83 84
51.10109 3519.64625 6215.32888 -9526.73043 -13950.91175 -5492.49047
85 86 87 88 89 90
-11900.08513 954.23129 -22205.73924 -13235.02665 5550.37559 -4254.82521
91 92 93 94 95 96
22753.42603 14153.28892 3720.98705 13228.21478 12804.27091 2269.03148
97 98 99 100 101 102
16723.45891 -8747.50290 -540.68399 -9704.78473 -12416.40203 758.52196
103 104 105 106 107 108
3603.02523 -8803.67370 -5605.10949 -3378.97123 -8725.04028 -5562.29147
109 110 111 112 113 114
-4845.91337 63214.36009 7444.52836 -10681.23105 -13505.63812 -4387.06419
115 116 117 118 119 120
-5525.47312 -5015.18742 -14814.00588 6565.18688 -16571.01215 9409.39359
121 122 123 124 125 126
-6831.58167 -10203.22729 -5087.53251 10647.48955 19308.60822 -5865.49536
127 128 129 130 131 132
-14139.76577 -13352.71204 2288.16743 -6674.49372 -6622.55231 64290.87776
133 134 135 136 137 138
-6063.55958 11893.54235 -5995.36385 -6306.12546 -5523.00956 932.99020
139 140 141 142 143 144
33345.73309 -6310.67544 -9248.21008 -12233.37292 -19119.22276 -3025.25401
> postscript(file="/var/wessaorg/rcomp/tmp/616321324566506.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 -15170.56482 NA
1 -5869.09125 -15170.56482
2 -2390.02015 -5869.09125
3 -15575.99010 -2390.02015
4 -14035.02751 -15575.99010
5 12069.00771 -14035.02751
6 -3446.91042 12069.00771
7 -13498.22898 -3446.91042
8 -10122.75084 -13498.22898
9 -8679.08094 -10122.75084
10 13554.55066 -8679.08094
11 -4260.12341 13554.55066
12 -12150.22586 -4260.12341
13 -17686.53976 -12150.22586
14 -2761.04751 -17686.53976
15 28310.71465 -2761.04751
16 18138.32436 28310.71465
17 409.14923 18138.32436
18 6785.80701 409.14923
19 34615.09742 6785.80701
20 -8463.73295 34615.09742
21 11914.43817 -8463.73295
22 4308.81882 11914.43817
23 3466.03855 4308.81882
24 -2493.54246 3466.03855
25 -16806.10608 -2493.54246
26 -2943.62102 -16806.10608
27 -12428.96422 -2943.62102
28 26024.26485 -12428.96422
29 -10147.70880 26024.26485
30 776.74670 -10147.70880
31 -6954.85867 776.74670
32 29232.10172 -6954.85867
33 15845.92648 29232.10172
34 -13580.31871 15845.92648
35 -3138.45407 -13580.31871
36 -1205.03777 -3138.45407
37 -10961.96553 -1205.03777
38 17886.01024 -10961.96553
39 17693.97771 17886.01024
40 -30110.66020 17693.97771
41 5202.38187 -30110.66020
42 38900.04239 5202.38187
43 5915.33450 38900.04239
44 -3606.03605 5915.33450
45 -9639.35104 -3606.03605
46 -10541.71866 -9639.35104
47 -3201.44425 -10541.71866
48 21416.01738 -3201.44425
49 -845.83256 21416.01738
50 7958.20044 -845.83256
51 -7222.21800 7958.20044
52 -11060.24176 -7222.21800
53 -13456.35538 -11060.24176
54 -12032.39459 -13456.35538
55 -11902.50193 -12032.39459
56 -6104.19534 -11902.50193
57 1226.23339 -6104.19534
58 1328.24456 1226.23339
59 13960.43856 1328.24456
60 -7999.40944 13960.43856
61 -3642.81808 -7999.40944
62 21753.51645 -3642.81808
63 -6883.00674 21753.51645
64 1396.31088 -6883.00674
65 328.49800 1396.31088
66 28299.74456 328.49800
67 -22899.18122 28299.74456
68 29326.98765 -22899.18122
69 -5377.65514 29326.98765
70 -34750.05065 -5377.65514
71 7006.89925 -34750.05065
72 -14104.07668 7006.89925
73 -22925.28718 -14104.07668
74 53236.71461 -22925.28718
75 -3951.03404 53236.71461
76 -7809.34069 -3951.03404
77 12900.34511 -7809.34069
78 51.10109 12900.34511
79 3519.64625 51.10109
80 6215.32888 3519.64625
81 -9526.73043 6215.32888
82 -13950.91175 -9526.73043
83 -5492.49047 -13950.91175
84 -11900.08513 -5492.49047
85 954.23129 -11900.08513
86 -22205.73924 954.23129
87 -13235.02665 -22205.73924
88 5550.37559 -13235.02665
89 -4254.82521 5550.37559
90 22753.42603 -4254.82521
91 14153.28892 22753.42603
92 3720.98705 14153.28892
93 13228.21478 3720.98705
94 12804.27091 13228.21478
95 2269.03148 12804.27091
96 16723.45891 2269.03148
97 -8747.50290 16723.45891
98 -540.68399 -8747.50290
99 -9704.78473 -540.68399
100 -12416.40203 -9704.78473
101 758.52196 -12416.40203
102 3603.02523 758.52196
103 -8803.67370 3603.02523
104 -5605.10949 -8803.67370
105 -3378.97123 -5605.10949
106 -8725.04028 -3378.97123
107 -5562.29147 -8725.04028
108 -4845.91337 -5562.29147
109 63214.36009 -4845.91337
110 7444.52836 63214.36009
111 -10681.23105 7444.52836
112 -13505.63812 -10681.23105
113 -4387.06419 -13505.63812
114 -5525.47312 -4387.06419
115 -5015.18742 -5525.47312
116 -14814.00588 -5015.18742
117 6565.18688 -14814.00588
118 -16571.01215 6565.18688
119 9409.39359 -16571.01215
120 -6831.58167 9409.39359
121 -10203.22729 -6831.58167
122 -5087.53251 -10203.22729
123 10647.48955 -5087.53251
124 19308.60822 10647.48955
125 -5865.49536 19308.60822
126 -14139.76577 -5865.49536
127 -13352.71204 -14139.76577
128 2288.16743 -13352.71204
129 -6674.49372 2288.16743
130 -6622.55231 -6674.49372
131 64290.87776 -6622.55231
132 -6063.55958 64290.87776
133 11893.54235 -6063.55958
134 -5995.36385 11893.54235
135 -6306.12546 -5995.36385
136 -5523.00956 -6306.12546
137 932.99020 -5523.00956
138 33345.73309 932.99020
139 -6310.67544 33345.73309
140 -9248.21008 -6310.67544
141 -12233.37292 -9248.21008
142 -19119.22276 -12233.37292
143 -3025.25401 -19119.22276
144 NA -3025.25401
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5869.09125 -15170.56482
[2,] -2390.02015 -5869.09125
[3,] -15575.99010 -2390.02015
[4,] -14035.02751 -15575.99010
[5,] 12069.00771 -14035.02751
[6,] -3446.91042 12069.00771
[7,] -13498.22898 -3446.91042
[8,] -10122.75084 -13498.22898
[9,] -8679.08094 -10122.75084
[10,] 13554.55066 -8679.08094
[11,] -4260.12341 13554.55066
[12,] -12150.22586 -4260.12341
[13,] -17686.53976 -12150.22586
[14,] -2761.04751 -17686.53976
[15,] 28310.71465 -2761.04751
[16,] 18138.32436 28310.71465
[17,] 409.14923 18138.32436
[18,] 6785.80701 409.14923
[19,] 34615.09742 6785.80701
[20,] -8463.73295 34615.09742
[21,] 11914.43817 -8463.73295
[22,] 4308.81882 11914.43817
[23,] 3466.03855 4308.81882
[24,] -2493.54246 3466.03855
[25,] -16806.10608 -2493.54246
[26,] -2943.62102 -16806.10608
[27,] -12428.96422 -2943.62102
[28,] 26024.26485 -12428.96422
[29,] -10147.70880 26024.26485
[30,] 776.74670 -10147.70880
[31,] -6954.85867 776.74670
[32,] 29232.10172 -6954.85867
[33,] 15845.92648 29232.10172
[34,] -13580.31871 15845.92648
[35,] -3138.45407 -13580.31871
[36,] -1205.03777 -3138.45407
[37,] -10961.96553 -1205.03777
[38,] 17886.01024 -10961.96553
[39,] 17693.97771 17886.01024
[40,] -30110.66020 17693.97771
[41,] 5202.38187 -30110.66020
[42,] 38900.04239 5202.38187
[43,] 5915.33450 38900.04239
[44,] -3606.03605 5915.33450
[45,] -9639.35104 -3606.03605
[46,] -10541.71866 -9639.35104
[47,] -3201.44425 -10541.71866
[48,] 21416.01738 -3201.44425
[49,] -845.83256 21416.01738
[50,] 7958.20044 -845.83256
[51,] -7222.21800 7958.20044
[52,] -11060.24176 -7222.21800
[53,] -13456.35538 -11060.24176
[54,] -12032.39459 -13456.35538
[55,] -11902.50193 -12032.39459
[56,] -6104.19534 -11902.50193
[57,] 1226.23339 -6104.19534
[58,] 1328.24456 1226.23339
[59,] 13960.43856 1328.24456
[60,] -7999.40944 13960.43856
[61,] -3642.81808 -7999.40944
[62,] 21753.51645 -3642.81808
[63,] -6883.00674 21753.51645
[64,] 1396.31088 -6883.00674
[65,] 328.49800 1396.31088
[66,] 28299.74456 328.49800
[67,] -22899.18122 28299.74456
[68,] 29326.98765 -22899.18122
[69,] -5377.65514 29326.98765
[70,] -34750.05065 -5377.65514
[71,] 7006.89925 -34750.05065
[72,] -14104.07668 7006.89925
[73,] -22925.28718 -14104.07668
[74,] 53236.71461 -22925.28718
[75,] -3951.03404 53236.71461
[76,] -7809.34069 -3951.03404
[77,] 12900.34511 -7809.34069
[78,] 51.10109 12900.34511
[79,] 3519.64625 51.10109
[80,] 6215.32888 3519.64625
[81,] -9526.73043 6215.32888
[82,] -13950.91175 -9526.73043
[83,] -5492.49047 -13950.91175
[84,] -11900.08513 -5492.49047
[85,] 954.23129 -11900.08513
[86,] -22205.73924 954.23129
[87,] -13235.02665 -22205.73924
[88,] 5550.37559 -13235.02665
[89,] -4254.82521 5550.37559
[90,] 22753.42603 -4254.82521
[91,] 14153.28892 22753.42603
[92,] 3720.98705 14153.28892
[93,] 13228.21478 3720.98705
[94,] 12804.27091 13228.21478
[95,] 2269.03148 12804.27091
[96,] 16723.45891 2269.03148
[97,] -8747.50290 16723.45891
[98,] -540.68399 -8747.50290
[99,] -9704.78473 -540.68399
[100,] -12416.40203 -9704.78473
[101,] 758.52196 -12416.40203
[102,] 3603.02523 758.52196
[103,] -8803.67370 3603.02523
[104,] -5605.10949 -8803.67370
[105,] -3378.97123 -5605.10949
[106,] -8725.04028 -3378.97123
[107,] -5562.29147 -8725.04028
[108,] -4845.91337 -5562.29147
[109,] 63214.36009 -4845.91337
[110,] 7444.52836 63214.36009
[111,] -10681.23105 7444.52836
[112,] -13505.63812 -10681.23105
[113,] -4387.06419 -13505.63812
[114,] -5525.47312 -4387.06419
[115,] -5015.18742 -5525.47312
[116,] -14814.00588 -5015.18742
[117,] 6565.18688 -14814.00588
[118,] -16571.01215 6565.18688
[119,] 9409.39359 -16571.01215
[120,] -6831.58167 9409.39359
[121,] -10203.22729 -6831.58167
[122,] -5087.53251 -10203.22729
[123,] 10647.48955 -5087.53251
[124,] 19308.60822 10647.48955
[125,] -5865.49536 19308.60822
[126,] -14139.76577 -5865.49536
[127,] -13352.71204 -14139.76577
[128,] 2288.16743 -13352.71204
[129,] -6674.49372 2288.16743
[130,] -6622.55231 -6674.49372
[131,] 64290.87776 -6622.55231
[132,] -6063.55958 64290.87776
[133,] 11893.54235 -6063.55958
[134,] -5995.36385 11893.54235
[135,] -6306.12546 -5995.36385
[136,] -5523.00956 -6306.12546
[137,] 932.99020 -5523.00956
[138,] 33345.73309 932.99020
[139,] -6310.67544 33345.73309
[140,] -9248.21008 -6310.67544
[141,] -12233.37292 -9248.21008
[142,] -19119.22276 -12233.37292
[143,] -3025.25401 -19119.22276
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5869.09125 -15170.56482
2 -2390.02015 -5869.09125
3 -15575.99010 -2390.02015
4 -14035.02751 -15575.99010
5 12069.00771 -14035.02751
6 -3446.91042 12069.00771
7 -13498.22898 -3446.91042
8 -10122.75084 -13498.22898
9 -8679.08094 -10122.75084
10 13554.55066 -8679.08094
11 -4260.12341 13554.55066
12 -12150.22586 -4260.12341
13 -17686.53976 -12150.22586
14 -2761.04751 -17686.53976
15 28310.71465 -2761.04751
16 18138.32436 28310.71465
17 409.14923 18138.32436
18 6785.80701 409.14923
19 34615.09742 6785.80701
20 -8463.73295 34615.09742
21 11914.43817 -8463.73295
22 4308.81882 11914.43817
23 3466.03855 4308.81882
24 -2493.54246 3466.03855
25 -16806.10608 -2493.54246
26 -2943.62102 -16806.10608
27 -12428.96422 -2943.62102
28 26024.26485 -12428.96422
29 -10147.70880 26024.26485
30 776.74670 -10147.70880
31 -6954.85867 776.74670
32 29232.10172 -6954.85867
33 15845.92648 29232.10172
34 -13580.31871 15845.92648
35 -3138.45407 -13580.31871
36 -1205.03777 -3138.45407
37 -10961.96553 -1205.03777
38 17886.01024 -10961.96553
39 17693.97771 17886.01024
40 -30110.66020 17693.97771
41 5202.38187 -30110.66020
42 38900.04239 5202.38187
43 5915.33450 38900.04239
44 -3606.03605 5915.33450
45 -9639.35104 -3606.03605
46 -10541.71866 -9639.35104
47 -3201.44425 -10541.71866
48 21416.01738 -3201.44425
49 -845.83256 21416.01738
50 7958.20044 -845.83256
51 -7222.21800 7958.20044
52 -11060.24176 -7222.21800
53 -13456.35538 -11060.24176
54 -12032.39459 -13456.35538
55 -11902.50193 -12032.39459
56 -6104.19534 -11902.50193
57 1226.23339 -6104.19534
58 1328.24456 1226.23339
59 13960.43856 1328.24456
60 -7999.40944 13960.43856
61 -3642.81808 -7999.40944
62 21753.51645 -3642.81808
63 -6883.00674 21753.51645
64 1396.31088 -6883.00674
65 328.49800 1396.31088
66 28299.74456 328.49800
67 -22899.18122 28299.74456
68 29326.98765 -22899.18122
69 -5377.65514 29326.98765
70 -34750.05065 -5377.65514
71 7006.89925 -34750.05065
72 -14104.07668 7006.89925
73 -22925.28718 -14104.07668
74 53236.71461 -22925.28718
75 -3951.03404 53236.71461
76 -7809.34069 -3951.03404
77 12900.34511 -7809.34069
78 51.10109 12900.34511
79 3519.64625 51.10109
80 6215.32888 3519.64625
81 -9526.73043 6215.32888
82 -13950.91175 -9526.73043
83 -5492.49047 -13950.91175
84 -11900.08513 -5492.49047
85 954.23129 -11900.08513
86 -22205.73924 954.23129
87 -13235.02665 -22205.73924
88 5550.37559 -13235.02665
89 -4254.82521 5550.37559
90 22753.42603 -4254.82521
91 14153.28892 22753.42603
92 3720.98705 14153.28892
93 13228.21478 3720.98705
94 12804.27091 13228.21478
95 2269.03148 12804.27091
96 16723.45891 2269.03148
97 -8747.50290 16723.45891
98 -540.68399 -8747.50290
99 -9704.78473 -540.68399
100 -12416.40203 -9704.78473
101 758.52196 -12416.40203
102 3603.02523 758.52196
103 -8803.67370 3603.02523
104 -5605.10949 -8803.67370
105 -3378.97123 -5605.10949
106 -8725.04028 -3378.97123
107 -5562.29147 -8725.04028
108 -4845.91337 -5562.29147
109 63214.36009 -4845.91337
110 7444.52836 63214.36009
111 -10681.23105 7444.52836
112 -13505.63812 -10681.23105
113 -4387.06419 -13505.63812
114 -5525.47312 -4387.06419
115 -5015.18742 -5525.47312
116 -14814.00588 -5015.18742
117 6565.18688 -14814.00588
118 -16571.01215 6565.18688
119 9409.39359 -16571.01215
120 -6831.58167 9409.39359
121 -10203.22729 -6831.58167
122 -5087.53251 -10203.22729
123 10647.48955 -5087.53251
124 19308.60822 10647.48955
125 -5865.49536 19308.60822
126 -14139.76577 -5865.49536
127 -13352.71204 -14139.76577
128 2288.16743 -13352.71204
129 -6674.49372 2288.16743
130 -6622.55231 -6674.49372
131 64290.87776 -6622.55231
132 -6063.55958 64290.87776
133 11893.54235 -6063.55958
134 -5995.36385 11893.54235
135 -6306.12546 -5995.36385
136 -5523.00956 -6306.12546
137 932.99020 -5523.00956
138 33345.73309 932.99020
139 -6310.67544 33345.73309
140 -9248.21008 -6310.67544
141 -12233.37292 -9248.21008
142 -19119.22276 -12233.37292
143 -3025.25401 -19119.22276
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7ow1t1324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8h3p51324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/983np1324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/106v3f1324566506.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11ah111324566506.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12jbn61324566506.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ym8z1324566506.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14sc1g1324566506.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1583aq1324566506.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/167zwf1324566506.tab")
+ }
>
> try(system("convert tmp/1n4dh1324566506.ps tmp/1n4dh1324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g37e1324566506.ps tmp/2g37e1324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/38fdm1324566506.ps tmp/38fdm1324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q2am1324566506.ps tmp/4q2am1324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mmz21324566506.ps tmp/5mmz21324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/616321324566506.ps tmp/616321324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ow1t1324566506.ps tmp/7ow1t1324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h3p51324566506.ps tmp/8h3p51324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/983np1324566506.ps tmp/983np1324566506.png",intern=TRUE))
character(0)
> try(system("convert tmp/106v3f1324566506.ps tmp/106v3f1324566506.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.463 0.643 5.132