R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(15,0,14.4,0,13,0,13.7,0,13.6,0,15.2,0,12.9,0,14,0,14.1,0,13.2,0,11.3,0,13.3,0,14.4,0,13.3,0,11.6,0,13.2,0,13.1,0,14.6,0,14,0,14.3,0,13.8,0,13.7,0,11,0,14.4,0,15.6,0,13.7,0,12.6,0,13.2,0,13.3,0,14.3,0,14,0,13.4,0,13.9,0,13.7,0,10.5,0,14.5,0,15,0,13.5,0,13.5,0,13.2,0,13.8,0,16.2,0,14.7,0,13.9,0,16,0,14.4,0,12.3,0,15.9,0,15.9,0,15.5,0,15.1,0,14.5,0,15.1,0,17.4,0,16.2,0,15.6,0,17.2,0,14.9,0,13.8,0,17.5,0,16.2,0,17.5,0,16.6,0,16.2,0,16.6,0,19.6,0,15.9,0,18,0,18.3,0,16.3,0,14.9,0,18.2,0,18.4,0,18.5,0,16,0,17.4,0,17.2,0,19.6,0,17.2,0,18.3,0,19.3,0,18.1,0,16.2,0,18.4,0,20.5,0,19,0,16.5,0,18.7,0,19,0,19.2,0,20.5,0,19.3,0,20.6,0,20.1,0,16.1,0,20.4,0,19.7,1,15.6,1,14.4,1,13.7,1,14.1,1,15,1,14.2,1,13.6,1,15.4,1,14.8,1,12.5,1,16.2,1,16.1,1,16,1,15.8,1,15.2,1,15.7,1,18.9,1,17.4,1,17,1,19.8,1,17.7,1,16,1,19.6,1,19.7,1),dim=c(2,121),dimnames=list(c('uitvoercijfer','X'),1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('uitvoercijfer','X'),1:121))
> 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
uitvoercijfer X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 15.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 14.4 0 0 1 0 0 0 0 0 0 0 0 0 2
3 13.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 13.7 0 0 0 0 1 0 0 0 0 0 0 0 4
5 13.6 0 0 0 0 0 1 0 0 0 0 0 0 5
6 15.2 0 0 0 0 0 0 1 0 0 0 0 0 6
7 12.9 0 0 0 0 0 0 0 1 0 0 0 0 7
8 14.0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 14.1 0 0 0 0 0 0 0 0 0 1 0 0 9
10 13.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 11.3 0 0 0 0 0 0 0 0 0 0 0 1 11
12 13.3 0 0 0 0 0 0 0 0 0 0 0 0 12
13 14.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 13.3 0 0 1 0 0 0 0 0 0 0 0 0 14
15 11.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 13.2 0 0 0 0 1 0 0 0 0 0 0 0 16
17 13.1 0 0 0 0 0 1 0 0 0 0 0 0 17
18 14.6 0 0 0 0 0 0 1 0 0 0 0 0 18
19 14.0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 14.3 0 0 0 0 0 0 0 0 1 0 0 0 20
21 13.8 0 0 0 0 0 0 0 0 0 1 0 0 21
22 13.7 0 0 0 0 0 0 0 0 0 0 1 0 22
23 11.0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 14.4 0 0 0 0 0 0 0 0 0 0 0 0 24
25 15.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 13.7 0 0 1 0 0 0 0 0 0 0 0 0 26
27 12.6 0 0 0 1 0 0 0 0 0 0 0 0 27
28 13.2 0 0 0 0 1 0 0 0 0 0 0 0 28
29 13.3 0 0 0 0 0 1 0 0 0 0 0 0 29
30 14.3 0 0 0 0 0 0 1 0 0 0 0 0 30
31 14.0 0 0 0 0 0 0 0 1 0 0 0 0 31
32 13.4 0 0 0 0 0 0 0 0 1 0 0 0 32
33 13.9 0 0 0 0 0 0 0 0 0 1 0 0 33
34 13.7 0 0 0 0 0 0 0 0 0 0 1 0 34
35 10.5 0 0 0 0 0 0 0 0 0 0 0 1 35
36 14.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 15.0 0 1 0 0 0 0 0 0 0 0 0 0 37
38 13.5 0 0 1 0 0 0 0 0 0 0 0 0 38
39 13.5 0 0 0 1 0 0 0 0 0 0 0 0 39
40 13.2 0 0 0 0 1 0 0 0 0 0 0 0 40
41 13.8 0 0 0 0 0 1 0 0 0 0 0 0 41
42 16.2 0 0 0 0 0 0 1 0 0 0 0 0 42
43 14.7 0 0 0 0 0 0 0 1 0 0 0 0 43
44 13.9 0 0 0 0 0 0 0 0 1 0 0 0 44
45 16.0 0 0 0 0 0 0 0 0 0 1 0 0 45
46 14.4 0 0 0 0 0 0 0 0 0 0 1 0 46
47 12.3 0 0 0 0 0 0 0 0 0 0 0 1 47
48 15.9 0 0 0 0 0 0 0 0 0 0 0 0 48
49 15.9 0 1 0 0 0 0 0 0 0 0 0 0 49
50 15.5 0 0 1 0 0 0 0 0 0 0 0 0 50
51 15.1 0 0 0 1 0 0 0 0 0 0 0 0 51
52 14.5 0 0 0 0 1 0 0 0 0 0 0 0 52
53 15.1 0 0 0 0 0 1 0 0 0 0 0 0 53
54 17.4 0 0 0 0 0 0 1 0 0 0 0 0 54
55 16.2 0 0 0 0 0 0 0 1 0 0 0 0 55
56 15.6 0 0 0 0 0 0 0 0 1 0 0 0 56
57 17.2 0 0 0 0 0 0 0 0 0 1 0 0 57
58 14.9 0 0 0 0 0 0 0 0 0 0 1 0 58
59 13.8 0 0 0 0 0 0 0 0 0 0 0 1 59
60 17.5 0 0 0 0 0 0 0 0 0 0 0 0 60
61 16.2 0 1 0 0 0 0 0 0 0 0 0 0 61
62 17.5 0 0 1 0 0 0 0 0 0 0 0 0 62
63 16.6 0 0 0 1 0 0 0 0 0 0 0 0 63
64 16.2 0 0 0 0 1 0 0 0 0 0 0 0 64
65 16.6 0 0 0 0 0 1 0 0 0 0 0 0 65
66 19.6 0 0 0 0 0 0 1 0 0 0 0 0 66
67 15.9 0 0 0 0 0 0 0 1 0 0 0 0 67
68 18.0 0 0 0 0 0 0 0 0 1 0 0 0 68
69 18.3 0 0 0 0 0 0 0 0 0 1 0 0 69
70 16.3 0 0 0 0 0 0 0 0 0 0 1 0 70
71 14.9 0 0 0 0 0 0 0 0 0 0 0 1 71
72 18.2 0 0 0 0 0 0 0 0 0 0 0 0 72
73 18.4 0 1 0 0 0 0 0 0 0 0 0 0 73
74 18.5 0 0 1 0 0 0 0 0 0 0 0 0 74
75 16.0 0 0 0 1 0 0 0 0 0 0 0 0 75
76 17.4 0 0 0 0 1 0 0 0 0 0 0 0 76
77 17.2 0 0 0 0 0 1 0 0 0 0 0 0 77
78 19.6 0 0 0 0 0 0 1 0 0 0 0 0 78
79 17.2 0 0 0 0 0 0 0 1 0 0 0 0 79
80 18.3 0 0 0 0 0 0 0 0 1 0 0 0 80
81 19.3 0 0 0 0 0 0 0 0 0 1 0 0 81
82 18.1 0 0 0 0 0 0 0 0 0 0 1 0 82
83 16.2 0 0 0 0 0 0 0 0 0 0 0 1 83
84 18.4 0 0 0 0 0 0 0 0 0 0 0 0 84
85 20.5 0 1 0 0 0 0 0 0 0 0 0 0 85
86 19.0 0 0 1 0 0 0 0 0 0 0 0 0 86
87 16.5 0 0 0 1 0 0 0 0 0 0 0 0 87
88 18.7 0 0 0 0 1 0 0 0 0 0 0 0 88
89 19.0 0 0 0 0 0 1 0 0 0 0 0 0 89
90 19.2 0 0 0 0 0 0 1 0 0 0 0 0 90
91 20.5 0 0 0 0 0 0 0 1 0 0 0 0 91
92 19.3 0 0 0 0 0 0 0 0 1 0 0 0 92
93 20.6 0 0 0 0 0 0 0 0 0 1 0 0 93
94 20.1 0 0 0 0 0 0 0 0 0 0 1 0 94
95 16.1 0 0 0 0 0 0 0 0 0 0 0 1 95
96 20.4 0 0 0 0 0 0 0 0 0 0 0 0 96
97 19.7 1 1 0 0 0 0 0 0 0 0 0 0 97
98 15.6 1 0 1 0 0 0 0 0 0 0 0 0 98
99 14.4 1 0 0 1 0 0 0 0 0 0 0 0 99
100 13.7 1 0 0 0 1 0 0 0 0 0 0 0 100
101 14.1 1 0 0 0 0 1 0 0 0 0 0 0 101
102 15.0 1 0 0 0 0 0 1 0 0 0 0 0 102
103 14.2 1 0 0 0 0 0 0 1 0 0 0 0 103
104 13.6 1 0 0 0 0 0 0 0 1 0 0 0 104
105 15.4 1 0 0 0 0 0 0 0 0 1 0 0 105
106 14.8 1 0 0 0 0 0 0 0 0 0 1 0 106
107 12.5 1 0 0 0 0 0 0 0 0 0 0 1 107
108 16.2 1 0 0 0 0 0 0 0 0 0 0 0 108
109 16.1 1 1 0 0 0 0 0 0 0 0 0 0 109
110 16.0 1 0 1 0 0 0 0 0 0 0 0 0 110
111 15.8 1 0 0 1 0 0 0 0 0 0 0 0 111
112 15.2 1 0 0 0 1 0 0 0 0 0 0 0 112
113 15.7 1 0 0 0 0 1 0 0 0 0 0 0 113
114 18.9 1 0 0 0 0 0 1 0 0 0 0 0 114
115 17.4 1 0 0 0 0 0 0 1 0 0 0 0 115
116 17.0 1 0 0 0 0 0 0 0 1 0 0 0 116
117 19.8 1 0 0 0 0 0 0 0 0 1 0 0 117
118 17.7 1 0 0 0 0 0 0 0 0 0 1 0 118
119 16.0 1 0 0 0 0 0 0 0 0 0 0 1 119
120 19.6 1 0 0 0 0 0 0 0 0 0 0 0 120
121 19.7 1 1 0 0 0 0 0 0 0 0 0 0 121
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
12.77577 -3.94654 0.76926 -0.40462 -1.66815 -1.35169
M5 M6 M7 M8 M9 M10
-1.17523 0.60123 -0.77231 -0.80585 0.22062 -1.00292
M11 t
-3.30646 0.07354
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.0714 -0.6205 -0.0630 0.5993 2.9683
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.775773 0.387137 33.001 < 2e-16 ***
X -3.946539 0.326344 -12.093 < 2e-16 ***
M1 0.769259 0.451594 1.703 0.091391 .
M2 -0.404616 0.462012 -0.876 0.383117
M3 -1.668154 0.461716 -3.613 0.000463 ***
M4 -1.351693 0.461452 -2.929 0.004154 **
M5 -1.175231 0.461218 -2.548 0.012251 *
M6 0.601230 0.461015 1.304 0.194983
M7 -0.772308 0.460844 -1.676 0.096686 .
M8 -0.805846 0.460703 -1.749 0.083130 .
M9 0.220615 0.460594 0.479 0.632930
M10 -1.002923 0.460516 -2.178 0.031615 *
M11 -3.306462 0.460469 -7.181 9.63e-11 ***
t 0.073538 0.003792 19.393 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.03 on 107 degrees of freedom
Multiple R-squared: 0.8258, Adjusted R-squared: 0.8047
F-statistic: 39.03 on 13 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,] 0.0581178847 0.1162357694 0.941882115
[2,] 0.0188892311 0.0377784622 0.981110769
[3,] 0.1832522281 0.3665044563 0.816747772
[4,] 0.1523134498 0.3046268997 0.847686550
[5,] 0.0864896137 0.1729792273 0.913510386
[6,] 0.0767844780 0.1535689559 0.923215522
[7,] 0.0436221792 0.0872443584 0.956377821
[8,] 0.0681279134 0.1362558269 0.931872087
[9,] 0.0858493971 0.1716987943 0.914150603
[10,] 0.0537478275 0.1074956551 0.946252172
[11,] 0.0349203497 0.0698406993 0.965079650
[12,] 0.0222950455 0.0445900909 0.977704955
[13,] 0.0131119948 0.0262239895 0.986888005
[14,] 0.0090170786 0.0180341572 0.990982921
[15,] 0.0071118428 0.0142236855 0.992888157
[16,] 0.0056653113 0.0113306227 0.994334689
[17,] 0.0031873923 0.0063747845 0.996812608
[18,] 0.0019070295 0.0038140590 0.998092970
[19,] 0.0013499455 0.0026998910 0.998650054
[20,] 0.0011322567 0.0022645135 0.998867743
[21,] 0.0005911840 0.0011823680 0.999408816
[22,] 0.0003443524 0.0006887049 0.999655648
[23,] 0.0006617246 0.0013234492 0.999338275
[24,] 0.0003531863 0.0007063726 0.999646814
[25,] 0.0002193983 0.0004387967 0.999780602
[26,] 0.0006993286 0.0013986573 0.999300671
[27,] 0.0007233434 0.0014466867 0.999276657
[28,] 0.0004149430 0.0008298861 0.999585057
[29,] 0.0021527801 0.0043055602 0.997847220
[30,] 0.0015010581 0.0030021163 0.998498942
[31,] 0.0015572439 0.0031144878 0.998442756
[32,] 0.0025892448 0.0051784897 0.997410755
[33,] 0.0017797969 0.0035595938 0.998220203
[34,] 0.0020809444 0.0041618888 0.997919056
[35,] 0.0050406083 0.0100812166 0.994959392
[36,] 0.0033558930 0.0067117859 0.996644107
[37,] 0.0027573085 0.0055146171 0.997242691
[38,] 0.0040205910 0.0080411820 0.995979409
[39,] 0.0052985760 0.0105971519 0.994701424
[40,] 0.0039587728 0.0079175455 0.996041227
[41,] 0.0063480758 0.0126961517 0.993651924
[42,] 0.0044222064 0.0088444128 0.995577794
[43,] 0.0051649937 0.0103299873 0.994835006
[44,] 0.0085018972 0.0170037943 0.991498103
[45,] 0.0101572138 0.0203144276 0.989842786
[46,] 0.0175467049 0.0350934099 0.982453295
[47,] 0.0317965139 0.0635930279 0.968203486
[48,] 0.0279215945 0.0558431889 0.972078406
[49,] 0.0265337424 0.0530674848 0.973466258
[50,] 0.0774106705 0.1548213411 0.922589329
[51,] 0.0609795648 0.1219591295 0.939020435
[52,] 0.1079781319 0.2159562639 0.892021868
[53,] 0.1057414723 0.2114829447 0.894258528
[54,] 0.0825120683 0.1650241365 0.917487932
[55,] 0.0776784452 0.1553568903 0.922321555
[56,] 0.0707521578 0.1415043157 0.929247842
[57,] 0.0599242427 0.1198484855 0.940075757
[58,] 0.0633399364 0.1266798729 0.936660064
[59,] 0.0470388165 0.0940776330 0.952961183
[60,] 0.0427148003 0.0854296005 0.957285200
[61,] 0.0319908231 0.0639816462 0.968009177
[62,] 0.0337895498 0.0675790997 0.966210450
[63,] 0.0247344504 0.0494689007 0.975265550
[64,] 0.0246106721 0.0492213442 0.975389328
[65,] 0.0204430355 0.0408860711 0.979556964
[66,] 0.0163033677 0.0326067354 0.983696632
[67,] 0.0166553368 0.0333106737 0.983344663
[68,] 0.0113744224 0.0227488448 0.988625578
[69,] 0.0103230307 0.0206460613 0.989676969
[70,] 0.0068872083 0.0137744165 0.993112792
[71,] 0.0086949183 0.0173898366 0.991305082
[72,] 0.0072160497 0.0144320994 0.992783950
[73,] 0.0059650673 0.0119301347 0.994034933
[74,] 0.0046872460 0.0093744921 0.995312754
[75,] 0.0081498433 0.0162996867 0.991850157
[76,] 0.0063699241 0.0127398482 0.993630076
[77,] 0.0044062047 0.0088124095 0.995593795
[78,] 0.0053213276 0.0106426552 0.994678672
[79,] 0.0031270977 0.0062541954 0.996872902
[80,] 0.0018520361 0.0037040722 0.998147964
[81,] 0.4206778023 0.8413556046 0.579322198
[82,] 0.7209489492 0.5581021016 0.279051051
[83,] 0.8048741016 0.3902517968 0.195125898
[84,] 0.9113272880 0.1773454239 0.088672712
[85,] 0.9918668882 0.0162662236 0.008133112
[86,] 0.9831491461 0.0337017078 0.016850854
[87,] 0.9585937268 0.0828125465 0.041406273
[88,] 0.8937833570 0.2124332859 0.106216643
> postscript(file="/var/www/html/rcomp/tmp/1uorf1292771876.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2uorf1292771876.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35f901292771876.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45f901292771876.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55f901292771876.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 = 121
Frequency = 1
1 2 3 4 5 6
1.381429969 1.881766031 1.671766031 1.981766031 1.631766031 1.381766031
7 8 9 10 11 12
0.381766031 1.441766031 0.441766031 0.691766031 1.021766031 -0.358233969
13 14 15 16 17 18
-0.101030895 -0.100694833 -0.610694833 0.599305167 0.249305167 -0.100694833
19 20 21 22 23 24
0.599305167 0.859305167 -0.740694833 0.309305167 -0.160694833 -0.140694833
25 26 27 28 29 30
0.216508240 -0.583155698 -0.493155698 -0.283155698 -0.433155698 -1.283155698
31 32 33 34 35 36
-0.283155698 -0.923155698 -1.523155698 -0.573155698 -1.543155698 -0.923155698
37 38 39 40 41 42
-1.265952625 -1.665616563 -0.475616563 -1.165616563 -0.815616563 -0.265616563
43 44 45 46 47 48
-0.465616563 -1.305616563 -0.305616563 -0.755616563 -0.625616563 -0.405616563
49 50 51 52 53 54
-1.248413489 -0.548077427 0.241922573 -0.748077427 -0.398077427 0.051922573
55 56 57 58 59 60
0.151922573 -0.488077427 0.011922573 -1.138077427 -0.008077427 0.311922573
61 62 63 64 65 66
-1.830874354 0.569461708 0.859461708 0.069461708 0.219461708 1.369461708
67 68 69 70 71 72
-1.030538292 1.029461708 0.229461708 -0.620538292 0.209461708 0.129461708
73 74 75 76 77 78
-0.513335218 0.687000844 -0.622999156 0.387000844 -0.062999156 0.487000844
79 80 81 82 83 84
-0.612999156 0.447000844 0.347000844 0.297000844 0.627000844 -0.552999156
85 86 87 88 89 90
0.704203917 0.304539979 -1.005460021 0.804539979 0.854539979 -0.795460021
91 92 93 94 95 96
1.804539979 0.564539979 0.764539979 1.414539979 -0.355460021 0.564539979
97 98 99 100 101 102
2.968282350 -0.031381588 -0.041381588 -1.131381588 -0.981381588 -1.931381588
103 104 105 106 107 108
-1.431381588 -2.071381588 -1.371381588 -0.821381588 -0.891381588 -0.571381588
109 110 111 112 113 114
-1.514178515 -0.513842453 0.476157547 -0.513842453 -0.263842453 1.086157547
115 116 117 118 119 120
0.886157547 0.446157547 2.146157547 1.196157547 1.726157547 1.946157547
121
1.203360620
> postscript(file="/var/www/html/rcomp/tmp/6y6ql1292771876.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 1.381429969 NA
1 1.881766031 1.381429969
2 1.671766031 1.881766031
3 1.981766031 1.671766031
4 1.631766031 1.981766031
5 1.381766031 1.631766031
6 0.381766031 1.381766031
7 1.441766031 0.381766031
8 0.441766031 1.441766031
9 0.691766031 0.441766031
10 1.021766031 0.691766031
11 -0.358233969 1.021766031
12 -0.101030895 -0.358233969
13 -0.100694833 -0.101030895
14 -0.610694833 -0.100694833
15 0.599305167 -0.610694833
16 0.249305167 0.599305167
17 -0.100694833 0.249305167
18 0.599305167 -0.100694833
19 0.859305167 0.599305167
20 -0.740694833 0.859305167
21 0.309305167 -0.740694833
22 -0.160694833 0.309305167
23 -0.140694833 -0.160694833
24 0.216508240 -0.140694833
25 -0.583155698 0.216508240
26 -0.493155698 -0.583155698
27 -0.283155698 -0.493155698
28 -0.433155698 -0.283155698
29 -1.283155698 -0.433155698
30 -0.283155698 -1.283155698
31 -0.923155698 -0.283155698
32 -1.523155698 -0.923155698
33 -0.573155698 -1.523155698
34 -1.543155698 -0.573155698
35 -0.923155698 -1.543155698
36 -1.265952625 -0.923155698
37 -1.665616563 -1.265952625
38 -0.475616563 -1.665616563
39 -1.165616563 -0.475616563
40 -0.815616563 -1.165616563
41 -0.265616563 -0.815616563
42 -0.465616563 -0.265616563
43 -1.305616563 -0.465616563
44 -0.305616563 -1.305616563
45 -0.755616563 -0.305616563
46 -0.625616563 -0.755616563
47 -0.405616563 -0.625616563
48 -1.248413489 -0.405616563
49 -0.548077427 -1.248413489
50 0.241922573 -0.548077427
51 -0.748077427 0.241922573
52 -0.398077427 -0.748077427
53 0.051922573 -0.398077427
54 0.151922573 0.051922573
55 -0.488077427 0.151922573
56 0.011922573 -0.488077427
57 -1.138077427 0.011922573
58 -0.008077427 -1.138077427
59 0.311922573 -0.008077427
60 -1.830874354 0.311922573
61 0.569461708 -1.830874354
62 0.859461708 0.569461708
63 0.069461708 0.859461708
64 0.219461708 0.069461708
65 1.369461708 0.219461708
66 -1.030538292 1.369461708
67 1.029461708 -1.030538292
68 0.229461708 1.029461708
69 -0.620538292 0.229461708
70 0.209461708 -0.620538292
71 0.129461708 0.209461708
72 -0.513335218 0.129461708
73 0.687000844 -0.513335218
74 -0.622999156 0.687000844
75 0.387000844 -0.622999156
76 -0.062999156 0.387000844
77 0.487000844 -0.062999156
78 -0.612999156 0.487000844
79 0.447000844 -0.612999156
80 0.347000844 0.447000844
81 0.297000844 0.347000844
82 0.627000844 0.297000844
83 -0.552999156 0.627000844
84 0.704203917 -0.552999156
85 0.304539979 0.704203917
86 -1.005460021 0.304539979
87 0.804539979 -1.005460021
88 0.854539979 0.804539979
89 -0.795460021 0.854539979
90 1.804539979 -0.795460021
91 0.564539979 1.804539979
92 0.764539979 0.564539979
93 1.414539979 0.764539979
94 -0.355460021 1.414539979
95 0.564539979 -0.355460021
96 2.968282350 0.564539979
97 -0.031381588 2.968282350
98 -0.041381588 -0.031381588
99 -1.131381588 -0.041381588
100 -0.981381588 -1.131381588
101 -1.931381588 -0.981381588
102 -1.431381588 -1.931381588
103 -2.071381588 -1.431381588
104 -1.371381588 -2.071381588
105 -0.821381588 -1.371381588
106 -0.891381588 -0.821381588
107 -0.571381588 -0.891381588
108 -1.514178515 -0.571381588
109 -0.513842453 -1.514178515
110 0.476157547 -0.513842453
111 -0.513842453 0.476157547
112 -0.263842453 -0.513842453
113 1.086157547 -0.263842453
114 0.886157547 1.086157547
115 0.446157547 0.886157547
116 2.146157547 0.446157547
117 1.196157547 2.146157547
118 1.726157547 1.196157547
119 1.946157547 1.726157547
120 1.203360620 1.946157547
121 NA 1.203360620
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.881766031 1.381429969
[2,] 1.671766031 1.881766031
[3,] 1.981766031 1.671766031
[4,] 1.631766031 1.981766031
[5,] 1.381766031 1.631766031
[6,] 0.381766031 1.381766031
[7,] 1.441766031 0.381766031
[8,] 0.441766031 1.441766031
[9,] 0.691766031 0.441766031
[10,] 1.021766031 0.691766031
[11,] -0.358233969 1.021766031
[12,] -0.101030895 -0.358233969
[13,] -0.100694833 -0.101030895
[14,] -0.610694833 -0.100694833
[15,] 0.599305167 -0.610694833
[16,] 0.249305167 0.599305167
[17,] -0.100694833 0.249305167
[18,] 0.599305167 -0.100694833
[19,] 0.859305167 0.599305167
[20,] -0.740694833 0.859305167
[21,] 0.309305167 -0.740694833
[22,] -0.160694833 0.309305167
[23,] -0.140694833 -0.160694833
[24,] 0.216508240 -0.140694833
[25,] -0.583155698 0.216508240
[26,] -0.493155698 -0.583155698
[27,] -0.283155698 -0.493155698
[28,] -0.433155698 -0.283155698
[29,] -1.283155698 -0.433155698
[30,] -0.283155698 -1.283155698
[31,] -0.923155698 -0.283155698
[32,] -1.523155698 -0.923155698
[33,] -0.573155698 -1.523155698
[34,] -1.543155698 -0.573155698
[35,] -0.923155698 -1.543155698
[36,] -1.265952625 -0.923155698
[37,] -1.665616563 -1.265952625
[38,] -0.475616563 -1.665616563
[39,] -1.165616563 -0.475616563
[40,] -0.815616563 -1.165616563
[41,] -0.265616563 -0.815616563
[42,] -0.465616563 -0.265616563
[43,] -1.305616563 -0.465616563
[44,] -0.305616563 -1.305616563
[45,] -0.755616563 -0.305616563
[46,] -0.625616563 -0.755616563
[47,] -0.405616563 -0.625616563
[48,] -1.248413489 -0.405616563
[49,] -0.548077427 -1.248413489
[50,] 0.241922573 -0.548077427
[51,] -0.748077427 0.241922573
[52,] -0.398077427 -0.748077427
[53,] 0.051922573 -0.398077427
[54,] 0.151922573 0.051922573
[55,] -0.488077427 0.151922573
[56,] 0.011922573 -0.488077427
[57,] -1.138077427 0.011922573
[58,] -0.008077427 -1.138077427
[59,] 0.311922573 -0.008077427
[60,] -1.830874354 0.311922573
[61,] 0.569461708 -1.830874354
[62,] 0.859461708 0.569461708
[63,] 0.069461708 0.859461708
[64,] 0.219461708 0.069461708
[65,] 1.369461708 0.219461708
[66,] -1.030538292 1.369461708
[67,] 1.029461708 -1.030538292
[68,] 0.229461708 1.029461708
[69,] -0.620538292 0.229461708
[70,] 0.209461708 -0.620538292
[71,] 0.129461708 0.209461708
[72,] -0.513335218 0.129461708
[73,] 0.687000844 -0.513335218
[74,] -0.622999156 0.687000844
[75,] 0.387000844 -0.622999156
[76,] -0.062999156 0.387000844
[77,] 0.487000844 -0.062999156
[78,] -0.612999156 0.487000844
[79,] 0.447000844 -0.612999156
[80,] 0.347000844 0.447000844
[81,] 0.297000844 0.347000844
[82,] 0.627000844 0.297000844
[83,] -0.552999156 0.627000844
[84,] 0.704203917 -0.552999156
[85,] 0.304539979 0.704203917
[86,] -1.005460021 0.304539979
[87,] 0.804539979 -1.005460021
[88,] 0.854539979 0.804539979
[89,] -0.795460021 0.854539979
[90,] 1.804539979 -0.795460021
[91,] 0.564539979 1.804539979
[92,] 0.764539979 0.564539979
[93,] 1.414539979 0.764539979
[94,] -0.355460021 1.414539979
[95,] 0.564539979 -0.355460021
[96,] 2.968282350 0.564539979
[97,] -0.031381588 2.968282350
[98,] -0.041381588 -0.031381588
[99,] -1.131381588 -0.041381588
[100,] -0.981381588 -1.131381588
[101,] -1.931381588 -0.981381588
[102,] -1.431381588 -1.931381588
[103,] -2.071381588 -1.431381588
[104,] -1.371381588 -2.071381588
[105,] -0.821381588 -1.371381588
[106,] -0.891381588 -0.821381588
[107,] -0.571381588 -0.891381588
[108,] -1.514178515 -0.571381588
[109,] -0.513842453 -1.514178515
[110,] 0.476157547 -0.513842453
[111,] -0.513842453 0.476157547
[112,] -0.263842453 -0.513842453
[113,] 1.086157547 -0.263842453
[114,] 0.886157547 1.086157547
[115,] 0.446157547 0.886157547
[116,] 2.146157547 0.446157547
[117,] 1.196157547 2.146157547
[118,] 1.726157547 1.196157547
[119,] 1.946157547 1.726157547
[120,] 1.203360620 1.946157547
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.881766031 1.381429969
2 1.671766031 1.881766031
3 1.981766031 1.671766031
4 1.631766031 1.981766031
5 1.381766031 1.631766031
6 0.381766031 1.381766031
7 1.441766031 0.381766031
8 0.441766031 1.441766031
9 0.691766031 0.441766031
10 1.021766031 0.691766031
11 -0.358233969 1.021766031
12 -0.101030895 -0.358233969
13 -0.100694833 -0.101030895
14 -0.610694833 -0.100694833
15 0.599305167 -0.610694833
16 0.249305167 0.599305167
17 -0.100694833 0.249305167
18 0.599305167 -0.100694833
19 0.859305167 0.599305167
20 -0.740694833 0.859305167
21 0.309305167 -0.740694833
22 -0.160694833 0.309305167
23 -0.140694833 -0.160694833
24 0.216508240 -0.140694833
25 -0.583155698 0.216508240
26 -0.493155698 -0.583155698
27 -0.283155698 -0.493155698
28 -0.433155698 -0.283155698
29 -1.283155698 -0.433155698
30 -0.283155698 -1.283155698
31 -0.923155698 -0.283155698
32 -1.523155698 -0.923155698
33 -0.573155698 -1.523155698
34 -1.543155698 -0.573155698
35 -0.923155698 -1.543155698
36 -1.265952625 -0.923155698
37 -1.665616563 -1.265952625
38 -0.475616563 -1.665616563
39 -1.165616563 -0.475616563
40 -0.815616563 -1.165616563
41 -0.265616563 -0.815616563
42 -0.465616563 -0.265616563
43 -1.305616563 -0.465616563
44 -0.305616563 -1.305616563
45 -0.755616563 -0.305616563
46 -0.625616563 -0.755616563
47 -0.405616563 -0.625616563
48 -1.248413489 -0.405616563
49 -0.548077427 -1.248413489
50 0.241922573 -0.548077427
51 -0.748077427 0.241922573
52 -0.398077427 -0.748077427
53 0.051922573 -0.398077427
54 0.151922573 0.051922573
55 -0.488077427 0.151922573
56 0.011922573 -0.488077427
57 -1.138077427 0.011922573
58 -0.008077427 -1.138077427
59 0.311922573 -0.008077427
60 -1.830874354 0.311922573
61 0.569461708 -1.830874354
62 0.859461708 0.569461708
63 0.069461708 0.859461708
64 0.219461708 0.069461708
65 1.369461708 0.219461708
66 -1.030538292 1.369461708
67 1.029461708 -1.030538292
68 0.229461708 1.029461708
69 -0.620538292 0.229461708
70 0.209461708 -0.620538292
71 0.129461708 0.209461708
72 -0.513335218 0.129461708
73 0.687000844 -0.513335218
74 -0.622999156 0.687000844
75 0.387000844 -0.622999156
76 -0.062999156 0.387000844
77 0.487000844 -0.062999156
78 -0.612999156 0.487000844
79 0.447000844 -0.612999156
80 0.347000844 0.447000844
81 0.297000844 0.347000844
82 0.627000844 0.297000844
83 -0.552999156 0.627000844
84 0.704203917 -0.552999156
85 0.304539979 0.704203917
86 -1.005460021 0.304539979
87 0.804539979 -1.005460021
88 0.854539979 0.804539979
89 -0.795460021 0.854539979
90 1.804539979 -0.795460021
91 0.564539979 1.804539979
92 0.764539979 0.564539979
93 1.414539979 0.764539979
94 -0.355460021 1.414539979
95 0.564539979 -0.355460021
96 2.968282350 0.564539979
97 -0.031381588 2.968282350
98 -0.041381588 -0.031381588
99 -1.131381588 -0.041381588
100 -0.981381588 -1.131381588
101 -1.931381588 -0.981381588
102 -1.431381588 -1.931381588
103 -2.071381588 -1.431381588
104 -1.371381588 -2.071381588
105 -0.821381588 -1.371381588
106 -0.891381588 -0.821381588
107 -0.571381588 -0.891381588
108 -1.514178515 -0.571381588
109 -0.513842453 -1.514178515
110 0.476157547 -0.513842453
111 -0.513842453 0.476157547
112 -0.263842453 -0.513842453
113 1.086157547 -0.263842453
114 0.886157547 1.086157547
115 0.446157547 0.886157547
116 2.146157547 0.446157547
117 1.196157547 2.146157547
118 1.726157547 1.196157547
119 1.946157547 1.726157547
120 1.203360620 1.946157547
> 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/7qfp61292771876.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qfp61292771876.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qfp61292771876.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1017or1292771876.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mpnf1292771876.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/12qq331292771876.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/134z1u1292771876.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/14f9ix1292771876.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/150rzk1292771876.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/16ejfb1292771876.tab")
+ }
>
> try(system("convert tmp/1uorf1292771876.ps tmp/1uorf1292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uorf1292771876.ps tmp/2uorf1292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/35f901292771876.ps tmp/35f901292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/45f901292771876.ps tmp/45f901292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/55f901292771876.ps tmp/55f901292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y6ql1292771876.ps tmp/6y6ql1292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qfp61292771876.ps tmp/7qfp61292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qfp61292771876.ps tmp/8qfp61292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qfp61292771876.ps tmp/9qfp61292771876.png",intern=TRUE))
character(0)
> try(system("convert tmp/1017or1292771876.ps tmp/1017or1292771876.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.432 1.794 17.538