R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(56,55,54,52,72,71,56,46,47,47,48,50,44,38,33,33,52,54,39,22,31,31,38,42,41,31,36,34,51,47,31,19,30,33,36,40,32,25,28,29,55,55,40,38,44,41,49,59,61,47,43,39,66,68,63,68,67,59,68,78,82,70,62,68,94,102,100,104,103,93,110,114,120,102,95,103,122,139,135,135,137,130,148,148,145,128,131,133,146,163,151,157,152,149,172,167,160,150,160,165,171,179,171,176,170,169,194,196,188,174,186,191,197,206,197,204,201,190,213,213),dim=c(1,120),dimnames=list(c('Omzet'),1:120))
> y <- array(NA,dim=c(1,120),dimnames=list(c('Omzet'),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'
> par3 <- 'Linear Trend'
> par2 <- 'Include Monthly Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, 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
Omzet M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 56 1 0 0 0 0 0 0 0 0 0 0 1
2 55 0 1 0 0 0 0 0 0 0 0 0 2
3 54 0 0 1 0 0 0 0 0 0 0 0 3
4 52 0 0 0 1 0 0 0 0 0 0 0 4
5 72 0 0 0 0 1 0 0 0 0 0 0 5
6 71 0 0 0 0 0 1 0 0 0 0 0 6
7 56 0 0 0 0 0 0 1 0 0 0 0 7
8 46 0 0 0 0 0 0 0 1 0 0 0 8
9 47 0 0 0 0 0 0 0 0 1 0 0 9
10 47 0 0 0 0 0 0 0 0 0 1 0 10
11 48 0 0 0 0 0 0 0 0 0 0 1 11
12 50 0 0 0 0 0 0 0 0 0 0 0 12
13 44 1 0 0 0 0 0 0 0 0 0 0 13
14 38 0 1 0 0 0 0 0 0 0 0 0 14
15 33 0 0 1 0 0 0 0 0 0 0 0 15
16 33 0 0 0 1 0 0 0 0 0 0 0 16
17 52 0 0 0 0 1 0 0 0 0 0 0 17
18 54 0 0 0 0 0 1 0 0 0 0 0 18
19 39 0 0 0 0 0 0 1 0 0 0 0 19
20 22 0 0 0 0 0 0 0 1 0 0 0 20
21 31 0 0 0 0 0 0 0 0 1 0 0 21
22 31 0 0 0 0 0 0 0 0 0 1 0 22
23 38 0 0 0 0 0 0 0 0 0 0 1 23
24 42 0 0 0 0 0 0 0 0 0 0 0 24
25 41 1 0 0 0 0 0 0 0 0 0 0 25
26 31 0 1 0 0 0 0 0 0 0 0 0 26
27 36 0 0 1 0 0 0 0 0 0 0 0 27
28 34 0 0 0 1 0 0 0 0 0 0 0 28
29 51 0 0 0 0 1 0 0 0 0 0 0 29
30 47 0 0 0 0 0 1 0 0 0 0 0 30
31 31 0 0 0 0 0 0 1 0 0 0 0 31
32 19 0 0 0 0 0 0 0 1 0 0 0 32
33 30 0 0 0 0 0 0 0 0 1 0 0 33
34 33 0 0 0 0 0 0 0 0 0 1 0 34
35 36 0 0 0 0 0 0 0 0 0 0 1 35
36 40 0 0 0 0 0 0 0 0 0 0 0 36
37 32 1 0 0 0 0 0 0 0 0 0 0 37
38 25 0 1 0 0 0 0 0 0 0 0 0 38
39 28 0 0 1 0 0 0 0 0 0 0 0 39
40 29 0 0 0 1 0 0 0 0 0 0 0 40
41 55 0 0 0 0 1 0 0 0 0 0 0 41
42 55 0 0 0 0 0 1 0 0 0 0 0 42
43 40 0 0 0 0 0 0 1 0 0 0 0 43
44 38 0 0 0 0 0 0 0 1 0 0 0 44
45 44 0 0 0 0 0 0 0 0 1 0 0 45
46 41 0 0 0 0 0 0 0 0 0 1 0 46
47 49 0 0 0 0 0 0 0 0 0 0 1 47
48 59 0 0 0 0 0 0 0 0 0 0 0 48
49 61 1 0 0 0 0 0 0 0 0 0 0 49
50 47 0 1 0 0 0 0 0 0 0 0 0 50
51 43 0 0 1 0 0 0 0 0 0 0 0 51
52 39 0 0 0 1 0 0 0 0 0 0 0 52
53 66 0 0 0 0 1 0 0 0 0 0 0 53
54 68 0 0 0 0 0 1 0 0 0 0 0 54
55 63 0 0 0 0 0 0 1 0 0 0 0 55
56 68 0 0 0 0 0 0 0 1 0 0 0 56
57 67 0 0 0 0 0 0 0 0 1 0 0 57
58 59 0 0 0 0 0 0 0 0 0 1 0 58
59 68 0 0 0 0 0 0 0 0 0 0 1 59
60 78 0 0 0 0 0 0 0 0 0 0 0 60
61 82 1 0 0 0 0 0 0 0 0 0 0 61
62 70 0 1 0 0 0 0 0 0 0 0 0 62
63 62 0 0 1 0 0 0 0 0 0 0 0 63
64 68 0 0 0 1 0 0 0 0 0 0 0 64
65 94 0 0 0 0 1 0 0 0 0 0 0 65
66 102 0 0 0 0 0 1 0 0 0 0 0 66
67 100 0 0 0 0 0 0 1 0 0 0 0 67
68 104 0 0 0 0 0 0 0 1 0 0 0 68
69 103 0 0 0 0 0 0 0 0 1 0 0 69
70 93 0 0 0 0 0 0 0 0 0 1 0 70
71 110 0 0 0 0 0 0 0 0 0 0 1 71
72 114 0 0 0 0 0 0 0 0 0 0 0 72
73 120 1 0 0 0 0 0 0 0 0 0 0 73
74 102 0 1 0 0 0 0 0 0 0 0 0 74
75 95 0 0 1 0 0 0 0 0 0 0 0 75
76 103 0 0 0 1 0 0 0 0 0 0 0 76
77 122 0 0 0 0 1 0 0 0 0 0 0 77
78 139 0 0 0 0 0 1 0 0 0 0 0 78
79 135 0 0 0 0 0 0 1 0 0 0 0 79
80 135 0 0 0 0 0 0 0 1 0 0 0 80
81 137 0 0 0 0 0 0 0 0 1 0 0 81
82 130 0 0 0 0 0 0 0 0 0 1 0 82
83 148 0 0 0 0 0 0 0 0 0 0 1 83
84 148 0 0 0 0 0 0 0 0 0 0 0 84
85 145 1 0 0 0 0 0 0 0 0 0 0 85
86 128 0 1 0 0 0 0 0 0 0 0 0 86
87 131 0 0 1 0 0 0 0 0 0 0 0 87
88 133 0 0 0 1 0 0 0 0 0 0 0 88
89 146 0 0 0 0 1 0 0 0 0 0 0 89
90 163 0 0 0 0 0 1 0 0 0 0 0 90
91 151 0 0 0 0 0 0 1 0 0 0 0 91
92 157 0 0 0 0 0 0 0 1 0 0 0 92
93 152 0 0 0 0 0 0 0 0 1 0 0 93
94 149 0 0 0 0 0 0 0 0 0 1 0 94
95 172 0 0 0 0 0 0 0 0 0 0 1 95
96 167 0 0 0 0 0 0 0 0 0 0 0 96
97 160 1 0 0 0 0 0 0 0 0 0 0 97
98 150 0 1 0 0 0 0 0 0 0 0 0 98
99 160 0 0 1 0 0 0 0 0 0 0 0 99
100 165 0 0 0 1 0 0 0 0 0 0 0 100
101 171 0 0 0 0 1 0 0 0 0 0 0 101
102 179 0 0 0 0 0 1 0 0 0 0 0 102
103 171 0 0 0 0 0 0 1 0 0 0 0 103
104 176 0 0 0 0 0 0 0 1 0 0 0 104
105 170 0 0 0 0 0 0 0 0 1 0 0 105
106 169 0 0 0 0 0 0 0 0 0 1 0 106
107 194 0 0 0 0 0 0 0 0 0 0 1 107
108 196 0 0 0 0 0 0 0 0 0 0 0 108
109 188 1 0 0 0 0 0 0 0 0 0 0 109
110 174 0 1 0 0 0 0 0 0 0 0 0 110
111 186 0 0 1 0 0 0 0 0 0 0 0 111
112 191 0 0 0 1 0 0 0 0 0 0 0 112
113 197 0 0 0 0 1 0 0 0 0 0 0 113
114 206 0 0 0 0 0 1 0 0 0 0 0 114
115 197 0 0 0 0 0 0 1 0 0 0 0 115
116 204 0 0 0 0 0 0 0 1 0 0 0 116
117 201 0 0 0 0 0 0 0 0 1 0 0 117
118 190 0 0 0 0 0 0 0 0 0 1 0 118
119 213 0 0 0 0 0 0 0 0 0 0 1 119
120 213 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
8.3694 -0.7449 -13.1954 -13.9458 -13.5963 2.7532
M6 M7 M8 M9 M10 M11
7.0028 -4.6477 -7.5981 -7.8486 -13.3991 -1.5495
t
1.5505
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-36.397 -20.349 1.736 13.461 56.725
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.36944 8.26395 1.013 0.313
M1 -0.74491 10.25001 -0.073 0.942
M2 -13.19537 10.24626 -1.288 0.201
M3 -13.94583 10.24285 -1.362 0.176
M4 -13.59630 10.23981 -1.328 0.187
M5 2.75324 10.23712 0.269 0.788
M6 7.00278 10.23479 0.684 0.495
M7 -4.64769 10.23282 -0.454 0.651
M8 -7.59815 10.23120 -0.743 0.459
M9 -7.84861 10.22995 -0.767 0.445
M10 -13.39907 10.22905 -1.310 0.193
M11 -1.54954 10.22851 -0.151 0.880
t 1.55046 0.06057 25.596 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22.87 on 107 degrees of freedom
Multiple R-squared: 0.8633, Adjusted R-squared: 0.8479
F-statistic: 56.29 on 12 and 107 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,] 1.177046e-02 2.354093e-02 9.882295e-01
[2,] 3.114967e-03 6.229933e-03 9.968850e-01
[3,] 6.933721e-04 1.386744e-03 9.993066e-01
[4,] 1.494235e-04 2.988469e-04 9.998506e-01
[5,] 1.543090e-04 3.086181e-04 9.998457e-01
[6,] 4.597352e-05 9.194705e-05 9.999540e-01
[7,] 1.567194e-05 3.134388e-05 9.999843e-01
[8,] 3.552560e-05 7.105119e-05 9.999645e-01
[9,] 9.529696e-05 1.905939e-04 9.999047e-01
[10,] 2.136755e-03 4.273509e-03 9.978632e-01
[11,] 2.447879e-03 4.895759e-03 9.975521e-01
[12,] 1.352269e-02 2.704538e-02 9.864773e-01
[13,] 3.403029e-02 6.806059e-02 9.659697e-01
[14,] 6.655607e-02 1.331121e-01 9.334439e-01
[15,] 6.725546e-02 1.345109e-01 9.327445e-01
[16,] 5.407500e-02 1.081500e-01 9.459250e-01
[17,] 3.612547e-02 7.225094e-02 9.638745e-01
[18,] 3.906822e-02 7.813644e-02 9.609318e-01
[19,] 7.928469e-02 1.585694e-01 9.207153e-01
[20,] 8.345875e-02 1.669175e-01 9.165412e-01
[21,] 9.250075e-02 1.850015e-01 9.074993e-01
[22,] 7.158765e-02 1.431753e-01 9.284124e-01
[23,] 5.503961e-02 1.100792e-01 9.449604e-01
[24,] 5.069750e-02 1.013950e-01 9.493025e-01
[25,] 5.055554e-02 1.011111e-01 9.494445e-01
[26,] 1.407809e-01 2.815617e-01 8.592191e-01
[27,] 2.244943e-01 4.489886e-01 7.755057e-01
[28,] 2.891906e-01 5.783813e-01 7.108094e-01
[29,] 6.199059e-01 7.601881e-01 3.800941e-01
[30,] 7.794629e-01 4.410743e-01 2.205371e-01
[31,] 8.265005e-01 3.469990e-01 1.734995e-01
[32,] 8.969328e-01 2.061344e-01 1.030672e-01
[33,] 9.577952e-01 8.440952e-02 4.220476e-02
[34,] 9.885947e-01 2.281052e-02 1.140526e-02
[35,] 9.915254e-01 1.694910e-02 8.474551e-03
[36,] 9.907941e-01 1.841189e-02 9.205947e-03
[37,] 9.922968e-01 1.540640e-02 7.703200e-03
[38,] 9.921325e-01 1.573502e-02 7.867508e-03
[39,] 9.944926e-01 1.101486e-02 5.507428e-03
[40,] 9.978847e-01 4.230505e-03 2.115253e-03
[41,] 9.998138e-01 3.723991e-04 1.861995e-04
[42,] 9.999343e-01 1.313808e-04 6.569039e-05
[43,] 9.999545e-01 9.100929e-05 4.550464e-05
[44,] 9.999973e-01 5.383529e-06 2.691764e-06
[45,] 9.999994e-01 1.132162e-06 5.660811e-07
[46,] 9.999998e-01 3.993104e-07 1.996552e-07
[47,] 9.999998e-01 3.198656e-07 1.599328e-07
[48,] 1.000000e+00 3.517671e-08 1.758836e-08
[49,] 1.000000e+00 1.549392e-09 7.746959e-10
[50,] 1.000000e+00 1.542267e-09 7.711337e-10
[51,] 1.000000e+00 4.739381e-10 2.369691e-10
[52,] 1.000000e+00 1.690969e-10 8.454843e-11
[53,] 1.000000e+00 3.282991e-11 1.641496e-11
[54,] 1.000000e+00 2.058417e-11 1.029209e-11
[55,] 1.000000e+00 1.760518e-11 8.802589e-12
[56,] 1.000000e+00 2.144528e-12 1.072264e-12
[57,] 1.000000e+00 1.035440e-12 5.177198e-13
[58,] 1.000000e+00 1.229599e-12 6.147997e-13
[59,] 1.000000e+00 2.684432e-12 1.342216e-12
[60,] 1.000000e+00 5.898439e-14 2.949220e-14
[61,] 1.000000e+00 1.643289e-15 8.216443e-16
[62,] 1.000000e+00 3.565127e-15 1.782564e-15
[63,] 1.000000e+00 8.303614e-15 4.151807e-15
[64,] 1.000000e+00 5.239596e-15 2.619798e-15
[65,] 1.000000e+00 1.049657e-14 5.248284e-15
[66,] 1.000000e+00 4.591324e-15 2.295662e-15
[67,] 1.000000e+00 3.986267e-15 1.993133e-15
[68,] 1.000000e+00 1.113341e-14 5.566704e-15
[69,] 1.000000e+00 3.378217e-14 1.689109e-14
[70,] 1.000000e+00 5.229894e-14 2.614947e-14
[71,] 1.000000e+00 2.737618e-13 1.368809e-13
[72,] 1.000000e+00 5.077447e-13 2.538724e-13
[73,] 1.000000e+00 1.225346e-13 6.126731e-14
[74,] 1.000000e+00 5.676690e-13 2.838345e-13
[75,] 1.000000e+00 1.354931e-12 6.774656e-13
[76,] 1.000000e+00 8.170342e-12 4.085171e-12
[77,] 1.000000e+00 4.302409e-11 2.151205e-11
[78,] 1.000000e+00 2.632150e-10 1.316075e-10
[79,] 1.000000e+00 6.197704e-10 3.098852e-10
[80,] 1.000000e+00 1.287283e-09 6.436413e-10
[81,] 1.000000e+00 1.011081e-08 5.055403e-09
[82,] 1.000000e+00 6.085018e-08 3.042509e-08
[83,] 9.999998e-01 4.784445e-07 2.392223e-07
[84,] 9.999983e-01 3.337795e-06 1.668898e-06
[85,] 9.999890e-01 2.204972e-05 1.102486e-05
[86,] 9.999275e-01 1.449704e-04 7.248518e-05
[87,] 9.995941e-01 8.118651e-04 4.059325e-04
[88,] 9.977053e-01 4.589499e-03 2.294749e-03
[89,] 9.905983e-01 1.880332e-02 9.401659e-03
> postscript(file="/var/fisher/rcomp/tmp/1yo941387555147.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2qijo1387555147.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3mief1387555147.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4hodm1387555147.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5oiv51387555147.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
46.8250000 56.7250000 54.9250000 51.0250000 53.1250000 46.3250000
7 8 9 10 11 12
41.4250000 32.8250000 32.5250000 36.5250000 24.1250000 23.0250000
13 14 15 16 17 18
16.2194444 21.1194444 15.3194444 13.4194444 14.5194444 10.7194444
19 20 21 22 23 24
5.8194444 -9.7805556 -2.0805556 1.9194444 -4.4805556 -3.5805556
25 26 27 28 29 30
-5.3861111 -4.4861111 -0.2861111 -4.1861111 -5.0861111 -14.8861111
31 32 33 34 35 36
-20.7861111 -31.3861111 -21.6861111 -14.6861111 -25.0861111 -24.1861111
37 38 39 40 41 42
-32.9916667 -29.0916667 -26.8916667 -27.7916667 -19.6916667 -25.4916667
43 44 45 46 47 48
-30.3916667 -30.9916667 -26.2916667 -25.2916667 -30.6916667 -23.7916667
49 50 51 52 53 54
-22.5972222 -25.6972222 -30.4972222 -36.3972222 -27.2972222 -31.0972222
55 56 57 58 59 60
-25.9972222 -19.5972222 -21.8972222 -25.8972222 -30.2972222 -23.3972222
61 62 63 64 65 66
-20.2027778 -21.3027778 -30.1027778 -26.0027778 -17.9027778 -15.7027778
67 68 69 70 71 72
-7.6027778 -2.2027778 -4.5027778 -10.5027778 -6.9027778 -6.0027778
73 74 75 76 77 78
-0.8083333 -7.9083333 -15.7083333 -9.6083333 -8.5083333 2.6916667
79 80 81 82 83 84
8.7916667 10.1916667 10.8916667 7.8916667 12.4916667 9.3916667
85 86 87 88 89 90
5.5861111 -0.5138889 1.6861111 1.7861111 -3.1138889 8.0861111
91 92 93 94 95 96
6.1861111 13.5861111 7.2861111 8.2861111 17.8861111 9.7861111
97 98 99 100 101 102
1.9805556 2.8805556 12.0805556 15.1805556 3.2805556 5.4805556
103 104 105 106 107 108
7.5805556 13.9805556 6.6805556 9.6805556 21.2805556 20.1805556
109 110 111 112 113 114
11.3750000 8.2750000 19.4750000 22.5750000 10.6750000 13.8750000
115 116 117 118 119 120
14.9750000 23.3750000 19.0750000 12.0750000 21.6750000 18.5750000
> postscript(file="/var/fisher/rcomp/tmp/68syy1387555147.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 46.8250000 NA
1 56.7250000 46.8250000
2 54.9250000 56.7250000
3 51.0250000 54.9250000
4 53.1250000 51.0250000
5 46.3250000 53.1250000
6 41.4250000 46.3250000
7 32.8250000 41.4250000
8 32.5250000 32.8250000
9 36.5250000 32.5250000
10 24.1250000 36.5250000
11 23.0250000 24.1250000
12 16.2194444 23.0250000
13 21.1194444 16.2194444
14 15.3194444 21.1194444
15 13.4194444 15.3194444
16 14.5194444 13.4194444
17 10.7194444 14.5194444
18 5.8194444 10.7194444
19 -9.7805556 5.8194444
20 -2.0805556 -9.7805556
21 1.9194444 -2.0805556
22 -4.4805556 1.9194444
23 -3.5805556 -4.4805556
24 -5.3861111 -3.5805556
25 -4.4861111 -5.3861111
26 -0.2861111 -4.4861111
27 -4.1861111 -0.2861111
28 -5.0861111 -4.1861111
29 -14.8861111 -5.0861111
30 -20.7861111 -14.8861111
31 -31.3861111 -20.7861111
32 -21.6861111 -31.3861111
33 -14.6861111 -21.6861111
34 -25.0861111 -14.6861111
35 -24.1861111 -25.0861111
36 -32.9916667 -24.1861111
37 -29.0916667 -32.9916667
38 -26.8916667 -29.0916667
39 -27.7916667 -26.8916667
40 -19.6916667 -27.7916667
41 -25.4916667 -19.6916667
42 -30.3916667 -25.4916667
43 -30.9916667 -30.3916667
44 -26.2916667 -30.9916667
45 -25.2916667 -26.2916667
46 -30.6916667 -25.2916667
47 -23.7916667 -30.6916667
48 -22.5972222 -23.7916667
49 -25.6972222 -22.5972222
50 -30.4972222 -25.6972222
51 -36.3972222 -30.4972222
52 -27.2972222 -36.3972222
53 -31.0972222 -27.2972222
54 -25.9972222 -31.0972222
55 -19.5972222 -25.9972222
56 -21.8972222 -19.5972222
57 -25.8972222 -21.8972222
58 -30.2972222 -25.8972222
59 -23.3972222 -30.2972222
60 -20.2027778 -23.3972222
61 -21.3027778 -20.2027778
62 -30.1027778 -21.3027778
63 -26.0027778 -30.1027778
64 -17.9027778 -26.0027778
65 -15.7027778 -17.9027778
66 -7.6027778 -15.7027778
67 -2.2027778 -7.6027778
68 -4.5027778 -2.2027778
69 -10.5027778 -4.5027778
70 -6.9027778 -10.5027778
71 -6.0027778 -6.9027778
72 -0.8083333 -6.0027778
73 -7.9083333 -0.8083333
74 -15.7083333 -7.9083333
75 -9.6083333 -15.7083333
76 -8.5083333 -9.6083333
77 2.6916667 -8.5083333
78 8.7916667 2.6916667
79 10.1916667 8.7916667
80 10.8916667 10.1916667
81 7.8916667 10.8916667
82 12.4916667 7.8916667
83 9.3916667 12.4916667
84 5.5861111 9.3916667
85 -0.5138889 5.5861111
86 1.6861111 -0.5138889
87 1.7861111 1.6861111
88 -3.1138889 1.7861111
89 8.0861111 -3.1138889
90 6.1861111 8.0861111
91 13.5861111 6.1861111
92 7.2861111 13.5861111
93 8.2861111 7.2861111
94 17.8861111 8.2861111
95 9.7861111 17.8861111
96 1.9805556 9.7861111
97 2.8805556 1.9805556
98 12.0805556 2.8805556
99 15.1805556 12.0805556
100 3.2805556 15.1805556
101 5.4805556 3.2805556
102 7.5805556 5.4805556
103 13.9805556 7.5805556
104 6.6805556 13.9805556
105 9.6805556 6.6805556
106 21.2805556 9.6805556
107 20.1805556 21.2805556
108 11.3750000 20.1805556
109 8.2750000 11.3750000
110 19.4750000 8.2750000
111 22.5750000 19.4750000
112 10.6750000 22.5750000
113 13.8750000 10.6750000
114 14.9750000 13.8750000
115 23.3750000 14.9750000
116 19.0750000 23.3750000
117 12.0750000 19.0750000
118 21.6750000 12.0750000
119 18.5750000 21.6750000
120 NA 18.5750000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 56.7250000 46.8250000
[2,] 54.9250000 56.7250000
[3,] 51.0250000 54.9250000
[4,] 53.1250000 51.0250000
[5,] 46.3250000 53.1250000
[6,] 41.4250000 46.3250000
[7,] 32.8250000 41.4250000
[8,] 32.5250000 32.8250000
[9,] 36.5250000 32.5250000
[10,] 24.1250000 36.5250000
[11,] 23.0250000 24.1250000
[12,] 16.2194444 23.0250000
[13,] 21.1194444 16.2194444
[14,] 15.3194444 21.1194444
[15,] 13.4194444 15.3194444
[16,] 14.5194444 13.4194444
[17,] 10.7194444 14.5194444
[18,] 5.8194444 10.7194444
[19,] -9.7805556 5.8194444
[20,] -2.0805556 -9.7805556
[21,] 1.9194444 -2.0805556
[22,] -4.4805556 1.9194444
[23,] -3.5805556 -4.4805556
[24,] -5.3861111 -3.5805556
[25,] -4.4861111 -5.3861111
[26,] -0.2861111 -4.4861111
[27,] -4.1861111 -0.2861111
[28,] -5.0861111 -4.1861111
[29,] -14.8861111 -5.0861111
[30,] -20.7861111 -14.8861111
[31,] -31.3861111 -20.7861111
[32,] -21.6861111 -31.3861111
[33,] -14.6861111 -21.6861111
[34,] -25.0861111 -14.6861111
[35,] -24.1861111 -25.0861111
[36,] -32.9916667 -24.1861111
[37,] -29.0916667 -32.9916667
[38,] -26.8916667 -29.0916667
[39,] -27.7916667 -26.8916667
[40,] -19.6916667 -27.7916667
[41,] -25.4916667 -19.6916667
[42,] -30.3916667 -25.4916667
[43,] -30.9916667 -30.3916667
[44,] -26.2916667 -30.9916667
[45,] -25.2916667 -26.2916667
[46,] -30.6916667 -25.2916667
[47,] -23.7916667 -30.6916667
[48,] -22.5972222 -23.7916667
[49,] -25.6972222 -22.5972222
[50,] -30.4972222 -25.6972222
[51,] -36.3972222 -30.4972222
[52,] -27.2972222 -36.3972222
[53,] -31.0972222 -27.2972222
[54,] -25.9972222 -31.0972222
[55,] -19.5972222 -25.9972222
[56,] -21.8972222 -19.5972222
[57,] -25.8972222 -21.8972222
[58,] -30.2972222 -25.8972222
[59,] -23.3972222 -30.2972222
[60,] -20.2027778 -23.3972222
[61,] -21.3027778 -20.2027778
[62,] -30.1027778 -21.3027778
[63,] -26.0027778 -30.1027778
[64,] -17.9027778 -26.0027778
[65,] -15.7027778 -17.9027778
[66,] -7.6027778 -15.7027778
[67,] -2.2027778 -7.6027778
[68,] -4.5027778 -2.2027778
[69,] -10.5027778 -4.5027778
[70,] -6.9027778 -10.5027778
[71,] -6.0027778 -6.9027778
[72,] -0.8083333 -6.0027778
[73,] -7.9083333 -0.8083333
[74,] -15.7083333 -7.9083333
[75,] -9.6083333 -15.7083333
[76,] -8.5083333 -9.6083333
[77,] 2.6916667 -8.5083333
[78,] 8.7916667 2.6916667
[79,] 10.1916667 8.7916667
[80,] 10.8916667 10.1916667
[81,] 7.8916667 10.8916667
[82,] 12.4916667 7.8916667
[83,] 9.3916667 12.4916667
[84,] 5.5861111 9.3916667
[85,] -0.5138889 5.5861111
[86,] 1.6861111 -0.5138889
[87,] 1.7861111 1.6861111
[88,] -3.1138889 1.7861111
[89,] 8.0861111 -3.1138889
[90,] 6.1861111 8.0861111
[91,] 13.5861111 6.1861111
[92,] 7.2861111 13.5861111
[93,] 8.2861111 7.2861111
[94,] 17.8861111 8.2861111
[95,] 9.7861111 17.8861111
[96,] 1.9805556 9.7861111
[97,] 2.8805556 1.9805556
[98,] 12.0805556 2.8805556
[99,] 15.1805556 12.0805556
[100,] 3.2805556 15.1805556
[101,] 5.4805556 3.2805556
[102,] 7.5805556 5.4805556
[103,] 13.9805556 7.5805556
[104,] 6.6805556 13.9805556
[105,] 9.6805556 6.6805556
[106,] 21.2805556 9.6805556
[107,] 20.1805556 21.2805556
[108,] 11.3750000 20.1805556
[109,] 8.2750000 11.3750000
[110,] 19.4750000 8.2750000
[111,] 22.5750000 19.4750000
[112,] 10.6750000 22.5750000
[113,] 13.8750000 10.6750000
[114,] 14.9750000 13.8750000
[115,] 23.3750000 14.9750000
[116,] 19.0750000 23.3750000
[117,] 12.0750000 19.0750000
[118,] 21.6750000 12.0750000
[119,] 18.5750000 21.6750000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 56.7250000 46.8250000
2 54.9250000 56.7250000
3 51.0250000 54.9250000
4 53.1250000 51.0250000
5 46.3250000 53.1250000
6 41.4250000 46.3250000
7 32.8250000 41.4250000
8 32.5250000 32.8250000
9 36.5250000 32.5250000
10 24.1250000 36.5250000
11 23.0250000 24.1250000
12 16.2194444 23.0250000
13 21.1194444 16.2194444
14 15.3194444 21.1194444
15 13.4194444 15.3194444
16 14.5194444 13.4194444
17 10.7194444 14.5194444
18 5.8194444 10.7194444
19 -9.7805556 5.8194444
20 -2.0805556 -9.7805556
21 1.9194444 -2.0805556
22 -4.4805556 1.9194444
23 -3.5805556 -4.4805556
24 -5.3861111 -3.5805556
25 -4.4861111 -5.3861111
26 -0.2861111 -4.4861111
27 -4.1861111 -0.2861111
28 -5.0861111 -4.1861111
29 -14.8861111 -5.0861111
30 -20.7861111 -14.8861111
31 -31.3861111 -20.7861111
32 -21.6861111 -31.3861111
33 -14.6861111 -21.6861111
34 -25.0861111 -14.6861111
35 -24.1861111 -25.0861111
36 -32.9916667 -24.1861111
37 -29.0916667 -32.9916667
38 -26.8916667 -29.0916667
39 -27.7916667 -26.8916667
40 -19.6916667 -27.7916667
41 -25.4916667 -19.6916667
42 -30.3916667 -25.4916667
43 -30.9916667 -30.3916667
44 -26.2916667 -30.9916667
45 -25.2916667 -26.2916667
46 -30.6916667 -25.2916667
47 -23.7916667 -30.6916667
48 -22.5972222 -23.7916667
49 -25.6972222 -22.5972222
50 -30.4972222 -25.6972222
51 -36.3972222 -30.4972222
52 -27.2972222 -36.3972222
53 -31.0972222 -27.2972222
54 -25.9972222 -31.0972222
55 -19.5972222 -25.9972222
56 -21.8972222 -19.5972222
57 -25.8972222 -21.8972222
58 -30.2972222 -25.8972222
59 -23.3972222 -30.2972222
60 -20.2027778 -23.3972222
61 -21.3027778 -20.2027778
62 -30.1027778 -21.3027778
63 -26.0027778 -30.1027778
64 -17.9027778 -26.0027778
65 -15.7027778 -17.9027778
66 -7.6027778 -15.7027778
67 -2.2027778 -7.6027778
68 -4.5027778 -2.2027778
69 -10.5027778 -4.5027778
70 -6.9027778 -10.5027778
71 -6.0027778 -6.9027778
72 -0.8083333 -6.0027778
73 -7.9083333 -0.8083333
74 -15.7083333 -7.9083333
75 -9.6083333 -15.7083333
76 -8.5083333 -9.6083333
77 2.6916667 -8.5083333
78 8.7916667 2.6916667
79 10.1916667 8.7916667
80 10.8916667 10.1916667
81 7.8916667 10.8916667
82 12.4916667 7.8916667
83 9.3916667 12.4916667
84 5.5861111 9.3916667
85 -0.5138889 5.5861111
86 1.6861111 -0.5138889
87 1.7861111 1.6861111
88 -3.1138889 1.7861111
89 8.0861111 -3.1138889
90 6.1861111 8.0861111
91 13.5861111 6.1861111
92 7.2861111 13.5861111
93 8.2861111 7.2861111
94 17.8861111 8.2861111
95 9.7861111 17.8861111
96 1.9805556 9.7861111
97 2.8805556 1.9805556
98 12.0805556 2.8805556
99 15.1805556 12.0805556
100 3.2805556 15.1805556
101 5.4805556 3.2805556
102 7.5805556 5.4805556
103 13.9805556 7.5805556
104 6.6805556 13.9805556
105 9.6805556 6.6805556
106 21.2805556 9.6805556
107 20.1805556 21.2805556
108 11.3750000 20.1805556
109 8.2750000 11.3750000
110 19.4750000 8.2750000
111 22.5750000 19.4750000
112 10.6750000 22.5750000
113 13.8750000 10.6750000
114 14.9750000 13.8750000
115 23.3750000 14.9750000
116 19.0750000 23.3750000
117 12.0750000 19.0750000
118 21.6750000 12.0750000
119 18.5750000 21.6750000
> 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/fisher/rcomp/tmp/7zxvd1387555147.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8qyyn1387555147.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9cign1387555147.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10p31n1387555147.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/fisher/rcomp/tmp/118xvm1387555147.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12e8no1387555147.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13res41387555147.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14lwci1387555147.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/150d4z1387555147.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/fisher/rcomp/tmp/169ut71387555147.tab")
+ }
>
> try(system("convert tmp/1yo941387555147.ps tmp/1yo941387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qijo1387555147.ps tmp/2qijo1387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mief1387555147.ps tmp/3mief1387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hodm1387555147.ps tmp/4hodm1387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oiv51387555147.ps tmp/5oiv51387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/68syy1387555147.ps tmp/68syy1387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zxvd1387555147.ps tmp/7zxvd1387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qyyn1387555147.ps tmp/8qyyn1387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cign1387555147.ps tmp/9cign1387555147.png",intern=TRUE))
character(0)
> try(system("convert tmp/10p31n1387555147.ps tmp/10p31n1387555147.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.003 2.500 13.571