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.
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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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> x <- array(list(101.76,102.37,102.38,102.86,102.87,102.92,102.95,103.02,104.08,104.16,104.24,104.33,104.73,104.86,105.03,105.62,105.63,105.63,105.94,106.61,107.69,107.78,107.93,108.48,108.14,108.48,108.48,108.89,108.93,109.21,109.47,109.80,111.73,111.85,112.12,112.15,112.17,112.67,112.80,113.44,113.53,114.53,114.51,115.05,116.67,117.07,116.92,117.00,117.02,117.35,117.36,117.82,117.88,118.24,118.50,118.80,119.76,120.09,120.16),dim=c(1,59),dimnames=list(c('cultuurbesteding'),1:59))
> y <- array(NA,dim=c(1,59),dimnames=list(c('cultuurbesteding'),1:59))
> 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 = '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
cultuurbesteding M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 101.76 1 0 0 0 0 0 0 0 0 0 0
2 102.37 0 1 0 0 0 0 0 0 0 0 0
3 102.38 0 0 1 0 0 0 0 0 0 0 0
4 102.86 0 0 0 1 0 0 0 0 0 0 0
5 102.87 0 0 0 0 1 0 0 0 0 0 0
6 102.92 0 0 0 0 0 1 0 0 0 0 0
7 102.95 0 0 0 0 0 0 1 0 0 0 0
8 103.02 0 0 0 0 0 0 0 1 0 0 0
9 104.08 0 0 0 0 0 0 0 0 1 0 0
10 104.16 0 0 0 0 0 0 0 0 0 1 0
11 104.24 0 0 0 0 0 0 0 0 0 0 1
12 104.33 0 0 0 0 0 0 0 0 0 0 0
13 104.73 1 0 0 0 0 0 0 0 0 0 0
14 104.86 0 1 0 0 0 0 0 0 0 0 0
15 105.03 0 0 1 0 0 0 0 0 0 0 0
16 105.62 0 0 0 1 0 0 0 0 0 0 0
17 105.63 0 0 0 0 1 0 0 0 0 0 0
18 105.63 0 0 0 0 0 1 0 0 0 0 0
19 105.94 0 0 0 0 0 0 1 0 0 0 0
20 106.61 0 0 0 0 0 0 0 1 0 0 0
21 107.69 0 0 0 0 0 0 0 0 1 0 0
22 107.78 0 0 0 0 0 0 0 0 0 1 0
23 107.93 0 0 0 0 0 0 0 0 0 0 1
24 108.48 0 0 0 0 0 0 0 0 0 0 0
25 108.14 1 0 0 0 0 0 0 0 0 0 0
26 108.48 0 1 0 0 0 0 0 0 0 0 0
27 108.48 0 0 1 0 0 0 0 0 0 0 0
28 108.89 0 0 0 1 0 0 0 0 0 0 0
29 108.93 0 0 0 0 1 0 0 0 0 0 0
30 109.21 0 0 0 0 0 1 0 0 0 0 0
31 109.47 0 0 0 0 0 0 1 0 0 0 0
32 109.80 0 0 0 0 0 0 0 1 0 0 0
33 111.73 0 0 0 0 0 0 0 0 1 0 0
34 111.85 0 0 0 0 0 0 0 0 0 1 0
35 112.12 0 0 0 0 0 0 0 0 0 0 1
36 112.15 0 0 0 0 0 0 0 0 0 0 0
37 112.17 1 0 0 0 0 0 0 0 0 0 0
38 112.67 0 1 0 0 0 0 0 0 0 0 0
39 112.80 0 0 1 0 0 0 0 0 0 0 0
40 113.44 0 0 0 1 0 0 0 0 0 0 0
41 113.53 0 0 0 0 1 0 0 0 0 0 0
42 114.53 0 0 0 0 0 1 0 0 0 0 0
43 114.51 0 0 0 0 0 0 1 0 0 0 0
44 115.05 0 0 0 0 0 0 0 1 0 0 0
45 116.67 0 0 0 0 0 0 0 0 1 0 0
46 117.07 0 0 0 0 0 0 0 0 0 1 0
47 116.92 0 0 0 0 0 0 0 0 0 0 1
48 117.00 0 0 0 0 0 0 0 0 0 0 0
49 117.02 1 0 0 0 0 0 0 0 0 0 0
50 117.35 0 1 0 0 0 0 0 0 0 0 0
51 117.36 0 0 1 0 0 0 0 0 0 0 0
52 117.82 0 0 0 1 0 0 0 0 0 0 0
53 117.88 0 0 0 0 1 0 0 0 0 0 0
54 118.24 0 0 0 0 0 1 0 0 0 0 0
55 118.50 0 0 0 0 0 0 1 0 0 0 0
56 118.80 0 0 0 0 0 0 0 1 0 0 0
57 119.76 0 0 0 0 0 0 0 0 1 0 0
58 120.09 0 0 0 0 0 0 0 0 0 1 0
59 120.16 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
110.490 -1.726 -1.344 -1.280 -0.764 -0.722
M6 M7 M8 M9 M10 M11
-0.384 -0.216 0.166 1.496 1.700 1.784
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.034 -4.339 -0.730 4.535 8.256
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 110.490 3.085 35.816 <2e-16 ***
M1 -1.726 4.139 -0.417 0.679
M2 -1.344 4.139 -0.325 0.747
M3 -1.280 4.139 -0.309 0.758
M4 -0.764 4.139 -0.185 0.854
M5 -0.722 4.139 -0.174 0.862
M6 -0.384 4.139 -0.093 0.926
M7 -0.216 4.139 -0.052 0.959
M8 0.166 4.139 0.040 0.968
M9 1.496 4.139 0.361 0.719
M10 1.700 4.139 0.411 0.683
M11 1.784 4.139 0.431 0.668
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.17 on 47 degrees of freedom
Multiple R-squared: 0.04259, Adjusted R-squared: -0.1815
F-statistic: 0.1901 on 11 and 47 DF, p-value: 0.9975
> 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,] 0.062543128 0.12508626 0.9374569
[2,] 0.034346368 0.06869274 0.9656536
[3,] 0.020063253 0.04012651 0.9799367
[4,] 0.012496658 0.02499332 0.9875033
[5,] 0.009041816 0.01808363 0.9909582
[6,] 0.008383090 0.01676618 0.9916169
[7,] 0.008377770 0.01675554 0.9916222
[8,] 0.009065838 0.01813168 0.9909342
[9,] 0.010693304 0.02138661 0.9893067
[10,] 0.011628981 0.02325796 0.9883710
[11,] 0.019911372 0.03982274 0.9800886
[12,] 0.030725785 0.06145157 0.9692742
[13,] 0.044120401 0.08824080 0.9558796
[14,] 0.060990701 0.12198140 0.9390093
[15,] 0.084383826 0.16876765 0.9156162
[16,] 0.126727757 0.25345551 0.8732722
[17,] 0.187037659 0.37407532 0.8129623
[18,] 0.274353474 0.54870695 0.7256465
[19,] 0.390595457 0.78119091 0.6094045
[20,] 0.541988045 0.91602391 0.4580120
[21,] 0.693945213 0.61210957 0.3060548
[22,] 0.733319254 0.53336149 0.2666807
[23,] 0.797414993 0.40517001 0.2025850
[24,] 0.840950056 0.31809989 0.1590499
[25,] 0.869832975 0.26033405 0.1301670
[26,] 0.887293198 0.22541360 0.1127068
[27,] 0.899020741 0.20195852 0.1009793
[28,] 0.894357511 0.21128498 0.1056425
[29,] 0.889182268 0.22163546 0.1108177
[30,] 0.869580708 0.26083858 0.1304193
> postscript(file="/var/www/html/rcomp/tmp/1s3qa1292690836.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2s3qa1292690836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3s3qa1292690836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lu8d1292690836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lu8d1292690836.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 = 59
Frequency = 1
1 2 3 4 5 6 7 8 9 10 11
-7.004 -6.776 -6.830 -6.866 -6.898 -7.186 -7.324 -7.636 -7.906 -8.030 -8.034
12 13 14 15 16 17 18 19 20 21 22
-6.160 -4.034 -4.286 -4.180 -4.106 -4.138 -4.476 -4.334 -4.046 -4.296 -4.410
23 24 25 26 27 28 29 30 31 32 33
-4.344 -2.010 -0.624 -0.666 -0.730 -0.836 -0.838 -0.896 -0.804 -0.856 -0.256
34 35 36 37 38 39 40 41 42 43 44
-0.340 -0.154 1.660 3.406 3.524 3.590 3.714 3.762 4.424 4.236 4.394
45 46 47 48 49 50 51 52 53 54 55
4.684 4.880 4.646 6.510 8.256 8.204 8.150 8.094 8.112 8.134 8.226
56 57 58 59
8.144 7.774 7.900 7.886
> postscript(file="/var/www/html/rcomp/tmp/6lu8d1292690836.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -7.004 NA
1 -6.776 -7.004
2 -6.830 -6.776
3 -6.866 -6.830
4 -6.898 -6.866
5 -7.186 -6.898
6 -7.324 -7.186
7 -7.636 -7.324
8 -7.906 -7.636
9 -8.030 -7.906
10 -8.034 -8.030
11 -6.160 -8.034
12 -4.034 -6.160
13 -4.286 -4.034
14 -4.180 -4.286
15 -4.106 -4.180
16 -4.138 -4.106
17 -4.476 -4.138
18 -4.334 -4.476
19 -4.046 -4.334
20 -4.296 -4.046
21 -4.410 -4.296
22 -4.344 -4.410
23 -2.010 -4.344
24 -0.624 -2.010
25 -0.666 -0.624
26 -0.730 -0.666
27 -0.836 -0.730
28 -0.838 -0.836
29 -0.896 -0.838
30 -0.804 -0.896
31 -0.856 -0.804
32 -0.256 -0.856
33 -0.340 -0.256
34 -0.154 -0.340
35 1.660 -0.154
36 3.406 1.660
37 3.524 3.406
38 3.590 3.524
39 3.714 3.590
40 3.762 3.714
41 4.424 3.762
42 4.236 4.424
43 4.394 4.236
44 4.684 4.394
45 4.880 4.684
46 4.646 4.880
47 6.510 4.646
48 8.256 6.510
49 8.204 8.256
50 8.150 8.204
51 8.094 8.150
52 8.112 8.094
53 8.134 8.112
54 8.226 8.134
55 8.144 8.226
56 7.774 8.144
57 7.900 7.774
58 7.886 7.900
59 NA 7.886
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.776 -7.004
[2,] -6.830 -6.776
[3,] -6.866 -6.830
[4,] -6.898 -6.866
[5,] -7.186 -6.898
[6,] -7.324 -7.186
[7,] -7.636 -7.324
[8,] -7.906 -7.636
[9,] -8.030 -7.906
[10,] -8.034 -8.030
[11,] -6.160 -8.034
[12,] -4.034 -6.160
[13,] -4.286 -4.034
[14,] -4.180 -4.286
[15,] -4.106 -4.180
[16,] -4.138 -4.106
[17,] -4.476 -4.138
[18,] -4.334 -4.476
[19,] -4.046 -4.334
[20,] -4.296 -4.046
[21,] -4.410 -4.296
[22,] -4.344 -4.410
[23,] -2.010 -4.344
[24,] -0.624 -2.010
[25,] -0.666 -0.624
[26,] -0.730 -0.666
[27,] -0.836 -0.730
[28,] -0.838 -0.836
[29,] -0.896 -0.838
[30,] -0.804 -0.896
[31,] -0.856 -0.804
[32,] -0.256 -0.856
[33,] -0.340 -0.256
[34,] -0.154 -0.340
[35,] 1.660 -0.154
[36,] 3.406 1.660
[37,] 3.524 3.406
[38,] 3.590 3.524
[39,] 3.714 3.590
[40,] 3.762 3.714
[41,] 4.424 3.762
[42,] 4.236 4.424
[43,] 4.394 4.236
[44,] 4.684 4.394
[45,] 4.880 4.684
[46,] 4.646 4.880
[47,] 6.510 4.646
[48,] 8.256 6.510
[49,] 8.204 8.256
[50,] 8.150 8.204
[51,] 8.094 8.150
[52,] 8.112 8.094
[53,] 8.134 8.112
[54,] 8.226 8.134
[55,] 8.144 8.226
[56,] 7.774 8.144
[57,] 7.900 7.774
[58,] 7.886 7.900
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.776 -7.004
2 -6.830 -6.776
3 -6.866 -6.830
4 -6.898 -6.866
5 -7.186 -6.898
6 -7.324 -7.186
7 -7.636 -7.324
8 -7.906 -7.636
9 -8.030 -7.906
10 -8.034 -8.030
11 -6.160 -8.034
12 -4.034 -6.160
13 -4.286 -4.034
14 -4.180 -4.286
15 -4.106 -4.180
16 -4.138 -4.106
17 -4.476 -4.138
18 -4.334 -4.476
19 -4.046 -4.334
20 -4.296 -4.046
21 -4.410 -4.296
22 -4.344 -4.410
23 -2.010 -4.344
24 -0.624 -2.010
25 -0.666 -0.624
26 -0.730 -0.666
27 -0.836 -0.730
28 -0.838 -0.836
29 -0.896 -0.838
30 -0.804 -0.896
31 -0.856 -0.804
32 -0.256 -0.856
33 -0.340 -0.256
34 -0.154 -0.340
35 1.660 -0.154
36 3.406 1.660
37 3.524 3.406
38 3.590 3.524
39 3.714 3.590
40 3.762 3.714
41 4.424 3.762
42 4.236 4.424
43 4.394 4.236
44 4.684 4.394
45 4.880 4.684
46 4.646 4.880
47 6.510 4.646
48 8.256 6.510
49 8.204 8.256
50 8.150 8.204
51 8.094 8.150
52 8.112 8.094
53 8.134 8.112
54 8.226 8.134
55 8.144 8.226
56 7.774 8.144
57 7.900 7.774
58 7.886 7.900
> 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/7em7f1292690836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86doi1292690836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96doi1292690836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106doi1292690836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sdn61292690836.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/12vwlc1292690836.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/137i7u1292690836.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/140rpx1292690836.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/15y7gx1292690836.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/16j7w21292690836.tab")
+ }
>
> try(system("convert tmp/1s3qa1292690836.ps tmp/1s3qa1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/2s3qa1292690836.ps tmp/2s3qa1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/3s3qa1292690836.ps tmp/3s3qa1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lu8d1292690836.ps tmp/4lu8d1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lu8d1292690836.ps tmp/5lu8d1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lu8d1292690836.ps tmp/6lu8d1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/7em7f1292690836.ps tmp/7em7f1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/86doi1292690836.ps tmp/86doi1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/96doi1292690836.ps tmp/96doi1292690836.png",intern=TRUE))
character(0)
> try(system("convert tmp/106doi1292690836.ps tmp/106doi1292690836.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.447 1.703 6.797