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.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(108.8235294
+ ,111.7647059
+ ,105.8823529
+ ,100
+ ,111.7647059
+ ,108.8235294
+ ,111.7647059
+ ,105.8823529
+ ,117.6470588
+ ,111.7647059
+ ,108.8235294
+ ,111.7647059
+ ,111.7647059
+ ,117.6470588
+ ,111.7647059
+ ,108.8235294
+ ,120.5882353
+ ,111.7647059
+ ,117.6470588
+ ,111.7647059
+ ,102.9411765
+ ,120.5882353
+ ,111.7647059
+ ,117.6470588
+ ,114.7058824
+ ,102.9411765
+ ,120.5882353
+ ,111.7647059
+ ,114.7058824
+ ,114.7058824
+ ,102.9411765
+ ,120.5882353
+ ,117.6470588
+ ,114.7058824
+ ,114.7058824
+ ,102.9411765
+ ,111.7647059
+ ,117.6470588
+ ,114.7058824
+ ,114.7058824
+ ,97.05882353
+ ,111.7647059
+ ,117.6470588
+ ,114.7058824
+ ,94.11764706
+ ,97.05882353
+ ,111.7647059
+ ,117.6470588
+ ,82.35294118
+ ,94.11764706
+ ,97.05882353
+ ,111.7647059
+ ,82.35294118
+ ,82.35294118
+ ,94.11764706
+ ,97.05882353
+ ,85.29411765
+ ,82.35294118
+ ,82.35294118
+ ,94.11764706
+ ,85.29411765
+ ,85.29411765
+ ,82.35294118
+ ,82.35294118
+ ,73.52941176
+ ,85.29411765
+ ,85.29411765
+ ,82.35294118
+ ,61.76470588
+ ,73.52941176
+ ,85.29411765
+ ,85.29411765
+ ,32.35294118
+ ,61.76470588
+ ,73.52941176
+ ,85.29411765
+ ,20.58823529
+ ,32.35294118
+ ,61.76470588
+ ,73.52941176
+ ,50
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+ ,50
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+ ,79.41176471
+ ,76.47058824
+ ,70.58823529
+ ,76.47058824
+ ,73.52941176
+ ,79.41176471
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+ ,73.52941176
+ ,76.47058824
+ ,73.52941176
+ ,79.41176471
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+ ,64.70588235
+ ,70.58823529
+ ,73.52941176
+ ,76.47058824
+ ,64.70588235
+ ,64.70588235
+ ,70.58823529
+ ,73.52941176
+ ,64.70588235
+ ,64.70588235
+ ,64.70588235
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+ ,61.76470588
+ ,64.70588235
+ ,64.70588235
+ ,64.70588235
+ ,50
+ ,61.76470588
+ ,64.70588235
+ ,64.70588235
+ ,47.05882353
+ ,50
+ ,61.76470588
+ ,64.70588235
+ ,35.29411765
+ ,47.05882353
+ ,50
+ ,61.76470588
+ ,20.58823529
+ ,35.29411765
+ ,47.05882353
+ ,50
+ ,41.17647059
+ ,20.58823529
+ ,35.29411765
+ ,47.05882353
+ ,47.05882353
+ ,41.17647059
+ ,20.58823529
+ ,35.29411765
+ ,44.11764706
+ ,47.05882353
+ ,41.17647059
+ ,20.58823529
+ ,35.29411765
+ ,44.11764706
+ ,47.05882353
+ ,41.17647059
+ ,41.17647059
+ ,35.29411765
+ ,44.11764706
+ ,47.05882353
+ ,58.82352941
+ ,41.17647059
+ ,35.29411765
+ ,44.11764706
+ ,29.41176471
+ ,58.82352941
+ ,41.17647059
+ ,35.29411765
+ ,55.88235294
+ ,29.41176471
+ ,58.82352941
+ ,41.17647059
+ ,55.88235294
+ ,55.88235294
+ ,29.41176471
+ ,58.82352941
+ ,64.70588235
+ ,55.88235294
+ ,55.88235294
+ ,29.41176471
+ ,70.58823529
+ ,64.70588235
+ ,55.88235294
+ ,55.88235294
+ ,64.70588235
+ ,70.58823529
+ ,64.70588235
+ ,55.88235294
+ ,61.76470588
+ ,64.70588235
+ ,70.58823529
+ ,64.70588235
+ ,55.88235294
+ ,61.76470588
+ ,64.70588235
+ ,70.58823529
+ ,73.52941176
+ ,55.88235294
+ ,61.76470588
+ ,64.70588235
+ ,61.76470588
+ ,73.52941176
+ ,55.88235294
+ ,61.76470588
+ ,67.64705882
+ ,61.76470588
+ ,73.52941176
+ ,55.88235294
+ ,67.64705882
+ ,67.64705882
+ ,61.76470588
+ ,73.52941176
+ ,55.88235294
+ ,67.64705882
+ ,67.64705882
+ ,61.76470588
+ ,52.94117647
+ ,55.88235294
+ ,67.64705882
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+ ,55.88235294
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+ ,52.94117647
+ ,67.64705882
+ ,64.70588235
+ ,55.88235294
+ ,55.88235294
+ ,58.82352941
+ ,67.64705882
+ ,64.70588235
+ ,55.88235294
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+ ,67.64705882
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+ ,41.17647059
+ ,58.82352941
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+ ,41.17647059
+ ,41.17647059
+ ,58.82352941
+ ,44.11764706
+ ,41.17647059
+ ,41.17647059
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+ ,32.35294118
+ ,44.11764706
+ ,41.17647059
+ ,41.17647059
+ ,50
+ ,32.35294118
+ ,44.11764706
+ ,41.17647059
+ ,47.05882353
+ ,50
+ ,32.35294118
+ ,44.11764706
+ ,58.82352941
+ ,47.05882353
+ ,50
+ ,32.35294118
+ ,70.58823529
+ ,58.82352941
+ ,47.05882353
+ ,50
+ ,67.64705882
+ ,70.58823529
+ ,58.82352941
+ ,47.05882353
+ ,58.82352941
+ ,67.64705882
+ ,70.58823529
+ ,58.82352941
+ ,61.76470588
+ ,58.82352941
+ ,67.64705882
+ ,70.58823529
+ ,55.88235294
+ ,61.76470588
+ ,58.82352941
+ ,67.64705882
+ ,67.64705882
+ ,55.88235294
+ ,61.76470588
+ ,58.82352941
+ ,67.64705882
+ ,67.64705882
+ ,55.88235294
+ ,61.76470588
+ ,67.64705882
+ ,67.64705882
+ ,67.64705882
+ ,55.88235294
+ ,67.64705882
+ ,67.64705882
+ ,67.64705882
+ ,67.64705882
+ ,79.41176471
+ ,67.64705882
+ ,67.64705882
+ ,67.64705882
+ ,76.47058824
+ ,79.41176471
+ ,67.64705882
+ ,67.64705882
+ ,50
+ ,76.47058824
+ ,79.41176471
+ ,67.64705882
+ ,70.58823529
+ ,50
+ ,76.47058824
+ ,79.41176471
+ ,76.47058824
+ ,70.58823529
+ ,50
+ ,76.47058824
+ ,70.58823529
+ ,76.47058824
+ ,70.58823529
+ ,50
+ ,79.41176471
+ ,70.58823529
+ ,76.47058824
+ ,70.58823529
+ ,79.41176471
+ ,79.41176471
+ ,70.58823529
+ ,76.47058824
+ ,76.47058824
+ ,79.41176471
+ ,79.41176471
+ ,70.58823529
+ ,70.58823529
+ ,76.47058824
+ ,79.41176471
+ ,79.41176471
+ ,67.64705882
+ ,70.58823529
+ ,76.47058824
+ ,79.41176471
+ ,67.64705882
+ ,67.64705882
+ ,70.58823529
+ ,76.47058824
+ ,70.58823529
+ ,67.64705882
+ ,67.64705882
+ ,70.58823529
+ ,50
+ ,70.58823529
+ ,67.64705882
+ ,67.64705882
+ ,61.76470588
+ ,50
+ ,70.58823529
+ ,67.64705882
+ ,55.88235294
+ ,61.76470588
+ ,50
+ ,70.58823529
+ ,64.70588235
+ ,55.88235294
+ ,61.76470588
+ ,50
+ ,64.70588235
+ ,64.70588235
+ ,55.88235294
+ ,61.76470588
+ ,52.94117647
+ ,64.70588235
+ ,64.70588235
+ ,55.88235294
+ ,47.05882353
+ ,52.94117647
+ ,64.70588235
+ ,64.70588235
+ ,41.17647059
+ ,47.05882353
+ ,52.94117647
+ ,64.70588235
+ ,35.29411765
+ ,41.17647059
+ ,47.05882353
+ ,52.94117647
+ ,41.17647059
+ ,35.29411765
+ ,41.17647059
+ ,47.05882353
+ ,47.05882353
+ ,41.17647059
+ ,35.29411765
+ ,41.17647059
+ ,23.52941176
+ ,47.05882353
+ ,41.17647059
+ ,35.29411765
+ ,8.823529412
+ ,23.52941176
+ ,47.05882353
+ ,41.17647059
+ ,0
+ ,8.823529412
+ ,23.52941176
+ ,47.05882353)
+ ,dim=c(4
+ ,105)
+ ,dimnames=list(c('X'
+ ,'Y0'
+ ,'Y1'
+ ,'Y2')
+ ,1:105))
> y <- array(NA,dim=c(4,105),dimnames=list(c('X','Y0','Y1','Y2'),1:105))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
X Y0 Y1 Y2
1 108.82353 111.76471 105.88235 100.00000
2 111.76471 108.82353 111.76471 105.88235
3 117.64706 111.76471 108.82353 111.76471
4 111.76471 117.64706 111.76471 108.82353
5 120.58824 111.76471 117.64706 111.76471
6 102.94118 120.58824 111.76471 117.64706
7 114.70588 102.94118 120.58824 111.76471
8 114.70588 114.70588 102.94118 120.58824
9 117.64706 114.70588 114.70588 102.94118
10 111.76471 117.64706 114.70588 114.70588
11 97.05882 111.76471 117.64706 114.70588
12 94.11765 97.05882 111.76471 117.64706
13 82.35294 94.11765 97.05882 111.76471
14 82.35294 82.35294 94.11765 97.05882
15 85.29412 82.35294 82.35294 94.11765
16 85.29412 85.29412 82.35294 82.35294
17 73.52941 85.29412 85.29412 82.35294
18 61.76471 73.52941 85.29412 85.29412
19 32.35294 61.76471 73.52941 85.29412
20 20.58824 32.35294 61.76471 73.52941
21 50.00000 20.58824 32.35294 61.76471
22 70.58824 50.00000 20.58824 32.35294
23 76.47059 70.58824 50.00000 20.58824
24 79.41176 76.47059 70.58824 50.00000
25 73.52941 79.41176 76.47059 70.58824
26 76.47059 73.52941 79.41176 76.47059
27 73.52941 76.47059 73.52941 79.41176
28 70.58824 73.52941 76.47059 73.52941
29 64.70588 70.58824 73.52941 76.47059
30 64.70588 64.70588 70.58824 73.52941
31 64.70588 64.70588 64.70588 70.58824
32 61.76471 64.70588 64.70588 64.70588
33 50.00000 61.76471 64.70588 64.70588
34 47.05882 50.00000 61.76471 64.70588
35 35.29412 47.05882 50.00000 61.76471
36 20.58824 35.29412 47.05882 50.00000
37 41.17647 20.58824 35.29412 47.05882
38 47.05882 41.17647 20.58824 35.29412
39 44.11765 47.05882 41.17647 20.58824
40 35.29412 44.11765 47.05882 41.17647
41 41.17647 35.29412 44.11765 47.05882
42 58.82353 41.17647 35.29412 44.11765
43 29.41176 58.82353 41.17647 35.29412
44 55.88235 29.41176 58.82353 41.17647
45 55.88235 55.88235 29.41176 58.82353
46 64.70588 55.88235 55.88235 29.41176
47 70.58824 64.70588 55.88235 55.88235
48 64.70588 70.58824 64.70588 55.88235
49 61.76471 64.70588 70.58824 64.70588
50 55.88235 61.76471 64.70588 70.58824
51 73.52941 55.88235 61.76471 64.70588
52 61.76471 73.52941 55.88235 61.76471
53 67.64706 61.76471 73.52941 55.88235
54 67.64706 67.64706 61.76471 73.52941
55 55.88235 67.64706 67.64706 61.76471
56 52.94118 55.88235 67.64706 67.64706
57 55.88235 52.94118 55.88235 67.64706
58 55.88235 55.88235 52.94118 55.88235
59 64.70588 55.88235 55.88235 52.94118
60 67.64706 64.70588 55.88235 55.88235
61 58.82353 67.64706 64.70588 55.88235
62 41.17647 58.82353 67.64706 64.70588
63 41.17647 41.17647 58.82353 67.64706
64 41.17647 41.17647 41.17647 58.82353
65 44.11765 41.17647 41.17647 41.17647
66 32.35294 44.11765 41.17647 41.17647
67 50.00000 32.35294 44.11765 41.17647
68 47.05882 50.00000 32.35294 44.11765
69 58.82353 47.05882 50.00000 32.35294
70 70.58824 58.82353 47.05882 50.00000
71 67.64706 70.58824 58.82353 47.05882
72 58.82353 67.64706 70.58824 58.82353
73 61.76471 58.82353 67.64706 70.58824
74 55.88235 61.76471 58.82353 67.64706
75 67.64706 55.88235 61.76471 58.82353
76 67.64706 67.64706 55.88235 61.76471
77 67.64706 67.64706 67.64706 55.88235
78 67.64706 67.64706 67.64706 67.64706
79 79.41176 67.64706 67.64706 67.64706
80 76.47059 79.41176 67.64706 67.64706
81 50.00000 76.47059 79.41176 67.64706
82 70.58824 50.00000 76.47059 79.41176
83 76.47059 70.58824 50.00000 76.47059
84 70.58824 76.47059 70.58824 50.00000
85 79.41176 70.58824 76.47059 70.58824
86 79.41176 79.41176 70.58824 76.47059
87 76.47059 79.41176 79.41176 70.58824
88 70.58824 76.47059 79.41176 79.41176
89 67.64706 70.58824 76.47059 79.41176
90 67.64706 67.64706 70.58824 76.47059
91 70.58824 67.64706 67.64706 70.58824
92 50.00000 70.58824 67.64706 67.64706
93 61.76471 50.00000 70.58824 67.64706
94 55.88235 61.76471 50.00000 70.58824
95 64.70588 55.88235 61.76471 50.00000
96 64.70588 64.70588 55.88235 61.76471
97 52.94118 64.70588 64.70588 55.88235
98 47.05882 52.94118 64.70588 64.70588
99 41.17647 47.05882 52.94118 64.70588
100 35.29412 41.17647 47.05882 52.94118
101 41.17647 35.29412 41.17647 47.05882
102 47.05882 41.17647 35.29412 41.17647
103 23.52941 47.05882 41.17647 35.29412
104 8.82353 23.52941 47.05882 41.17647
105 0.00000 8.82353 23.52941 47.05882
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y0 Y1 Y2
4.6206826 0.8643692 0.0466691 0.0007214
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-29.1483 -6.3890 0.4477 6.0458 26.0290
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6206826 3.4050337 1.357 0.178
Y0 0.8643692 0.0997952 8.661 7.86e-14 ***
Y1 0.0466691 0.1317849 0.354 0.724
Y2 0.0007214 0.1014515 0.007 0.994
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.77 on 101 degrees of freedom
Multiple R-squared: 0.7932, Adjusted R-squared: 0.7871
F-statistic: 129.1 on 3 and 101 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,] 0.24661456 0.49322913 0.75338544
[2,] 0.12971102 0.25942205 0.87028898
[3,] 0.09829279 0.19658557 0.90170721
[4,] 0.04695654 0.09391309 0.95304346
[5,] 0.18127037 0.36254075 0.81872963
[6,] 0.25254779 0.50509558 0.74745221
[7,] 0.27345342 0.54690683 0.72654658
[8,] 0.19690755 0.39381510 0.80309245
[9,] 0.15165616 0.30331233 0.84834384
[10,] 0.10639550 0.21279100 0.89360450
[11,] 0.13424614 0.26849228 0.86575386
[12,] 0.14391302 0.28782604 0.85608698
[13,] 0.34264558 0.68529116 0.65735442
[14,] 0.30375835 0.60751669 0.69624165
[15,] 0.91590289 0.16819423 0.08409711
[16,] 0.92668467 0.14663066 0.07331533
[17,] 0.91155522 0.17688955 0.08844478
[18,] 0.88485156 0.23029688 0.11514844
[19,] 0.85981427 0.28037145 0.14018573
[20,] 0.82396801 0.35206398 0.17603199
[21,] 0.78278519 0.43442963 0.21721481
[22,] 0.73368359 0.53263282 0.26631641
[23,] 0.69216112 0.61567776 0.30783888
[24,] 0.63253689 0.73492622 0.36746311
[25,] 0.57141577 0.85716846 0.42858423
[26,] 0.51547923 0.96904154 0.48452077
[27,] 0.52878167 0.94243666 0.47121833
[28,] 0.47145658 0.94291315 0.52854342
[29,] 0.50075815 0.99848369 0.49924185
[30,] 0.56380283 0.87239434 0.43619717
[31,] 0.68125049 0.63749901 0.31874951
[32,] 0.64236053 0.71527894 0.35763947
[33,] 0.59323132 0.81353735 0.40676868
[34,] 0.57628014 0.84743971 0.42371986
[35,] 0.52983264 0.94033472 0.47016736
[36,] 0.59718125 0.80563751 0.40281875
[37,] 0.87461788 0.25076423 0.12538212
[38,] 0.95625757 0.08748485 0.04374243
[39,] 0.94176914 0.11646172 0.05823086
[40,] 0.93824539 0.12350922 0.06175461
[41,] 0.92863040 0.14273920 0.07136960
[42,] 0.91022046 0.17955908 0.08977954
[43,] 0.88586072 0.22827857 0.11413928
[44,] 0.86431529 0.27136942 0.13568471
[45,] 0.90677025 0.18645950 0.09322975
[46,] 0.89893038 0.20213925 0.10106962
[47,] 0.88163781 0.23672438 0.11836219
[48,] 0.85064987 0.29870027 0.14935013
[49,] 0.84804753 0.30390494 0.15195247
[50,] 0.81543750 0.36912501 0.18456250
[51,] 0.77621577 0.44756845 0.22378423
[52,] 0.73072668 0.53854664 0.26927332
[53,] 0.72043152 0.55913695 0.27956848
[54,] 0.68067446 0.63865108 0.31932554
[55,] 0.64898808 0.70202385 0.35101192
[56,] 0.73514538 0.52970924 0.26485462
[57,] 0.68738665 0.62522669 0.31261335
[58,] 0.63309878 0.73380243 0.36690122
[59,] 0.58281845 0.83436310 0.41718155
[60,] 0.58335191 0.83329619 0.41664809
[61,] 0.66603293 0.66793414 0.33396707
[62,] 0.61045638 0.77908725 0.38954362
[63,] 0.68123923 0.63752155 0.31876077
[64,] 0.75294171 0.49411657 0.24705829
[65,] 0.71850645 0.56298710 0.28149355
[66,] 0.68065437 0.63869126 0.31934563
[67,] 0.62281602 0.75436797 0.37718398
[68,] 0.57488929 0.85022143 0.42511071
[69,] 0.61504280 0.76991440 0.38495720
[70,] 0.56024469 0.87951063 0.43975531
[71,] 0.51335044 0.97329912 0.48664956
[72,] 0.44778511 0.89557023 0.55221489
[73,] 0.49321179 0.98642358 0.50678821
[74,] 0.42778755 0.85557511 0.57221245
[75,] 0.70899446 0.58201108 0.29100554
[76,] 0.78501904 0.42996192 0.21498096
[77,] 0.76596420 0.46807159 0.23403580
[78,] 0.70336686 0.59326627 0.29663314
[79,] 0.70366920 0.59266160 0.29633080
[80,] 0.63721918 0.72556165 0.36278082
[81,] 0.55705141 0.88589718 0.44294859
[82,] 0.48783176 0.97566352 0.51216824
[83,] 0.40826822 0.81653643 0.59173178
[84,] 0.32406495 0.64812991 0.67593505
[85,] 0.26367888 0.52735776 0.73632112
[86,] 0.41458805 0.82917609 0.58541195
[87,] 0.43743353 0.87486706 0.56256647
[88,] 0.36844912 0.73689825 0.63155088
[89,] 0.58653255 0.82693490 0.41346745
[90,] 0.45469998 0.90939996 0.54530002
[91,] 0.33123764 0.66247528 0.66876236
[92,] 0.20438969 0.40877938 0.79561031
> postscript(file="/var/www/html/rcomp/tmp/1zs8y1258451089.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bwpq1258451089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3dnw31258451089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4574t1258451089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59i831258451089.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 = 105
Frequency = 1
1 2 3 4 5
2.583303283 7.787974201 11.261083497 0.159065700 13.790473466
6 7 8 9 10
-11.213091234 15.397645136 6.045803916 8.450662117 0.017560018
11 12 13 14 15
-9.741060056 0.301477354 -8.230411883 2.086508063 5.578854993
16 17 18 19 20
3.045079748 -8.856888319 -10.454666933 -29.148333914 -14.932881564
21 22 23 24 25
26.029040943 21.764919927 8.487302346 5.361901544 -3.352090276
26 27 28 29 30
4.532105037 -0.678931101 -1.210863984 -4.415814246 0.808094188
31 32 33 34 35
1.084740294 -1.852192672 -11.074636300 -3.709501583 -12.380774750
36 37 38 39 40
-16.771858916 17.078858115 5.860173173 -3.115754284 -9.686398058
41 42 43 44 45
3.955760313 16.932202911 -28.001294402 23.064099785 1.543630774
46 47 48 49 50
9.153018108 7.389488524 -3.989175452 -2.126717027 -5.196526864
51 52 53 54 55
17.676562142 -9.065071146 6.167001244 1.618794937 -10.411948290
56 57 58 59 60
-3.188319254 2.844168177 0.447655109 9.136044094 4.448312054
61 62 63 64 65
-7.329266139 -17.493165635 -1.829927340 -0.999989022 1.953917959
66 67 68 69 70
-12.353050174 15.325795479 -2.322027549 11.169854528 12.890043064
71 72 73 74 75
-0.767109372 -7.605912245 3.090826152 -4.919880757 11.798452706
76 77 78 79 80
1.901806298 1.357001094 1.348514086 13.113219976 0.002994488
81 82 83 84 85
-24.474380209 19.122990532 8.446989061 -3.461627876 10.157049440
86 87 88 89 90
2.800543525 -0.548175973 -3.894631926 -1.614021705 1.204886654
91 92 93 94 95
4.287568805 -18.840806986 10.582472484 -4.510215978 8.863641491
96 97 98 99 100
1.502892081 -10.669356827 -6.389026013 -6.637805739 -7.152622813
101 102 103 104 105
4.093022490 5.169618783 -23.714598343 -18.361150521 -13.379514758
> postscript(file="/var/www/html/rcomp/tmp/6q1u71258451089.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 = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 2.583303283 NA
1 7.787974201 2.583303283
2 11.261083497 7.787974201
3 0.159065700 11.261083497
4 13.790473466 0.159065700
5 -11.213091234 13.790473466
6 15.397645136 -11.213091234
7 6.045803916 15.397645136
8 8.450662117 6.045803916
9 0.017560018 8.450662117
10 -9.741060056 0.017560018
11 0.301477354 -9.741060056
12 -8.230411883 0.301477354
13 2.086508063 -8.230411883
14 5.578854993 2.086508063
15 3.045079748 5.578854993
16 -8.856888319 3.045079748
17 -10.454666933 -8.856888319
18 -29.148333914 -10.454666933
19 -14.932881564 -29.148333914
20 26.029040943 -14.932881564
21 21.764919927 26.029040943
22 8.487302346 21.764919927
23 5.361901544 8.487302346
24 -3.352090276 5.361901544
25 4.532105037 -3.352090276
26 -0.678931101 4.532105037
27 -1.210863984 -0.678931101
28 -4.415814246 -1.210863984
29 0.808094188 -4.415814246
30 1.084740294 0.808094188
31 -1.852192672 1.084740294
32 -11.074636300 -1.852192672
33 -3.709501583 -11.074636300
34 -12.380774750 -3.709501583
35 -16.771858916 -12.380774750
36 17.078858115 -16.771858916
37 5.860173173 17.078858115
38 -3.115754284 5.860173173
39 -9.686398058 -3.115754284
40 3.955760313 -9.686398058
41 16.932202911 3.955760313
42 -28.001294402 16.932202911
43 23.064099785 -28.001294402
44 1.543630774 23.064099785
45 9.153018108 1.543630774
46 7.389488524 9.153018108
47 -3.989175452 7.389488524
48 -2.126717027 -3.989175452
49 -5.196526864 -2.126717027
50 17.676562142 -5.196526864
51 -9.065071146 17.676562142
52 6.167001244 -9.065071146
53 1.618794937 6.167001244
54 -10.411948290 1.618794937
55 -3.188319254 -10.411948290
56 2.844168177 -3.188319254
57 0.447655109 2.844168177
58 9.136044094 0.447655109
59 4.448312054 9.136044094
60 -7.329266139 4.448312054
61 -17.493165635 -7.329266139
62 -1.829927340 -17.493165635
63 -0.999989022 -1.829927340
64 1.953917959 -0.999989022
65 -12.353050174 1.953917959
66 15.325795479 -12.353050174
67 -2.322027549 15.325795479
68 11.169854528 -2.322027549
69 12.890043064 11.169854528
70 -0.767109372 12.890043064
71 -7.605912245 -0.767109372
72 3.090826152 -7.605912245
73 -4.919880757 3.090826152
74 11.798452706 -4.919880757
75 1.901806298 11.798452706
76 1.357001094 1.901806298
77 1.348514086 1.357001094
78 13.113219976 1.348514086
79 0.002994488 13.113219976
80 -24.474380209 0.002994488
81 19.122990532 -24.474380209
82 8.446989061 19.122990532
83 -3.461627876 8.446989061
84 10.157049440 -3.461627876
85 2.800543525 10.157049440
86 -0.548175973 2.800543525
87 -3.894631926 -0.548175973
88 -1.614021705 -3.894631926
89 1.204886654 -1.614021705
90 4.287568805 1.204886654
91 -18.840806986 4.287568805
92 10.582472484 -18.840806986
93 -4.510215978 10.582472484
94 8.863641491 -4.510215978
95 1.502892081 8.863641491
96 -10.669356827 1.502892081
97 -6.389026013 -10.669356827
98 -6.637805739 -6.389026013
99 -7.152622813 -6.637805739
100 4.093022490 -7.152622813
101 5.169618783 4.093022490
102 -23.714598343 5.169618783
103 -18.361150521 -23.714598343
104 -13.379514758 -18.361150521
105 NA -13.379514758
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.787974201 2.583303283
[2,] 11.261083497 7.787974201
[3,] 0.159065700 11.261083497
[4,] 13.790473466 0.159065700
[5,] -11.213091234 13.790473466
[6,] 15.397645136 -11.213091234
[7,] 6.045803916 15.397645136
[8,] 8.450662117 6.045803916
[9,] 0.017560018 8.450662117
[10,] -9.741060056 0.017560018
[11,] 0.301477354 -9.741060056
[12,] -8.230411883 0.301477354
[13,] 2.086508063 -8.230411883
[14,] 5.578854993 2.086508063
[15,] 3.045079748 5.578854993
[16,] -8.856888319 3.045079748
[17,] -10.454666933 -8.856888319
[18,] -29.148333914 -10.454666933
[19,] -14.932881564 -29.148333914
[20,] 26.029040943 -14.932881564
[21,] 21.764919927 26.029040943
[22,] 8.487302346 21.764919927
[23,] 5.361901544 8.487302346
[24,] -3.352090276 5.361901544
[25,] 4.532105037 -3.352090276
[26,] -0.678931101 4.532105037
[27,] -1.210863984 -0.678931101
[28,] -4.415814246 -1.210863984
[29,] 0.808094188 -4.415814246
[30,] 1.084740294 0.808094188
[31,] -1.852192672 1.084740294
[32,] -11.074636300 -1.852192672
[33,] -3.709501583 -11.074636300
[34,] -12.380774750 -3.709501583
[35,] -16.771858916 -12.380774750
[36,] 17.078858115 -16.771858916
[37,] 5.860173173 17.078858115
[38,] -3.115754284 5.860173173
[39,] -9.686398058 -3.115754284
[40,] 3.955760313 -9.686398058
[41,] 16.932202911 3.955760313
[42,] -28.001294402 16.932202911
[43,] 23.064099785 -28.001294402
[44,] 1.543630774 23.064099785
[45,] 9.153018108 1.543630774
[46,] 7.389488524 9.153018108
[47,] -3.989175452 7.389488524
[48,] -2.126717027 -3.989175452
[49,] -5.196526864 -2.126717027
[50,] 17.676562142 -5.196526864
[51,] -9.065071146 17.676562142
[52,] 6.167001244 -9.065071146
[53,] 1.618794937 6.167001244
[54,] -10.411948290 1.618794937
[55,] -3.188319254 -10.411948290
[56,] 2.844168177 -3.188319254
[57,] 0.447655109 2.844168177
[58,] 9.136044094 0.447655109
[59,] 4.448312054 9.136044094
[60,] -7.329266139 4.448312054
[61,] -17.493165635 -7.329266139
[62,] -1.829927340 -17.493165635
[63,] -0.999989022 -1.829927340
[64,] 1.953917959 -0.999989022
[65,] -12.353050174 1.953917959
[66,] 15.325795479 -12.353050174
[67,] -2.322027549 15.325795479
[68,] 11.169854528 -2.322027549
[69,] 12.890043064 11.169854528
[70,] -0.767109372 12.890043064
[71,] -7.605912245 -0.767109372
[72,] 3.090826152 -7.605912245
[73,] -4.919880757 3.090826152
[74,] 11.798452706 -4.919880757
[75,] 1.901806298 11.798452706
[76,] 1.357001094 1.901806298
[77,] 1.348514086 1.357001094
[78,] 13.113219976 1.348514086
[79,] 0.002994488 13.113219976
[80,] -24.474380209 0.002994488
[81,] 19.122990532 -24.474380209
[82,] 8.446989061 19.122990532
[83,] -3.461627876 8.446989061
[84,] 10.157049440 -3.461627876
[85,] 2.800543525 10.157049440
[86,] -0.548175973 2.800543525
[87,] -3.894631926 -0.548175973
[88,] -1.614021705 -3.894631926
[89,] 1.204886654 -1.614021705
[90,] 4.287568805 1.204886654
[91,] -18.840806986 4.287568805
[92,] 10.582472484 -18.840806986
[93,] -4.510215978 10.582472484
[94,] 8.863641491 -4.510215978
[95,] 1.502892081 8.863641491
[96,] -10.669356827 1.502892081
[97,] -6.389026013 -10.669356827
[98,] -6.637805739 -6.389026013
[99,] -7.152622813 -6.637805739
[100,] 4.093022490 -7.152622813
[101,] 5.169618783 4.093022490
[102,] -23.714598343 5.169618783
[103,] -18.361150521 -23.714598343
[104,] -13.379514758 -18.361150521
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.787974201 2.583303283
2 11.261083497 7.787974201
3 0.159065700 11.261083497
4 13.790473466 0.159065700
5 -11.213091234 13.790473466
6 15.397645136 -11.213091234
7 6.045803916 15.397645136
8 8.450662117 6.045803916
9 0.017560018 8.450662117
10 -9.741060056 0.017560018
11 0.301477354 -9.741060056
12 -8.230411883 0.301477354
13 2.086508063 -8.230411883
14 5.578854993 2.086508063
15 3.045079748 5.578854993
16 -8.856888319 3.045079748
17 -10.454666933 -8.856888319
18 -29.148333914 -10.454666933
19 -14.932881564 -29.148333914
20 26.029040943 -14.932881564
21 21.764919927 26.029040943
22 8.487302346 21.764919927
23 5.361901544 8.487302346
24 -3.352090276 5.361901544
25 4.532105037 -3.352090276
26 -0.678931101 4.532105037
27 -1.210863984 -0.678931101
28 -4.415814246 -1.210863984
29 0.808094188 -4.415814246
30 1.084740294 0.808094188
31 -1.852192672 1.084740294
32 -11.074636300 -1.852192672
33 -3.709501583 -11.074636300
34 -12.380774750 -3.709501583
35 -16.771858916 -12.380774750
36 17.078858115 -16.771858916
37 5.860173173 17.078858115
38 -3.115754284 5.860173173
39 -9.686398058 -3.115754284
40 3.955760313 -9.686398058
41 16.932202911 3.955760313
42 -28.001294402 16.932202911
43 23.064099785 -28.001294402
44 1.543630774 23.064099785
45 9.153018108 1.543630774
46 7.389488524 9.153018108
47 -3.989175452 7.389488524
48 -2.126717027 -3.989175452
49 -5.196526864 -2.126717027
50 17.676562142 -5.196526864
51 -9.065071146 17.676562142
52 6.167001244 -9.065071146
53 1.618794937 6.167001244
54 -10.411948290 1.618794937
55 -3.188319254 -10.411948290
56 2.844168177 -3.188319254
57 0.447655109 2.844168177
58 9.136044094 0.447655109
59 4.448312054 9.136044094
60 -7.329266139 4.448312054
61 -17.493165635 -7.329266139
62 -1.829927340 -17.493165635
63 -0.999989022 -1.829927340
64 1.953917959 -0.999989022
65 -12.353050174 1.953917959
66 15.325795479 -12.353050174
67 -2.322027549 15.325795479
68 11.169854528 -2.322027549
69 12.890043064 11.169854528
70 -0.767109372 12.890043064
71 -7.605912245 -0.767109372
72 3.090826152 -7.605912245
73 -4.919880757 3.090826152
74 11.798452706 -4.919880757
75 1.901806298 11.798452706
76 1.357001094 1.901806298
77 1.348514086 1.357001094
78 13.113219976 1.348514086
79 0.002994488 13.113219976
80 -24.474380209 0.002994488
81 19.122990532 -24.474380209
82 8.446989061 19.122990532
83 -3.461627876 8.446989061
84 10.157049440 -3.461627876
85 2.800543525 10.157049440
86 -0.548175973 2.800543525
87 -3.894631926 -0.548175973
88 -1.614021705 -3.894631926
89 1.204886654 -1.614021705
90 4.287568805 1.204886654
91 -18.840806986 4.287568805
92 10.582472484 -18.840806986
93 -4.510215978 10.582472484
94 8.863641491 -4.510215978
95 1.502892081 8.863641491
96 -10.669356827 1.502892081
97 -6.389026013 -10.669356827
98 -6.637805739 -6.389026013
99 -7.152622813 -6.637805739
100 4.093022490 -7.152622813
101 5.169618783 4.093022490
102 -23.714598343 5.169618783
103 -18.361150521 -23.714598343
104 -13.379514758 -18.361150521
> 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/750ut1258451089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cgkv1258451089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94fz41258451089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zfbr1258451089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pjf61258451089.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/12u20s1258451089.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/13mx6l1258451089.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/143dke1258451089.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/15p20c1258451089.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/1666nr1258451089.tab")
+ }
>
> system("convert tmp/1zs8y1258451089.ps tmp/1zs8y1258451089.png")
> system("convert tmp/2bwpq1258451089.ps tmp/2bwpq1258451089.png")
> system("convert tmp/3dnw31258451089.ps tmp/3dnw31258451089.png")
> system("convert tmp/4574t1258451089.ps tmp/4574t1258451089.png")
> system("convert tmp/59i831258451089.ps tmp/59i831258451089.png")
> system("convert tmp/6q1u71258451089.ps tmp/6q1u71258451089.png")
> system("convert tmp/750ut1258451089.ps tmp/750ut1258451089.png")
> system("convert tmp/8cgkv1258451089.ps tmp/8cgkv1258451089.png")
> system("convert tmp/94fz41258451089.ps tmp/94fz41258451089.png")
> system("convert tmp/10zfbr1258451089.ps tmp/10zfbr1258451089.png")
>
>
> proc.time()
user system elapsed
3.063 1.607 3.751