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
'help.start()' for an HTML browser interface to help.
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> x <- array(list(100.35,102.1,100.35,102.86,100.36,102.99,100.39,103.73,100.34,105.02,100.34,104.43,100.35,104.63,100.43,104.93,100.47,105.87,100.67,105.66,100.75,106.76,100.78,106,100.79,107.22,100.67,107.33,100.64,107.11,100.64,108.86,100.76,107.72,100.79,107.88,100.79,108.38,100.9,107.72,100.98,108.41,101.11,109.9,101.18,111.45,101.22,112.18,101.23,113.34,101.09,113.46,101.26,114.06,101.28,115.54,101.43,116.39,101.53,115.94,101.54,116.97,101.54,115.94,101.79,115.91,102.18,116.43,102.37,116.26,102.46,116.35,102.46,117.9,102.03,117.7,102.26,117.53,102.33,117.86,102.44,117.65,102.5,116.51,102.52,115.93,102.66,115.31,102.72,115),dim=c(2,45),dimnames=list(c('Ktot','Vmtot'),1:45))
> y <- array(NA,dim=c(2,45),dimnames=list(c('Ktot','Vmtot'),1:45))
> 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
Ktot Vmtot M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.35 102.10 1 0 0 0 0 0 0 0 0 0 0 1
2 100.35 102.86 0 1 0 0 0 0 0 0 0 0 0 2
3 100.36 102.99 0 0 1 0 0 0 0 0 0 0 0 3
4 100.39 103.73 0 0 0 1 0 0 0 0 0 0 0 4
5 100.34 105.02 0 0 0 0 1 0 0 0 0 0 0 5
6 100.34 104.43 0 0 0 0 0 1 0 0 0 0 0 6
7 100.35 104.63 0 0 0 0 0 0 1 0 0 0 0 7
8 100.43 104.93 0 0 0 0 0 0 0 1 0 0 0 8
9 100.47 105.87 0 0 0 0 0 0 0 0 1 0 0 9
10 100.67 105.66 0 0 0 0 0 0 0 0 0 1 0 10
11 100.75 106.76 0 0 0 0 0 0 0 0 0 0 1 11
12 100.78 106.00 0 0 0 0 0 0 0 0 0 0 0 12
13 100.79 107.22 1 0 0 0 0 0 0 0 0 0 0 13
14 100.67 107.33 0 1 0 0 0 0 0 0 0 0 0 14
15 100.64 107.11 0 0 1 0 0 0 0 0 0 0 0 15
16 100.64 108.86 0 0 0 1 0 0 0 0 0 0 0 16
17 100.76 107.72 0 0 0 0 1 0 0 0 0 0 0 17
18 100.79 107.88 0 0 0 0 0 1 0 0 0 0 0 18
19 100.79 108.38 0 0 0 0 0 0 1 0 0 0 0 19
20 100.90 107.72 0 0 0 0 0 0 0 1 0 0 0 20
21 100.98 108.41 0 0 0 0 0 0 0 0 1 0 0 21
22 101.11 109.90 0 0 0 0 0 0 0 0 0 1 0 22
23 101.18 111.45 0 0 0 0 0 0 0 0 0 0 1 23
24 101.22 112.18 0 0 0 0 0 0 0 0 0 0 0 24
25 101.23 113.34 1 0 0 0 0 0 0 0 0 0 0 25
26 101.09 113.46 0 1 0 0 0 0 0 0 0 0 0 26
27 101.26 114.06 0 0 1 0 0 0 0 0 0 0 0 27
28 101.28 115.54 0 0 0 1 0 0 0 0 0 0 0 28
29 101.43 116.39 0 0 0 0 1 0 0 0 0 0 0 29
30 101.53 115.94 0 0 0 0 0 1 0 0 0 0 0 30
31 101.54 116.97 0 0 0 0 0 0 1 0 0 0 0 31
32 101.54 115.94 0 0 0 0 0 0 0 1 0 0 0 32
33 101.79 115.91 0 0 0 0 0 0 0 0 1 0 0 33
34 102.18 116.43 0 0 0 0 0 0 0 0 0 1 0 34
35 102.37 116.26 0 0 0 0 0 0 0 0 0 0 1 35
36 102.46 116.35 0 0 0 0 0 0 0 0 0 0 0 36
37 102.46 117.90 1 0 0 0 0 0 0 0 0 0 0 37
38 102.03 117.70 0 1 0 0 0 0 0 0 0 0 0 38
39 102.26 117.53 0 0 1 0 0 0 0 0 0 0 0 39
40 102.33 117.86 0 0 0 1 0 0 0 0 0 0 0 40
41 102.44 117.65 0 0 0 0 1 0 0 0 0 0 0 41
42 102.50 116.51 0 0 0 0 0 1 0 0 0 0 0 42
43 102.52 115.93 0 0 0 0 0 0 1 0 0 0 0 43
44 102.66 115.31 0 0 0 0 0 0 0 1 0 0 0 44
45 102.72 115.00 0 0 0 0 0 0 0 0 1 0 0 45
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Vmtot M1 M2 M3 M4
105.06587 -0.04862 0.03801 -0.20165 -0.17927 -0.17376
M5 M6 M7 M8 M9 M10
-0.15842 -0.21222 -0.26500 -0.28369 -0.23727 -0.05432
M11 t
0.02245 0.07676
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.28246 -0.18022 0.02782 0.10541 0.33307
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 105.065867 2.265547 46.376 < 2e-16 ***
Vmtot -0.048618 0.022122 -2.198 0.0356 *
M1 0.038006 0.156693 0.243 0.8100
M2 -0.201648 0.156255 -1.291 0.2064
M3 -0.179271 0.155919 -1.150 0.2590
M4 -0.173763 0.156724 -1.109 0.2761
M5 -0.158417 0.156277 -1.014 0.3186
M6 -0.212224 0.155908 -1.361 0.1833
M7 -0.265003 0.156041 -1.698 0.0995 .
M8 -0.283689 0.158481 -1.790 0.0832 .
M9 -0.237265 0.158833 -1.494 0.1453
M10 -0.054318 0.166577 -0.326 0.7466
M11 0.022450 0.166715 0.135 0.8937
t 0.076756 0.008868 8.656 8.94e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2039 on 31 degrees of freedom
Multiple R-squared: 0.9523, Adjusted R-squared: 0.9323
F-statistic: 47.59 on 13 and 31 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,] 0.15988969 0.3197794 0.8401103
[2,] 0.09554630 0.1910926 0.9044537
[3,] 0.05758855 0.1151771 0.9424114
[4,] 0.05774495 0.1154899 0.9422551
[5,] 0.18986969 0.3797394 0.8101303
[6,] 0.16205992 0.3241198 0.8379401
[7,] 0.12904415 0.2580883 0.8709558
[8,] 0.24605948 0.4921190 0.7539405
[9,] 0.66183617 0.6763277 0.3381638
[10,] 0.59006456 0.8198709 0.4099354
[11,] 0.60177936 0.7964413 0.3982206
[12,] 0.49500894 0.9900179 0.5049911
> postscript(file="/var/www/html/rcomp/tmp/1ovgo1258761365.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xlor1258761365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38bx91258761365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41irk1258761365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ml031258761365.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 = 45
Frequency = 1
1 2 3 4 5 6
0.13321913 0.33306648 0.25025427 0.23396740 0.15458204 0.10294955
7 8 9 10 11 12
0.09869551 0.13521107 0.09773238 0.02781967 0.00777512 -0.05347987
13 14 15 16 17 18
-0.09892849 -0.05068253 -0.19051087 -0.18769405 -0.21521997 -0.20038932
19 20 21 22 23 24
-0.20005810 -0.18021536 -0.18984842 -0.24711137 -0.25527803 -0.23409293
25 26 27 28 29 30
-0.28245860 -0.25372646 -0.15368844 -0.14399835 -0.04477542 0.01039854
31 32 33 34 35 36
0.04649705 -0.06164869 0.06371364 0.21929170 0.24750291 0.28757281
37 38 39 40 41 42
0.24816797 -0.02865749 0.09394504 0.09772499 0.10541335 0.08704123
43 44 45
0.05486554 0.10665298 0.02840240
> postscript(file="/var/www/html/rcomp/tmp/6c2so1258761365.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 = 45
Frequency = 1
lag(myerror, k = 1) myerror
0 0.13321913 NA
1 0.33306648 0.13321913
2 0.25025427 0.33306648
3 0.23396740 0.25025427
4 0.15458204 0.23396740
5 0.10294955 0.15458204
6 0.09869551 0.10294955
7 0.13521107 0.09869551
8 0.09773238 0.13521107
9 0.02781967 0.09773238
10 0.00777512 0.02781967
11 -0.05347987 0.00777512
12 -0.09892849 -0.05347987
13 -0.05068253 -0.09892849
14 -0.19051087 -0.05068253
15 -0.18769405 -0.19051087
16 -0.21521997 -0.18769405
17 -0.20038932 -0.21521997
18 -0.20005810 -0.20038932
19 -0.18021536 -0.20005810
20 -0.18984842 -0.18021536
21 -0.24711137 -0.18984842
22 -0.25527803 -0.24711137
23 -0.23409293 -0.25527803
24 -0.28245860 -0.23409293
25 -0.25372646 -0.28245860
26 -0.15368844 -0.25372646
27 -0.14399835 -0.15368844
28 -0.04477542 -0.14399835
29 0.01039854 -0.04477542
30 0.04649705 0.01039854
31 -0.06164869 0.04649705
32 0.06371364 -0.06164869
33 0.21929170 0.06371364
34 0.24750291 0.21929170
35 0.28757281 0.24750291
36 0.24816797 0.28757281
37 -0.02865749 0.24816797
38 0.09394504 -0.02865749
39 0.09772499 0.09394504
40 0.10541335 0.09772499
41 0.08704123 0.10541335
42 0.05486554 0.08704123
43 0.10665298 0.05486554
44 0.02840240 0.10665298
45 NA 0.02840240
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.33306648 0.13321913
[2,] 0.25025427 0.33306648
[3,] 0.23396740 0.25025427
[4,] 0.15458204 0.23396740
[5,] 0.10294955 0.15458204
[6,] 0.09869551 0.10294955
[7,] 0.13521107 0.09869551
[8,] 0.09773238 0.13521107
[9,] 0.02781967 0.09773238
[10,] 0.00777512 0.02781967
[11,] -0.05347987 0.00777512
[12,] -0.09892849 -0.05347987
[13,] -0.05068253 -0.09892849
[14,] -0.19051087 -0.05068253
[15,] -0.18769405 -0.19051087
[16,] -0.21521997 -0.18769405
[17,] -0.20038932 -0.21521997
[18,] -0.20005810 -0.20038932
[19,] -0.18021536 -0.20005810
[20,] -0.18984842 -0.18021536
[21,] -0.24711137 -0.18984842
[22,] -0.25527803 -0.24711137
[23,] -0.23409293 -0.25527803
[24,] -0.28245860 -0.23409293
[25,] -0.25372646 -0.28245860
[26,] -0.15368844 -0.25372646
[27,] -0.14399835 -0.15368844
[28,] -0.04477542 -0.14399835
[29,] 0.01039854 -0.04477542
[30,] 0.04649705 0.01039854
[31,] -0.06164869 0.04649705
[32,] 0.06371364 -0.06164869
[33,] 0.21929170 0.06371364
[34,] 0.24750291 0.21929170
[35,] 0.28757281 0.24750291
[36,] 0.24816797 0.28757281
[37,] -0.02865749 0.24816797
[38,] 0.09394504 -0.02865749
[39,] 0.09772499 0.09394504
[40,] 0.10541335 0.09772499
[41,] 0.08704123 0.10541335
[42,] 0.05486554 0.08704123
[43,] 0.10665298 0.05486554
[44,] 0.02840240 0.10665298
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.33306648 0.13321913
2 0.25025427 0.33306648
3 0.23396740 0.25025427
4 0.15458204 0.23396740
5 0.10294955 0.15458204
6 0.09869551 0.10294955
7 0.13521107 0.09869551
8 0.09773238 0.13521107
9 0.02781967 0.09773238
10 0.00777512 0.02781967
11 -0.05347987 0.00777512
12 -0.09892849 -0.05347987
13 -0.05068253 -0.09892849
14 -0.19051087 -0.05068253
15 -0.18769405 -0.19051087
16 -0.21521997 -0.18769405
17 -0.20038932 -0.21521997
18 -0.20005810 -0.20038932
19 -0.18021536 -0.20005810
20 -0.18984842 -0.18021536
21 -0.24711137 -0.18984842
22 -0.25527803 -0.24711137
23 -0.23409293 -0.25527803
24 -0.28245860 -0.23409293
25 -0.25372646 -0.28245860
26 -0.15368844 -0.25372646
27 -0.14399835 -0.15368844
28 -0.04477542 -0.14399835
29 0.01039854 -0.04477542
30 0.04649705 0.01039854
31 -0.06164869 0.04649705
32 0.06371364 -0.06164869
33 0.21929170 0.06371364
34 0.24750291 0.21929170
35 0.28757281 0.24750291
36 0.24816797 0.28757281
37 -0.02865749 0.24816797
38 0.09394504 -0.02865749
39 0.09772499 0.09394504
40 0.10541335 0.09772499
41 0.08704123 0.10541335
42 0.05486554 0.08704123
43 0.10665298 0.05486554
44 0.02840240 0.10665298
> 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/7zusx1258761365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jtb31258761365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z95j1258761365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10sqzs1258761365.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11219l1258761365.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/12dger1258761365.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/13olvn1258761365.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/149uvb1258761365.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/151rlk1258761365.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/16e3jc1258761365.tab")
+ }
>
> system("convert tmp/1ovgo1258761365.ps tmp/1ovgo1258761365.png")
> system("convert tmp/2xlor1258761365.ps tmp/2xlor1258761365.png")
> system("convert tmp/38bx91258761365.ps tmp/38bx91258761365.png")
> system("convert tmp/41irk1258761365.ps tmp/41irk1258761365.png")
> system("convert tmp/5ml031258761365.ps tmp/5ml031258761365.png")
> system("convert tmp/6c2so1258761365.ps tmp/6c2so1258761365.png")
> system("convert tmp/7zusx1258761365.ps tmp/7zusx1258761365.png")
> system("convert tmp/8jtb31258761365.ps tmp/8jtb31258761365.png")
> system("convert tmp/9z95j1258761365.ps tmp/9z95j1258761365.png")
> system("convert tmp/10sqzs1258761365.ps tmp/10sqzs1258761365.png")
>
>
> proc.time()
user system elapsed
2.196 1.515 2.832