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(17.848,19.592,21.092,20.899,25.890,24.965,22.225,20.977,22.897,22.785,22.769,19.637,20.203,20.450,23.083,21.738,26.766,25.280,22.574,22.729,21.378,22.902,24.989,21.116,15.169,15.846,20.927,18.273,22.538,15.596,14.034,11.366,14.861,15.149,13.577,13.026,13.190,13.196,15.826,14.733,16.307,15.703,14.589,12.043,15.057,14.053,12.698,10.888,10.045,11.549,13.767,12.434,13.116,14.211,12.266,12.602,15.714,13.742,12.745,10.491,10.057,10.900,11.771,11.992,11.933,14.504,11.727,11.477,13.578,11.555,11.846,11.397,10.066,10.269,14.279,13.870,13.695,14.420,11.424,9.704,12.464,14.301,13.464,9.893,11.572,12.380,16.692,16.052,16.459,14.761,13.654,13.480,18.068,16.560,14.530,10.650,11.651,13.735,13.360,17.818,20.613,16.231,13.862,12.004,17.734,15.034,12.609,12.320,10.833,11.350,13.648,14.890,16.325,18.045,15.616,11.926,16.855,15.083,12.520,12.355),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 17.848 1 0 0 0 0 0 0 0 0 0 0 1
2 19.592 0 1 0 0 0 0 0 0 0 0 0 2
3 21.092 0 0 1 0 0 0 0 0 0 0 0 3
4 20.899 0 0 0 1 0 0 0 0 0 0 0 4
5 25.890 0 0 0 0 1 0 0 0 0 0 0 5
6 24.965 0 0 0 0 0 1 0 0 0 0 0 6
7 22.225 0 0 0 0 0 0 1 0 0 0 0 7
8 20.977 0 0 0 0 0 0 0 1 0 0 0 8
9 22.897 0 0 0 0 0 0 0 0 1 0 0 9
10 22.785 0 0 0 0 0 0 0 0 0 1 0 10
11 22.769 0 0 0 0 0 0 0 0 0 0 1 11
12 19.637 0 0 0 0 0 0 0 0 0 0 0 12
13 20.203 1 0 0 0 0 0 0 0 0 0 0 13
14 20.450 0 1 0 0 0 0 0 0 0 0 0 14
15 23.083 0 0 1 0 0 0 0 0 0 0 0 15
16 21.738 0 0 0 1 0 0 0 0 0 0 0 16
17 26.766 0 0 0 0 1 0 0 0 0 0 0 17
18 25.280 0 0 0 0 0 1 0 0 0 0 0 18
19 22.574 0 0 0 0 0 0 1 0 0 0 0 19
20 22.729 0 0 0 0 0 0 0 1 0 0 0 20
21 21.378 0 0 0 0 0 0 0 0 1 0 0 21
22 22.902 0 0 0 0 0 0 0 0 0 1 0 22
23 24.989 0 0 0 0 0 0 0 0 0 0 1 23
24 21.116 0 0 0 0 0 0 0 0 0 0 0 24
25 15.169 1 0 0 0 0 0 0 0 0 0 0 25
26 15.846 0 1 0 0 0 0 0 0 0 0 0 26
27 20.927 0 0 1 0 0 0 0 0 0 0 0 27
28 18.273 0 0 0 1 0 0 0 0 0 0 0 28
29 22.538 0 0 0 0 1 0 0 0 0 0 0 29
30 15.596 0 0 0 0 0 1 0 0 0 0 0 30
31 14.034 0 0 0 0 0 0 1 0 0 0 0 31
32 11.366 0 0 0 0 0 0 0 1 0 0 0 32
33 14.861 0 0 0 0 0 0 0 0 1 0 0 33
34 15.149 0 0 0 0 0 0 0 0 0 1 0 34
35 13.577 0 0 0 0 0 0 0 0 0 0 1 35
36 13.026 0 0 0 0 0 0 0 0 0 0 0 36
37 13.190 1 0 0 0 0 0 0 0 0 0 0 37
38 13.196 0 1 0 0 0 0 0 0 0 0 0 38
39 15.826 0 0 1 0 0 0 0 0 0 0 0 39
40 14.733 0 0 0 1 0 0 0 0 0 0 0 40
41 16.307 0 0 0 0 1 0 0 0 0 0 0 41
42 15.703 0 0 0 0 0 1 0 0 0 0 0 42
43 14.589 0 0 0 0 0 0 1 0 0 0 0 43
44 12.043 0 0 0 0 0 0 0 1 0 0 0 44
45 15.057 0 0 0 0 0 0 0 0 1 0 0 45
46 14.053 0 0 0 0 0 0 0 0 0 1 0 46
47 12.698 0 0 0 0 0 0 0 0 0 0 1 47
48 10.888 0 0 0 0 0 0 0 0 0 0 0 48
49 10.045 1 0 0 0 0 0 0 0 0 0 0 49
50 11.549 0 1 0 0 0 0 0 0 0 0 0 50
51 13.767 0 0 1 0 0 0 0 0 0 0 0 51
52 12.434 0 0 0 1 0 0 0 0 0 0 0 52
53 13.116 0 0 0 0 1 0 0 0 0 0 0 53
54 14.211 0 0 0 0 0 1 0 0 0 0 0 54
55 12.266 0 0 0 0 0 0 1 0 0 0 0 55
56 12.602 0 0 0 0 0 0 0 1 0 0 0 56
57 15.714 0 0 0 0 0 0 0 0 1 0 0 57
58 13.742 0 0 0 0 0 0 0 0 0 1 0 58
59 12.745 0 0 0 0 0 0 0 0 0 0 1 59
60 10.491 0 0 0 0 0 0 0 0 0 0 0 60
61 10.057 1 0 0 0 0 0 0 0 0 0 0 61
62 10.900 0 1 0 0 0 0 0 0 0 0 0 62
63 11.771 0 0 1 0 0 0 0 0 0 0 0 63
64 11.992 0 0 0 1 0 0 0 0 0 0 0 64
65 11.933 0 0 0 0 1 0 0 0 0 0 0 65
66 14.504 0 0 0 0 0 1 0 0 0 0 0 66
67 11.727 0 0 0 0 0 0 1 0 0 0 0 67
68 11.477 0 0 0 0 0 0 0 1 0 0 0 68
69 13.578 0 0 0 0 0 0 0 0 1 0 0 69
70 11.555 0 0 0 0 0 0 0 0 0 1 0 70
71 11.846 0 0 0 0 0 0 0 0 0 0 1 71
72 11.397 0 0 0 0 0 0 0 0 0 0 0 72
73 10.066 1 0 0 0 0 0 0 0 0 0 0 73
74 10.269 0 1 0 0 0 0 0 0 0 0 0 74
75 14.279 0 0 1 0 0 0 0 0 0 0 0 75
76 13.870 0 0 0 1 0 0 0 0 0 0 0 76
77 13.695 0 0 0 0 1 0 0 0 0 0 0 77
78 14.420 0 0 0 0 0 1 0 0 0 0 0 78
79 11.424 0 0 0 0 0 0 1 0 0 0 0 79
80 9.704 0 0 0 0 0 0 0 1 0 0 0 80
81 12.464 0 0 0 0 0 0 0 0 1 0 0 81
82 14.301 0 0 0 0 0 0 0 0 0 1 0 82
83 13.464 0 0 0 0 0 0 0 0 0 0 1 83
84 9.893 0 0 0 0 0 0 0 0 0 0 0 84
85 11.572 1 0 0 0 0 0 0 0 0 0 0 85
86 12.380 0 1 0 0 0 0 0 0 0 0 0 86
87 16.692 0 0 1 0 0 0 0 0 0 0 0 87
88 16.052 0 0 0 1 0 0 0 0 0 0 0 88
89 16.459 0 0 0 0 1 0 0 0 0 0 0 89
90 14.761 0 0 0 0 0 1 0 0 0 0 0 90
91 13.654 0 0 0 0 0 0 1 0 0 0 0 91
92 13.480 0 0 0 0 0 0 0 1 0 0 0 92
93 18.068 0 0 0 0 0 0 0 0 1 0 0 93
94 16.560 0 0 0 0 0 0 0 0 0 1 0 94
95 14.530 0 0 0 0 0 0 0 0 0 0 1 95
96 10.650 0 0 0 0 0 0 0 0 0 0 0 96
97 11.651 1 0 0 0 0 0 0 0 0 0 0 97
98 13.735 0 1 0 0 0 0 0 0 0 0 0 98
99 13.360 0 0 1 0 0 0 0 0 0 0 0 99
100 17.818 0 0 0 1 0 0 0 0 0 0 0 100
101 20.613 0 0 0 0 1 0 0 0 0 0 0 101
102 16.231 0 0 0 0 0 1 0 0 0 0 0 102
103 13.862 0 0 0 0 0 0 1 0 0 0 0 103
104 12.004 0 0 0 0 0 0 0 1 0 0 0 104
105 17.734 0 0 0 0 0 0 0 0 1 0 0 105
106 15.034 0 0 0 0 0 0 0 0 0 1 0 106
107 12.609 0 0 0 0 0 0 0 0 0 0 1 107
108 12.320 0 0 0 0 0 0 0 0 0 0 0 108
109 10.833 1 0 0 0 0 0 0 0 0 0 0 109
110 11.350 0 1 0 0 0 0 0 0 0 0 0 110
111 13.648 0 0 1 0 0 0 0 0 0 0 0 111
112 14.890 0 0 0 1 0 0 0 0 0 0 0 112
113 16.325 0 0 0 0 1 0 0 0 0 0 0 113
114 18.045 0 0 0 0 0 1 0 0 0 0 0 114
115 15.616 0 0 0 0 0 0 1 0 0 0 0 115
116 11.926 0 0 0 0 0 0 0 1 0 0 0 116
117 16.855 0 0 0 0 0 0 0 0 1 0 0 117
118 15.083 0 0 0 0 0 0 0 0 0 1 0 118
119 12.520 0 0 0 0 0 0 0 0 0 0 1 119
120 12.355 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
17.76507 -0.87853 0.05428 2.64160 2.53651 4.70032
M6 M7 M8 M9 M10 M11
3.77723 1.67224 0.37545 3.47477 2.80008 1.92789
t
-0.06951
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.014 -2.851 -0.031 2.380 6.895
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.765067 1.143005 15.542 < 2e-16 ***
M1 -0.878528 1.417703 -0.620 0.53678
M2 0.054284 1.417183 0.038 0.96952
M3 2.641595 1.416712 1.865 0.06498 .
M4 2.536507 1.416291 1.791 0.07613 .
M5 4.700319 1.415919 3.320 0.00123 **
M6 3.777230 1.415597 2.668 0.00881 **
M7 1.672242 1.415324 1.182 0.24001
M8 0.375454 1.415101 0.265 0.79127
M9 3.474765 1.414927 2.456 0.01567 *
M10 2.800077 1.414803 1.979 0.05037 .
M11 1.927888 1.414729 1.363 0.17583
t -0.069512 0.008378 -8.297 3.49e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.163 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/18vvg1292063046.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28vvg1292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38vvg1292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/404c11292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/504c11292063046.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
1.03097273 1.91167273 0.89387273 0.87547273 3.77217273 3.83977273
7 8 9 10 11 12
3.27427273 3.39257273 2.28277273 2.91497273 3.84067273 2.70607273
13 14 15 16 17 18
4.22011212 3.60381212 3.71901212 2.54861212 5.48231212 4.98891212
19 20 21 22 23 24
4.45741212 5.97871212 1.59791212 3.86611212 6.89481212 5.01921212
25 26 27 28 29 30
0.02025152 -0.16604848 2.39715152 -0.08224848 2.08845152 -3.86094848
31 32 33 34 35 36
-3.24844848 -4.55014848 -4.08494848 -3.05274848 -3.68304848 -2.23664848
37 38 39 40 41 42
-1.12460909 -1.98190909 -1.86970909 -2.78810909 -3.30840909 -2.91980909
43 44 45 46 47 48
-1.85930909 -3.03900909 -3.05480909 -3.31460909 -3.72790909 -3.54050909
49 50 51 52 53 54
-3.43546970 -2.79476970 -3.09456970 -4.25296970 -5.66526970 -3.57766970
55 56 57 58 59 60
-3.34816970 -1.64586970 -1.56366970 -2.79146970 -2.84676970 -3.10336970
61 62 63 64 65 66
-2.58933030 -2.60963030 -4.25643030 -3.86083030 -6.01413030 -2.45053030
67 68 69 70 71 72
-3.05303030 -1.93673030 -2.86553030 -4.14433030 -2.91163030 -1.36323030
73 74 75 76 77 78
-1.74619091 -2.40649091 -0.91429091 -1.14869091 -3.41799091 -1.70039091
79 80 81 82 83 84
-2.52189091 -2.87559091 -3.14539091 -0.56419091 -0.45949091 -2.03309091
85 86 87 88 89 90
0.59394848 0.53864848 2.33284848 1.86744848 0.18014848 -0.52525152
91 92 93 94 95 96
0.54224848 1.73454848 3.29274848 2.52894848 1.44064848 -0.44195152
97 98 99 100 101 102
1.50708788 2.72778788 -0.16501212 4.46758788 5.16828788 1.77888788
103 104 105 106 107 108
1.58438788 1.09268788 3.79288788 1.83708788 0.35378788 2.06218788
109 110 111 112 113 114
1.52322727 1.17692727 0.95712727 2.37372727 1.71442727 4.42702727
115 116 117 118 119 120
4.17252727 1.84882727 3.74802727 2.72022727 1.09892727 2.93132727
> postscript(file="/var/www/html/rcomp/tmp/6tvc41292063046.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 1.03097273 NA
1 1.91167273 1.03097273
2 0.89387273 1.91167273
3 0.87547273 0.89387273
4 3.77217273 0.87547273
5 3.83977273 3.77217273
6 3.27427273 3.83977273
7 3.39257273 3.27427273
8 2.28277273 3.39257273
9 2.91497273 2.28277273
10 3.84067273 2.91497273
11 2.70607273 3.84067273
12 4.22011212 2.70607273
13 3.60381212 4.22011212
14 3.71901212 3.60381212
15 2.54861212 3.71901212
16 5.48231212 2.54861212
17 4.98891212 5.48231212
18 4.45741212 4.98891212
19 5.97871212 4.45741212
20 1.59791212 5.97871212
21 3.86611212 1.59791212
22 6.89481212 3.86611212
23 5.01921212 6.89481212
24 0.02025152 5.01921212
25 -0.16604848 0.02025152
26 2.39715152 -0.16604848
27 -0.08224848 2.39715152
28 2.08845152 -0.08224848
29 -3.86094848 2.08845152
30 -3.24844848 -3.86094848
31 -4.55014848 -3.24844848
32 -4.08494848 -4.55014848
33 -3.05274848 -4.08494848
34 -3.68304848 -3.05274848
35 -2.23664848 -3.68304848
36 -1.12460909 -2.23664848
37 -1.98190909 -1.12460909
38 -1.86970909 -1.98190909
39 -2.78810909 -1.86970909
40 -3.30840909 -2.78810909
41 -2.91980909 -3.30840909
42 -1.85930909 -2.91980909
43 -3.03900909 -1.85930909
44 -3.05480909 -3.03900909
45 -3.31460909 -3.05480909
46 -3.72790909 -3.31460909
47 -3.54050909 -3.72790909
48 -3.43546970 -3.54050909
49 -2.79476970 -3.43546970
50 -3.09456970 -2.79476970
51 -4.25296970 -3.09456970
52 -5.66526970 -4.25296970
53 -3.57766970 -5.66526970
54 -3.34816970 -3.57766970
55 -1.64586970 -3.34816970
56 -1.56366970 -1.64586970
57 -2.79146970 -1.56366970
58 -2.84676970 -2.79146970
59 -3.10336970 -2.84676970
60 -2.58933030 -3.10336970
61 -2.60963030 -2.58933030
62 -4.25643030 -2.60963030
63 -3.86083030 -4.25643030
64 -6.01413030 -3.86083030
65 -2.45053030 -6.01413030
66 -3.05303030 -2.45053030
67 -1.93673030 -3.05303030
68 -2.86553030 -1.93673030
69 -4.14433030 -2.86553030
70 -2.91163030 -4.14433030
71 -1.36323030 -2.91163030
72 -1.74619091 -1.36323030
73 -2.40649091 -1.74619091
74 -0.91429091 -2.40649091
75 -1.14869091 -0.91429091
76 -3.41799091 -1.14869091
77 -1.70039091 -3.41799091
78 -2.52189091 -1.70039091
79 -2.87559091 -2.52189091
80 -3.14539091 -2.87559091
81 -0.56419091 -3.14539091
82 -0.45949091 -0.56419091
83 -2.03309091 -0.45949091
84 0.59394848 -2.03309091
85 0.53864848 0.59394848
86 2.33284848 0.53864848
87 1.86744848 2.33284848
88 0.18014848 1.86744848
89 -0.52525152 0.18014848
90 0.54224848 -0.52525152
91 1.73454848 0.54224848
92 3.29274848 1.73454848
93 2.52894848 3.29274848
94 1.44064848 2.52894848
95 -0.44195152 1.44064848
96 1.50708788 -0.44195152
97 2.72778788 1.50708788
98 -0.16501212 2.72778788
99 4.46758788 -0.16501212
100 5.16828788 4.46758788
101 1.77888788 5.16828788
102 1.58438788 1.77888788
103 1.09268788 1.58438788
104 3.79288788 1.09268788
105 1.83708788 3.79288788
106 0.35378788 1.83708788
107 2.06218788 0.35378788
108 1.52322727 2.06218788
109 1.17692727 1.52322727
110 0.95712727 1.17692727
111 2.37372727 0.95712727
112 1.71442727 2.37372727
113 4.42702727 1.71442727
114 4.17252727 4.42702727
115 1.84882727 4.17252727
116 3.74802727 1.84882727
117 2.72022727 3.74802727
118 1.09892727 2.72022727
119 2.93132727 1.09892727
120 NA 2.93132727
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.91167273 1.03097273
[2,] 0.89387273 1.91167273
[3,] 0.87547273 0.89387273
[4,] 3.77217273 0.87547273
[5,] 3.83977273 3.77217273
[6,] 3.27427273 3.83977273
[7,] 3.39257273 3.27427273
[8,] 2.28277273 3.39257273
[9,] 2.91497273 2.28277273
[10,] 3.84067273 2.91497273
[11,] 2.70607273 3.84067273
[12,] 4.22011212 2.70607273
[13,] 3.60381212 4.22011212
[14,] 3.71901212 3.60381212
[15,] 2.54861212 3.71901212
[16,] 5.48231212 2.54861212
[17,] 4.98891212 5.48231212
[18,] 4.45741212 4.98891212
[19,] 5.97871212 4.45741212
[20,] 1.59791212 5.97871212
[21,] 3.86611212 1.59791212
[22,] 6.89481212 3.86611212
[23,] 5.01921212 6.89481212
[24,] 0.02025152 5.01921212
[25,] -0.16604848 0.02025152
[26,] 2.39715152 -0.16604848
[27,] -0.08224848 2.39715152
[28,] 2.08845152 -0.08224848
[29,] -3.86094848 2.08845152
[30,] -3.24844848 -3.86094848
[31,] -4.55014848 -3.24844848
[32,] -4.08494848 -4.55014848
[33,] -3.05274848 -4.08494848
[34,] -3.68304848 -3.05274848
[35,] -2.23664848 -3.68304848
[36,] -1.12460909 -2.23664848
[37,] -1.98190909 -1.12460909
[38,] -1.86970909 -1.98190909
[39,] -2.78810909 -1.86970909
[40,] -3.30840909 -2.78810909
[41,] -2.91980909 -3.30840909
[42,] -1.85930909 -2.91980909
[43,] -3.03900909 -1.85930909
[44,] -3.05480909 -3.03900909
[45,] -3.31460909 -3.05480909
[46,] -3.72790909 -3.31460909
[47,] -3.54050909 -3.72790909
[48,] -3.43546970 -3.54050909
[49,] -2.79476970 -3.43546970
[50,] -3.09456970 -2.79476970
[51,] -4.25296970 -3.09456970
[52,] -5.66526970 -4.25296970
[53,] -3.57766970 -5.66526970
[54,] -3.34816970 -3.57766970
[55,] -1.64586970 -3.34816970
[56,] -1.56366970 -1.64586970
[57,] -2.79146970 -1.56366970
[58,] -2.84676970 -2.79146970
[59,] -3.10336970 -2.84676970
[60,] -2.58933030 -3.10336970
[61,] -2.60963030 -2.58933030
[62,] -4.25643030 -2.60963030
[63,] -3.86083030 -4.25643030
[64,] -6.01413030 -3.86083030
[65,] -2.45053030 -6.01413030
[66,] -3.05303030 -2.45053030
[67,] -1.93673030 -3.05303030
[68,] -2.86553030 -1.93673030
[69,] -4.14433030 -2.86553030
[70,] -2.91163030 -4.14433030
[71,] -1.36323030 -2.91163030
[72,] -1.74619091 -1.36323030
[73,] -2.40649091 -1.74619091
[74,] -0.91429091 -2.40649091
[75,] -1.14869091 -0.91429091
[76,] -3.41799091 -1.14869091
[77,] -1.70039091 -3.41799091
[78,] -2.52189091 -1.70039091
[79,] -2.87559091 -2.52189091
[80,] -3.14539091 -2.87559091
[81,] -0.56419091 -3.14539091
[82,] -0.45949091 -0.56419091
[83,] -2.03309091 -0.45949091
[84,] 0.59394848 -2.03309091
[85,] 0.53864848 0.59394848
[86,] 2.33284848 0.53864848
[87,] 1.86744848 2.33284848
[88,] 0.18014848 1.86744848
[89,] -0.52525152 0.18014848
[90,] 0.54224848 -0.52525152
[91,] 1.73454848 0.54224848
[92,] 3.29274848 1.73454848
[93,] 2.52894848 3.29274848
[94,] 1.44064848 2.52894848
[95,] -0.44195152 1.44064848
[96,] 1.50708788 -0.44195152
[97,] 2.72778788 1.50708788
[98,] -0.16501212 2.72778788
[99,] 4.46758788 -0.16501212
[100,] 5.16828788 4.46758788
[101,] 1.77888788 5.16828788
[102,] 1.58438788 1.77888788
[103,] 1.09268788 1.58438788
[104,] 3.79288788 1.09268788
[105,] 1.83708788 3.79288788
[106,] 0.35378788 1.83708788
[107,] 2.06218788 0.35378788
[108,] 1.52322727 2.06218788
[109,] 1.17692727 1.52322727
[110,] 0.95712727 1.17692727
[111,] 2.37372727 0.95712727
[112,] 1.71442727 2.37372727
[113,] 4.42702727 1.71442727
[114,] 4.17252727 4.42702727
[115,] 1.84882727 4.17252727
[116,] 3.74802727 1.84882727
[117,] 2.72022727 3.74802727
[118,] 1.09892727 2.72022727
[119,] 2.93132727 1.09892727
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.91167273 1.03097273
2 0.89387273 1.91167273
3 0.87547273 0.89387273
4 3.77217273 0.87547273
5 3.83977273 3.77217273
6 3.27427273 3.83977273
7 3.39257273 3.27427273
8 2.28277273 3.39257273
9 2.91497273 2.28277273
10 3.84067273 2.91497273
11 2.70607273 3.84067273
12 4.22011212 2.70607273
13 3.60381212 4.22011212
14 3.71901212 3.60381212
15 2.54861212 3.71901212
16 5.48231212 2.54861212
17 4.98891212 5.48231212
18 4.45741212 4.98891212
19 5.97871212 4.45741212
20 1.59791212 5.97871212
21 3.86611212 1.59791212
22 6.89481212 3.86611212
23 5.01921212 6.89481212
24 0.02025152 5.01921212
25 -0.16604848 0.02025152
26 2.39715152 -0.16604848
27 -0.08224848 2.39715152
28 2.08845152 -0.08224848
29 -3.86094848 2.08845152
30 -3.24844848 -3.86094848
31 -4.55014848 -3.24844848
32 -4.08494848 -4.55014848
33 -3.05274848 -4.08494848
34 -3.68304848 -3.05274848
35 -2.23664848 -3.68304848
36 -1.12460909 -2.23664848
37 -1.98190909 -1.12460909
38 -1.86970909 -1.98190909
39 -2.78810909 -1.86970909
40 -3.30840909 -2.78810909
41 -2.91980909 -3.30840909
42 -1.85930909 -2.91980909
43 -3.03900909 -1.85930909
44 -3.05480909 -3.03900909
45 -3.31460909 -3.05480909
46 -3.72790909 -3.31460909
47 -3.54050909 -3.72790909
48 -3.43546970 -3.54050909
49 -2.79476970 -3.43546970
50 -3.09456970 -2.79476970
51 -4.25296970 -3.09456970
52 -5.66526970 -4.25296970
53 -3.57766970 -5.66526970
54 -3.34816970 -3.57766970
55 -1.64586970 -3.34816970
56 -1.56366970 -1.64586970
57 -2.79146970 -1.56366970
58 -2.84676970 -2.79146970
59 -3.10336970 -2.84676970
60 -2.58933030 -3.10336970
61 -2.60963030 -2.58933030
62 -4.25643030 -2.60963030
63 -3.86083030 -4.25643030
64 -6.01413030 -3.86083030
65 -2.45053030 -6.01413030
66 -3.05303030 -2.45053030
67 -1.93673030 -3.05303030
68 -2.86553030 -1.93673030
69 -4.14433030 -2.86553030
70 -2.91163030 -4.14433030
71 -1.36323030 -2.91163030
72 -1.74619091 -1.36323030
73 -2.40649091 -1.74619091
74 -0.91429091 -2.40649091
75 -1.14869091 -0.91429091
76 -3.41799091 -1.14869091
77 -1.70039091 -3.41799091
78 -2.52189091 -1.70039091
79 -2.87559091 -2.52189091
80 -3.14539091 -2.87559091
81 -0.56419091 -3.14539091
82 -0.45949091 -0.56419091
83 -2.03309091 -0.45949091
84 0.59394848 -2.03309091
85 0.53864848 0.59394848
86 2.33284848 0.53864848
87 1.86744848 2.33284848
88 0.18014848 1.86744848
89 -0.52525152 0.18014848
90 0.54224848 -0.52525152
91 1.73454848 0.54224848
92 3.29274848 1.73454848
93 2.52894848 3.29274848
94 1.44064848 2.52894848
95 -0.44195152 1.44064848
96 1.50708788 -0.44195152
97 2.72778788 1.50708788
98 -0.16501212 2.72778788
99 4.46758788 -0.16501212
100 5.16828788 4.46758788
101 1.77888788 5.16828788
102 1.58438788 1.77888788
103 1.09268788 1.58438788
104 3.79288788 1.09268788
105 1.83708788 3.79288788
106 0.35378788 1.83708788
107 2.06218788 0.35378788
108 1.52322727 2.06218788
109 1.17692727 1.52322727
110 0.95712727 1.17692727
111 2.37372727 0.95712727
112 1.71442727 2.37372727
113 4.42702727 1.71442727
114 4.17252727 4.42702727
115 1.84882727 4.17252727
116 3.74802727 1.84882727
117 2.72022727 3.74802727
118 1.09892727 2.72022727
119 2.93132727 1.09892727
> 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/7tvc41292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/845b71292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/945b71292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1045b71292063046.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11iw8f1292063046.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/12bo801292063046.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/13i75u1292063046.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/14ay4f1292063046.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/15wg3l1292063046.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/16sq0u1292063046.tab")
+ }
>
> try(system("convert tmp/18vvg1292063046.ps tmp/18vvg1292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/28vvg1292063046.ps tmp/28vvg1292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/38vvg1292063046.ps tmp/38vvg1292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/404c11292063046.ps tmp/404c11292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/504c11292063046.ps tmp/504c11292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tvc41292063046.ps tmp/6tvc41292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tvc41292063046.ps tmp/7tvc41292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/845b71292063046.ps tmp/845b71292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/945b71292063046.ps tmp/945b71292063046.png",intern=TRUE))
character(0)
> try(system("convert tmp/1045b71292063046.ps tmp/1045b71292063046.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.359 1.682 7.811