R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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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
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Type 'q()' to quit R.
> x <- array(list(107.11,107.56,107.57,107.70,107.81,107.67,108.75,107.67,109.43,107.72,109.62,108.35,109.54,108.25,109.53,108.26,109.84,108.31,109.67,108.33,109.79,108.36,109.56,108.36,110.22,108.97,110.40,109.62,110.69,109.60,110.72,109.64,110.89,109.65,110.58,109.64,110.94,109.93,110.91,109.81,111.22,109.77,111.09,110.10,111.00,110.40,111.06,110.50,111.55,111.89,112.32,112.10,112.64,111.92,112.36,112.15,112.04,112.16,112.37,112.17,112.59,112.32,112.89,112.38,113.22,112.34,112.85,113.14,113.06,113.18,112.99,113.21,113.32,113.76,113.74,113.99,113.91,113.95,114.52,113.93,114.96,114.01,114.91,114.10,115.30,114.11,115.44,114.10,115.52,114.12,116.08,114.68,115.94,114.71,115.56,114.73,115.88,115.81,116.66,116.01,117.41,116.12,117.68,116.49,117.85,116.51,118.21,116.60,118.92,117.01,119.03,117.01,119.17,117.12,118.95,117.22,118.92,118.38,118.90,118.80),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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
Y X
1 107.11 107.56
2 107.57 107.70
3 107.81 107.67
4 108.75 107.67
5 109.43 107.72
6 109.62 108.35
7 109.54 108.25
8 109.53 108.26
9 109.84 108.31
10 109.67 108.33
11 109.79 108.36
12 109.56 108.36
13 110.22 108.97
14 110.40 109.62
15 110.69 109.60
16 110.72 109.64
17 110.89 109.65
18 110.58 109.64
19 110.94 109.93
20 110.91 109.81
21 111.22 109.77
22 111.09 110.10
23 111.00 110.40
24 111.06 110.50
25 111.55 111.89
26 112.32 112.10
27 112.64 111.92
28 112.36 112.15
29 112.04 112.16
30 112.37 112.17
31 112.59 112.32
32 112.89 112.38
33 113.22 112.34
34 112.85 113.14
35 113.06 113.18
36 112.99 113.21
37 113.32 113.76
38 113.74 113.99
39 113.91 113.95
40 114.52 113.93
41 114.96 114.01
42 114.91 114.10
43 115.30 114.11
44 115.44 114.10
45 115.52 114.12
46 116.08 114.68
47 115.94 114.71
48 115.56 114.73
49 115.88 115.81
50 116.66 116.01
51 117.41 116.12
52 117.68 116.49
53 117.85 116.51
54 118.21 116.60
55 118.92 117.01
56 119.03 117.01
57 119.17 117.12
58 118.95 117.22
59 118.92 118.38
60 118.90 118.80
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
-0.2983 1.0100
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.2748 -0.6009 0.1689 0.4893 1.1817
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.29834 3.05351 -0.098 0.923
X 1.00996 0.02719 37.150 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6698 on 58 degrees of freedom
Multiple R-squared: 0.9597, Adjusted R-squared: 0.959
F-statistic: 1380 on 1 and 58 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,] 0.76761809 0.464763817 0.232381909
[2,] 0.83445848 0.331083048 0.165541524
[3,] 0.73786710 0.524265795 0.262132897
[4,] 0.62835035 0.743299303 0.371649652
[5,] 0.53072215 0.938555701 0.469277851
[6,] 0.42945876 0.858917526 0.570541237
[7,] 0.34258818 0.685176366 0.657411817
[8,] 0.27187825 0.543756490 0.728121755
[9,] 0.31954542 0.639090837 0.680454581
[10,] 0.47422257 0.948445136 0.525777432
[11,] 0.41851435 0.837028692 0.581485654
[12,] 0.35830222 0.716604443 0.641697779
[13,] 0.30533366 0.610667321 0.694666339
[14,] 0.25989531 0.519790624 0.740104688
[15,] 0.21927227 0.438544536 0.780727732
[16,] 0.18744035 0.374880701 0.812559650
[17,] 0.20801729 0.416034587 0.791982707
[18,] 0.20139596 0.402791915 0.798604042
[19,] 0.21121179 0.422423577 0.788788212
[20,] 0.21346338 0.426926750 0.786536625
[21,] 0.36131277 0.722625543 0.638687229
[22,] 0.30980285 0.619605695 0.690197153
[23,] 0.26311290 0.526225805 0.736887097
[24,] 0.21497795 0.429955898 0.785022051
[25,] 0.19936822 0.398736440 0.800631780
[26,] 0.15338453 0.306769060 0.846615470
[27,] 0.11287290 0.225745808 0.887127096
[28,] 0.08251288 0.165025762 0.917487119
[29,] 0.07705510 0.154110197 0.922944902
[30,] 0.07825125 0.156502509 0.921748746
[31,] 0.06776814 0.135536277 0.932231862
[32,] 0.06847862 0.136957249 0.931521376
[33,] 0.11081223 0.221624466 0.889187767
[34,] 0.16621781 0.332435614 0.833782193
[35,] 0.23419203 0.468384064 0.765807968
[36,] 0.24502194 0.490043881 0.754978060
[37,] 0.25676813 0.513536254 0.743231873
[38,] 0.25659452 0.513189041 0.743405479
[39,] 0.26232690 0.524653799 0.737673100
[40,] 0.26537273 0.530745467 0.734627266
[41,] 0.25833182 0.516663635 0.741668183
[42,] 0.24316831 0.486336630 0.756831685
[43,] 0.20189262 0.403785244 0.798107378
[44,] 0.16951758 0.339035167 0.830482416
[45,] 0.41396384 0.827927681 0.586036160
[46,] 0.64170571 0.716588588 0.358294294
[47,] 0.70177667 0.596446666 0.298223333
[48,] 0.79372240 0.412555209 0.206277605
[49,] 0.92368989 0.152620230 0.076310115
[50,] 0.99877615 0.002447703 0.001223852
[51,] 0.99627149 0.007457027 0.003728514
> postscript(file="/var/www/html/rcomp/tmp/1yl7b1258745601.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26qiq1258745601.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jxdx1258745601.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xmev1258745601.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pu8p1258745601.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 = 60
Frequency = 1
1 2 3 4 5 6
-1.22305131 -0.90444582 -0.63414700 0.30585300 0.93535496 0.48907964
7 8 9 10 11 12
0.51007572 0.48997611 0.74947807 0.55927886 0.64898003 0.41898003
13 14 15 16 17 18
0.46290393 -0.01357061 0.29662861 0.28623017 0.44613057 0.14623017
19 20 21 22 23 24
0.21334154 0.30453683 0.65493527 0.19164820 -0.20134005 -0.24233614
25 26 27 28 29 30
-1.15618168 -0.59827346 -0.09648051 -0.60877150 -0.93887111 -0.61897071
31 32 33 34 35 36
-0.55046484 -0.31106249 0.05933594 -1.11863272 -0.94903115 -1.04932997
37 38 39 40 41 42
-1.27480843 -1.08709942 -0.87670098 -0.24650177 0.11270137 -0.02819511
43 44 45 46 47 48
0.35170528 0.50180489 0.56160567 0.55602761 0.38572879 -0.01447043
49 50 51 52 53 54
-0.78522812 -0.20722029 0.43168402 0.32799852 0.47779930 0.74690283
55 56 57 58 59 60
1.04281889 1.15281889 1.18172320 0.86072711 -0.34082744 -0.78501099
> postscript(file="/var/www/html/rcomp/tmp/6v1g61258745601.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.22305131 NA
1 -0.90444582 -1.22305131
2 -0.63414700 -0.90444582
3 0.30585300 -0.63414700
4 0.93535496 0.30585300
5 0.48907964 0.93535496
6 0.51007572 0.48907964
7 0.48997611 0.51007572
8 0.74947807 0.48997611
9 0.55927886 0.74947807
10 0.64898003 0.55927886
11 0.41898003 0.64898003
12 0.46290393 0.41898003
13 -0.01357061 0.46290393
14 0.29662861 -0.01357061
15 0.28623017 0.29662861
16 0.44613057 0.28623017
17 0.14623017 0.44613057
18 0.21334154 0.14623017
19 0.30453683 0.21334154
20 0.65493527 0.30453683
21 0.19164820 0.65493527
22 -0.20134005 0.19164820
23 -0.24233614 -0.20134005
24 -1.15618168 -0.24233614
25 -0.59827346 -1.15618168
26 -0.09648051 -0.59827346
27 -0.60877150 -0.09648051
28 -0.93887111 -0.60877150
29 -0.61897071 -0.93887111
30 -0.55046484 -0.61897071
31 -0.31106249 -0.55046484
32 0.05933594 -0.31106249
33 -1.11863272 0.05933594
34 -0.94903115 -1.11863272
35 -1.04932997 -0.94903115
36 -1.27480843 -1.04932997
37 -1.08709942 -1.27480843
38 -0.87670098 -1.08709942
39 -0.24650177 -0.87670098
40 0.11270137 -0.24650177
41 -0.02819511 0.11270137
42 0.35170528 -0.02819511
43 0.50180489 0.35170528
44 0.56160567 0.50180489
45 0.55602761 0.56160567
46 0.38572879 0.55602761
47 -0.01447043 0.38572879
48 -0.78522812 -0.01447043
49 -0.20722029 -0.78522812
50 0.43168402 -0.20722029
51 0.32799852 0.43168402
52 0.47779930 0.32799852
53 0.74690283 0.47779930
54 1.04281889 0.74690283
55 1.15281889 1.04281889
56 1.18172320 1.15281889
57 0.86072711 1.18172320
58 -0.34082744 0.86072711
59 -0.78501099 -0.34082744
60 NA -0.78501099
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.90444582 -1.22305131
[2,] -0.63414700 -0.90444582
[3,] 0.30585300 -0.63414700
[4,] 0.93535496 0.30585300
[5,] 0.48907964 0.93535496
[6,] 0.51007572 0.48907964
[7,] 0.48997611 0.51007572
[8,] 0.74947807 0.48997611
[9,] 0.55927886 0.74947807
[10,] 0.64898003 0.55927886
[11,] 0.41898003 0.64898003
[12,] 0.46290393 0.41898003
[13,] -0.01357061 0.46290393
[14,] 0.29662861 -0.01357061
[15,] 0.28623017 0.29662861
[16,] 0.44613057 0.28623017
[17,] 0.14623017 0.44613057
[18,] 0.21334154 0.14623017
[19,] 0.30453683 0.21334154
[20,] 0.65493527 0.30453683
[21,] 0.19164820 0.65493527
[22,] -0.20134005 0.19164820
[23,] -0.24233614 -0.20134005
[24,] -1.15618168 -0.24233614
[25,] -0.59827346 -1.15618168
[26,] -0.09648051 -0.59827346
[27,] -0.60877150 -0.09648051
[28,] -0.93887111 -0.60877150
[29,] -0.61897071 -0.93887111
[30,] -0.55046484 -0.61897071
[31,] -0.31106249 -0.55046484
[32,] 0.05933594 -0.31106249
[33,] -1.11863272 0.05933594
[34,] -0.94903115 -1.11863272
[35,] -1.04932997 -0.94903115
[36,] -1.27480843 -1.04932997
[37,] -1.08709942 -1.27480843
[38,] -0.87670098 -1.08709942
[39,] -0.24650177 -0.87670098
[40,] 0.11270137 -0.24650177
[41,] -0.02819511 0.11270137
[42,] 0.35170528 -0.02819511
[43,] 0.50180489 0.35170528
[44,] 0.56160567 0.50180489
[45,] 0.55602761 0.56160567
[46,] 0.38572879 0.55602761
[47,] -0.01447043 0.38572879
[48,] -0.78522812 -0.01447043
[49,] -0.20722029 -0.78522812
[50,] 0.43168402 -0.20722029
[51,] 0.32799852 0.43168402
[52,] 0.47779930 0.32799852
[53,] 0.74690283 0.47779930
[54,] 1.04281889 0.74690283
[55,] 1.15281889 1.04281889
[56,] 1.18172320 1.15281889
[57,] 0.86072711 1.18172320
[58,] -0.34082744 0.86072711
[59,] -0.78501099 -0.34082744
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.90444582 -1.22305131
2 -0.63414700 -0.90444582
3 0.30585300 -0.63414700
4 0.93535496 0.30585300
5 0.48907964 0.93535496
6 0.51007572 0.48907964
7 0.48997611 0.51007572
8 0.74947807 0.48997611
9 0.55927886 0.74947807
10 0.64898003 0.55927886
11 0.41898003 0.64898003
12 0.46290393 0.41898003
13 -0.01357061 0.46290393
14 0.29662861 -0.01357061
15 0.28623017 0.29662861
16 0.44613057 0.28623017
17 0.14623017 0.44613057
18 0.21334154 0.14623017
19 0.30453683 0.21334154
20 0.65493527 0.30453683
21 0.19164820 0.65493527
22 -0.20134005 0.19164820
23 -0.24233614 -0.20134005
24 -1.15618168 -0.24233614
25 -0.59827346 -1.15618168
26 -0.09648051 -0.59827346
27 -0.60877150 -0.09648051
28 -0.93887111 -0.60877150
29 -0.61897071 -0.93887111
30 -0.55046484 -0.61897071
31 -0.31106249 -0.55046484
32 0.05933594 -0.31106249
33 -1.11863272 0.05933594
34 -0.94903115 -1.11863272
35 -1.04932997 -0.94903115
36 -1.27480843 -1.04932997
37 -1.08709942 -1.27480843
38 -0.87670098 -1.08709942
39 -0.24650177 -0.87670098
40 0.11270137 -0.24650177
41 -0.02819511 0.11270137
42 0.35170528 -0.02819511
43 0.50180489 0.35170528
44 0.56160567 0.50180489
45 0.55602761 0.56160567
46 0.38572879 0.55602761
47 -0.01447043 0.38572879
48 -0.78522812 -0.01447043
49 -0.20722029 -0.78522812
50 0.43168402 -0.20722029
51 0.32799852 0.43168402
52 0.47779930 0.32799852
53 0.74690283 0.47779930
54 1.04281889 0.74690283
55 1.15281889 1.04281889
56 1.18172320 1.15281889
57 0.86072711 1.18172320
58 -0.34082744 0.86072711
59 -0.78501099 -0.34082744
> 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/7mnmn1258745601.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xqmg1258745601.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wpoh1258745601.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1060ax1258745601.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z0lc1258745601.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/1216us1258745601.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/13iujo1258745601.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/140b5j1258745601.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/15z8lw1258745601.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/16tsoe1258745601.tab")
+ }
> system("convert tmp/1yl7b1258745601.ps tmp/1yl7b1258745601.png")
> system("convert tmp/26qiq1258745601.ps tmp/26qiq1258745601.png")
> system("convert tmp/3jxdx1258745601.ps tmp/3jxdx1258745601.png")
> system("convert tmp/4xmev1258745601.ps tmp/4xmev1258745601.png")
> system("convert tmp/5pu8p1258745601.ps tmp/5pu8p1258745601.png")
> system("convert tmp/6v1g61258745601.ps tmp/6v1g61258745601.png")
> system("convert tmp/7mnmn1258745601.ps tmp/7mnmn1258745601.png")
> system("convert tmp/8xqmg1258745601.ps tmp/8xqmg1258745601.png")
> system("convert tmp/9wpoh1258745601.ps tmp/9wpoh1258745601.png")
> system("convert tmp/1060ax1258745601.ps tmp/1060ax1258745601.png")
>
>
> proc.time()
user system elapsed
2.451 1.530 2.849