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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> x <- array(list(17848,19592,21092,20899,25890,24965,22225,20977,22897,22785,22769,19637,20203,20450,23083,21738,26766,25280,22574,22729,21378,22902,24989,21116,15169,15846,20927,18273,22538,15596,14034,11366,14861,15149,13577,13026,13190,13196,15826,14733,16307,15703,14589,12043,15057,14053,12698,10888,10045,11549,13767,12434,13116,14211,12266,12602,15714,13742,12745,10491,10057,10900,11771,11992,11933,14504,11727,11477,13578,11555,11846,11397,10066,10269,14279,13870,13695,14420,11424,9704,12464,14301,13464,9893,11572,12380,16692,16052,16459,14761,13654,13480,18068,16560,14530,10650,11651,13735,13360,17818,20613,16231,13862,12004,17734,15034,12609,12320,10833,11350,13648,14890,16325,18045,15616,11926,16855,15083,12520,12355),dim=c(1,120),dimnames=list(c('Pas'),1:120))
> y <- array(NA,dim=c(1,120),dimnames=list(c('Pas'),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 = 'Include Monthly 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
Pas M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 17848 1 0 0 0 0 0 0 0 0 0 0 1
2 19592 0 1 0 0 0 0 0 0 0 0 0 2
3 21092 0 0 1 0 0 0 0 0 0 0 0 3
4 20899 0 0 0 1 0 0 0 0 0 0 0 4
5 25890 0 0 0 0 1 0 0 0 0 0 0 5
6 24965 0 0 0 0 0 1 0 0 0 0 0 6
7 22225 0 0 0 0 0 0 1 0 0 0 0 7
8 20977 0 0 0 0 0 0 0 1 0 0 0 8
9 22897 0 0 0 0 0 0 0 0 1 0 0 9
10 22785 0 0 0 0 0 0 0 0 0 1 0 10
11 22769 0 0 0 0 0 0 0 0 0 0 1 11
12 19637 0 0 0 0 0 0 0 0 0 0 0 12
13 20203 1 0 0 0 0 0 0 0 0 0 0 13
14 20450 0 1 0 0 0 0 0 0 0 0 0 14
15 23083 0 0 1 0 0 0 0 0 0 0 0 15
16 21738 0 0 0 1 0 0 0 0 0 0 0 16
17 26766 0 0 0 0 1 0 0 0 0 0 0 17
18 25280 0 0 0 0 0 1 0 0 0 0 0 18
19 22574 0 0 0 0 0 0 1 0 0 0 0 19
20 22729 0 0 0 0 0 0 0 1 0 0 0 20
21 21378 0 0 0 0 0 0 0 0 1 0 0 21
22 22902 0 0 0 0 0 0 0 0 0 1 0 22
23 24989 0 0 0 0 0 0 0 0 0 0 1 23
24 21116 0 0 0 0 0 0 0 0 0 0 0 24
25 15169 1 0 0 0 0 0 0 0 0 0 0 25
26 15846 0 1 0 0 0 0 0 0 0 0 0 26
27 20927 0 0 1 0 0 0 0 0 0 0 0 27
28 18273 0 0 0 1 0 0 0 0 0 0 0 28
29 22538 0 0 0 0 1 0 0 0 0 0 0 29
30 15596 0 0 0 0 0 1 0 0 0 0 0 30
31 14034 0 0 0 0 0 0 1 0 0 0 0 31
32 11366 0 0 0 0 0 0 0 1 0 0 0 32
33 14861 0 0 0 0 0 0 0 0 1 0 0 33
34 15149 0 0 0 0 0 0 0 0 0 1 0 34
35 13577 0 0 0 0 0 0 0 0 0 0 1 35
36 13026 0 0 0 0 0 0 0 0 0 0 0 36
37 13190 1 0 0 0 0 0 0 0 0 0 0 37
38 13196 0 1 0 0 0 0 0 0 0 0 0 38
39 15826 0 0 1 0 0 0 0 0 0 0 0 39
40 14733 0 0 0 1 0 0 0 0 0 0 0 40
41 16307 0 0 0 0 1 0 0 0 0 0 0 41
42 15703 0 0 0 0 0 1 0 0 0 0 0 42
43 14589 0 0 0 0 0 0 1 0 0 0 0 43
44 12043 0 0 0 0 0 0 0 1 0 0 0 44
45 15057 0 0 0 0 0 0 0 0 1 0 0 45
46 14053 0 0 0 0 0 0 0 0 0 1 0 46
47 12698 0 0 0 0 0 0 0 0 0 0 1 47
48 10888 0 0 0 0 0 0 0 0 0 0 0 48
49 10045 1 0 0 0 0 0 0 0 0 0 0 49
50 11549 0 1 0 0 0 0 0 0 0 0 0 50
51 13767 0 0 1 0 0 0 0 0 0 0 0 51
52 12434 0 0 0 1 0 0 0 0 0 0 0 52
53 13116 0 0 0 0 1 0 0 0 0 0 0 53
54 14211 0 0 0 0 0 1 0 0 0 0 0 54
55 12266 0 0 0 0 0 0 1 0 0 0 0 55
56 12602 0 0 0 0 0 0 0 1 0 0 0 56
57 15714 0 0 0 0 0 0 0 0 1 0 0 57
58 13742 0 0 0 0 0 0 0 0 0 1 0 58
59 12745 0 0 0 0 0 0 0 0 0 0 1 59
60 10491 0 0 0 0 0 0 0 0 0 0 0 60
61 10057 1 0 0 0 0 0 0 0 0 0 0 61
62 10900 0 1 0 0 0 0 0 0 0 0 0 62
63 11771 0 0 1 0 0 0 0 0 0 0 0 63
64 11992 0 0 0 1 0 0 0 0 0 0 0 64
65 11933 0 0 0 0 1 0 0 0 0 0 0 65
66 14504 0 0 0 0 0 1 0 0 0 0 0 66
67 11727 0 0 0 0 0 0 1 0 0 0 0 67
68 11477 0 0 0 0 0 0 0 1 0 0 0 68
69 13578 0 0 0 0 0 0 0 0 1 0 0 69
70 11555 0 0 0 0 0 0 0 0 0 1 0 70
71 11846 0 0 0 0 0 0 0 0 0 0 1 71
72 11397 0 0 0 0 0 0 0 0 0 0 0 72
73 10066 1 0 0 0 0 0 0 0 0 0 0 73
74 10269 0 1 0 0 0 0 0 0 0 0 0 74
75 14279 0 0 1 0 0 0 0 0 0 0 0 75
76 13870 0 0 0 1 0 0 0 0 0 0 0 76
77 13695 0 0 0 0 1 0 0 0 0 0 0 77
78 14420 0 0 0 0 0 1 0 0 0 0 0 78
79 11424 0 0 0 0 0 0 1 0 0 0 0 79
80 9704 0 0 0 0 0 0 0 1 0 0 0 80
81 12464 0 0 0 0 0 0 0 0 1 0 0 81
82 14301 0 0 0 0 0 0 0 0 0 1 0 82
83 13464 0 0 0 0 0 0 0 0 0 0 1 83
84 9893 0 0 0 0 0 0 0 0 0 0 0 84
85 11572 1 0 0 0 0 0 0 0 0 0 0 85
86 12380 0 1 0 0 0 0 0 0 0 0 0 86
87 16692 0 0 1 0 0 0 0 0 0 0 0 87
88 16052 0 0 0 1 0 0 0 0 0 0 0 88
89 16459 0 0 0 0 1 0 0 0 0 0 0 89
90 14761 0 0 0 0 0 1 0 0 0 0 0 90
91 13654 0 0 0 0 0 0 1 0 0 0 0 91
92 13480 0 0 0 0 0 0 0 1 0 0 0 92
93 18068 0 0 0 0 0 0 0 0 1 0 0 93
94 16560 0 0 0 0 0 0 0 0 0 1 0 94
95 14530 0 0 0 0 0 0 0 0 0 0 1 95
96 10650 0 0 0 0 0 0 0 0 0 0 0 96
97 11651 1 0 0 0 0 0 0 0 0 0 0 97
98 13735 0 1 0 0 0 0 0 0 0 0 0 98
99 13360 0 0 1 0 0 0 0 0 0 0 0 99
100 17818 0 0 0 1 0 0 0 0 0 0 0 100
101 20613 0 0 0 0 1 0 0 0 0 0 0 101
102 16231 0 0 0 0 0 1 0 0 0 0 0 102
103 13862 0 0 0 0 0 0 1 0 0 0 0 103
104 12004 0 0 0 0 0 0 0 1 0 0 0 104
105 17734 0 0 0 0 0 0 0 0 1 0 0 105
106 15034 0 0 0 0 0 0 0 0 0 1 0 106
107 12609 0 0 0 0 0 0 0 0 0 0 1 107
108 12320 0 0 0 0 0 0 0 0 0 0 0 108
109 10833 1 0 0 0 0 0 0 0 0 0 0 109
110 11350 0 1 0 0 0 0 0 0 0 0 0 110
111 13648 0 0 1 0 0 0 0 0 0 0 0 111
112 14890 0 0 0 1 0 0 0 0 0 0 0 112
113 16325 0 0 0 0 1 0 0 0 0 0 0 113
114 18045 0 0 0 0 0 1 0 0 0 0 0 114
115 15616 0 0 0 0 0 0 1 0 0 0 0 115
116 11926 0 0 0 0 0 0 0 1 0 0 0 116
117 16855 0 0 0 0 0 0 0 0 1 0 0 117
118 15083 0 0 0 0 0 0 0 0 0 1 0 118
119 12520 0 0 0 0 0 0 0 0 0 0 1 119
120 12355 0 0 0 0 0 0 0 0 0 0 0 120
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
17765.07 -878.53 54.28 2641.60 2536.51 4700.32
M6 M7 M8 M9 M10 M11
3777.23 1672.24 375.45 3474.77 2800.08 1927.89
t
-69.51
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6014 -2851 -31 2380 6895
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17765.067 1143.005 15.542 < 2e-16 ***
M1 -878.528 1417.703 -0.620 0.53678
M2 54.284 1417.183 0.038 0.96952
M3 2641.595 1416.712 1.865 0.06498 .
M4 2536.507 1416.291 1.791 0.07613 .
M5 4700.319 1415.919 3.320 0.00123 **
M6 3777.230 1415.597 2.668 0.00881 **
M7 1672.242 1415.324 1.182 0.24001
M8 375.454 1415.101 0.265 0.79127
M9 3474.765 1414.927 2.456 0.01567 *
M10 2800.077 1414.803 1.979 0.05037 .
M11 1927.888 1414.729 1.363 0.17583
t -69.512 8.378 -8.297 3.49e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3163 on 107 degrees of freedom
Multiple R-squared: 0.4856, Adjusted R-squared: 0.4279
F-statistic: 8.419 on 12 and 107 DF, p-value: 5.109e-11
> 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,] 0.0081843845 1.636877e-02 9.918156e-01
[2,] 0.0019187113 3.837423e-03 9.980813e-01
[3,] 0.0008436644 1.687329e-03 9.991563e-01
[4,] 0.0003041990 6.083980e-04 9.996958e-01
[5,] 0.0001182823 2.365645e-04 9.998817e-01
[6,] 0.0008768266 1.753653e-03 9.991232e-01
[7,] 0.0004455692 8.911383e-04 9.995544e-01
[8,] 0.0008004583 1.600917e-03 9.991995e-01
[9,] 0.0008605387 1.721077e-03 9.991395e-01
[10,] 0.0802174726 1.604349e-01 9.197825e-01
[11,] 0.2490160518 4.980321e-01 7.509839e-01
[12,] 0.2982822286 5.965645e-01 7.017178e-01
[13,] 0.3643403813 7.286808e-01 6.356596e-01
[14,] 0.6516880233 6.966240e-01 3.483120e-01
[15,] 0.9844014267 3.119715e-02 1.559857e-02
[16,] 0.9974140816 5.171837e-03 2.585918e-03
[17,] 0.9998326639 3.346723e-04 1.673361e-04
[18,] 0.9998620999 2.758001e-04 1.379001e-04
[19,] 0.9999045163 1.909674e-04 9.548368e-05
[20,] 0.9999777820 4.443608e-05 2.221804e-05
[21,] 0.9999840039 3.199224e-05 1.599612e-05
[22,] 0.9999880323 2.393533e-05 1.196767e-05
[23,] 0.9999857993 2.840147e-05 1.420074e-05
[24,] 0.9999869638 2.607232e-05 1.303616e-05
[25,] 0.9999785660 4.286793e-05 2.143397e-05
[26,] 0.9999826252 3.474954e-05 1.737477e-05
[27,] 0.9999739626 5.207485e-05 2.603743e-05
[28,] 0.9999781218 4.375633e-05 2.187816e-05
[29,] 0.9999684171 6.316573e-05 3.158287e-05
[30,] 0.9999492723 1.014555e-04 5.072774e-05
[31,] 0.9999203156 1.593687e-04 7.968437e-05
[32,] 0.9998969142 2.061715e-04 1.030858e-04
[33,] 0.9998543585 2.912830e-04 1.456415e-04
[34,] 0.9997659908 4.680183e-04 2.340092e-04
[35,] 0.9996987109 6.025782e-04 3.012891e-04
[36,] 0.9995809205 8.381590e-04 4.190795e-04
[37,] 0.9993502705 1.299459e-03 6.497295e-04
[38,] 0.9992324017 1.535197e-03 7.675983e-04
[39,] 0.9988078758 2.384248e-03 1.192124e-03
[40,] 0.9982256407 3.548719e-03 1.774359e-03
[41,] 0.9987795198 2.440960e-03 1.220480e-03
[42,] 0.9991860461 1.627908e-03 8.139539e-04
[43,] 0.9989287420 2.142516e-03 1.071258e-03
[44,] 0.9986689916 2.662017e-03 1.331008e-03
[45,] 0.9981550971 3.689806e-03 1.844903e-03
[46,] 0.9978067674 4.386465e-03 2.193233e-03
[47,] 0.9973019618 5.396076e-03 2.698038e-03
[48,] 0.9960711398 7.857720e-03 3.928860e-03
[49,] 0.9959401897 8.119621e-03 4.059810e-03
[50,] 0.9974150241 5.169952e-03 2.584976e-03
[51,] 0.9968221661 6.355668e-03 3.177834e-03
[52,] 0.9956265228 8.746954e-03 4.373477e-03
[53,] 0.9951250467 9.749907e-03 4.874953e-03
[54,] 0.9944979271 1.100415e-02 5.502073e-03
[55,] 0.9945417772 1.091645e-02 5.458223e-03
[56,] 0.9920586634 1.588267e-02 7.941337e-03
[57,] 0.9919915165 1.601697e-02 8.008483e-03
[58,] 0.9909208619 1.815828e-02 9.079138e-03
[59,] 0.9893643814 2.127124e-02 1.063562e-02
[60,] 0.9891108787 2.177824e-02 1.088912e-02
[61,] 0.9902291067 1.954179e-02 9.770893e-03
[62,] 0.9932006727 1.359865e-02 6.799327e-03
[63,] 0.9920278491 1.594430e-02 7.972151e-03
[64,] 0.9924679908 1.506402e-02 7.532009e-03
[65,] 0.9925456282 1.490874e-02 7.454372e-03
[66,] 0.9991514936 1.697013e-03 8.485064e-04
[67,] 0.9991610015 1.677997e-03 8.389985e-04
[68,] 0.9987725340 2.454932e-03 1.227466e-03
[69,] 0.9988912214 2.217557e-03 1.108779e-03
[70,] 0.9986294727 2.741055e-03 1.370527e-03
[71,] 0.9982576713 3.484657e-03 1.742329e-03
[72,] 0.9993243430 1.351314e-03 6.756570e-04
[73,] 0.9991967211 1.606558e-03 8.032789e-04
[74,] 0.9993450738 1.309852e-03 6.549262e-04
[75,] 0.9996587670 6.824660e-04 3.412330e-04
[76,] 0.9996432207 7.135586e-04 3.567793e-04
[77,] 0.9993976180 1.204764e-03 6.023820e-04
[78,] 0.9990992527 1.801495e-03 9.007473e-04
[79,] 0.9985062259 2.987548e-03 1.493774e-03
[80,] 0.9976908547 4.618291e-03 2.309145e-03
[81,] 0.9977823788 4.435242e-03 2.217621e-03
[82,] 0.9951798440 9.640312e-03 4.820156e-03
[83,] 0.9932743879 1.345122e-02 6.725612e-03
[84,] 0.9853985226 2.920295e-02 1.460148e-02
[85,] 0.9858839799 2.823204e-02 1.411602e-02
[86,] 0.9993727054 1.254589e-03 6.272946e-04
[87,] 0.9991684388 1.663122e-03 8.315612e-04
[88,] 0.9998803027 2.393947e-04 1.196973e-04
[89,] 0.9986169469 2.766106e-03 1.383053e-03
> postscript(file="/var/www/html/rcomp/tmp/1data1292064021.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2data1292064021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/351sv1292064021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/451sv1292064021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yb9y1292064021.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 = 120
Frequency = 1
1 2 3 4 5 6
1030.97273 1911.67273 893.87273 875.47273 3772.17273 3839.77273
7 8 9 10 11 12
3274.27273 3392.57273 2282.77273 2914.97273 3840.67273 2706.07273
13 14 15 16 17 18
4220.11212 3603.81212 3719.01212 2548.61212 5482.31212 4988.91212
19 20 21 22 23 24
4457.41212 5978.71212 1597.91212 3866.11212 6894.81212 5019.21212
25 26 27 28 29 30
20.25152 -166.04848 2397.15152 -82.24848 2088.45152 -3860.94848
31 32 33 34 35 36
-3248.44848 -4550.14848 -4084.94848 -3052.74848 -3683.04848 -2236.64848
37 38 39 40 41 42
-1124.60909 -1981.90909 -1869.70909 -2788.10909 -3308.40909 -2919.80909
43 44 45 46 47 48
-1859.30909 -3039.00909 -3054.80909 -3314.60909 -3727.90909 -3540.50909
49 50 51 52 53 54
-3435.46970 -2794.76970 -3094.56970 -4252.96970 -5665.26970 -3577.66970
55 56 57 58 59 60
-3348.16970 -1645.86970 -1563.66970 -2791.46970 -2846.76970 -3103.36970
61 62 63 64 65 66
-2589.33030 -2609.63030 -4256.43030 -3860.83030 -6014.13030 -2450.53030
67 68 69 70 71 72
-3053.03030 -1936.73030 -2865.53030 -4144.33030 -2911.63030 -1363.23030
73 74 75 76 77 78
-1746.19091 -2406.49091 -914.29091 -1148.69091 -3417.99091 -1700.39091
79 80 81 82 83 84
-2521.89091 -2875.59091 -3145.39091 -564.19091 -459.49091 -2033.09091
85 86 87 88 89 90
593.94848 538.64848 2332.84848 1867.44848 180.14848 -525.25152
91 92 93 94 95 96
542.24848 1734.54848 3292.74848 2528.94848 1440.64848 -441.95152
97 98 99 100 101 102
1507.08788 2727.78788 -165.01212 4467.58788 5168.28788 1778.88788
103 104 105 106 107 108
1584.38788 1092.68788 3792.88788 1837.08788 353.78788 2062.18788
109 110 111 112 113 114
1523.22727 1176.92727 957.12727 2373.72727 1714.42727 4427.02727
115 116 117 118 119 120
4172.52727 1848.82727 3748.02727 2720.22727 1098.92727 2931.32727
> postscript(file="/var/www/html/rcomp/tmp/6yb9y1292064021.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 1030.97273 NA
1 1911.67273 1030.97273
2 893.87273 1911.67273
3 875.47273 893.87273
4 3772.17273 875.47273
5 3839.77273 3772.17273
6 3274.27273 3839.77273
7 3392.57273 3274.27273
8 2282.77273 3392.57273
9 2914.97273 2282.77273
10 3840.67273 2914.97273
11 2706.07273 3840.67273
12 4220.11212 2706.07273
13 3603.81212 4220.11212
14 3719.01212 3603.81212
15 2548.61212 3719.01212
16 5482.31212 2548.61212
17 4988.91212 5482.31212
18 4457.41212 4988.91212
19 5978.71212 4457.41212
20 1597.91212 5978.71212
21 3866.11212 1597.91212
22 6894.81212 3866.11212
23 5019.21212 6894.81212
24 20.25152 5019.21212
25 -166.04848 20.25152
26 2397.15152 -166.04848
27 -82.24848 2397.15152
28 2088.45152 -82.24848
29 -3860.94848 2088.45152
30 -3248.44848 -3860.94848
31 -4550.14848 -3248.44848
32 -4084.94848 -4550.14848
33 -3052.74848 -4084.94848
34 -3683.04848 -3052.74848
35 -2236.64848 -3683.04848
36 -1124.60909 -2236.64848
37 -1981.90909 -1124.60909
38 -1869.70909 -1981.90909
39 -2788.10909 -1869.70909
40 -3308.40909 -2788.10909
41 -2919.80909 -3308.40909
42 -1859.30909 -2919.80909
43 -3039.00909 -1859.30909
44 -3054.80909 -3039.00909
45 -3314.60909 -3054.80909
46 -3727.90909 -3314.60909
47 -3540.50909 -3727.90909
48 -3435.46970 -3540.50909
49 -2794.76970 -3435.46970
50 -3094.56970 -2794.76970
51 -4252.96970 -3094.56970
52 -5665.26970 -4252.96970
53 -3577.66970 -5665.26970
54 -3348.16970 -3577.66970
55 -1645.86970 -3348.16970
56 -1563.66970 -1645.86970
57 -2791.46970 -1563.66970
58 -2846.76970 -2791.46970
59 -3103.36970 -2846.76970
60 -2589.33030 -3103.36970
61 -2609.63030 -2589.33030
62 -4256.43030 -2609.63030
63 -3860.83030 -4256.43030
64 -6014.13030 -3860.83030
65 -2450.53030 -6014.13030
66 -3053.03030 -2450.53030
67 -1936.73030 -3053.03030
68 -2865.53030 -1936.73030
69 -4144.33030 -2865.53030
70 -2911.63030 -4144.33030
71 -1363.23030 -2911.63030
72 -1746.19091 -1363.23030
73 -2406.49091 -1746.19091
74 -914.29091 -2406.49091
75 -1148.69091 -914.29091
76 -3417.99091 -1148.69091
77 -1700.39091 -3417.99091
78 -2521.89091 -1700.39091
79 -2875.59091 -2521.89091
80 -3145.39091 -2875.59091
81 -564.19091 -3145.39091
82 -459.49091 -564.19091
83 -2033.09091 -459.49091
84 593.94848 -2033.09091
85 538.64848 593.94848
86 2332.84848 538.64848
87 1867.44848 2332.84848
88 180.14848 1867.44848
89 -525.25152 180.14848
90 542.24848 -525.25152
91 1734.54848 542.24848
92 3292.74848 1734.54848
93 2528.94848 3292.74848
94 1440.64848 2528.94848
95 -441.95152 1440.64848
96 1507.08788 -441.95152
97 2727.78788 1507.08788
98 -165.01212 2727.78788
99 4467.58788 -165.01212
100 5168.28788 4467.58788
101 1778.88788 5168.28788
102 1584.38788 1778.88788
103 1092.68788 1584.38788
104 3792.88788 1092.68788
105 1837.08788 3792.88788
106 353.78788 1837.08788
107 2062.18788 353.78788
108 1523.22727 2062.18788
109 1176.92727 1523.22727
110 957.12727 1176.92727
111 2373.72727 957.12727
112 1714.42727 2373.72727
113 4427.02727 1714.42727
114 4172.52727 4427.02727
115 1848.82727 4172.52727
116 3748.02727 1848.82727
117 2720.22727 3748.02727
118 1098.92727 2720.22727
119 2931.32727 1098.92727
120 NA 2931.32727
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1911.67273 1030.97273
[2,] 893.87273 1911.67273
[3,] 875.47273 893.87273
[4,] 3772.17273 875.47273
[5,] 3839.77273 3772.17273
[6,] 3274.27273 3839.77273
[7,] 3392.57273 3274.27273
[8,] 2282.77273 3392.57273
[9,] 2914.97273 2282.77273
[10,] 3840.67273 2914.97273
[11,] 2706.07273 3840.67273
[12,] 4220.11212 2706.07273
[13,] 3603.81212 4220.11212
[14,] 3719.01212 3603.81212
[15,] 2548.61212 3719.01212
[16,] 5482.31212 2548.61212
[17,] 4988.91212 5482.31212
[18,] 4457.41212 4988.91212
[19,] 5978.71212 4457.41212
[20,] 1597.91212 5978.71212
[21,] 3866.11212 1597.91212
[22,] 6894.81212 3866.11212
[23,] 5019.21212 6894.81212
[24,] 20.25152 5019.21212
[25,] -166.04848 20.25152
[26,] 2397.15152 -166.04848
[27,] -82.24848 2397.15152
[28,] 2088.45152 -82.24848
[29,] -3860.94848 2088.45152
[30,] -3248.44848 -3860.94848
[31,] -4550.14848 -3248.44848
[32,] -4084.94848 -4550.14848
[33,] -3052.74848 -4084.94848
[34,] -3683.04848 -3052.74848
[35,] -2236.64848 -3683.04848
[36,] -1124.60909 -2236.64848
[37,] -1981.90909 -1124.60909
[38,] -1869.70909 -1981.90909
[39,] -2788.10909 -1869.70909
[40,] -3308.40909 -2788.10909
[41,] -2919.80909 -3308.40909
[42,] -1859.30909 -2919.80909
[43,] -3039.00909 -1859.30909
[44,] -3054.80909 -3039.00909
[45,] -3314.60909 -3054.80909
[46,] -3727.90909 -3314.60909
[47,] -3540.50909 -3727.90909
[48,] -3435.46970 -3540.50909
[49,] -2794.76970 -3435.46970
[50,] -3094.56970 -2794.76970
[51,] -4252.96970 -3094.56970
[52,] -5665.26970 -4252.96970
[53,] -3577.66970 -5665.26970
[54,] -3348.16970 -3577.66970
[55,] -1645.86970 -3348.16970
[56,] -1563.66970 -1645.86970
[57,] -2791.46970 -1563.66970
[58,] -2846.76970 -2791.46970
[59,] -3103.36970 -2846.76970
[60,] -2589.33030 -3103.36970
[61,] -2609.63030 -2589.33030
[62,] -4256.43030 -2609.63030
[63,] -3860.83030 -4256.43030
[64,] -6014.13030 -3860.83030
[65,] -2450.53030 -6014.13030
[66,] -3053.03030 -2450.53030
[67,] -1936.73030 -3053.03030
[68,] -2865.53030 -1936.73030
[69,] -4144.33030 -2865.53030
[70,] -2911.63030 -4144.33030
[71,] -1363.23030 -2911.63030
[72,] -1746.19091 -1363.23030
[73,] -2406.49091 -1746.19091
[74,] -914.29091 -2406.49091
[75,] -1148.69091 -914.29091
[76,] -3417.99091 -1148.69091
[77,] -1700.39091 -3417.99091
[78,] -2521.89091 -1700.39091
[79,] -2875.59091 -2521.89091
[80,] -3145.39091 -2875.59091
[81,] -564.19091 -3145.39091
[82,] -459.49091 -564.19091
[83,] -2033.09091 -459.49091
[84,] 593.94848 -2033.09091
[85,] 538.64848 593.94848
[86,] 2332.84848 538.64848
[87,] 1867.44848 2332.84848
[88,] 180.14848 1867.44848
[89,] -525.25152 180.14848
[90,] 542.24848 -525.25152
[91,] 1734.54848 542.24848
[92,] 3292.74848 1734.54848
[93,] 2528.94848 3292.74848
[94,] 1440.64848 2528.94848
[95,] -441.95152 1440.64848
[96,] 1507.08788 -441.95152
[97,] 2727.78788 1507.08788
[98,] -165.01212 2727.78788
[99,] 4467.58788 -165.01212
[100,] 5168.28788 4467.58788
[101,] 1778.88788 5168.28788
[102,] 1584.38788 1778.88788
[103,] 1092.68788 1584.38788
[104,] 3792.88788 1092.68788
[105,] 1837.08788 3792.88788
[106,] 353.78788 1837.08788
[107,] 2062.18788 353.78788
[108,] 1523.22727 2062.18788
[109,] 1176.92727 1523.22727
[110,] 957.12727 1176.92727
[111,] 2373.72727 957.12727
[112,] 1714.42727 2373.72727
[113,] 4427.02727 1714.42727
[114,] 4172.52727 4427.02727
[115,] 1848.82727 4172.52727
[116,] 3748.02727 1848.82727
[117,] 2720.22727 3748.02727
[118,] 1098.92727 2720.22727
[119,] 2931.32727 1098.92727
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1911.67273 1030.97273
2 893.87273 1911.67273
3 875.47273 893.87273
4 3772.17273 875.47273
5 3839.77273 3772.17273
6 3274.27273 3839.77273
7 3392.57273 3274.27273
8 2282.77273 3392.57273
9 2914.97273 2282.77273
10 3840.67273 2914.97273
11 2706.07273 3840.67273
12 4220.11212 2706.07273
13 3603.81212 4220.11212
14 3719.01212 3603.81212
15 2548.61212 3719.01212
16 5482.31212 2548.61212
17 4988.91212 5482.31212
18 4457.41212 4988.91212
19 5978.71212 4457.41212
20 1597.91212 5978.71212
21 3866.11212 1597.91212
22 6894.81212 3866.11212
23 5019.21212 6894.81212
24 20.25152 5019.21212
25 -166.04848 20.25152
26 2397.15152 -166.04848
27 -82.24848 2397.15152
28 2088.45152 -82.24848
29 -3860.94848 2088.45152
30 -3248.44848 -3860.94848
31 -4550.14848 -3248.44848
32 -4084.94848 -4550.14848
33 -3052.74848 -4084.94848
34 -3683.04848 -3052.74848
35 -2236.64848 -3683.04848
36 -1124.60909 -2236.64848
37 -1981.90909 -1124.60909
38 -1869.70909 -1981.90909
39 -2788.10909 -1869.70909
40 -3308.40909 -2788.10909
41 -2919.80909 -3308.40909
42 -1859.30909 -2919.80909
43 -3039.00909 -1859.30909
44 -3054.80909 -3039.00909
45 -3314.60909 -3054.80909
46 -3727.90909 -3314.60909
47 -3540.50909 -3727.90909
48 -3435.46970 -3540.50909
49 -2794.76970 -3435.46970
50 -3094.56970 -2794.76970
51 -4252.96970 -3094.56970
52 -5665.26970 -4252.96970
53 -3577.66970 -5665.26970
54 -3348.16970 -3577.66970
55 -1645.86970 -3348.16970
56 -1563.66970 -1645.86970
57 -2791.46970 -1563.66970
58 -2846.76970 -2791.46970
59 -3103.36970 -2846.76970
60 -2589.33030 -3103.36970
61 -2609.63030 -2589.33030
62 -4256.43030 -2609.63030
63 -3860.83030 -4256.43030
64 -6014.13030 -3860.83030
65 -2450.53030 -6014.13030
66 -3053.03030 -2450.53030
67 -1936.73030 -3053.03030
68 -2865.53030 -1936.73030
69 -4144.33030 -2865.53030
70 -2911.63030 -4144.33030
71 -1363.23030 -2911.63030
72 -1746.19091 -1363.23030
73 -2406.49091 -1746.19091
74 -914.29091 -2406.49091
75 -1148.69091 -914.29091
76 -3417.99091 -1148.69091
77 -1700.39091 -3417.99091
78 -2521.89091 -1700.39091
79 -2875.59091 -2521.89091
80 -3145.39091 -2875.59091
81 -564.19091 -3145.39091
82 -459.49091 -564.19091
83 -2033.09091 -459.49091
84 593.94848 -2033.09091
85 538.64848 593.94848
86 2332.84848 538.64848
87 1867.44848 2332.84848
88 180.14848 1867.44848
89 -525.25152 180.14848
90 542.24848 -525.25152
91 1734.54848 542.24848
92 3292.74848 1734.54848
93 2528.94848 3292.74848
94 1440.64848 2528.94848
95 -441.95152 1440.64848
96 1507.08788 -441.95152
97 2727.78788 1507.08788
98 -165.01212 2727.78788
99 4467.58788 -165.01212
100 5168.28788 4467.58788
101 1778.88788 5168.28788
102 1584.38788 1778.88788
103 1092.68788 1584.38788
104 3792.88788 1092.68788
105 1837.08788 3792.88788
106 353.78788 1837.08788
107 2062.18788 353.78788
108 1523.22727 2062.18788
109 1176.92727 1523.22727
110 957.12727 1176.92727
111 2373.72727 957.12727
112 1714.42727 2373.72727
113 4427.02727 1714.42727
114 4172.52727 4427.02727
115 1848.82727 4172.52727
116 3748.02727 1848.82727
117 2720.22727 3748.02727
118 1098.92727 2720.22727
119 2931.32727 1098.92727
> 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/79kqj1292064021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89kqj1292064021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99kqj1292064021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jbq41292064021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115u6a1292064021.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/12qu5x1292064021.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/13fd291292064021.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/14q41u1292064021.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/15b5z01292064021.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/167ffr1292064021.tab")
+ }
>
> try(system("convert tmp/1data1292064021.ps tmp/1data1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/2data1292064021.ps tmp/2data1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/351sv1292064021.ps tmp/351sv1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/451sv1292064021.ps tmp/451sv1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yb9y1292064021.ps tmp/5yb9y1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yb9y1292064021.ps tmp/6yb9y1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/79kqj1292064021.ps tmp/79kqj1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/89kqj1292064021.ps tmp/89kqj1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/99kqj1292064021.ps tmp/99kqj1292064021.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jbq41292064021.ps tmp/10jbq41292064021.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.340 1.729 12.091