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 '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(97.4,116.7,97,109,105.4,119.5,102.7,115.1,98.1,107.1,104.5,109.7,87.4,110.4,89.9,105,109.8,115.8,111.7,116.4,98.6,111.1,96.9,119.5,95.1,110.9,97,115.1,112.7,125.2,102.9,116,97.4,112.9,111.4,121.7,87.4,123.2,96.8,116.6,114.1,136.2,110.3,120.9,103.9,119.6,101.6,125.9,94.6,116.1,95.9,107.5,104.7,116.7,102.8,112.5,98.1,113,113.9,126.4,80.9,114.1,95.7,112.5,113.2,112.4,105.9,113.1,108.8,116.3,102.3,111.7,99,118.8,100.7,116.5,115.5,125.1,100.7,113.1,109.9,119.6,114.6,114.4,85.4,114,100.5,117.8,114.8,117,116.5,120.9,112.9,115,102,117.3,106,119.4,105.3,114.9,118.8,125.8,106.1,117.6,109.3,117.6,117.2,114.9,92.5,121.9,104.2,117,112.5,106.4,122.4,110.5,113.3,113.6,100,114.2),dim=c(2,60),dimnames=list(c('Tip','ipchn'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Tip','ipchn'),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 = '2'
> #'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
ipchn Tip
1 116.7 97.4
2 109.0 97.0
3 119.5 105.4
4 115.1 102.7
5 107.1 98.1
6 109.7 104.5
7 110.4 87.4
8 105.0 89.9
9 115.8 109.8
10 116.4 111.7
11 111.1 98.6
12 119.5 96.9
13 110.9 95.1
14 115.1 97.0
15 125.2 112.7
16 116.0 102.9
17 112.9 97.4
18 121.7 111.4
19 123.2 87.4
20 116.6 96.8
21 136.2 114.1
22 120.9 110.3
23 119.6 103.9
24 125.9 101.6
25 116.1 94.6
26 107.5 95.9
27 116.7 104.7
28 112.5 102.8
29 113.0 98.1
30 126.4 113.9
31 114.1 80.9
32 112.5 95.7
33 112.4 113.2
34 113.1 105.9
35 116.3 108.8
36 111.7 102.3
37 118.8 99.0
38 116.5 100.7
39 125.1 115.5
40 113.1 100.7
41 119.6 109.9
42 114.4 114.6
43 114.0 85.4
44 117.8 100.5
45 117.0 114.8
46 120.9 116.5
47 115.0 112.9
48 117.3 102.0
49 119.4 106.0
50 114.9 105.3
51 125.8 118.8
52 117.6 106.1
53 117.6 109.3
54 114.9 117.2
55 121.9 92.5
56 117.0 104.2
57 106.4 112.5
58 110.5 122.4
59 113.6 113.3
60 114.2 100.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Tip
95.1965 0.2029
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.6204 -3.5597 0.0757 2.5425 17.8550
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 95.19648 8.03171 11.853 <2e-16 ***
Tip 0.20288 0.07711 2.631 0.0109 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.273 on 58 degrees of freedom
Multiple R-squared: 0.1066, Adjusted R-squared: 0.09122
F-statistic: 6.922 on 1 and 58 DF, p-value: 0.01089
> 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,] 0.43098194 0.8619639 0.56901806
[2,] 0.52347544 0.9530491 0.47652456
[3,] 0.41258815 0.8251763 0.58741185
[4,] 0.38860945 0.7772189 0.61139055
[5,] 0.27562567 0.5512513 0.72437433
[6,] 0.18516382 0.3703276 0.81483618
[7,] 0.12439491 0.2487898 0.87560509
[8,] 0.24633466 0.4926693 0.75366534
[9,] 0.18083235 0.3616647 0.81916765
[10,] 0.14221285 0.2844257 0.85778715
[11,] 0.20131897 0.4026379 0.79868103
[12,] 0.14377924 0.2875585 0.85622076
[13,] 0.10136613 0.2027323 0.89863387
[14,] 0.07728332 0.1545666 0.92271668
[15,] 0.41778899 0.8355780 0.58221101
[16,] 0.35453119 0.7090624 0.64546881
[17,] 0.91672298 0.1665540 0.08327702
[18,] 0.89262130 0.2147574 0.10737870
[19,] 0.86452941 0.2709412 0.13547059
[20,] 0.94431163 0.1113767 0.05568837
[21,] 0.92337807 0.1532439 0.07662193
[22,] 0.94715259 0.1056948 0.05284741
[23,] 0.92421971 0.1515606 0.07578029
[24,] 0.91352589 0.1729482 0.08647411
[25,] 0.88766629 0.2246674 0.11233371
[26,] 0.93602720 0.1279456 0.06397280
[27,] 0.93238719 0.1352256 0.06761281
[28,] 0.91512672 0.1697466 0.08487328
[29,] 0.93050571 0.1389886 0.06949429
[30,] 0.91994634 0.1601073 0.08005366
[31,] 0.88937654 0.2212469 0.11062346
[32,] 0.88750691 0.2249862 0.11249309
[33,] 0.85760331 0.2847934 0.14239669
[34,] 0.80709020 0.3858196 0.19290980
[35,] 0.87666472 0.2466706 0.12333528
[36,] 0.85331704 0.2933659 0.14668296
[37,] 0.81930203 0.3613959 0.18069797
[38,] 0.78909922 0.4218016 0.21090078
[39,] 0.76752226 0.4649555 0.23247774
[40,] 0.69607587 0.6078483 0.30392413
[41,] 0.62339982 0.7532004 0.37660018
[42,] 0.62516795 0.7496641 0.37483205
[43,] 0.54554744 0.9089051 0.45445256
[44,] 0.44696719 0.8939344 0.55303281
[45,] 0.37480032 0.7496006 0.62519968
[46,] 0.28868383 0.5773677 0.71131617
[47,] 0.82444177 0.3511165 0.17555823
[48,] 0.74773416 0.5045317 0.25226584
[49,] 0.69606670 0.6078666 0.30393330
[50,] 0.68281879 0.6343624 0.31718121
[51,] 0.61946776 0.7610645 0.38053224
> postscript(file="/var/www/html/rcomp/tmp/1h4xq1259061832.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ho5l1259061832.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3uj9q1259061832.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mbcs1259061832.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5oxhj1259061832.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
1.74304369 -5.87580445 2.92000642 -0.93221850 -7.99897207 -6.69740189
7 8 9 10 11 12
-2.52815973 -8.43535887 -1.67266408 -1.45813543 -4.10041190 4.64448352
13 14 15 16 17 18
-3.59033310 0.22419555 7.13898491 -0.07279443 -2.05695631 3.90272847
19 20 21 22 23 24
10.27184027 1.76477148 17.85495339 3.32589609 3.32432591 10.09094912
25 26 27 28 29 30
1.71110673 -7.15263682 0.26202218 -3.55250647 -2.09897207 8.09552932
31 32 33 34 35 36
2.49055805 -2.11206089 -5.76245492 -3.58143341 -0.96978442 -4.25106664
37 38 39 40 41 42
3.51843623 0.87354081 6.47092187 -2.52645919 2.10704796 -4.04648644
43 44 45 46 47 48
1.47759959 2.21411675 -1.48706237 2.06804221 -3.10159102 1.40979726
49 50 51 52 53 54
2.69827862 -1.65970561 6.50141899 0.87799066 0.22877575 -4.07397355
55 56 57 58 59 60
7.93715401 0.66346201 -11.62043916 -9.52894778 -4.58274288 -1.28444342
> postscript(file="/var/www/html/rcomp/tmp/6quu41259061832.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 1.74304369 NA
1 -5.87580445 1.74304369
2 2.92000642 -5.87580445
3 -0.93221850 2.92000642
4 -7.99897207 -0.93221850
5 -6.69740189 -7.99897207
6 -2.52815973 -6.69740189
7 -8.43535887 -2.52815973
8 -1.67266408 -8.43535887
9 -1.45813543 -1.67266408
10 -4.10041190 -1.45813543
11 4.64448352 -4.10041190
12 -3.59033310 4.64448352
13 0.22419555 -3.59033310
14 7.13898491 0.22419555
15 -0.07279443 7.13898491
16 -2.05695631 -0.07279443
17 3.90272847 -2.05695631
18 10.27184027 3.90272847
19 1.76477148 10.27184027
20 17.85495339 1.76477148
21 3.32589609 17.85495339
22 3.32432591 3.32589609
23 10.09094912 3.32432591
24 1.71110673 10.09094912
25 -7.15263682 1.71110673
26 0.26202218 -7.15263682
27 -3.55250647 0.26202218
28 -2.09897207 -3.55250647
29 8.09552932 -2.09897207
30 2.49055805 8.09552932
31 -2.11206089 2.49055805
32 -5.76245492 -2.11206089
33 -3.58143341 -5.76245492
34 -0.96978442 -3.58143341
35 -4.25106664 -0.96978442
36 3.51843623 -4.25106664
37 0.87354081 3.51843623
38 6.47092187 0.87354081
39 -2.52645919 6.47092187
40 2.10704796 -2.52645919
41 -4.04648644 2.10704796
42 1.47759959 -4.04648644
43 2.21411675 1.47759959
44 -1.48706237 2.21411675
45 2.06804221 -1.48706237
46 -3.10159102 2.06804221
47 1.40979726 -3.10159102
48 2.69827862 1.40979726
49 -1.65970561 2.69827862
50 6.50141899 -1.65970561
51 0.87799066 6.50141899
52 0.22877575 0.87799066
53 -4.07397355 0.22877575
54 7.93715401 -4.07397355
55 0.66346201 7.93715401
56 -11.62043916 0.66346201
57 -9.52894778 -11.62043916
58 -4.58274288 -9.52894778
59 -1.28444342 -4.58274288
60 NA -1.28444342
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.87580445 1.74304369
[2,] 2.92000642 -5.87580445
[3,] -0.93221850 2.92000642
[4,] -7.99897207 -0.93221850
[5,] -6.69740189 -7.99897207
[6,] -2.52815973 -6.69740189
[7,] -8.43535887 -2.52815973
[8,] -1.67266408 -8.43535887
[9,] -1.45813543 -1.67266408
[10,] -4.10041190 -1.45813543
[11,] 4.64448352 -4.10041190
[12,] -3.59033310 4.64448352
[13,] 0.22419555 -3.59033310
[14,] 7.13898491 0.22419555
[15,] -0.07279443 7.13898491
[16,] -2.05695631 -0.07279443
[17,] 3.90272847 -2.05695631
[18,] 10.27184027 3.90272847
[19,] 1.76477148 10.27184027
[20,] 17.85495339 1.76477148
[21,] 3.32589609 17.85495339
[22,] 3.32432591 3.32589609
[23,] 10.09094912 3.32432591
[24,] 1.71110673 10.09094912
[25,] -7.15263682 1.71110673
[26,] 0.26202218 -7.15263682
[27,] -3.55250647 0.26202218
[28,] -2.09897207 -3.55250647
[29,] 8.09552932 -2.09897207
[30,] 2.49055805 8.09552932
[31,] -2.11206089 2.49055805
[32,] -5.76245492 -2.11206089
[33,] -3.58143341 -5.76245492
[34,] -0.96978442 -3.58143341
[35,] -4.25106664 -0.96978442
[36,] 3.51843623 -4.25106664
[37,] 0.87354081 3.51843623
[38,] 6.47092187 0.87354081
[39,] -2.52645919 6.47092187
[40,] 2.10704796 -2.52645919
[41,] -4.04648644 2.10704796
[42,] 1.47759959 -4.04648644
[43,] 2.21411675 1.47759959
[44,] -1.48706237 2.21411675
[45,] 2.06804221 -1.48706237
[46,] -3.10159102 2.06804221
[47,] 1.40979726 -3.10159102
[48,] 2.69827862 1.40979726
[49,] -1.65970561 2.69827862
[50,] 6.50141899 -1.65970561
[51,] 0.87799066 6.50141899
[52,] 0.22877575 0.87799066
[53,] -4.07397355 0.22877575
[54,] 7.93715401 -4.07397355
[55,] 0.66346201 7.93715401
[56,] -11.62043916 0.66346201
[57,] -9.52894778 -11.62043916
[58,] -4.58274288 -9.52894778
[59,] -1.28444342 -4.58274288
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.87580445 1.74304369
2 2.92000642 -5.87580445
3 -0.93221850 2.92000642
4 -7.99897207 -0.93221850
5 -6.69740189 -7.99897207
6 -2.52815973 -6.69740189
7 -8.43535887 -2.52815973
8 -1.67266408 -8.43535887
9 -1.45813543 -1.67266408
10 -4.10041190 -1.45813543
11 4.64448352 -4.10041190
12 -3.59033310 4.64448352
13 0.22419555 -3.59033310
14 7.13898491 0.22419555
15 -0.07279443 7.13898491
16 -2.05695631 -0.07279443
17 3.90272847 -2.05695631
18 10.27184027 3.90272847
19 1.76477148 10.27184027
20 17.85495339 1.76477148
21 3.32589609 17.85495339
22 3.32432591 3.32589609
23 10.09094912 3.32432591
24 1.71110673 10.09094912
25 -7.15263682 1.71110673
26 0.26202218 -7.15263682
27 -3.55250647 0.26202218
28 -2.09897207 -3.55250647
29 8.09552932 -2.09897207
30 2.49055805 8.09552932
31 -2.11206089 2.49055805
32 -5.76245492 -2.11206089
33 -3.58143341 -5.76245492
34 -0.96978442 -3.58143341
35 -4.25106664 -0.96978442
36 3.51843623 -4.25106664
37 0.87354081 3.51843623
38 6.47092187 0.87354081
39 -2.52645919 6.47092187
40 2.10704796 -2.52645919
41 -4.04648644 2.10704796
42 1.47759959 -4.04648644
43 2.21411675 1.47759959
44 -1.48706237 2.21411675
45 2.06804221 -1.48706237
46 -3.10159102 2.06804221
47 1.40979726 -3.10159102
48 2.69827862 1.40979726
49 -1.65970561 2.69827862
50 6.50141899 -1.65970561
51 0.87799066 6.50141899
52 0.22877575 0.87799066
53 -4.07397355 0.22877575
54 7.93715401 -4.07397355
55 0.66346201 7.93715401
56 -11.62043916 0.66346201
57 -9.52894778 -11.62043916
58 -4.58274288 -9.52894778
59 -1.28444342 -4.58274288
> 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/79gzb1259061832.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/898071259061832.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ae831259061832.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bu8a1259061832.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113ekt1259061832.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/12t9zq1259061832.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/13ajg61259061832.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/14mn4p1259061832.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/15yog31259061832.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/162yib1259061832.tab")
+ }
>
> system("convert tmp/1h4xq1259061832.ps tmp/1h4xq1259061832.png")
> system("convert tmp/2ho5l1259061832.ps tmp/2ho5l1259061832.png")
> system("convert tmp/3uj9q1259061832.ps tmp/3uj9q1259061832.png")
> system("convert tmp/4mbcs1259061832.ps tmp/4mbcs1259061832.png")
> system("convert tmp/5oxhj1259061832.ps tmp/5oxhj1259061832.png")
> system("convert tmp/6quu41259061832.ps tmp/6quu41259061832.png")
> system("convert tmp/79gzb1259061832.ps tmp/79gzb1259061832.png")
> system("convert tmp/898071259061832.ps tmp/898071259061832.png")
> system("convert tmp/9ae831259061832.ps tmp/9ae831259061832.png")
> system("convert tmp/10bu8a1259061832.ps tmp/10bu8a1259061832.png")
>
>
> proc.time()
user system elapsed
2.457 1.572 3.880