R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(8310,0,7649,0,7279,0,6857,0,6496,0,6280,0,8962,0,11205,0,10363,0,9175,0,8234,0,8121,0,7438,0,6876,0,6489,0,6319,0,5952,0,6055,0,9107,0,11493,0,10213,0,9238,0,8218,0,7995,0,7581,0,7051,0,6668,0,6433,0,6135,0,6365,0,10095,0,12029,0,12184,0,11331,0,9961,0,9739,0,9080,0,8507,0,8097,0,7772,0,7440,0,7902,0,13539,0,14992,0,15436,0,14156,0,12846,0,12302,0,11691,0,10648,0,10064,0,10016,0,9691,0,10260,0,16882,0,18573,0,18227,0,16346,0,14694,0,14453,0,13949,0,13277,0,12726,0,12279,0,11819,0,12207,0,18637,0,20519,0,19974,0,17802,0,15997,0,15430,0,14452,0,13614,0,13080,0,12290,0,11890,0,12292,0,18700,0,20388,0,19170,0,17530,0,15564,0,15163,0,13406,0,12763,0,12083,0,12054,0,11770,0,12266,0,17549,0,18655,0,17279,0,14788,0,13138,0,12494,0,11767,0,10928,0,10104,0,9760,0,9536,0,9978,0,14846,0,15565,0,13587,0,11804,0,10611,0,10915,0,9988,0,9376,0,9319,0,8852,0,8392,0,9050,0,13250,1,14037,1,12486,1,11182,1,10287,1),dim=c(2,119),dimnames=list(c('NWWZM','Dummy'),1:119))
> y <- array(NA,dim=c(2,119),dimnames=list(c('NWWZM','Dummy'),1:119))
> 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
NWWZM Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8310 0 1 0 0 0 0 0 0 0 0 0 0 1
2 7649 0 0 1 0 0 0 0 0 0 0 0 0 2
3 7279 0 0 0 1 0 0 0 0 0 0 0 0 3
4 6857 0 0 0 0 1 0 0 0 0 0 0 0 4
5 6496 0 0 0 0 0 1 0 0 0 0 0 0 5
6 6280 0 0 0 0 0 0 1 0 0 0 0 0 6
7 8962 0 0 0 0 0 0 0 1 0 0 0 0 7
8 11205 0 0 0 0 0 0 0 0 1 0 0 0 8
9 10363 0 0 0 0 0 0 0 0 0 1 0 0 9
10 9175 0 0 0 0 0 0 0 0 0 0 1 0 10
11 8234 0 0 0 0 0 0 0 0 0 0 0 1 11
12 8121 0 0 0 0 0 0 0 0 0 0 0 0 12
13 7438 0 1 0 0 0 0 0 0 0 0 0 0 13
14 6876 0 0 1 0 0 0 0 0 0 0 0 0 14
15 6489 0 0 0 1 0 0 0 0 0 0 0 0 15
16 6319 0 0 0 0 1 0 0 0 0 0 0 0 16
17 5952 0 0 0 0 0 1 0 0 0 0 0 0 17
18 6055 0 0 0 0 0 0 1 0 0 0 0 0 18
19 9107 0 0 0 0 0 0 0 1 0 0 0 0 19
20 11493 0 0 0 0 0 0 0 0 1 0 0 0 20
21 10213 0 0 0 0 0 0 0 0 0 1 0 0 21
22 9238 0 0 0 0 0 0 0 0 0 0 1 0 22
23 8218 0 0 0 0 0 0 0 0 0 0 0 1 23
24 7995 0 0 0 0 0 0 0 0 0 0 0 0 24
25 7581 0 1 0 0 0 0 0 0 0 0 0 0 25
26 7051 0 0 1 0 0 0 0 0 0 0 0 0 26
27 6668 0 0 0 1 0 0 0 0 0 0 0 0 27
28 6433 0 0 0 0 1 0 0 0 0 0 0 0 28
29 6135 0 0 0 0 0 1 0 0 0 0 0 0 29
30 6365 0 0 0 0 0 0 1 0 0 0 0 0 30
31 10095 0 0 0 0 0 0 0 1 0 0 0 0 31
32 12029 0 0 0 0 0 0 0 0 1 0 0 0 32
33 12184 0 0 0 0 0 0 0 0 0 1 0 0 33
34 11331 0 0 0 0 0 0 0 0 0 0 1 0 34
35 9961 0 0 0 0 0 0 0 0 0 0 0 1 35
36 9739 0 0 0 0 0 0 0 0 0 0 0 0 36
37 9080 0 1 0 0 0 0 0 0 0 0 0 0 37
38 8507 0 0 1 0 0 0 0 0 0 0 0 0 38
39 8097 0 0 0 1 0 0 0 0 0 0 0 0 39
40 7772 0 0 0 0 1 0 0 0 0 0 0 0 40
41 7440 0 0 0 0 0 1 0 0 0 0 0 0 41
42 7902 0 0 0 0 0 0 1 0 0 0 0 0 42
43 13539 0 0 0 0 0 0 0 1 0 0 0 0 43
44 14992 0 0 0 0 0 0 0 0 1 0 0 0 44
45 15436 0 0 0 0 0 0 0 0 0 1 0 0 45
46 14156 0 0 0 0 0 0 0 0 0 0 1 0 46
47 12846 0 0 0 0 0 0 0 0 0 0 0 1 47
48 12302 0 0 0 0 0 0 0 0 0 0 0 0 48
49 11691 0 1 0 0 0 0 0 0 0 0 0 0 49
50 10648 0 0 1 0 0 0 0 0 0 0 0 0 50
51 10064 0 0 0 1 0 0 0 0 0 0 0 0 51
52 10016 0 0 0 0 1 0 0 0 0 0 0 0 52
53 9691 0 0 0 0 0 1 0 0 0 0 0 0 53
54 10260 0 0 0 0 0 0 1 0 0 0 0 0 54
55 16882 0 0 0 0 0 0 0 1 0 0 0 0 55
56 18573 0 0 0 0 0 0 0 0 1 0 0 0 56
57 18227 0 0 0 0 0 0 0 0 0 1 0 0 57
58 16346 0 0 0 0 0 0 0 0 0 0 1 0 58
59 14694 0 0 0 0 0 0 0 0 0 0 0 1 59
60 14453 0 0 0 0 0 0 0 0 0 0 0 0 60
61 13949 0 1 0 0 0 0 0 0 0 0 0 0 61
62 13277 0 0 1 0 0 0 0 0 0 0 0 0 62
63 12726 0 0 0 1 0 0 0 0 0 0 0 0 63
64 12279 0 0 0 0 1 0 0 0 0 0 0 0 64
65 11819 0 0 0 0 0 1 0 0 0 0 0 0 65
66 12207 0 0 0 0 0 0 1 0 0 0 0 0 66
67 18637 0 0 0 0 0 0 0 1 0 0 0 0 67
68 20519 0 0 0 0 0 0 0 0 1 0 0 0 68
69 19974 0 0 0 0 0 0 0 0 0 1 0 0 69
70 17802 0 0 0 0 0 0 0 0 0 0 1 0 70
71 15997 0 0 0 0 0 0 0 0 0 0 0 1 71
72 15430 0 0 0 0 0 0 0 0 0 0 0 0 72
73 14452 0 1 0 0 0 0 0 0 0 0 0 0 73
74 13614 0 0 1 0 0 0 0 0 0 0 0 0 74
75 13080 0 0 0 1 0 0 0 0 0 0 0 0 75
76 12290 0 0 0 0 1 0 0 0 0 0 0 0 76
77 11890 0 0 0 0 0 1 0 0 0 0 0 0 77
78 12292 0 0 0 0 0 0 1 0 0 0 0 0 78
79 18700 0 0 0 0 0 0 0 1 0 0 0 0 79
80 20388 0 0 0 0 0 0 0 0 1 0 0 0 80
81 19170 0 0 0 0 0 0 0 0 0 1 0 0 81
82 17530 0 0 0 0 0 0 0 0 0 0 1 0 82
83 15564 0 0 0 0 0 0 0 0 0 0 0 1 83
84 15163 0 0 0 0 0 0 0 0 0 0 0 0 84
85 13406 0 1 0 0 0 0 0 0 0 0 0 0 85
86 12763 0 0 1 0 0 0 0 0 0 0 0 0 86
87 12083 0 0 0 1 0 0 0 0 0 0 0 0 87
88 12054 0 0 0 0 1 0 0 0 0 0 0 0 88
89 11770 0 0 0 0 0 1 0 0 0 0 0 0 89
90 12266 0 0 0 0 0 0 1 0 0 0 0 0 90
91 17549 0 0 0 0 0 0 0 1 0 0 0 0 91
92 18655 0 0 0 0 0 0 0 0 1 0 0 0 92
93 17279 0 0 0 0 0 0 0 0 0 1 0 0 93
94 14788 0 0 0 0 0 0 0 0 0 0 1 0 94
95 13138 0 0 0 0 0 0 0 0 0 0 0 1 95
96 12494 0 0 0 0 0 0 0 0 0 0 0 0 96
97 11767 0 1 0 0 0 0 0 0 0 0 0 0 97
98 10928 0 0 1 0 0 0 0 0 0 0 0 0 98
99 10104 0 0 0 1 0 0 0 0 0 0 0 0 99
100 9760 0 0 0 0 1 0 0 0 0 0 0 0 100
101 9536 0 0 0 0 0 1 0 0 0 0 0 0 101
102 9978 0 0 0 0 0 0 1 0 0 0 0 0 102
103 14846 0 0 0 0 0 0 0 1 0 0 0 0 103
104 15565 0 0 0 0 0 0 0 0 1 0 0 0 104
105 13587 0 0 0 0 0 0 0 0 0 1 0 0 105
106 11804 0 0 0 0 0 0 0 0 0 0 1 0 106
107 10611 0 0 0 0 0 0 0 0 0 0 0 1 107
108 10915 0 0 0 0 0 0 0 0 0 0 0 0 108
109 9988 0 1 0 0 0 0 0 0 0 0 0 0 109
110 9376 0 0 1 0 0 0 0 0 0 0 0 0 110
111 9319 0 0 0 1 0 0 0 0 0 0 0 0 111
112 8852 0 0 0 0 1 0 0 0 0 0 0 0 112
113 8392 0 0 0 0 0 1 0 0 0 0 0 0 113
114 9050 0 0 0 0 0 0 1 0 0 0 0 0 114
115 13250 1 0 0 0 0 0 0 1 0 0 0 0 115
116 14037 1 0 0 0 0 0 0 0 1 0 0 0 116
117 12486 1 0 0 0 0 0 0 0 0 1 0 0 117
118 11182 1 0 0 0 0 0 0 0 0 0 1 0 118
119 10287 1 0 0 0 0 0 0 0 0 0 0 1 119
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
8479.37 -5331.39 -799.04 -1552.45 -2086.56 -2470.36
M5 M6 M7 M8 M9 M10
-2877.57 -2580.28 2787.95 4320.75 3410.94 1798.13
M11 t
361.83 56.11
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4420.8 -1499.7 -276.8 1667.9 4212.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8479.368 844.620 10.039 < 2e-16 ***
Dummy -5331.387 1130.551 -4.716 7.44e-06 ***
M1 -799.044 1036.378 -0.771 0.44244
M2 -1552.450 1036.199 -1.498 0.13708
M3 -2086.557 1036.059 -2.014 0.04657 *
M4 -2470.364 1035.960 -2.385 0.01889 *
M5 -2877.571 1035.900 -2.778 0.00648 **
M6 -2580.278 1035.880 -2.491 0.01431 *
M7 2787.954 1041.813 2.676 0.00864 **
M8 4320.747 1041.635 4.148 6.82e-05 ***
M9 3410.940 1041.496 3.275 0.00143 **
M10 1798.134 1041.397 1.727 0.08717 .
M11 361.827 1041.338 0.347 0.72894
t 56.107 6.425 8.732 4.22e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2255 on 105 degrees of freedom
Multiple R-squared: 0.6596, Adjusted R-squared: 0.6175
F-statistic: 15.65 on 13 and 105 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.992256e-04 3.984512e-04 9.998008e-01
[2,] 7.777427e-05 1.555485e-04 9.999222e-01
[3,] 7.163845e-05 1.432769e-04 9.999284e-01
[4,] 3.898769e-05 7.797538e-05 9.999610e-01
[5,] 6.460971e-06 1.292194e-05 9.999935e-01
[6,] 1.410206e-06 2.820411e-06 9.999986e-01
[7,] 2.497903e-07 4.995806e-07 9.999998e-01
[8,] 3.720359e-08 7.440718e-08 1.000000e+00
[9,] 5.223703e-09 1.044741e-08 1.000000e+00
[10,] 7.679220e-10 1.535844e-09 1.000000e+00
[11,] 1.064246e-10 2.128492e-10 1.000000e+00
[12,] 1.553214e-11 3.106428e-11 1.000000e+00
[13,] 2.478555e-12 4.957110e-12 1.000000e+00
[14,] 9.859217e-13 1.971843e-12 1.000000e+00
[15,] 5.291143e-11 1.058229e-10 1.000000e+00
[16,] 7.194135e-11 1.438827e-10 1.000000e+00
[17,] 7.515690e-09 1.503138e-08 1.000000e+00
[18,] 1.213274e-07 2.426548e-07 9.999999e-01
[19,] 2.613137e-07 5.226274e-07 9.999997e-01
[20,] 3.914779e-07 7.829558e-07 9.999996e-01
[21,] 2.815487e-07 5.630973e-07 9.999997e-01
[22,] 1.967320e-07 3.934640e-07 9.999998e-01
[23,] 1.342013e-07 2.684025e-07 9.999999e-01
[24,] 9.341520e-08 1.868304e-07 9.999999e-01
[25,] 7.143795e-08 1.428759e-07 9.999999e-01
[26,] 9.156694e-08 1.831339e-07 9.999999e-01
[27,] 2.701069e-05 5.402137e-05 9.999730e-01
[28,] 2.673088e-04 5.346176e-04 9.997327e-01
[29,] 3.519124e-03 7.038249e-03 9.964809e-01
[30,] 1.261888e-02 2.523776e-02 9.873811e-01
[31,] 2.746209e-02 5.492418e-02 9.725379e-01
[32,] 4.555747e-02 9.111494e-02 9.544425e-01
[33,] 5.699991e-02 1.139998e-01 9.430001e-01
[34,] 7.416673e-02 1.483335e-01 9.258333e-01
[35,] 1.050490e-01 2.100981e-01 8.949510e-01
[36,] 1.488971e-01 2.977941e-01 8.511029e-01
[37,] 2.276677e-01 4.553354e-01 7.723323e-01
[38,] 3.791399e-01 7.582797e-01 6.208601e-01
[39,] 7.491741e-01 5.016518e-01 2.508259e-01
[40,] 9.113313e-01 1.773373e-01 8.866867e-02
[41,] 9.603158e-01 7.936843e-02 3.968422e-02
[42,] 9.766158e-01 4.676841e-02 2.338421e-02
[43,] 9.870405e-01 2.591893e-02 1.295947e-02
[44,] 9.925801e-01 1.483982e-02 7.419910e-03
[45,] 9.936782e-01 1.264362e-02 6.321812e-03
[46,] 9.947985e-01 1.040296e-02 5.201482e-03
[47,] 9.961459e-01 7.708252e-03 3.854126e-03
[48,] 9.976108e-01 4.778397e-03 2.389199e-03
[49,] 9.990412e-01 1.917502e-03 9.587511e-04
[50,] 9.998392e-01 3.215849e-04 1.607925e-04
[51,] 9.999491e-01 1.018593e-04 5.092966e-05
[52,] 9.999596e-01 8.076889e-05 4.038445e-05
[53,] 9.999508e-01 9.837514e-05 4.918757e-05
[54,] 9.999159e-01 1.681768e-04 8.408841e-05
[55,] 9.998646e-01 2.707135e-04 1.353567e-04
[56,] 9.997950e-01 4.099824e-04 2.049912e-04
[57,] 9.996820e-01 6.360973e-04 3.180486e-04
[58,] 9.995698e-01 8.604504e-04 4.302252e-04
[59,] 9.994568e-01 1.086493e-03 5.432465e-04
[60,] 9.996759e-01 6.482574e-04 3.241287e-04
[61,] 9.998861e-01 2.277722e-04 1.138861e-04
[62,] 9.999890e-01 2.195316e-05 1.097658e-05
[63,] 9.999797e-01 4.067334e-05 2.033667e-05
[64,] 9.999590e-01 8.198099e-05 4.099049e-05
[65,] 9.999479e-01 1.042691e-04 5.213457e-05
[66,] 9.999629e-01 7.425395e-05 3.712697e-05
[67,] 9.999396e-01 1.208683e-04 6.043415e-05
[68,] 9.999123e-01 1.753900e-04 8.769502e-05
[69,] 9.998787e-01 2.426259e-04 1.213130e-04
[70,] 9.998125e-01 3.750076e-04 1.875038e-04
[71,] 9.997461e-01 5.078855e-04 2.539427e-04
[72,] 9.995489e-01 9.021081e-04 4.510541e-04
[73,] 9.991655e-01 1.668959e-03 8.344797e-04
[74,] 9.984172e-01 3.165511e-03 1.582756e-03
[75,] 9.978032e-01 4.393600e-03 2.196800e-03
[76,] 9.985312e-01 2.937604e-03 1.468802e-03
[77,] 9.999141e-01 1.717788e-04 8.588942e-05
[78,] 9.999858e-01 2.830462e-05 1.415231e-05
[79,] 9.999928e-01 1.437667e-05 7.188334e-06
[80,] 9.999820e-01 3.592530e-05 1.796265e-05
[81,] 9.999744e-01 5.119395e-05 2.559697e-05
[82,] 9.999478e-01 1.044521e-04 5.222605e-05
[83,] 9.997799e-01 4.402398e-04 2.201199e-04
[84,] 9.989935e-01 2.013087e-03 1.006543e-03
[85,] 9.954543e-01 9.091415e-03 4.545708e-03
[86,] 9.792902e-01 4.141964e-02 2.070982e-02
> postscript(file="/var/www/html/rcomp/tmp/1wo191229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wb171229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3bcrl1229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lbip1229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52lwu1229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 119
Frequency = 1
1 2 3 4 5 6
573.56842 609.86842 717.86842 623.56842 613.66842 44.26842
7 8 9 10 11 12
-2698.07029 -2043.97029 -2032.27029 -1663.57029 -1224.37029 -1031.65029
13 14 15 16 17 18
-971.71345 -836.41345 -745.41345 -587.71345 -603.61345 -854.01345
19 20 21 22 23 24
-3226.35216 -2429.25216 -2855.55216 -2273.85216 -1913.65216 -1830.93216
25 26 27 28 29 30
-1501.99532 -1334.69532 -1239.69532 -1146.99532 -1093.89532 -1217.29532
31 32 33 34 35 36
-2911.63404 -2566.53404 -1557.83404 -854.13404 -843.93404 -760.21404
37 38 39 40 41 42
-676.27719 -551.97719 -483.97719 -481.27719 -462.17719 -353.57719
43 44 45 46 47 48
-140.91591 -276.81591 1020.88409 1297.58409 1367.78409 1129.50409
49 50 51 52 53 54
1261.44094 915.74094 809.74094 1089.44094 1115.54094 1331.14094
55 56 57 58 59 60
2528.80222 2630.90222 3138.60222 2814.30222 2542.50222 2607.22222
61 62 63 64 65 66
2846.15906 2871.45906 2798.45906 2679.15906 2570.25906 2604.85906
67 68 69 70 71 72
3610.52035 3903.62035 4212.32035 3597.02035 3172.22035 2910.94035
73 74 75 76 77 78
2675.87719 2535.17719 2479.17719 2016.87719 1967.97719 2016.57719
79 80 81 82 83 84
3000.23848 3099.33848 2735.03848 2651.73848 2065.93848 1970.65848
85 86 87 88 89 90
956.59532 1010.89532 808.89532 1107.59532 1174.69532 1317.29532
91 92 93 94 95 96
1175.95661 693.05661 170.75661 -763.54339 -1033.34339 -1371.62339
97 98 99 100 101 102
-1355.68655 -1497.38655 -1843.38655 -1859.68655 -1732.58655 -1643.98655
103 104 105 106 107 108
-2200.32526 -3070.22526 -4194.52526 -4420.82526 -4233.62526 -3623.90526
109 110 111 112 113 114
-3807.96842 -3722.66842 -3301.66842 -3440.96842 -3549.86842 -3245.26842
115 116 117 118 119
861.78000 59.88000 -637.42000 -384.72000 100.48000
> postscript(file="/var/www/html/rcomp/tmp/61zsk1229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 119
Frequency = 1
lag(myerror, k = 1) myerror
0 573.56842 NA
1 609.86842 573.56842
2 717.86842 609.86842
3 623.56842 717.86842
4 613.66842 623.56842
5 44.26842 613.66842
6 -2698.07029 44.26842
7 -2043.97029 -2698.07029
8 -2032.27029 -2043.97029
9 -1663.57029 -2032.27029
10 -1224.37029 -1663.57029
11 -1031.65029 -1224.37029
12 -971.71345 -1031.65029
13 -836.41345 -971.71345
14 -745.41345 -836.41345
15 -587.71345 -745.41345
16 -603.61345 -587.71345
17 -854.01345 -603.61345
18 -3226.35216 -854.01345
19 -2429.25216 -3226.35216
20 -2855.55216 -2429.25216
21 -2273.85216 -2855.55216
22 -1913.65216 -2273.85216
23 -1830.93216 -1913.65216
24 -1501.99532 -1830.93216
25 -1334.69532 -1501.99532
26 -1239.69532 -1334.69532
27 -1146.99532 -1239.69532
28 -1093.89532 -1146.99532
29 -1217.29532 -1093.89532
30 -2911.63404 -1217.29532
31 -2566.53404 -2911.63404
32 -1557.83404 -2566.53404
33 -854.13404 -1557.83404
34 -843.93404 -854.13404
35 -760.21404 -843.93404
36 -676.27719 -760.21404
37 -551.97719 -676.27719
38 -483.97719 -551.97719
39 -481.27719 -483.97719
40 -462.17719 -481.27719
41 -353.57719 -462.17719
42 -140.91591 -353.57719
43 -276.81591 -140.91591
44 1020.88409 -276.81591
45 1297.58409 1020.88409
46 1367.78409 1297.58409
47 1129.50409 1367.78409
48 1261.44094 1129.50409
49 915.74094 1261.44094
50 809.74094 915.74094
51 1089.44094 809.74094
52 1115.54094 1089.44094
53 1331.14094 1115.54094
54 2528.80222 1331.14094
55 2630.90222 2528.80222
56 3138.60222 2630.90222
57 2814.30222 3138.60222
58 2542.50222 2814.30222
59 2607.22222 2542.50222
60 2846.15906 2607.22222
61 2871.45906 2846.15906
62 2798.45906 2871.45906
63 2679.15906 2798.45906
64 2570.25906 2679.15906
65 2604.85906 2570.25906
66 3610.52035 2604.85906
67 3903.62035 3610.52035
68 4212.32035 3903.62035
69 3597.02035 4212.32035
70 3172.22035 3597.02035
71 2910.94035 3172.22035
72 2675.87719 2910.94035
73 2535.17719 2675.87719
74 2479.17719 2535.17719
75 2016.87719 2479.17719
76 1967.97719 2016.87719
77 2016.57719 1967.97719
78 3000.23848 2016.57719
79 3099.33848 3000.23848
80 2735.03848 3099.33848
81 2651.73848 2735.03848
82 2065.93848 2651.73848
83 1970.65848 2065.93848
84 956.59532 1970.65848
85 1010.89532 956.59532
86 808.89532 1010.89532
87 1107.59532 808.89532
88 1174.69532 1107.59532
89 1317.29532 1174.69532
90 1175.95661 1317.29532
91 693.05661 1175.95661
92 170.75661 693.05661
93 -763.54339 170.75661
94 -1033.34339 -763.54339
95 -1371.62339 -1033.34339
96 -1355.68655 -1371.62339
97 -1497.38655 -1355.68655
98 -1843.38655 -1497.38655
99 -1859.68655 -1843.38655
100 -1732.58655 -1859.68655
101 -1643.98655 -1732.58655
102 -2200.32526 -1643.98655
103 -3070.22526 -2200.32526
104 -4194.52526 -3070.22526
105 -4420.82526 -4194.52526
106 -4233.62526 -4420.82526
107 -3623.90526 -4233.62526
108 -3807.96842 -3623.90526
109 -3722.66842 -3807.96842
110 -3301.66842 -3722.66842
111 -3440.96842 -3301.66842
112 -3549.86842 -3440.96842
113 -3245.26842 -3549.86842
114 861.78000 -3245.26842
115 59.88000 861.78000
116 -637.42000 59.88000
117 -384.72000 -637.42000
118 100.48000 -384.72000
119 NA 100.48000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 609.86842 573.56842
[2,] 717.86842 609.86842
[3,] 623.56842 717.86842
[4,] 613.66842 623.56842
[5,] 44.26842 613.66842
[6,] -2698.07029 44.26842
[7,] -2043.97029 -2698.07029
[8,] -2032.27029 -2043.97029
[9,] -1663.57029 -2032.27029
[10,] -1224.37029 -1663.57029
[11,] -1031.65029 -1224.37029
[12,] -971.71345 -1031.65029
[13,] -836.41345 -971.71345
[14,] -745.41345 -836.41345
[15,] -587.71345 -745.41345
[16,] -603.61345 -587.71345
[17,] -854.01345 -603.61345
[18,] -3226.35216 -854.01345
[19,] -2429.25216 -3226.35216
[20,] -2855.55216 -2429.25216
[21,] -2273.85216 -2855.55216
[22,] -1913.65216 -2273.85216
[23,] -1830.93216 -1913.65216
[24,] -1501.99532 -1830.93216
[25,] -1334.69532 -1501.99532
[26,] -1239.69532 -1334.69532
[27,] -1146.99532 -1239.69532
[28,] -1093.89532 -1146.99532
[29,] -1217.29532 -1093.89532
[30,] -2911.63404 -1217.29532
[31,] -2566.53404 -2911.63404
[32,] -1557.83404 -2566.53404
[33,] -854.13404 -1557.83404
[34,] -843.93404 -854.13404
[35,] -760.21404 -843.93404
[36,] -676.27719 -760.21404
[37,] -551.97719 -676.27719
[38,] -483.97719 -551.97719
[39,] -481.27719 -483.97719
[40,] -462.17719 -481.27719
[41,] -353.57719 -462.17719
[42,] -140.91591 -353.57719
[43,] -276.81591 -140.91591
[44,] 1020.88409 -276.81591
[45,] 1297.58409 1020.88409
[46,] 1367.78409 1297.58409
[47,] 1129.50409 1367.78409
[48,] 1261.44094 1129.50409
[49,] 915.74094 1261.44094
[50,] 809.74094 915.74094
[51,] 1089.44094 809.74094
[52,] 1115.54094 1089.44094
[53,] 1331.14094 1115.54094
[54,] 2528.80222 1331.14094
[55,] 2630.90222 2528.80222
[56,] 3138.60222 2630.90222
[57,] 2814.30222 3138.60222
[58,] 2542.50222 2814.30222
[59,] 2607.22222 2542.50222
[60,] 2846.15906 2607.22222
[61,] 2871.45906 2846.15906
[62,] 2798.45906 2871.45906
[63,] 2679.15906 2798.45906
[64,] 2570.25906 2679.15906
[65,] 2604.85906 2570.25906
[66,] 3610.52035 2604.85906
[67,] 3903.62035 3610.52035
[68,] 4212.32035 3903.62035
[69,] 3597.02035 4212.32035
[70,] 3172.22035 3597.02035
[71,] 2910.94035 3172.22035
[72,] 2675.87719 2910.94035
[73,] 2535.17719 2675.87719
[74,] 2479.17719 2535.17719
[75,] 2016.87719 2479.17719
[76,] 1967.97719 2016.87719
[77,] 2016.57719 1967.97719
[78,] 3000.23848 2016.57719
[79,] 3099.33848 3000.23848
[80,] 2735.03848 3099.33848
[81,] 2651.73848 2735.03848
[82,] 2065.93848 2651.73848
[83,] 1970.65848 2065.93848
[84,] 956.59532 1970.65848
[85,] 1010.89532 956.59532
[86,] 808.89532 1010.89532
[87,] 1107.59532 808.89532
[88,] 1174.69532 1107.59532
[89,] 1317.29532 1174.69532
[90,] 1175.95661 1317.29532
[91,] 693.05661 1175.95661
[92,] 170.75661 693.05661
[93,] -763.54339 170.75661
[94,] -1033.34339 -763.54339
[95,] -1371.62339 -1033.34339
[96,] -1355.68655 -1371.62339
[97,] -1497.38655 -1355.68655
[98,] -1843.38655 -1497.38655
[99,] -1859.68655 -1843.38655
[100,] -1732.58655 -1859.68655
[101,] -1643.98655 -1732.58655
[102,] -2200.32526 -1643.98655
[103,] -3070.22526 -2200.32526
[104,] -4194.52526 -3070.22526
[105,] -4420.82526 -4194.52526
[106,] -4233.62526 -4420.82526
[107,] -3623.90526 -4233.62526
[108,] -3807.96842 -3623.90526
[109,] -3722.66842 -3807.96842
[110,] -3301.66842 -3722.66842
[111,] -3440.96842 -3301.66842
[112,] -3549.86842 -3440.96842
[113,] -3245.26842 -3549.86842
[114,] 861.78000 -3245.26842
[115,] 59.88000 861.78000
[116,] -637.42000 59.88000
[117,] -384.72000 -637.42000
[118,] 100.48000 -384.72000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 609.86842 573.56842
2 717.86842 609.86842
3 623.56842 717.86842
4 613.66842 623.56842
5 44.26842 613.66842
6 -2698.07029 44.26842
7 -2043.97029 -2698.07029
8 -2032.27029 -2043.97029
9 -1663.57029 -2032.27029
10 -1224.37029 -1663.57029
11 -1031.65029 -1224.37029
12 -971.71345 -1031.65029
13 -836.41345 -971.71345
14 -745.41345 -836.41345
15 -587.71345 -745.41345
16 -603.61345 -587.71345
17 -854.01345 -603.61345
18 -3226.35216 -854.01345
19 -2429.25216 -3226.35216
20 -2855.55216 -2429.25216
21 -2273.85216 -2855.55216
22 -1913.65216 -2273.85216
23 -1830.93216 -1913.65216
24 -1501.99532 -1830.93216
25 -1334.69532 -1501.99532
26 -1239.69532 -1334.69532
27 -1146.99532 -1239.69532
28 -1093.89532 -1146.99532
29 -1217.29532 -1093.89532
30 -2911.63404 -1217.29532
31 -2566.53404 -2911.63404
32 -1557.83404 -2566.53404
33 -854.13404 -1557.83404
34 -843.93404 -854.13404
35 -760.21404 -843.93404
36 -676.27719 -760.21404
37 -551.97719 -676.27719
38 -483.97719 -551.97719
39 -481.27719 -483.97719
40 -462.17719 -481.27719
41 -353.57719 -462.17719
42 -140.91591 -353.57719
43 -276.81591 -140.91591
44 1020.88409 -276.81591
45 1297.58409 1020.88409
46 1367.78409 1297.58409
47 1129.50409 1367.78409
48 1261.44094 1129.50409
49 915.74094 1261.44094
50 809.74094 915.74094
51 1089.44094 809.74094
52 1115.54094 1089.44094
53 1331.14094 1115.54094
54 2528.80222 1331.14094
55 2630.90222 2528.80222
56 3138.60222 2630.90222
57 2814.30222 3138.60222
58 2542.50222 2814.30222
59 2607.22222 2542.50222
60 2846.15906 2607.22222
61 2871.45906 2846.15906
62 2798.45906 2871.45906
63 2679.15906 2798.45906
64 2570.25906 2679.15906
65 2604.85906 2570.25906
66 3610.52035 2604.85906
67 3903.62035 3610.52035
68 4212.32035 3903.62035
69 3597.02035 4212.32035
70 3172.22035 3597.02035
71 2910.94035 3172.22035
72 2675.87719 2910.94035
73 2535.17719 2675.87719
74 2479.17719 2535.17719
75 2016.87719 2479.17719
76 1967.97719 2016.87719
77 2016.57719 1967.97719
78 3000.23848 2016.57719
79 3099.33848 3000.23848
80 2735.03848 3099.33848
81 2651.73848 2735.03848
82 2065.93848 2651.73848
83 1970.65848 2065.93848
84 956.59532 1970.65848
85 1010.89532 956.59532
86 808.89532 1010.89532
87 1107.59532 808.89532
88 1174.69532 1107.59532
89 1317.29532 1174.69532
90 1175.95661 1317.29532
91 693.05661 1175.95661
92 170.75661 693.05661
93 -763.54339 170.75661
94 -1033.34339 -763.54339
95 -1371.62339 -1033.34339
96 -1355.68655 -1371.62339
97 -1497.38655 -1355.68655
98 -1843.38655 -1497.38655
99 -1859.68655 -1843.38655
100 -1732.58655 -1859.68655
101 -1643.98655 -1732.58655
102 -2200.32526 -1643.98655
103 -3070.22526 -2200.32526
104 -4194.52526 -3070.22526
105 -4420.82526 -4194.52526
106 -4233.62526 -4420.82526
107 -3623.90526 -4233.62526
108 -3807.96842 -3623.90526
109 -3722.66842 -3807.96842
110 -3301.66842 -3722.66842
111 -3440.96842 -3301.66842
112 -3549.86842 -3440.96842
113 -3245.26842 -3549.86842
114 861.78000 -3245.26842
115 59.88000 861.78000
116 -637.42000 59.88000
117 -384.72000 -637.42000
118 100.48000 -384.72000
> 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/77sx21229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81cgz1229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vxfl1229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kp6l1229094566.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11215o1229094566.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/12y4bq1229094566.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/13zr601229094566.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/14glfr1229094567.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/15hcmn1229094567.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/169t5b1229094567.tab")
+ }
>
> system("convert tmp/1wo191229094566.ps tmp/1wo191229094566.png")
> system("convert tmp/2wb171229094566.ps tmp/2wb171229094566.png")
> system("convert tmp/3bcrl1229094566.ps tmp/3bcrl1229094566.png")
> system("convert tmp/4lbip1229094566.ps tmp/4lbip1229094566.png")
> system("convert tmp/52lwu1229094566.ps tmp/52lwu1229094566.png")
> system("convert tmp/61zsk1229094566.ps tmp/61zsk1229094566.png")
> system("convert tmp/77sx21229094566.ps tmp/77sx21229094566.png")
> system("convert tmp/81cgz1229094566.ps tmp/81cgz1229094566.png")
> system("convert tmp/9vxfl1229094566.ps tmp/9vxfl1229094566.png")
> system("convert tmp/10kp6l1229094566.ps tmp/10kp6l1229094566.png")
>
>
> proc.time()
user system elapsed
3.331 1.668 4.406