R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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.
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(11881.4
+ ,423.4
+ ,10374.2
+ ,404.1
+ ,13828
+ ,500
+ ,13490.5
+ ,472.6
+ ,13092.2
+ ,496.1
+ ,13184.4
+ ,562
+ ,12398.4
+ ,434.8
+ ,13882.3
+ ,538.2
+ ,15861.5
+ ,577.6
+ ,13286.1
+ ,518.1
+ ,15634.9
+ ,625.2
+ ,14211
+ ,561.2
+ ,13646.8
+ ,523.3
+ ,12224.6
+ ,536.1
+ ,15916.4
+ ,607.3
+ ,16535.9
+ ,637.3
+ ,15796
+ ,606.9
+ ,14418.6
+ ,652.9
+ ,15044.5
+ ,617.2
+ ,14944.2
+ ,670.4
+ ,16754.8
+ ,729.9
+ ,14254
+ ,677.2
+ ,15454.9
+ ,710
+ ,15644.8
+ ,844.3
+ ,14568.3
+ ,748.2
+ ,12520.2
+ ,653.9
+ ,14803
+ ,742.6
+ ,15873.2
+ ,854.2
+ ,14755.3
+ ,808.4
+ ,12875.1
+ ,1819
+ ,14291.1
+ ,1936.5
+ ,14205.3
+ ,1966.1
+ ,15859.4
+ ,2083.1
+ ,15258.9
+ ,1620.1
+ ,15498.6
+ ,1527.6
+ ,15106.5
+ ,1795
+ ,15023.6
+ ,1685.1
+ ,12083
+ ,1851.8
+ ,15761.3
+ ,2164.4
+ ,16943
+ ,1981.8
+ ,15070.3
+ ,1726.5
+ ,13659.6
+ ,2144.6
+ ,14768.9
+ ,1758.2
+ ,14725.1
+ ,1672.9
+ ,15998.1
+ ,1837.3
+ ,15370.6
+ ,1596.1
+ ,14956.9
+ ,1446
+ ,15469.7
+ ,1898.4
+ ,15101.8
+ ,1964.1
+ ,11703.7
+ ,1755.9
+ ,16283.6
+ ,2255.3
+ ,16726.5
+ ,1881.2
+ ,14968.9
+ ,2117.9
+ ,14861
+ ,1656.5
+ ,14583.3
+ ,1544.1
+ ,15305.8
+ ,2098.9
+ ,17903.9
+ ,2133.3
+ ,16379.4
+ ,1963.5
+ ,15420.3
+ ,1801.2
+ ,17870.5
+ ,2365.4
+ ,15912.8
+ ,1936.5
+ ,13866.5
+ ,1667.6
+ ,17823.2
+ ,1983.5
+ ,17872
+ ,2058.6
+ ,17420.4
+ ,2448.3
+ ,16704.4
+ ,1858.1
+ ,15991.2
+ ,1625.4
+ ,16583.6
+ ,2130.6
+ ,19123.5
+ ,2515.7
+ ,17838.7
+ ,2230.2
+ ,17209.4
+ ,2086.9
+ ,18586.5
+ ,2235
+ ,16258.1
+ ,2100.2
+ ,15141.6
+ ,2288.6
+ ,19202.1
+ ,2490
+ ,17746.5
+ ,2573.7
+ ,19090.1
+ ,2543.8
+ ,18040.3
+ ,2004.7
+ ,17515.5
+ ,2390
+ ,17751.8
+ ,2338.4
+ ,21072.4
+ ,2724.5
+ ,17170
+ ,2292.5
+ ,19439.5
+ ,2386
+ ,19795.4
+ ,2477.9
+ ,17574.9
+ ,2337
+ ,16165.4
+ ,2605.1
+ ,19464.6
+ ,2560.8
+ ,19932.1
+ ,2839.3
+ ,19961.2
+ ,2407.2
+ ,17343.4
+ ,2085.2
+ ,18924.2
+ ,2735.6
+ ,18574.1
+ ,2798.7
+ ,21350.6
+ ,3053.2
+ ,18594.6
+ ,2405
+ ,19823.1
+ ,2471.9
+ ,20844.4
+ ,2727.3
+ ,19640.2
+ ,2790.7
+ ,17735.4
+ ,2385.4
+ ,19813.6
+ ,3206.6
+ ,22160
+ ,2705.6
+ ,20664.3
+ ,3518.4
+ ,17877.4
+ ,1954.9
+ ,20906.5
+ ,2584.3
+ ,21164.1
+ ,2535.8
+ ,21374.4
+ ,2685.9
+ ,22952.3
+ ,2866
+ ,21343.5
+ ,2236.6
+ ,23899.3
+ ,2934.9
+ ,22392.9
+ ,2668.6
+ ,18274.1
+ ,2371.2
+ ,22786.7
+ ,3165.9
+ ,22321.5
+ ,2887.2
+ ,17842.2
+ ,3112.2
+ ,16373.5
+ ,2671.2
+ ,15993.8
+ ,2432.6
+ ,16446.1
+ ,2812.3
+ ,17729
+ ,3095.7
+ ,16643
+ ,2862.9
+ ,16196.7
+ ,2607.3
+ ,18252.1
+ ,2862.5)
+ ,dim=c(2
+ ,120)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:120))
> y <- array(NA,dim=c(2,120),dimnames=list(c('Y','X'),1:120))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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
Y X t
1 11881.4 423.4 1
2 10374.2 404.1 2
3 13828.0 500.0 3
4 13490.5 472.6 4
5 13092.2 496.1 5
6 13184.4 562.0 6
7 12398.4 434.8 7
8 13882.3 538.2 8
9 15861.5 577.6 9
10 13286.1 518.1 10
11 15634.9 625.2 11
12 14211.0 561.2 12
13 13646.8 523.3 13
14 12224.6 536.1 14
15 15916.4 607.3 15
16 16535.9 637.3 16
17 15796.0 606.9 17
18 14418.6 652.9 18
19 15044.5 617.2 19
20 14944.2 670.4 20
21 16754.8 729.9 21
22 14254.0 677.2 22
23 15454.9 710.0 23
24 15644.8 844.3 24
25 14568.3 748.2 25
26 12520.2 653.9 26
27 14803.0 742.6 27
28 15873.2 854.2 28
29 14755.3 808.4 29
30 12875.1 1819.0 30
31 14291.1 1936.5 31
32 14205.3 1966.1 32
33 15859.4 2083.1 33
34 15258.9 1620.1 34
35 15498.6 1527.6 35
36 15106.5 1795.0 36
37 15023.6 1685.1 37
38 12083.0 1851.8 38
39 15761.3 2164.4 39
40 16943.0 1981.8 40
41 15070.3 1726.5 41
42 13659.6 2144.6 42
43 14768.9 1758.2 43
44 14725.1 1672.9 44
45 15998.1 1837.3 45
46 15370.6 1596.1 46
47 14956.9 1446.0 47
48 15469.7 1898.4 48
49 15101.8 1964.1 49
50 11703.7 1755.9 50
51 16283.6 2255.3 51
52 16726.5 1881.2 52
53 14968.9 2117.9 53
54 14861.0 1656.5 54
55 14583.3 1544.1 55
56 15305.8 2098.9 56
57 17903.9 2133.3 57
58 16379.4 1963.5 58
59 15420.3 1801.2 59
60 17870.5 2365.4 60
61 15912.8 1936.5 61
62 13866.5 1667.6 62
63 17823.2 1983.5 63
64 17872.0 2058.6 64
65 17420.4 2448.3 65
66 16704.4 1858.1 66
67 15991.2 1625.4 67
68 16583.6 2130.6 68
69 19123.5 2515.7 69
70 17838.7 2230.2 70
71 17209.4 2086.9 71
72 18586.5 2235.0 72
73 16258.1 2100.2 73
74 15141.6 2288.6 74
75 19202.1 2490.0 75
76 17746.5 2573.7 76
77 19090.1 2543.8 77
78 18040.3 2004.7 78
79 17515.5 2390.0 79
80 17751.8 2338.4 80
81 21072.4 2724.5 81
82 17170.0 2292.5 82
83 19439.5 2386.0 83
84 19795.4 2477.9 84
85 17574.9 2337.0 85
86 16165.4 2605.1 86
87 19464.6 2560.8 87
88 19932.1 2839.3 88
89 19961.2 2407.2 89
90 17343.4 2085.2 90
91 18924.2 2735.6 91
92 18574.1 2798.7 92
93 21350.6 3053.2 93
94 18594.6 2405.0 94
95 19823.1 2471.9 95
96 20844.4 2727.3 96
97 19640.2 2790.7 97
98 17735.4 2385.4 98
99 19813.6 3206.6 99
100 22160.0 2705.6 100
101 20664.3 3518.4 101
102 17877.4 1954.9 102
103 20906.5 2584.3 103
104 21164.1 2535.8 104
105 21374.4 2685.9 105
106 22952.3 2866.0 106
107 21343.5 2236.6 107
108 23899.3 2934.9 108
109 22392.9 2668.6 109
110 18274.1 2371.2 110
111 22786.7 3165.9 111
112 22321.5 2887.2 112
113 17842.2 3112.2 113
114 16373.5 2671.2 114
115 15993.8 2432.6 115
116 16446.1 2812.3 116
117 17729.0 3095.7 117
118 16643.0 2862.9 118
119 16196.7 2607.3 119
120 18252.1 2862.5 120
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X t
1.262e+04 7.514e-01 4.446e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4456.112 -1044.131 -4.972 1127.711 4274.633
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.262e+04 4.125e+02 30.585 < 2e-16 ***
X 7.514e-01 4.616e-01 1.628 0.106
t 4.446e+01 1.094e+01 4.064 8.76e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1712 on 117 degrees of freedom
Multiple R-squared: 0.6108, Adjusted R-squared: 0.6041
F-statistic: 91.81 on 2 and 117 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,] 0.2077326480 0.415465296 0.79226735
[2,] 0.1181866141 0.236373228 0.88181339
[3,] 0.0525843633 0.105168727 0.94741564
[4,] 0.0435058410 0.087011682 0.95649416
[5,] 0.0238629574 0.047725915 0.97613704
[6,] 0.0108915017 0.021783003 0.98910850
[7,] 0.0047841591 0.009568318 0.99521584
[8,] 0.0018725015 0.003745003 0.99812750
[9,] 0.0032584874 0.006516975 0.99674151
[10,] 0.0024138611 0.004827722 0.99758614
[11,] 0.0015484225 0.003096845 0.99845158
[12,] 0.0008690942 0.001738188 0.99913091
[13,] 0.0022203974 0.004440795 0.99777960
[14,] 0.0011031728 0.002206346 0.99889683
[15,] 0.0010726455 0.002145291 0.99892735
[16,] 0.0007122679 0.001424536 0.99928773
[17,] 0.0010653933 0.002130787 0.99893461
[18,] 0.0007660032 0.001532006 0.99923400
[19,] 0.0046976659 0.009395332 0.99530233
[20,] 0.0055707906 0.011141581 0.99442921
[21,] 0.0074197445 0.014839489 0.99258026
[22,] 0.0053651904 0.010730381 0.99463481
[23,] 0.0058410155 0.011682031 0.99415898
[24,] 0.0060397337 0.012079467 0.99396027
[25,] 0.1144602902 0.228920580 0.88553971
[26,] 0.0886404077 0.177280815 0.91135959
[27,] 0.0680161456 0.136032291 0.93198385
[28,] 0.0634543841 0.126908768 0.93654562
[29,] 0.0466777434 0.093355487 0.95332226
[30,] 0.0347473439 0.069494688 0.96525266
[31,] 0.0244553941 0.048910788 0.97554461
[32,] 0.0172838141 0.034567628 0.98271619
[33,] 0.0537713912 0.107542782 0.94622861
[34,] 0.0443990364 0.088798073 0.95560096
[35,] 0.0460143436 0.092028687 0.95398566
[36,] 0.0347212258 0.069442452 0.96527877
[37,] 0.0419618680 0.083923736 0.95803813
[38,] 0.0333019647 0.066603929 0.96669804
[39,] 0.0267056102 0.053411220 0.97329439
[40,] 0.0194760088 0.038952018 0.98052399
[41,] 0.0141905835 0.028381167 0.98580942
[42,] 0.0113250478 0.022650096 0.98867495
[43,] 0.0078309290 0.015661858 0.99216907
[44,] 0.0057447757 0.011489551 0.99425522
[45,] 0.0418335755 0.083667151 0.95816642
[46,] 0.0355532213 0.071106443 0.96444678
[47,] 0.0295922881 0.059184576 0.97040771
[48,] 0.0266996446 0.053399289 0.97330036
[49,] 0.0215568758 0.043113752 0.97844312
[50,] 0.0180997577 0.036199515 0.98190024
[51,] 0.0154116344 0.030823269 0.98458837
[52,] 0.0182794594 0.036558919 0.98172054
[53,] 0.0135513292 0.027102658 0.98644867
[54,] 0.0102777865 0.020555573 0.98972221
[55,] 0.0107297126 0.021459425 0.98927029
[56,] 0.0079062507 0.015812501 0.99209375
[57,] 0.0117510233 0.023502047 0.98824898
[58,] 0.0112839104 0.022567821 0.98871609
[59,] 0.0102924483 0.020584897 0.98970755
[60,] 0.0087422738 0.017484548 0.99125773
[61,] 0.0060919069 0.012183814 0.99390809
[62,] 0.0042724108 0.008544822 0.99572759
[63,] 0.0031532896 0.006306579 0.99684671
[64,] 0.0039498576 0.007899715 0.99605014
[65,] 0.0029682263 0.005936453 0.99703177
[66,] 0.0020217891 0.004043578 0.99797821
[67,] 0.0017215258 0.003443052 0.99827847
[68,] 0.0014149917 0.002829983 0.99858501
[69,] 0.0029393415 0.005878683 0.99706066
[70,] 0.0029196069 0.005839214 0.99708039
[71,] 0.0024960856 0.004992171 0.99750391
[72,] 0.0021982622 0.004396524 0.99780174
[73,] 0.0014661182 0.002932236 0.99853388
[74,] 0.0011703109 0.002340622 0.99882969
[75,] 0.0008789861 0.001757972 0.99912101
[76,] 0.0016038865 0.003207773 0.99839611
[77,] 0.0014391640 0.002878328 0.99856084
[78,] 0.0011008501 0.002201700 0.99889915
[79,] 0.0008832640 0.001766528 0.99911674
[80,] 0.0007301461 0.001460292 0.99926985
[81,] 0.0025927589 0.005185518 0.99740724
[82,] 0.0020059821 0.004011964 0.99799402
[83,] 0.0017304354 0.003460871 0.99826956
[84,] 0.0012671695 0.002534339 0.99873283
[85,] 0.0013314126 0.002662825 0.99866859
[86,] 0.0012912771 0.002582554 0.99870872
[87,] 0.0018130372 0.003626074 0.99818696
[88,] 0.0018809117 0.003761823 0.99811909
[89,] 0.0019436987 0.003887397 0.99805630
[90,] 0.0014923598 0.002984720 0.99850764
[91,] 0.0011767800 0.002353560 0.99882322
[92,] 0.0012238540 0.002447708 0.99877615
[93,] 0.0039212727 0.007842545 0.99607873
[94,] 0.0111765591 0.022353118 0.98882344
[95,] 0.0102667415 0.020533483 0.98973326
[96,] 0.1289000426 0.257800085 0.87109996
[97,] 0.1580272858 0.316054572 0.84197271
[98,] 0.1866261267 0.373252253 0.81337387
[99,] 0.1852679637 0.370535927 0.81473204
[100,] 0.2038876068 0.407775214 0.79611239
[101,] 0.1837977503 0.367595501 0.81620225
[102,] 0.1468513682 0.293702736 0.85314863
[103,] 0.1579138651 0.315827730 0.84208613
[104,] 0.1953805055 0.390761011 0.80461949
[105,] 0.1432878353 0.286575671 0.85671216
[106,] 0.1681243584 0.336248717 0.83187564
[107,] 0.9806502203 0.038699559 0.01934978
[108,] 0.9591096636 0.081780673 0.04089034
[109,] 0.9057899960 0.188420008 0.09421000
> postscript(file="/var/www/html/rcomp/tmp/1el551261946521.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nuoa1261946521.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3ho8p1261946521.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fh4m1261946521.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54z1z1261946521.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 = 120
Frequency = 1
1 2 3 4 5
-1098.3834264 -2635.5443693 701.7282275 340.3539888 -120.0687987
6 7 8 9 10
-121.8528529 -856.7328839 505.0038757 2410.1331132 -165.0197421
11 12 13 14 15
2058.8366711 638.5653182 58.3812516 -1417.9010748 2175.9322127
16 17 18 19 20
2728.4250329 1966.9051290 510.4748298 1118.7375842 933.9968812
21 22 23 24 25
2655.4220749 149.7593938 1281.5481680 1326.0652782 277.3153087
26 27 28 29 30
-1744.3872618 427.2957388 1369.1706497 241.2229984 -2442.8511156
31 32 33 34 35
-1159.6097300 -1312.1163318 209.6007762 -87.4440351 177.3007935
36 37 38 39 40
-460.1994214 -504.9794504 -3615.3091571 -216.3746845 1058.0753356
41 42 43 44 45
-667.2445953 -2436.5875666 -1081.3930624 -1105.5586376 -0.5600209
46 47 48 49 50
-491.2753258 -836.6472672 -708.2648009 -1169.9985660 -4456.1115548
51 52 53 54 55
-295.9470019 383.6047293 -1596.3261251 -1401.9732484 -1639.6746650
56 57 58 59 60
-1378.5401633 1149.2462991 -292.1221765 -1173.7264892 808.0444299
61 62 63 64 65
-871.8246548 -2760.5249342 914.3297701 862.2324223 73.3304880
66 67 68 69 70
-243.6305236 -826.4331107 -658.1269386 1547.9277739 433.2014811
71 72 73 74 75
-132.8802861 1088.4668836 -1183.1021658 -2485.6382283 1379.0569246
76 77 78 79 80
-183.9028499 1137.7015238 448.5416744 -410.2539020 -179.6431726
81 82 83 84 85
2806.3600950 -815.8795103 1338.8965545 1581.2749313 -577.8103038
86 87 88 89 90
-2233.2365301 1054.7886511 1268.5473971 1577.8829363 -842.4156154
91 92 93 94 95
205.1807476 -236.7992606 2303.9941646 -9.3830390 1124.3814620
96 97 98 99 100
1909.2985867 612.9931450 -1031.7100409 384.9395218 3063.3496191
101 102 103 104 105
912.4113195 -744.0683061 1767.6084011 2017.1896512 2070.2339309
106 107 108 109 110
3468.3348616 2288.0304926 4274.6326417 2923.8786055 -1015.9054923
111 112 113 114 115
2855.0573620 2554.8212433 -2138.0177050 -3319.7943056 -3564.6633674
116 117 118 119 120
-3442.1508520 -2416.6741864 -3372.2016290 -3670.8961264 -1851.7287126
> postscript(file="/var/www/html/rcomp/tmp/6tjbv1261946521.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 -1098.3834264 NA
1 -2635.5443693 -1098.3834264
2 701.7282275 -2635.5443693
3 340.3539888 701.7282275
4 -120.0687987 340.3539888
5 -121.8528529 -120.0687987
6 -856.7328839 -121.8528529
7 505.0038757 -856.7328839
8 2410.1331132 505.0038757
9 -165.0197421 2410.1331132
10 2058.8366711 -165.0197421
11 638.5653182 2058.8366711
12 58.3812516 638.5653182
13 -1417.9010748 58.3812516
14 2175.9322127 -1417.9010748
15 2728.4250329 2175.9322127
16 1966.9051290 2728.4250329
17 510.4748298 1966.9051290
18 1118.7375842 510.4748298
19 933.9968812 1118.7375842
20 2655.4220749 933.9968812
21 149.7593938 2655.4220749
22 1281.5481680 149.7593938
23 1326.0652782 1281.5481680
24 277.3153087 1326.0652782
25 -1744.3872618 277.3153087
26 427.2957388 -1744.3872618
27 1369.1706497 427.2957388
28 241.2229984 1369.1706497
29 -2442.8511156 241.2229984
30 -1159.6097300 -2442.8511156
31 -1312.1163318 -1159.6097300
32 209.6007762 -1312.1163318
33 -87.4440351 209.6007762
34 177.3007935 -87.4440351
35 -460.1994214 177.3007935
36 -504.9794504 -460.1994214
37 -3615.3091571 -504.9794504
38 -216.3746845 -3615.3091571
39 1058.0753356 -216.3746845
40 -667.2445953 1058.0753356
41 -2436.5875666 -667.2445953
42 -1081.3930624 -2436.5875666
43 -1105.5586376 -1081.3930624
44 -0.5600209 -1105.5586376
45 -491.2753258 -0.5600209
46 -836.6472672 -491.2753258
47 -708.2648009 -836.6472672
48 -1169.9985660 -708.2648009
49 -4456.1115548 -1169.9985660
50 -295.9470019 -4456.1115548
51 383.6047293 -295.9470019
52 -1596.3261251 383.6047293
53 -1401.9732484 -1596.3261251
54 -1639.6746650 -1401.9732484
55 -1378.5401633 -1639.6746650
56 1149.2462991 -1378.5401633
57 -292.1221765 1149.2462991
58 -1173.7264892 -292.1221765
59 808.0444299 -1173.7264892
60 -871.8246548 808.0444299
61 -2760.5249342 -871.8246548
62 914.3297701 -2760.5249342
63 862.2324223 914.3297701
64 73.3304880 862.2324223
65 -243.6305236 73.3304880
66 -826.4331107 -243.6305236
67 -658.1269386 -826.4331107
68 1547.9277739 -658.1269386
69 433.2014811 1547.9277739
70 -132.8802861 433.2014811
71 1088.4668836 -132.8802861
72 -1183.1021658 1088.4668836
73 -2485.6382283 -1183.1021658
74 1379.0569246 -2485.6382283
75 -183.9028499 1379.0569246
76 1137.7015238 -183.9028499
77 448.5416744 1137.7015238
78 -410.2539020 448.5416744
79 -179.6431726 -410.2539020
80 2806.3600950 -179.6431726
81 -815.8795103 2806.3600950
82 1338.8965545 -815.8795103
83 1581.2749313 1338.8965545
84 -577.8103038 1581.2749313
85 -2233.2365301 -577.8103038
86 1054.7886511 -2233.2365301
87 1268.5473971 1054.7886511
88 1577.8829363 1268.5473971
89 -842.4156154 1577.8829363
90 205.1807476 -842.4156154
91 -236.7992606 205.1807476
92 2303.9941646 -236.7992606
93 -9.3830390 2303.9941646
94 1124.3814620 -9.3830390
95 1909.2985867 1124.3814620
96 612.9931450 1909.2985867
97 -1031.7100409 612.9931450
98 384.9395218 -1031.7100409
99 3063.3496191 384.9395218
100 912.4113195 3063.3496191
101 -744.0683061 912.4113195
102 1767.6084011 -744.0683061
103 2017.1896512 1767.6084011
104 2070.2339309 2017.1896512
105 3468.3348616 2070.2339309
106 2288.0304926 3468.3348616
107 4274.6326417 2288.0304926
108 2923.8786055 4274.6326417
109 -1015.9054923 2923.8786055
110 2855.0573620 -1015.9054923
111 2554.8212433 2855.0573620
112 -2138.0177050 2554.8212433
113 -3319.7943056 -2138.0177050
114 -3564.6633674 -3319.7943056
115 -3442.1508520 -3564.6633674
116 -2416.6741864 -3442.1508520
117 -3372.2016290 -2416.6741864
118 -3670.8961264 -3372.2016290
119 -1851.7287126 -3670.8961264
120 NA -1851.7287126
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2635.5443693 -1098.3834264
[2,] 701.7282275 -2635.5443693
[3,] 340.3539888 701.7282275
[4,] -120.0687987 340.3539888
[5,] -121.8528529 -120.0687987
[6,] -856.7328839 -121.8528529
[7,] 505.0038757 -856.7328839
[8,] 2410.1331132 505.0038757
[9,] -165.0197421 2410.1331132
[10,] 2058.8366711 -165.0197421
[11,] 638.5653182 2058.8366711
[12,] 58.3812516 638.5653182
[13,] -1417.9010748 58.3812516
[14,] 2175.9322127 -1417.9010748
[15,] 2728.4250329 2175.9322127
[16,] 1966.9051290 2728.4250329
[17,] 510.4748298 1966.9051290
[18,] 1118.7375842 510.4748298
[19,] 933.9968812 1118.7375842
[20,] 2655.4220749 933.9968812
[21,] 149.7593938 2655.4220749
[22,] 1281.5481680 149.7593938
[23,] 1326.0652782 1281.5481680
[24,] 277.3153087 1326.0652782
[25,] -1744.3872618 277.3153087
[26,] 427.2957388 -1744.3872618
[27,] 1369.1706497 427.2957388
[28,] 241.2229984 1369.1706497
[29,] -2442.8511156 241.2229984
[30,] -1159.6097300 -2442.8511156
[31,] -1312.1163318 -1159.6097300
[32,] 209.6007762 -1312.1163318
[33,] -87.4440351 209.6007762
[34,] 177.3007935 -87.4440351
[35,] -460.1994214 177.3007935
[36,] -504.9794504 -460.1994214
[37,] -3615.3091571 -504.9794504
[38,] -216.3746845 -3615.3091571
[39,] 1058.0753356 -216.3746845
[40,] -667.2445953 1058.0753356
[41,] -2436.5875666 -667.2445953
[42,] -1081.3930624 -2436.5875666
[43,] -1105.5586376 -1081.3930624
[44,] -0.5600209 -1105.5586376
[45,] -491.2753258 -0.5600209
[46,] -836.6472672 -491.2753258
[47,] -708.2648009 -836.6472672
[48,] -1169.9985660 -708.2648009
[49,] -4456.1115548 -1169.9985660
[50,] -295.9470019 -4456.1115548
[51,] 383.6047293 -295.9470019
[52,] -1596.3261251 383.6047293
[53,] -1401.9732484 -1596.3261251
[54,] -1639.6746650 -1401.9732484
[55,] -1378.5401633 -1639.6746650
[56,] 1149.2462991 -1378.5401633
[57,] -292.1221765 1149.2462991
[58,] -1173.7264892 -292.1221765
[59,] 808.0444299 -1173.7264892
[60,] -871.8246548 808.0444299
[61,] -2760.5249342 -871.8246548
[62,] 914.3297701 -2760.5249342
[63,] 862.2324223 914.3297701
[64,] 73.3304880 862.2324223
[65,] -243.6305236 73.3304880
[66,] -826.4331107 -243.6305236
[67,] -658.1269386 -826.4331107
[68,] 1547.9277739 -658.1269386
[69,] 433.2014811 1547.9277739
[70,] -132.8802861 433.2014811
[71,] 1088.4668836 -132.8802861
[72,] -1183.1021658 1088.4668836
[73,] -2485.6382283 -1183.1021658
[74,] 1379.0569246 -2485.6382283
[75,] -183.9028499 1379.0569246
[76,] 1137.7015238 -183.9028499
[77,] 448.5416744 1137.7015238
[78,] -410.2539020 448.5416744
[79,] -179.6431726 -410.2539020
[80,] 2806.3600950 -179.6431726
[81,] -815.8795103 2806.3600950
[82,] 1338.8965545 -815.8795103
[83,] 1581.2749313 1338.8965545
[84,] -577.8103038 1581.2749313
[85,] -2233.2365301 -577.8103038
[86,] 1054.7886511 -2233.2365301
[87,] 1268.5473971 1054.7886511
[88,] 1577.8829363 1268.5473971
[89,] -842.4156154 1577.8829363
[90,] 205.1807476 -842.4156154
[91,] -236.7992606 205.1807476
[92,] 2303.9941646 -236.7992606
[93,] -9.3830390 2303.9941646
[94,] 1124.3814620 -9.3830390
[95,] 1909.2985867 1124.3814620
[96,] 612.9931450 1909.2985867
[97,] -1031.7100409 612.9931450
[98,] 384.9395218 -1031.7100409
[99,] 3063.3496191 384.9395218
[100,] 912.4113195 3063.3496191
[101,] -744.0683061 912.4113195
[102,] 1767.6084011 -744.0683061
[103,] 2017.1896512 1767.6084011
[104,] 2070.2339309 2017.1896512
[105,] 3468.3348616 2070.2339309
[106,] 2288.0304926 3468.3348616
[107,] 4274.6326417 2288.0304926
[108,] 2923.8786055 4274.6326417
[109,] -1015.9054923 2923.8786055
[110,] 2855.0573620 -1015.9054923
[111,] 2554.8212433 2855.0573620
[112,] -2138.0177050 2554.8212433
[113,] -3319.7943056 -2138.0177050
[114,] -3564.6633674 -3319.7943056
[115,] -3442.1508520 -3564.6633674
[116,] -2416.6741864 -3442.1508520
[117,] -3372.2016290 -2416.6741864
[118,] -3670.8961264 -3372.2016290
[119,] -1851.7287126 -3670.8961264
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2635.5443693 -1098.3834264
2 701.7282275 -2635.5443693
3 340.3539888 701.7282275
4 -120.0687987 340.3539888
5 -121.8528529 -120.0687987
6 -856.7328839 -121.8528529
7 505.0038757 -856.7328839
8 2410.1331132 505.0038757
9 -165.0197421 2410.1331132
10 2058.8366711 -165.0197421
11 638.5653182 2058.8366711
12 58.3812516 638.5653182
13 -1417.9010748 58.3812516
14 2175.9322127 -1417.9010748
15 2728.4250329 2175.9322127
16 1966.9051290 2728.4250329
17 510.4748298 1966.9051290
18 1118.7375842 510.4748298
19 933.9968812 1118.7375842
20 2655.4220749 933.9968812
21 149.7593938 2655.4220749
22 1281.5481680 149.7593938
23 1326.0652782 1281.5481680
24 277.3153087 1326.0652782
25 -1744.3872618 277.3153087
26 427.2957388 -1744.3872618
27 1369.1706497 427.2957388
28 241.2229984 1369.1706497
29 -2442.8511156 241.2229984
30 -1159.6097300 -2442.8511156
31 -1312.1163318 -1159.6097300
32 209.6007762 -1312.1163318
33 -87.4440351 209.6007762
34 177.3007935 -87.4440351
35 -460.1994214 177.3007935
36 -504.9794504 -460.1994214
37 -3615.3091571 -504.9794504
38 -216.3746845 -3615.3091571
39 1058.0753356 -216.3746845
40 -667.2445953 1058.0753356
41 -2436.5875666 -667.2445953
42 -1081.3930624 -2436.5875666
43 -1105.5586376 -1081.3930624
44 -0.5600209 -1105.5586376
45 -491.2753258 -0.5600209
46 -836.6472672 -491.2753258
47 -708.2648009 -836.6472672
48 -1169.9985660 -708.2648009
49 -4456.1115548 -1169.9985660
50 -295.9470019 -4456.1115548
51 383.6047293 -295.9470019
52 -1596.3261251 383.6047293
53 -1401.9732484 -1596.3261251
54 -1639.6746650 -1401.9732484
55 -1378.5401633 -1639.6746650
56 1149.2462991 -1378.5401633
57 -292.1221765 1149.2462991
58 -1173.7264892 -292.1221765
59 808.0444299 -1173.7264892
60 -871.8246548 808.0444299
61 -2760.5249342 -871.8246548
62 914.3297701 -2760.5249342
63 862.2324223 914.3297701
64 73.3304880 862.2324223
65 -243.6305236 73.3304880
66 -826.4331107 -243.6305236
67 -658.1269386 -826.4331107
68 1547.9277739 -658.1269386
69 433.2014811 1547.9277739
70 -132.8802861 433.2014811
71 1088.4668836 -132.8802861
72 -1183.1021658 1088.4668836
73 -2485.6382283 -1183.1021658
74 1379.0569246 -2485.6382283
75 -183.9028499 1379.0569246
76 1137.7015238 -183.9028499
77 448.5416744 1137.7015238
78 -410.2539020 448.5416744
79 -179.6431726 -410.2539020
80 2806.3600950 -179.6431726
81 -815.8795103 2806.3600950
82 1338.8965545 -815.8795103
83 1581.2749313 1338.8965545
84 -577.8103038 1581.2749313
85 -2233.2365301 -577.8103038
86 1054.7886511 -2233.2365301
87 1268.5473971 1054.7886511
88 1577.8829363 1268.5473971
89 -842.4156154 1577.8829363
90 205.1807476 -842.4156154
91 -236.7992606 205.1807476
92 2303.9941646 -236.7992606
93 -9.3830390 2303.9941646
94 1124.3814620 -9.3830390
95 1909.2985867 1124.3814620
96 612.9931450 1909.2985867
97 -1031.7100409 612.9931450
98 384.9395218 -1031.7100409
99 3063.3496191 384.9395218
100 912.4113195 3063.3496191
101 -744.0683061 912.4113195
102 1767.6084011 -744.0683061
103 2017.1896512 1767.6084011
104 2070.2339309 2017.1896512
105 3468.3348616 2070.2339309
106 2288.0304926 3468.3348616
107 4274.6326417 2288.0304926
108 2923.8786055 4274.6326417
109 -1015.9054923 2923.8786055
110 2855.0573620 -1015.9054923
111 2554.8212433 2855.0573620
112 -2138.0177050 2554.8212433
113 -3319.7943056 -2138.0177050
114 -3564.6633674 -3319.7943056
115 -3442.1508520 -3564.6633674
116 -2416.6741864 -3442.1508520
117 -3372.2016290 -2416.6741864
118 -3670.8961264 -3372.2016290
119 -1851.7287126 -3670.8961264
> 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/html/rcomp/tmp/7jjuj1261946521.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87apc1261946521.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/969c41261946521.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dhh81261946521.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11w6du1261946521.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/html/rcomp/tmp/12ytub1261946521.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/html/rcomp/tmp/13uqae1261946521.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/html/rcomp/tmp/14s1cz1261946521.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/html/rcomp/tmp/15k6du1261946521.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/html/rcomp/tmp/167dwt1261946521.tab")
+ }
> try(system("convert tmp/1el551261946521.ps tmp/1el551261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nuoa1261946521.ps tmp/2nuoa1261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ho8p1261946521.ps tmp/3ho8p1261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fh4m1261946521.ps tmp/4fh4m1261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/54z1z1261946521.ps tmp/54z1z1261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tjbv1261946521.ps tmp/6tjbv1261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jjuj1261946521.ps tmp/7jjuj1261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/87apc1261946521.ps tmp/87apc1261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/969c41261946521.ps tmp/969c41261946521.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dhh81261946521.ps tmp/10dhh81261946521.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.233 1.759 6.440