R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1.3067
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+ ,dimnames=list(c('WSK'
+ ,'WER'
+ ,'INP'
+ ,'BE2'
+ ,'Uit'
+ ,'INV'
+ ,'CE-AES'
+ ,'CE-CV'
+ ,'CE-WER')
+ ,1:117))
> y <- array(NA,dim=c(9,117),dimnames=list(c('WSK','WER','INP','BE2','Uit','INV','CE-AES','CE-CV','CE-WER'),1:117))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'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
WER WSK INP BE2 Uit INV CE-AES CE-CV CE-WER
1 8.7 1.3067 113.0 2579.39 19.6 18.9 3 -2 16
2 8.9 1.2894 95.4 2504.58 16.0 16.6 3 -4 17
3 8.9 1.2770 86.2 2462.32 17.7 17.2 7 -4 23
4 8.1 1.2208 111.7 2467.38 19.8 19.2 4 -7 24
5 8.0 1.2565 97.5 2446.66 17.0 17.1 -4 -9 27
6 8.3 1.3406 99.7 2656.32 17.4 17.7 -6 -13 31
7 8.5 1.3569 111.5 2626.15 18.9 18.7 8 -8 40
8 8.7 1.3686 91.8 2482.60 15.7 15.9 2 -13 47
9 8.6 1.4272 86.3 2539.91 15.2 16.0 -1 -15 43
10 8.3 1.4614 88.7 2502.66 15.8 16.8 -2 -15 60
11 7.9 1.4914 95.1 2466.92 16.0 16.0 0 -15 64
12 7.9 1.4816 105.1 2513.17 16.1 16.8 10 -10 65
13 8.1 1.4562 104.5 2443.27 16.2 16.3 3 -12 65
14 8.3 1.4268 89.1 2293.41 12.5 13.6 6 -11 55
15 8.1 1.4088 82.6 2070.83 14.8 14.3 7 -11 57
16 7.4 1.4016 102.7 2029.60 15.4 15.5 -4 -17 57
17 7.3 1.3650 91.8 2052.02 13.6 13.9 -5 -18 57
18 7.7 1.3190 94.1 1864.44 14.2 14.3 -7 -19 65
19 8.0 1.3050 103.1 1670.07 15.0 15.8 -10 -22 69
20 8.0 1.2785 93.2 1810.99 14.1 14.5 -21 -24 70
21 7.7 1.3239 91.0 1905.41 13.7 15.1 -22 -24 71
22 6.9 1.3449 94.3 1862.83 14.4 15.8 -16 -20 71
23 6.6 1.2732 99.4 2014.45 15.6 17.2 -25 -25 73
24 6.9 1.3322 115.7 2197.82 19.7 20.4 -22 -22 68
25 7.5 1.4369 116.8 2962.34 20.4 21.3 -22 -17 65
26 7.9 1.4975 99.8 3047.03 16.1 18.2 -19 -9 57
27 7.7 1.5770 96.0 3032.60 20.1 20.2 -21 -11 41
28 6.5 1.5553 115.9 3504.37 20.6 21.1 -31 -13 21
29 6.1 1.5557 109.1 3801.06 19.3 19.7 -28 -11 21
30 6.4 1.5750 117.3 3857.62 20.5 21.5 -23 -9 17
31 6.8 1.5527 109.8 3674.40 19.2 20.2 -17 -7 9
32 7.1 1.4748 112.8 3720.98 19.0 19.0 -12 -3 11
33 7.3 1.4718 110.7 3844.49 18.7 20.2 -14 -3 6
34 7.2 1.4570 100.0 4116.68 16.5 18.0 -18 -6 -2
35 7.0 1.4684 113.3 4105.18 19.0 19.5 -16 -4 0
36 7.0 1.4227 122.4 4435.23 20.5 20.3 -22 -8 5
37 7.0 1.3896 112.5 4296.49 18.4 18.0 -9 -1 3
38 7.3 1.3622 104.2 4202.52 16.2 16.4 -10 -2 7
39 7.5 1.3716 92.5 4562.84 18.1 17.8 -10 -2 4
40 7.2 1.3419 117.2 4621.40 19.3 18.5 0 -1 8
41 7.7 1.3511 109.3 4696.96 18.3 18.2 3 1 9
42 8.0 1.3516 106.1 4591.27 17.2 16.7 2 2 14
43 7.9 1.3242 118.8 4356.98 19.6 19.1 4 2 12
44 8.0 1.3074 105.3 4502.64 17.2 16.8 -3 -1 12
45 8.0 1.2999 106.0 4443.91 17.4 17.5 0 1 7
46 7.9 1.3213 102.0 4290.89 16.0 16.2 -1 -1 15
47 7.9 1.2881 112.9 4199.75 18.5 17.9 -7 -8 14
48 8.0 1.2611 116.5 4138.52 18.4 17.7 2 1 19
49 8.1 1.2727 114.8 3970.10 18.2 17.2 3 2 39
50 8.1 1.2811 100.5 3862.27 14.9 15.7 -3 -2 12
51 8.2 1.2684 85.4 3701.61 16.3 15.2 -5 -2 11
52 8.0 1.2650 114.6 3570.12 18.3 17.7 0 -2 17
53 8.3 1.2770 109.9 3801.06 18.0 17.4 -3 -2 16
54 8.5 1.2271 100.7 3895.51 15.9 15.9 -7 -6 25
55 8.6 1.2020 115.5 3917.96 19.6 19.7 -7 -4 24
56 8.7 1.1938 100.7 3813.06 16.6 16.7 -7 -5 28
57 8.7 1.2103 99.0 3667.03 16.2 16.9 -4 -2 25
58 8.5 1.1856 102.3 3494.17 16.6 18.0 -3 -1 31
59 8.4 1.1786 108.8 3363.99 17.5 17.6 -6 -5 24
60 8.5 1.2015 105.9 3295.32 16.2 15.2 -10 -9 24
61 8.7 1.2256 113.2 3277.01 17.5 16.5 -10 -8 33
62 8.7 1.2292 95.7 3257.16 13.8 14.7 -23 -14 37
63 8.6 1.2037 80.9 3161.69 14.9 14.1 -13 -10 35
64 7.9 1.2165 113.9 3097.31 17.2 16.9 -18 -11 37
65 8.1 1.2694 98.1 3061.26 15.6 15.2 -16 -11 38
66 8.2 1.2938 102.8 3119.31 16.2 15.4 -15 -11 42
67 8.5 1.3201 104.7 3106.22 17.4 16.8 -5 -5 43
68 8.6 1.3014 95.9 3080.58 15.1 14.8 2 -2 44
69 8.5 1.3119 94.6 2981.85 14.5 14.1 -2 -3 32
70 8.3 1.3408 101.6 2921.44 15.1 15.0 -4 -6 32
71 8.2 1.2991 103.9 2849.27 15.5 14.8 -4 -6 37
72 8.7 1.2490 110.3 2756.76 15.9 15.0 -6 -7 38
73 9.3 1.2218 114.1 2645.64 15.9 15.1 -7 -6 39
74 9.3 1.2176 96.8 2497.84 12.3 12.8 0 -2 38
75 8.8 1.2266 87.4 2448.05 14.4 13.0 1 -2 39
76 7.4 1.2138 111.4 2454.62 16.0 15.7 -3 -4 30
77 7.2 1.2007 97.4 2407.60 13.9 12.8 6 0 28
78 7.5 1.1985 102.9 2472.81 14.7 13.9 -2 -6 31
79 8.3 1.2262 112.7 2408.64 16.2 15.4 2 -4 28
80 8.8 1.2646 97.0 2440.25 13.8 13.2 5 -3 38
81 8.9 1.2613 95.1 2350.44 13.2 12.7 7 -1 37
82 8.6 1.2286 96.9 2196.72 13.5 13.5 4 -3 34
83 8.4 1.1702 98.6 2174.56 13.5 12.8 0 -6 32
84 8.4 1.1692 111.7 2120.88 15.0 13.9 0 -6 33
85 8.4 1.1222 109.8 2093.48 14.5 13.3 -13 -15 39
86 8.4 1.1139 89.9 2061.41 10.5 10.7 -2 -5 42
87 8.3 1.1372 87.4 1969.60 13.7 12.3 -10 -11 57
88 7.6 1.1663 104.5 1959.67 13.9 12.9 -12 -13 36
89 7.6 1.1582 98.1 1910.43 13.4 12.5 -9 -10 42
90 7.9 1.0848 102.7 1833.42 14.0 13.0 -4 -9 49
91 8.0 1.0807 105.4 1635.25 14.3 13.9 -11 -11 44
92 8.2 1.0773 97.0 1765.90 13.3 13.1 -28 -18 44
93 8.3 1.0622 97.4 1946.81 13.2 13.1 -19 -13 43
94 8.2 1.0183 92.0 1995.37 12.6 13.0 -16 -9 50
95 8.1 1.0014 101.7 2042.00 13.7 12.8 -8 -8 45
96 8.0 0.9811 112.6 1940.49 15.6 14.2 -1 -4 40
97 7.8 0.9808 106.9 2065.81 14.4 13.0 -2 -3 38
98 7.6 0.9778 92.1 2214.95 11.0 11.2 -4 -3 29
99 7.5 0.9922 86.0 2304.98 13.7 12.1 -5 -3 27
100 6.8 0.9554 104.7 2555.28 13.8 12.9 0 -1 27
101 6.9 0.9170 102.0 2799.43 14.3 13.2 5 0 27
102 7.1 0.8858 103.1 2811.70 14.0 13.2 5 1 32
103 7.3 0.8758 106.0 2735.70 14.6 13.5 2 0 24
104 7.4 0.8700 96.1 2745.88 13.1 12.4 6 2 22
105 7.6 0.8833 96.2 2720.25 13.2 12.4 3 1 22
106 7.6 0.8924 90.7 2638.53 11.6 11.6 1 -1 23
107 7.5 0.8883 102.3 2659.81 13.3 12.6 -9 -8 23
108 7.5 0.9059 109.4 2641.65 14.4 13.1 -26 -18 28
109 6.8 0.9111 101.0 2604.42 13.3 12.3 -25 -14 36
110 6.4 0.9005 94.7 2892.63 11.3 11.4 -13 -4 60
111 6.2 0.8607 81.0 2915.03 13.2 11.8 -6 0 43
112 6.0 0.8532 106.2 2845.26 14.1 13.4 -1 4 23
113 6.3 0.8742 101.9 2794.83 14.0 13.6 1 4 15
114 6.3 0.8920 96.4 2848.96 12.9 12.9 1 3 7
115 6.1 0.9095 110.4 2833.18 15.2 14.5 -2 3 6
116 6.1 0.9217 100.5 2995.55 13.6 13.3 2 7 8
117 6.3 0.9383 98.8 2987.10 13.7 13.5 3 8 5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WSK INP BE2 Uit INV
6.8229298 1.5645010 -0.0059542 0.0001044 0.1986281 -0.2484010
`CE-AES` `CE-CV` `CE-WER`
0.0354669 -0.0377754 0.0022919
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.36795 -0.50314 0.09235 0.43952 1.49372
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.8229298 0.9484969 7.193 8.73e-11 ***
WSK 1.5645010 0.6435334 2.431 0.01670 *
INP -0.0059542 0.0101019 -0.589 0.55682
BE2 0.0001044 0.0001556 0.671 0.50370
Uit 0.1986281 0.1087282 1.827 0.07049 .
INV -0.2484010 0.1048766 -2.369 0.01964 *
`CE-AES` 0.0354669 0.0110082 3.222 0.00168 **
`CE-CV` -0.0377754 0.0233113 -1.620 0.10805
`CE-WER` 0.0022919 0.0066699 0.344 0.73180
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6768 on 108 degrees of freedom
Multiple R-squared: 0.2679, Adjusted R-squared: 0.2137
F-statistic: 4.941 on 8 and 108 DF, p-value: 3.178e-05
> 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,] 0.108352427 0.216704854 0.891647573
[2,] 0.062215899 0.124431797 0.937784101
[3,] 0.023346382 0.046692763 0.976653618
[4,] 0.008277837 0.016555675 0.991722163
[5,] 0.003625098 0.007250197 0.996374902
[6,] 0.001874789 0.003749577 0.998125211
[7,] 0.008274510 0.016549020 0.991725490
[8,] 0.017665301 0.035330603 0.982334699
[9,] 0.015269245 0.030538490 0.984730755
[10,] 0.011386887 0.022773774 0.988613113
[11,] 0.058103857 0.116207715 0.941896143
[12,] 0.167793039 0.335586078 0.832206961
[13,] 0.219428796 0.438857593 0.780571204
[14,] 0.247285823 0.494571646 0.752714177
[15,] 0.201214383 0.402428767 0.798785617
[16,] 0.163330082 0.326660163 0.836669918
[17,] 0.258425276 0.516850551 0.741574724
[18,] 0.455588197 0.911176394 0.544411803
[19,] 0.545427013 0.909145974 0.454572987
[20,] 0.605442315 0.789115371 0.394557685
[21,] 0.553746359 0.892507282 0.446253641
[22,] 0.511800946 0.976398108 0.488199054
[23,] 0.476498619 0.952997238 0.523501381
[24,] 0.434642635 0.869285270 0.565357365
[25,] 0.421433296 0.842866591 0.578566704
[26,] 0.414881648 0.829763296 0.585118352
[27,] 0.361026057 0.722052115 0.638973943
[28,] 0.337080627 0.674161254 0.662919373
[29,] 0.345352067 0.690704134 0.654647933
[30,] 0.323025538 0.646051076 0.676974462
[31,] 0.290897255 0.581794510 0.709102745
[32,] 0.246259733 0.492519467 0.753740267
[33,] 0.225034300 0.450068601 0.774965700
[34,] 0.182975236 0.365950471 0.817024764
[35,] 0.155408637 0.310817275 0.844591363
[36,] 0.199320309 0.398640618 0.800679691
[37,] 0.161730785 0.323461570 0.838269215
[38,] 0.128770549 0.257541099 0.871229451
[39,] 0.108047429 0.216094858 0.891952571
[40,] 0.087044241 0.174088483 0.912955759
[41,] 0.070140998 0.140281996 0.929859002
[42,] 0.067429778 0.134859556 0.932570222
[43,] 0.075398889 0.150797778 0.924601111
[44,] 0.075872659 0.151745319 0.924127341
[45,] 0.073381684 0.146763367 0.926618316
[46,] 0.060722019 0.121444037 0.939277981
[47,] 0.050376412 0.100752824 0.949623588
[48,] 0.038661736 0.077323472 0.961338264
[49,] 0.059736259 0.119472518 0.940263741
[50,] 0.117062065 0.234124131 0.882937935
[51,] 0.187492565 0.374985131 0.812507435
[52,] 0.163103203 0.326206407 0.836896797
[53,] 0.131790930 0.263581860 0.868209070
[54,] 0.106722510 0.213445020 0.893277490
[55,] 0.090855579 0.181711159 0.909144421
[56,] 0.073039341 0.146078681 0.926960659
[57,] 0.055251427 0.110502853 0.944748573
[58,] 0.042303058 0.084606116 0.957696942
[59,] 0.036264740 0.072529481 0.963735260
[60,] 0.029223875 0.058447751 0.970776125
[61,] 0.028003602 0.056007205 0.971996398
[62,] 0.198487117 0.396974234 0.801512883
[63,] 0.427931056 0.855862112 0.572068944
[64,] 0.620600819 0.758798362 0.379399181
[65,] 0.744530775 0.510938450 0.255469225
[66,] 0.925959164 0.148081673 0.074040836
[67,] 0.957565951 0.084868098 0.042434049
[68,] 0.941108777 0.117782447 0.058891223
[69,] 0.933860417 0.132279166 0.066139583
[70,] 0.962776347 0.074447306 0.037223653
[71,] 0.956322971 0.087354059 0.043677029
[72,] 0.947718369 0.104563261 0.052281631
[73,] 0.969183266 0.061633468 0.030816734
[74,] 0.974553508 0.050892985 0.025446492
[75,] 0.980448658 0.039102684 0.019551342
[76,] 0.981904708 0.036190584 0.018095292
[77,] 0.973555561 0.052888878 0.026444439
[78,] 0.968300097 0.063399805 0.031699903
[79,] 0.979501376 0.040997247 0.020498624
[80,] 0.993846035 0.012307929 0.006153965
[81,] 0.991134523 0.017730954 0.008865477
[82,] 0.984080075 0.031839850 0.015919925
[83,] 0.981781746 0.036436507 0.018218254
[84,] 0.973341544 0.053316913 0.026658456
[85,] 0.967455436 0.065089128 0.032544564
[86,] 0.963781964 0.072436072 0.036218036
[87,] 0.948600667 0.102798665 0.051399333
[88,] 0.957884135 0.084231729 0.042115865
[89,] 0.970513593 0.058972815 0.029486407
[90,] 0.997409723 0.005180553 0.002590277
[91,] 0.998368069 0.003263863 0.001631931
[92,] 0.995038310 0.009923381 0.004961690
[93,] 0.989346721 0.021306558 0.010653279
[94,] 0.960008235 0.079983529 0.039991765
> postscript(file="/var/www/html/rcomp/tmp/1szve1292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/239uh1292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/339uh1292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/439uh1292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e0uk1292950429.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 = 117
Frequency = 1
1 2 3 4 5 6
0.81940039 1.01537709 0.64016271 0.14985601 0.14744223 0.28733934
7 8 9 10 11 12
0.15741746 0.18476598 0.11852893 -0.14075131 -0.86439760 -0.78357689
13 14 15 16 17 18
-0.51146161 -0.32298359 -0.83330965 -1.05567582 -1.20787465 -0.70762956
19 20 21 22 23 24
-0.11424778 0.02169545 -0.11061945 -0.94624023 -0.88437436 -0.59987195
25 26 27 28 29 30
0.04335850 0.53668622 -0.07446828 -0.72206402 -1.31453708 -0.88565889
31 32 33 34 35 36
-0.65992471 -0.51422127 0.10514129 -0.12644429 -0.38782488 -0.34557152
37 38 39 40 41 42
-0.68451424 -0.35319496 -0.29792639 -0.80103915 -0.27939029 -0.08052320
43 44 45 46 47 48
0.01551859 0.08654665 0.22334246 -0.02124555 -0.01851505 0.13105120
49 50 51 52 53 54
0.09235340 0.41177412 0.12944878 0.15501258 0.47791258 0.70600196
55 56 57 58 59 60
1.21789169 1.05728551 1.17952241 1.23820174 0.89465850 0.60155150
61 62 63 64 65 66
0.89107606 1.29636988 0.59159177 0.44842568 0.29764793 0.26726891
67 68 69 70 71 72
0.51790055 0.42024480 0.38327271 0.24803406 0.09390934 0.72114777
73 74 75 76 77 78
1.49371561 1.46156914 0.49151819 -0.30642040 -1.03113677 -0.53720706
79 80 81 82 83 84
0.29972232 0.48154708 0.58665545 0.54143325 0.30447954 0.36265278
85 86 87 88 89 90
0.48534422 0.51260118 0.15538023 -0.33446595 -0.36163350 -0.06195179
91 92 93 94 95 96
0.42937066 0.90945666 0.90841102 0.96286636 0.43952378 0.33143838
97 98 99 100 101 102
0.10298868 0.12377061 -0.31715900 -0.79729118 -0.84312442 -0.50313875
103 104 105 106 107 108
-0.21999077 -0.20795709 0.02326787 0.09698718 0.07122335 0.20729129
109 110 111 112 113 114
-0.42990401 -0.81007041 -1.36795017 -1.16060831 -0.89685797 -0.93793300
115 116 117
-1.03101722 -1.10162218 -0.89783143
> postscript(file="/var/www/html/rcomp/tmp/6e0uk1292950429.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 = 117
Frequency = 1
lag(myerror, k = 1) myerror
0 0.81940039 NA
1 1.01537709 0.81940039
2 0.64016271 1.01537709
3 0.14985601 0.64016271
4 0.14744223 0.14985601
5 0.28733934 0.14744223
6 0.15741746 0.28733934
7 0.18476598 0.15741746
8 0.11852893 0.18476598
9 -0.14075131 0.11852893
10 -0.86439760 -0.14075131
11 -0.78357689 -0.86439760
12 -0.51146161 -0.78357689
13 -0.32298359 -0.51146161
14 -0.83330965 -0.32298359
15 -1.05567582 -0.83330965
16 -1.20787465 -1.05567582
17 -0.70762956 -1.20787465
18 -0.11424778 -0.70762956
19 0.02169545 -0.11424778
20 -0.11061945 0.02169545
21 -0.94624023 -0.11061945
22 -0.88437436 -0.94624023
23 -0.59987195 -0.88437436
24 0.04335850 -0.59987195
25 0.53668622 0.04335850
26 -0.07446828 0.53668622
27 -0.72206402 -0.07446828
28 -1.31453708 -0.72206402
29 -0.88565889 -1.31453708
30 -0.65992471 -0.88565889
31 -0.51422127 -0.65992471
32 0.10514129 -0.51422127
33 -0.12644429 0.10514129
34 -0.38782488 -0.12644429
35 -0.34557152 -0.38782488
36 -0.68451424 -0.34557152
37 -0.35319496 -0.68451424
38 -0.29792639 -0.35319496
39 -0.80103915 -0.29792639
40 -0.27939029 -0.80103915
41 -0.08052320 -0.27939029
42 0.01551859 -0.08052320
43 0.08654665 0.01551859
44 0.22334246 0.08654665
45 -0.02124555 0.22334246
46 -0.01851505 -0.02124555
47 0.13105120 -0.01851505
48 0.09235340 0.13105120
49 0.41177412 0.09235340
50 0.12944878 0.41177412
51 0.15501258 0.12944878
52 0.47791258 0.15501258
53 0.70600196 0.47791258
54 1.21789169 0.70600196
55 1.05728551 1.21789169
56 1.17952241 1.05728551
57 1.23820174 1.17952241
58 0.89465850 1.23820174
59 0.60155150 0.89465850
60 0.89107606 0.60155150
61 1.29636988 0.89107606
62 0.59159177 1.29636988
63 0.44842568 0.59159177
64 0.29764793 0.44842568
65 0.26726891 0.29764793
66 0.51790055 0.26726891
67 0.42024480 0.51790055
68 0.38327271 0.42024480
69 0.24803406 0.38327271
70 0.09390934 0.24803406
71 0.72114777 0.09390934
72 1.49371561 0.72114777
73 1.46156914 1.49371561
74 0.49151819 1.46156914
75 -0.30642040 0.49151819
76 -1.03113677 -0.30642040
77 -0.53720706 -1.03113677
78 0.29972232 -0.53720706
79 0.48154708 0.29972232
80 0.58665545 0.48154708
81 0.54143325 0.58665545
82 0.30447954 0.54143325
83 0.36265278 0.30447954
84 0.48534422 0.36265278
85 0.51260118 0.48534422
86 0.15538023 0.51260118
87 -0.33446595 0.15538023
88 -0.36163350 -0.33446595
89 -0.06195179 -0.36163350
90 0.42937066 -0.06195179
91 0.90945666 0.42937066
92 0.90841102 0.90945666
93 0.96286636 0.90841102
94 0.43952378 0.96286636
95 0.33143838 0.43952378
96 0.10298868 0.33143838
97 0.12377061 0.10298868
98 -0.31715900 0.12377061
99 -0.79729118 -0.31715900
100 -0.84312442 -0.79729118
101 -0.50313875 -0.84312442
102 -0.21999077 -0.50313875
103 -0.20795709 -0.21999077
104 0.02326787 -0.20795709
105 0.09698718 0.02326787
106 0.07122335 0.09698718
107 0.20729129 0.07122335
108 -0.42990401 0.20729129
109 -0.81007041 -0.42990401
110 -1.36795017 -0.81007041
111 -1.16060831 -1.36795017
112 -0.89685797 -1.16060831
113 -0.93793300 -0.89685797
114 -1.03101722 -0.93793300
115 -1.10162218 -1.03101722
116 -0.89783143 -1.10162218
117 NA -0.89783143
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.01537709 0.81940039
[2,] 0.64016271 1.01537709
[3,] 0.14985601 0.64016271
[4,] 0.14744223 0.14985601
[5,] 0.28733934 0.14744223
[6,] 0.15741746 0.28733934
[7,] 0.18476598 0.15741746
[8,] 0.11852893 0.18476598
[9,] -0.14075131 0.11852893
[10,] -0.86439760 -0.14075131
[11,] -0.78357689 -0.86439760
[12,] -0.51146161 -0.78357689
[13,] -0.32298359 -0.51146161
[14,] -0.83330965 -0.32298359
[15,] -1.05567582 -0.83330965
[16,] -1.20787465 -1.05567582
[17,] -0.70762956 -1.20787465
[18,] -0.11424778 -0.70762956
[19,] 0.02169545 -0.11424778
[20,] -0.11061945 0.02169545
[21,] -0.94624023 -0.11061945
[22,] -0.88437436 -0.94624023
[23,] -0.59987195 -0.88437436
[24,] 0.04335850 -0.59987195
[25,] 0.53668622 0.04335850
[26,] -0.07446828 0.53668622
[27,] -0.72206402 -0.07446828
[28,] -1.31453708 -0.72206402
[29,] -0.88565889 -1.31453708
[30,] -0.65992471 -0.88565889
[31,] -0.51422127 -0.65992471
[32,] 0.10514129 -0.51422127
[33,] -0.12644429 0.10514129
[34,] -0.38782488 -0.12644429
[35,] -0.34557152 -0.38782488
[36,] -0.68451424 -0.34557152
[37,] -0.35319496 -0.68451424
[38,] -0.29792639 -0.35319496
[39,] -0.80103915 -0.29792639
[40,] -0.27939029 -0.80103915
[41,] -0.08052320 -0.27939029
[42,] 0.01551859 -0.08052320
[43,] 0.08654665 0.01551859
[44,] 0.22334246 0.08654665
[45,] -0.02124555 0.22334246
[46,] -0.01851505 -0.02124555
[47,] 0.13105120 -0.01851505
[48,] 0.09235340 0.13105120
[49,] 0.41177412 0.09235340
[50,] 0.12944878 0.41177412
[51,] 0.15501258 0.12944878
[52,] 0.47791258 0.15501258
[53,] 0.70600196 0.47791258
[54,] 1.21789169 0.70600196
[55,] 1.05728551 1.21789169
[56,] 1.17952241 1.05728551
[57,] 1.23820174 1.17952241
[58,] 0.89465850 1.23820174
[59,] 0.60155150 0.89465850
[60,] 0.89107606 0.60155150
[61,] 1.29636988 0.89107606
[62,] 0.59159177 1.29636988
[63,] 0.44842568 0.59159177
[64,] 0.29764793 0.44842568
[65,] 0.26726891 0.29764793
[66,] 0.51790055 0.26726891
[67,] 0.42024480 0.51790055
[68,] 0.38327271 0.42024480
[69,] 0.24803406 0.38327271
[70,] 0.09390934 0.24803406
[71,] 0.72114777 0.09390934
[72,] 1.49371561 0.72114777
[73,] 1.46156914 1.49371561
[74,] 0.49151819 1.46156914
[75,] -0.30642040 0.49151819
[76,] -1.03113677 -0.30642040
[77,] -0.53720706 -1.03113677
[78,] 0.29972232 -0.53720706
[79,] 0.48154708 0.29972232
[80,] 0.58665545 0.48154708
[81,] 0.54143325 0.58665545
[82,] 0.30447954 0.54143325
[83,] 0.36265278 0.30447954
[84,] 0.48534422 0.36265278
[85,] 0.51260118 0.48534422
[86,] 0.15538023 0.51260118
[87,] -0.33446595 0.15538023
[88,] -0.36163350 -0.33446595
[89,] -0.06195179 -0.36163350
[90,] 0.42937066 -0.06195179
[91,] 0.90945666 0.42937066
[92,] 0.90841102 0.90945666
[93,] 0.96286636 0.90841102
[94,] 0.43952378 0.96286636
[95,] 0.33143838 0.43952378
[96,] 0.10298868 0.33143838
[97,] 0.12377061 0.10298868
[98,] -0.31715900 0.12377061
[99,] -0.79729118 -0.31715900
[100,] -0.84312442 -0.79729118
[101,] -0.50313875 -0.84312442
[102,] -0.21999077 -0.50313875
[103,] -0.20795709 -0.21999077
[104,] 0.02326787 -0.20795709
[105,] 0.09698718 0.02326787
[106,] 0.07122335 0.09698718
[107,] 0.20729129 0.07122335
[108,] -0.42990401 0.20729129
[109,] -0.81007041 -0.42990401
[110,] -1.36795017 -0.81007041
[111,] -1.16060831 -1.36795017
[112,] -0.89685797 -1.16060831
[113,] -0.93793300 -0.89685797
[114,] -1.03101722 -0.93793300
[115,] -1.10162218 -1.03101722
[116,] -0.89783143 -1.10162218
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.01537709 0.81940039
2 0.64016271 1.01537709
3 0.14985601 0.64016271
4 0.14744223 0.14985601
5 0.28733934 0.14744223
6 0.15741746 0.28733934
7 0.18476598 0.15741746
8 0.11852893 0.18476598
9 -0.14075131 0.11852893
10 -0.86439760 -0.14075131
11 -0.78357689 -0.86439760
12 -0.51146161 -0.78357689
13 -0.32298359 -0.51146161
14 -0.83330965 -0.32298359
15 -1.05567582 -0.83330965
16 -1.20787465 -1.05567582
17 -0.70762956 -1.20787465
18 -0.11424778 -0.70762956
19 0.02169545 -0.11424778
20 -0.11061945 0.02169545
21 -0.94624023 -0.11061945
22 -0.88437436 -0.94624023
23 -0.59987195 -0.88437436
24 0.04335850 -0.59987195
25 0.53668622 0.04335850
26 -0.07446828 0.53668622
27 -0.72206402 -0.07446828
28 -1.31453708 -0.72206402
29 -0.88565889 -1.31453708
30 -0.65992471 -0.88565889
31 -0.51422127 -0.65992471
32 0.10514129 -0.51422127
33 -0.12644429 0.10514129
34 -0.38782488 -0.12644429
35 -0.34557152 -0.38782488
36 -0.68451424 -0.34557152
37 -0.35319496 -0.68451424
38 -0.29792639 -0.35319496
39 -0.80103915 -0.29792639
40 -0.27939029 -0.80103915
41 -0.08052320 -0.27939029
42 0.01551859 -0.08052320
43 0.08654665 0.01551859
44 0.22334246 0.08654665
45 -0.02124555 0.22334246
46 -0.01851505 -0.02124555
47 0.13105120 -0.01851505
48 0.09235340 0.13105120
49 0.41177412 0.09235340
50 0.12944878 0.41177412
51 0.15501258 0.12944878
52 0.47791258 0.15501258
53 0.70600196 0.47791258
54 1.21789169 0.70600196
55 1.05728551 1.21789169
56 1.17952241 1.05728551
57 1.23820174 1.17952241
58 0.89465850 1.23820174
59 0.60155150 0.89465850
60 0.89107606 0.60155150
61 1.29636988 0.89107606
62 0.59159177 1.29636988
63 0.44842568 0.59159177
64 0.29764793 0.44842568
65 0.26726891 0.29764793
66 0.51790055 0.26726891
67 0.42024480 0.51790055
68 0.38327271 0.42024480
69 0.24803406 0.38327271
70 0.09390934 0.24803406
71 0.72114777 0.09390934
72 1.49371561 0.72114777
73 1.46156914 1.49371561
74 0.49151819 1.46156914
75 -0.30642040 0.49151819
76 -1.03113677 -0.30642040
77 -0.53720706 -1.03113677
78 0.29972232 -0.53720706
79 0.48154708 0.29972232
80 0.58665545 0.48154708
81 0.54143325 0.58665545
82 0.30447954 0.54143325
83 0.36265278 0.30447954
84 0.48534422 0.36265278
85 0.51260118 0.48534422
86 0.15538023 0.51260118
87 -0.33446595 0.15538023
88 -0.36163350 -0.33446595
89 -0.06195179 -0.36163350
90 0.42937066 -0.06195179
91 0.90945666 0.42937066
92 0.90841102 0.90945666
93 0.96286636 0.90841102
94 0.43952378 0.96286636
95 0.33143838 0.43952378
96 0.10298868 0.33143838
97 0.12377061 0.10298868
98 -0.31715900 0.12377061
99 -0.79729118 -0.31715900
100 -0.84312442 -0.79729118
101 -0.50313875 -0.84312442
102 -0.21999077 -0.50313875
103 -0.20795709 -0.21999077
104 0.02326787 -0.20795709
105 0.09698718 0.02326787
106 0.07122335 0.09698718
107 0.20729129 0.07122335
108 -0.42990401 0.20729129
109 -0.81007041 -0.42990401
110 -1.36795017 -0.81007041
111 -1.16060831 -1.36795017
112 -0.89685797 -1.16060831
113 -0.93793300 -0.89685797
114 -1.03101722 -0.93793300
115 -1.10162218 -1.03101722
116 -0.89783143 -1.10162218
> 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/7orb51292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8orb51292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hiaq1292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hiaq1292950429.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kj9w1292950429.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/126kp21292950429.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/132u5t1292950429.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/14nc4z1292950429.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/15rc241292950429.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/16cd1a1292950429.tab")
+ }
>
> try(system("convert tmp/1szve1292950429.ps tmp/1szve1292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/239uh1292950429.ps tmp/239uh1292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/339uh1292950429.ps tmp/339uh1292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/439uh1292950429.ps tmp/439uh1292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e0uk1292950429.ps tmp/5e0uk1292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e0uk1292950429.ps tmp/6e0uk1292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/7orb51292950429.ps tmp/7orb51292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/8orb51292950429.ps tmp/8orb51292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hiaq1292950429.ps tmp/9hiaq1292950429.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hiaq1292950429.ps tmp/10hiaq1292950429.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.554 1.756 8.646