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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> x <- array(list(8.9,1.4,8.8,1.2,8.3,1,7.5,1.7,7.2,2.4,7.4,2,8.8,2.1,9.3,2,9.3,1.8,8.7,2.7,8.2,2.3,8.3,1.9,8.5,2,8.6,2.3,8.5,2.8,8.2,2.4,8.1,2.3,7.9,2.7,8.6,2.7,8.7,2.9,8.7,3,8.5,2.2,8.4,2.3,8.5,2.8,8.7,2.8,8.7,2.8,8.6,2.2,8.5,2.6,8.3,2.8,8,2.5,8.2,2.4,8.1,2.3,8.1,1.9,8,1.7,7.9,2,7.9,2.1,8,1.7,8,1.8,7.9,1.8,8,1.8,7.7,1.3,7.2,1.3,7.5,1.3,7.3,1.2,7,1.4,7,2.2,7,2.9,7.2,3.1,7.3,3.5,7.1,3.6,6.8,4.4,6.4,4.1,6.1,5.1,6.5,5.8,7.7,5.9,7.9,5.4,7.5,5.5,6.9,4.8,6.6,3.2,6.9,2.7),dim=c(2,60),dimnames=list(c('werkloosheid','inflatie'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('werkloosheid','inflatie'),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 = '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
werkloosheid inflatie t
1 8.9 1.4 1
2 8.8 1.2 2
3 8.3 1.0 3
4 7.5 1.7 4
5 7.2 2.4 5
6 7.4 2.0 6
7 8.8 2.1 7
8 9.3 2.0 8
9 9.3 1.8 9
10 8.7 2.7 10
11 8.2 2.3 11
12 8.3 1.9 12
13 8.5 2.0 13
14 8.6 2.3 14
15 8.5 2.8 15
16 8.2 2.4 16
17 8.1 2.3 17
18 7.9 2.7 18
19 8.6 2.7 19
20 8.7 2.9 20
21 8.7 3.0 21
22 8.5 2.2 22
23 8.4 2.3 23
24 8.5 2.8 24
25 8.7 2.8 25
26 8.7 2.8 26
27 8.6 2.2 27
28 8.5 2.6 28
29 8.3 2.8 29
30 8.0 2.5 30
31 8.2 2.4 31
32 8.1 2.3 32
33 8.1 1.9 33
34 8.0 1.7 34
35 7.9 2.0 35
36 7.9 2.1 36
37 8.0 1.7 37
38 8.0 1.8 38
39 7.9 1.8 39
40 8.0 1.8 40
41 7.7 1.3 41
42 7.2 1.3 42
43 7.5 1.3 43
44 7.3 1.2 44
45 7.0 1.4 45
46 7.0 2.2 46
47 7.0 2.9 47
48 7.2 3.1 48
49 7.3 3.5 49
50 7.1 3.6 50
51 6.8 4.4 51
52 6.4 4.1 52
53 6.1 5.1 53
54 6.5 5.8 54
55 7.7 5.9 55
56 7.9 5.4 56
57 7.5 5.5 57
58 6.9 4.8 58
59 6.6 3.2 59
60 6.9 2.7 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) inflatie t
8.88296 -0.01403 -0.03081
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.4953 -0.2482 0.1084 0.3293 0.8180
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.882963 0.167938 52.894 < 2e-16 ***
inflatie -0.014029 0.068795 -0.204 0.839
t -0.030808 0.004597 -6.702 9.98e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5117 on 57 degrees of freedom
Multiple R-squared: 0.5422, Adjusted R-squared: 0.5262
F-statistic: 33.76 on 2 and 57 DF, p-value: 2.130e-10
> 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,] 0.3323098 0.664619673 0.667690163
[2,] 0.9894722 0.021055548 0.010527774
[3,] 0.9973923 0.005215353 0.002607676
[4,] 0.9952099 0.009580266 0.004790133
[5,] 0.9928535 0.014293053 0.007146527
[6,] 0.9957330 0.008534041 0.004267021
[7,] 0.9976856 0.004628896 0.002314448
[8,] 0.9964294 0.007141167 0.003570583
[9,] 0.9933897 0.013220582 0.006610291
[10,] 0.9889665 0.022066950 0.011033475
[11,] 0.9894600 0.021079980 0.010539990
[12,] 0.9922939 0.015412128 0.007706064
[13,] 0.9966993 0.006601422 0.003300711
[14,] 0.9948422 0.010315693 0.005157846
[15,] 0.9922359 0.015528143 0.007764071
[16,] 0.9879881 0.024023869 0.012011935
[17,] 0.9825785 0.034842900 0.017421450
[18,] 0.9761955 0.047609017 0.023804509
[19,] 0.9638596 0.072280826 0.036140413
[20,] 0.9467948 0.106410353 0.053205177
[21,] 0.9249660 0.150067965 0.075033982
[22,] 0.9000387 0.199922502 0.099961251
[23,] 0.8669349 0.266130271 0.133065136
[24,] 0.8298820 0.340235941 0.170117970
[25,] 0.8251514 0.349697264 0.174848632
[26,] 0.7839291 0.432141858 0.216070929
[27,] 0.7409640 0.518072099 0.259036050
[28,] 0.6884079 0.623184205 0.311592103
[29,] 0.6304226 0.739154896 0.369577448
[30,] 0.5710697 0.857860676 0.428930338
[31,] 0.5050018 0.989996422 0.494998211
[32,] 0.4415563 0.883112592 0.558443704
[33,] 0.3929947 0.785989360 0.607005320
[34,] 0.3512999 0.702599708 0.648700146
[35,] 0.3629543 0.725908569 0.637045716
[36,] 0.3449823 0.689964508 0.655017746
[37,] 0.3129926 0.625985172 0.687007414
[38,] 0.2850170 0.570034043 0.714982979
[39,] 0.2514526 0.502905109 0.748547445
[40,] 0.2190795 0.438158970 0.780920515
[41,] 0.2014143 0.402828535 0.798585732
[42,] 0.1870368 0.374073556 0.812963222
[43,] 0.1697962 0.339592315 0.830203843
[44,] 0.1989569 0.397913768 0.801043116
[45,] 0.3014972 0.602994314 0.698502843
[46,] 0.3506363 0.701272599 0.649363700
[47,] 0.4563755 0.912751089 0.543624455
[48,] 0.3612170 0.722434082 0.638782959
[49,] 0.7346469 0.530706277 0.265353138
> postscript(file="/var/www/html/rcomp/tmp/1xpmc1258564161.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2slbl1258564161.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3v4ry1258564161.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49brp1258564161.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57g6b1258564161.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
0.067485654 -0.004512557 -0.476510768 -1.235882633 -1.495254497 -1.270058563
7 8 9 10 11 12
0.162152008 0.691556725 0.719558514 0.162992504 -0.311811561 -0.186615627
13 14 15 16 17 18
0.045594944 0.180611370 0.118433651 -0.156370415 -0.226965698 -0.390546345
19 20 21 22 23 24
0.340261299 0.473874798 0.506085369 0.325669594 0.257880165 0.395702446
25 26 27 28 29 30
0.626510090 0.657317734 0.579707813 0.516127167 0.349740665 0.076339527
31 32 33 34 35 36
0.305744244 0.235148960 0.260344895 0.188346684 0.123363110 0.155573681
37 38 39 40 41 42
0.280769616 0.312980187 0.243787831 0.374595475 0.098388482 -0.370803874
43 44 45 46 47 48
-0.039996230 -0.210591514 -0.476978015 -0.434946953 -0.394318817 -0.160705319
49 50 51 52 53 54
-0.024285966 -0.192075395 -0.450044332 -0.823445470 -1.078608553 -0.637980418
55 56 57 58 59 60
0.594230153 0.818023161 0.450233732 -0.128779115 -0.420418309 -0.096625302
> postscript(file="/var/www/html/rcomp/tmp/6aek41258564161.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 0.067485654 NA
1 -0.004512557 0.067485654
2 -0.476510768 -0.004512557
3 -1.235882633 -0.476510768
4 -1.495254497 -1.235882633
5 -1.270058563 -1.495254497
6 0.162152008 -1.270058563
7 0.691556725 0.162152008
8 0.719558514 0.691556725
9 0.162992504 0.719558514
10 -0.311811561 0.162992504
11 -0.186615627 -0.311811561
12 0.045594944 -0.186615627
13 0.180611370 0.045594944
14 0.118433651 0.180611370
15 -0.156370415 0.118433651
16 -0.226965698 -0.156370415
17 -0.390546345 -0.226965698
18 0.340261299 -0.390546345
19 0.473874798 0.340261299
20 0.506085369 0.473874798
21 0.325669594 0.506085369
22 0.257880165 0.325669594
23 0.395702446 0.257880165
24 0.626510090 0.395702446
25 0.657317734 0.626510090
26 0.579707813 0.657317734
27 0.516127167 0.579707813
28 0.349740665 0.516127167
29 0.076339527 0.349740665
30 0.305744244 0.076339527
31 0.235148960 0.305744244
32 0.260344895 0.235148960
33 0.188346684 0.260344895
34 0.123363110 0.188346684
35 0.155573681 0.123363110
36 0.280769616 0.155573681
37 0.312980187 0.280769616
38 0.243787831 0.312980187
39 0.374595475 0.243787831
40 0.098388482 0.374595475
41 -0.370803874 0.098388482
42 -0.039996230 -0.370803874
43 -0.210591514 -0.039996230
44 -0.476978015 -0.210591514
45 -0.434946953 -0.476978015
46 -0.394318817 -0.434946953
47 -0.160705319 -0.394318817
48 -0.024285966 -0.160705319
49 -0.192075395 -0.024285966
50 -0.450044332 -0.192075395
51 -0.823445470 -0.450044332
52 -1.078608553 -0.823445470
53 -0.637980418 -1.078608553
54 0.594230153 -0.637980418
55 0.818023161 0.594230153
56 0.450233732 0.818023161
57 -0.128779115 0.450233732
58 -0.420418309 -0.128779115
59 -0.096625302 -0.420418309
60 NA -0.096625302
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.004512557 0.067485654
[2,] -0.476510768 -0.004512557
[3,] -1.235882633 -0.476510768
[4,] -1.495254497 -1.235882633
[5,] -1.270058563 -1.495254497
[6,] 0.162152008 -1.270058563
[7,] 0.691556725 0.162152008
[8,] 0.719558514 0.691556725
[9,] 0.162992504 0.719558514
[10,] -0.311811561 0.162992504
[11,] -0.186615627 -0.311811561
[12,] 0.045594944 -0.186615627
[13,] 0.180611370 0.045594944
[14,] 0.118433651 0.180611370
[15,] -0.156370415 0.118433651
[16,] -0.226965698 -0.156370415
[17,] -0.390546345 -0.226965698
[18,] 0.340261299 -0.390546345
[19,] 0.473874798 0.340261299
[20,] 0.506085369 0.473874798
[21,] 0.325669594 0.506085369
[22,] 0.257880165 0.325669594
[23,] 0.395702446 0.257880165
[24,] 0.626510090 0.395702446
[25,] 0.657317734 0.626510090
[26,] 0.579707813 0.657317734
[27,] 0.516127167 0.579707813
[28,] 0.349740665 0.516127167
[29,] 0.076339527 0.349740665
[30,] 0.305744244 0.076339527
[31,] 0.235148960 0.305744244
[32,] 0.260344895 0.235148960
[33,] 0.188346684 0.260344895
[34,] 0.123363110 0.188346684
[35,] 0.155573681 0.123363110
[36,] 0.280769616 0.155573681
[37,] 0.312980187 0.280769616
[38,] 0.243787831 0.312980187
[39,] 0.374595475 0.243787831
[40,] 0.098388482 0.374595475
[41,] -0.370803874 0.098388482
[42,] -0.039996230 -0.370803874
[43,] -0.210591514 -0.039996230
[44,] -0.476978015 -0.210591514
[45,] -0.434946953 -0.476978015
[46,] -0.394318817 -0.434946953
[47,] -0.160705319 -0.394318817
[48,] -0.024285966 -0.160705319
[49,] -0.192075395 -0.024285966
[50,] -0.450044332 -0.192075395
[51,] -0.823445470 -0.450044332
[52,] -1.078608553 -0.823445470
[53,] -0.637980418 -1.078608553
[54,] 0.594230153 -0.637980418
[55,] 0.818023161 0.594230153
[56,] 0.450233732 0.818023161
[57,] -0.128779115 0.450233732
[58,] -0.420418309 -0.128779115
[59,] -0.096625302 -0.420418309
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.004512557 0.067485654
2 -0.476510768 -0.004512557
3 -1.235882633 -0.476510768
4 -1.495254497 -1.235882633
5 -1.270058563 -1.495254497
6 0.162152008 -1.270058563
7 0.691556725 0.162152008
8 0.719558514 0.691556725
9 0.162992504 0.719558514
10 -0.311811561 0.162992504
11 -0.186615627 -0.311811561
12 0.045594944 -0.186615627
13 0.180611370 0.045594944
14 0.118433651 0.180611370
15 -0.156370415 0.118433651
16 -0.226965698 -0.156370415
17 -0.390546345 -0.226965698
18 0.340261299 -0.390546345
19 0.473874798 0.340261299
20 0.506085369 0.473874798
21 0.325669594 0.506085369
22 0.257880165 0.325669594
23 0.395702446 0.257880165
24 0.626510090 0.395702446
25 0.657317734 0.626510090
26 0.579707813 0.657317734
27 0.516127167 0.579707813
28 0.349740665 0.516127167
29 0.076339527 0.349740665
30 0.305744244 0.076339527
31 0.235148960 0.305744244
32 0.260344895 0.235148960
33 0.188346684 0.260344895
34 0.123363110 0.188346684
35 0.155573681 0.123363110
36 0.280769616 0.155573681
37 0.312980187 0.280769616
38 0.243787831 0.312980187
39 0.374595475 0.243787831
40 0.098388482 0.374595475
41 -0.370803874 0.098388482
42 -0.039996230 -0.370803874
43 -0.210591514 -0.039996230
44 -0.476978015 -0.210591514
45 -0.434946953 -0.476978015
46 -0.394318817 -0.434946953
47 -0.160705319 -0.394318817
48 -0.024285966 -0.160705319
49 -0.192075395 -0.024285966
50 -0.450044332 -0.192075395
51 -0.823445470 -0.450044332
52 -1.078608553 -0.823445470
53 -0.637980418 -1.078608553
54 0.594230153 -0.637980418
55 0.818023161 0.594230153
56 0.450233732 0.818023161
57 -0.128779115 0.450233732
58 -0.420418309 -0.128779115
59 -0.096625302 -0.420418309
> 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/7ibmk1258564161.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81spn1258564161.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98uay1258564161.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107tm61258564161.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wyrr1258564161.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/12i9d41258564161.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/13rbkg1258564161.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/14r6wc1258564161.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/1523of1258564161.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/16xazz1258564161.tab")
+ }
>
> system("convert tmp/1xpmc1258564161.ps tmp/1xpmc1258564161.png")
> system("convert tmp/2slbl1258564161.ps tmp/2slbl1258564161.png")
> system("convert tmp/3v4ry1258564161.ps tmp/3v4ry1258564161.png")
> system("convert tmp/49brp1258564161.ps tmp/49brp1258564161.png")
> system("convert tmp/57g6b1258564161.ps tmp/57g6b1258564161.png")
> system("convert tmp/6aek41258564161.ps tmp/6aek41258564161.png")
> system("convert tmp/7ibmk1258564161.ps tmp/7ibmk1258564161.png")
> system("convert tmp/81spn1258564161.ps tmp/81spn1258564161.png")
> system("convert tmp/98uay1258564161.ps tmp/98uay1258564161.png")
> system("convert tmp/107tm61258564161.ps tmp/107tm61258564161.png")
>
>
> proc.time()
user system elapsed
2.420 1.541 2.873