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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
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(3956.2,3977.7,3142.7,3983.4,3884.3,4152.9,3892.2,4286.1,3613,4348.1,3730.5,3949.3,3481.3,4166.7,3649.5,4217.9,4215.2,4528.2,4066.6,4232.2,4196.8,4470.9,4536.6,5121.2,4441.6,4170.8,3548.3,4398.6,4735.9,4491.4,4130.6,4251.8,4356.2,4901.9,4159.6,4745.2,3988,4666.9,4167.8,4210.4,4902.2,5273.6,3909.4,4095.3,4697.6,4610.1,4308.9,4718.1,4420.4,4185.5,3544.2,4314.7,4433,4422.6,4479.7,5059.2,4533.2,5043.6,4237.5,4436.6,4207.4,4922.6,4394,4454.8,5148.4,5058.7,4202.2,4768.9,4682.5,5171.8,4884.3,4989.3,5288.9,5202.1,4505.2,4838.4,4611.5,4876.5,5104,5875.5,4586.6,5717.9,4529.3,4778.8,4504.1,6195.9,4604.9,4625.4,4795.4,5549.8,5391.1,6397.6,5213.9,5856.7,5415,6343.8,5990.3,6615.5,4241.8,5904.6,5677.6,6861,5164.2,6553.5,3962.3,5481,4011,5435.3,3310.3,5278,3837.3,4671.8,4145.3,4891.5,3796.7,4241.6,3849.6,4152.1,4285,4484.4,4189.6,4124.7),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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 3956.2 3977.7
2 3142.7 3983.4
3 3884.3 4152.9
4 3892.2 4286.1
5 3613.0 4348.1
6 3730.5 3949.3
7 3481.3 4166.7
8 3649.5 4217.9
9 4215.2 4528.2
10 4066.6 4232.2
11 4196.8 4470.9
12 4536.6 5121.2
13 4441.6 4170.8
14 3548.3 4398.6
15 4735.9 4491.4
16 4130.6 4251.8
17 4356.2 4901.9
18 4159.6 4745.2
19 3988.0 4666.9
20 4167.8 4210.4
21 4902.2 5273.6
22 3909.4 4095.3
23 4697.6 4610.1
24 4308.9 4718.1
25 4420.4 4185.5
26 3544.2 4314.7
27 4433.0 4422.6
28 4479.7 5059.2
29 4533.2 5043.6
30 4237.5 4436.6
31 4207.4 4922.6
32 4394.0 4454.8
33 5148.4 5058.7
34 4202.2 4768.9
35 4682.5 5171.8
36 4884.3 4989.3
37 5288.9 5202.1
38 4505.2 4838.4
39 4611.5 4876.5
40 5104.0 5875.5
41 4586.6 5717.9
42 4529.3 4778.8
43 4504.1 6195.9
44 4604.9 4625.4
45 4795.4 5549.8
46 5391.1 6397.6
47 5213.9 5856.7
48 5415.0 6343.8
49 5990.3 6615.5
50 4241.8 5904.6
51 5677.6 6861.0
52 5164.2 6553.5
53 3962.3 5481.0
54 4011.0 5435.3
55 3310.3 5278.0
56 3837.3 4671.8
57 4145.3 4891.5
58 3796.7 4241.6
59 3849.6 4152.1
60 4285.0 4484.4
61 4189.6 4124.7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
1538.6271 0.5778
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1277.8 -192.6 60.3 229.6 744.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.539e+03 3.437e+02 4.477 3.52e-05 ***
X 5.778e-01 6.964e-02 8.297 1.73e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 396.4 on 59 degrees of freedom
Multiple R-squared: 0.5385, Adjusted R-squared: 0.5306
F-statistic: 68.83 on 1 and 59 DF, p-value: 1.727e-11
> 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,] 0.55289063 0.89421874 0.4471094
[2,] 0.39283594 0.78567189 0.6071641
[3,] 0.31156704 0.62313408 0.6884330
[4,] 0.20619439 0.41238877 0.7938056
[5,] 0.18244329 0.36488659 0.8175567
[6,] 0.15153712 0.30307423 0.8484629
[7,] 0.10347479 0.20694959 0.8965252
[8,] 0.06279222 0.12558444 0.9372078
[9,] 0.17395928 0.34791856 0.8260407
[10,] 0.21321681 0.42643362 0.7867832
[11,] 0.36100512 0.72201024 0.6389949
[12,] 0.30047424 0.60094849 0.6995258
[13,] 0.23066820 0.46133640 0.7693318
[14,] 0.17722496 0.35444993 0.8227750
[15,] 0.14633258 0.29266516 0.8536674
[16,] 0.12263190 0.24526380 0.8773681
[17,] 0.09799449 0.19598898 0.9020055
[18,] 0.06895856 0.13791711 0.9310414
[19,] 0.08598882 0.17197764 0.9140112
[20,] 0.05841466 0.11682933 0.9415853
[21,] 0.07689814 0.15379629 0.9231019
[22,] 0.09411852 0.18823704 0.9058815
[23,] 0.08543358 0.17086717 0.9145664
[24,] 0.06025043 0.12050085 0.9397496
[25,] 0.04064863 0.08129727 0.9593514
[26,] 0.02773899 0.05547798 0.9722610
[27,] 0.02094779 0.04189558 0.9790522
[28,] 0.01652714 0.03305428 0.9834729
[29,] 0.03171139 0.06342278 0.9682886
[30,] 0.02176125 0.04352250 0.9782387
[31,] 0.01414920 0.02829840 0.9858508
[32,] 0.01446963 0.02893926 0.9855304
[33,] 0.03310287 0.06620574 0.9668971
[34,] 0.02350475 0.04700949 0.9764953
[35,] 0.01829775 0.03659550 0.9817022
[36,] 0.01453482 0.02906965 0.9854652
[37,] 0.01571137 0.03142274 0.9842886
[38,] 0.01235064 0.02470129 0.9876494
[39,] 0.03209750 0.06419500 0.9679025
[40,] 0.04011038 0.08022077 0.9598896
[41,] 0.02684278 0.05368556 0.9731572
[42,] 0.01776377 0.03552755 0.9822362
[43,] 0.01562663 0.03125325 0.9843734
[44,] 0.01195627 0.02391255 0.9880437
[45,] 0.05361936 0.10723873 0.9463806
[46,] 0.07726255 0.15452509 0.9227375
[47,] 0.16006484 0.32012968 0.8399352
[48,] 0.65228542 0.69542916 0.3477146
[49,] 0.61998726 0.76002547 0.3800127
[50,] 0.64137047 0.71725905 0.3586295
[51,] 0.86071333 0.27857333 0.1392867
[52,] 0.82175220 0.35649560 0.1782478
> postscript(file="/var/www/html/rcomp/tmp/19wze1258736035.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kwsz1258736035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39xtx1258736035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4idl41258736035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5axo01258736035.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 = 61
Frequency = 1
1 2 3 4 5 6
119.361161 -697.432151 -53.764848 -122.824347 -437.846337 -89.930057
7 8 9 10 11 12
-464.738129 -326.120159 60.296562 82.717672 75.003013 39.076566
13 14 15 16 17 18
493.192997 -531.723925 602.258646 135.393302 -14.617591 -120.680401
19 20 21 22 23 24
-247.040695 196.513146 316.623805 4.614936 495.376869 44.277275
25 26 27 28 29 30
463.499719 -487.348684 339.109498 17.998556 80.511830 135.520662
31 32 33 34 35 36
-175.377513 281.505175 686.987443 -91.773646 155.741201 462.984960
37 38 39 40 41 42
744.634648 171.071060 255.357870 170.661621 -255.681193 229.606392
43 44 45 46 47 48
-614.357176 393.836927 50.242620 156.105804 291.423773 211.090046
49 50 51 52 53 54
629.408844 -708.351603 174.865322 -160.869166 -743.106527 -668.002254
55 56 57 58 59 60
-1277.818400 -400.571788 -219.508741 -192.613403 -88.002628 155.403064
61
267.828380
> postscript(file="/var/www/html/rcomp/tmp/6wi061258736035.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 119.361161 NA
1 -697.432151 119.361161
2 -53.764848 -697.432151
3 -122.824347 -53.764848
4 -437.846337 -122.824347
5 -89.930057 -437.846337
6 -464.738129 -89.930057
7 -326.120159 -464.738129
8 60.296562 -326.120159
9 82.717672 60.296562
10 75.003013 82.717672
11 39.076566 75.003013
12 493.192997 39.076566
13 -531.723925 493.192997
14 602.258646 -531.723925
15 135.393302 602.258646
16 -14.617591 135.393302
17 -120.680401 -14.617591
18 -247.040695 -120.680401
19 196.513146 -247.040695
20 316.623805 196.513146
21 4.614936 316.623805
22 495.376869 4.614936
23 44.277275 495.376869
24 463.499719 44.277275
25 -487.348684 463.499719
26 339.109498 -487.348684
27 17.998556 339.109498
28 80.511830 17.998556
29 135.520662 80.511830
30 -175.377513 135.520662
31 281.505175 -175.377513
32 686.987443 281.505175
33 -91.773646 686.987443
34 155.741201 -91.773646
35 462.984960 155.741201
36 744.634648 462.984960
37 171.071060 744.634648
38 255.357870 171.071060
39 170.661621 255.357870
40 -255.681193 170.661621
41 229.606392 -255.681193
42 -614.357176 229.606392
43 393.836927 -614.357176
44 50.242620 393.836927
45 156.105804 50.242620
46 291.423773 156.105804
47 211.090046 291.423773
48 629.408844 211.090046
49 -708.351603 629.408844
50 174.865322 -708.351603
51 -160.869166 174.865322
52 -743.106527 -160.869166
53 -668.002254 -743.106527
54 -1277.818400 -668.002254
55 -400.571788 -1277.818400
56 -219.508741 -400.571788
57 -192.613403 -219.508741
58 -88.002628 -192.613403
59 155.403064 -88.002628
60 267.828380 155.403064
61 NA 267.828380
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -697.432151 119.361161
[2,] -53.764848 -697.432151
[3,] -122.824347 -53.764848
[4,] -437.846337 -122.824347
[5,] -89.930057 -437.846337
[6,] -464.738129 -89.930057
[7,] -326.120159 -464.738129
[8,] 60.296562 -326.120159
[9,] 82.717672 60.296562
[10,] 75.003013 82.717672
[11,] 39.076566 75.003013
[12,] 493.192997 39.076566
[13,] -531.723925 493.192997
[14,] 602.258646 -531.723925
[15,] 135.393302 602.258646
[16,] -14.617591 135.393302
[17,] -120.680401 -14.617591
[18,] -247.040695 -120.680401
[19,] 196.513146 -247.040695
[20,] 316.623805 196.513146
[21,] 4.614936 316.623805
[22,] 495.376869 4.614936
[23,] 44.277275 495.376869
[24,] 463.499719 44.277275
[25,] -487.348684 463.499719
[26,] 339.109498 -487.348684
[27,] 17.998556 339.109498
[28,] 80.511830 17.998556
[29,] 135.520662 80.511830
[30,] -175.377513 135.520662
[31,] 281.505175 -175.377513
[32,] 686.987443 281.505175
[33,] -91.773646 686.987443
[34,] 155.741201 -91.773646
[35,] 462.984960 155.741201
[36,] 744.634648 462.984960
[37,] 171.071060 744.634648
[38,] 255.357870 171.071060
[39,] 170.661621 255.357870
[40,] -255.681193 170.661621
[41,] 229.606392 -255.681193
[42,] -614.357176 229.606392
[43,] 393.836927 -614.357176
[44,] 50.242620 393.836927
[45,] 156.105804 50.242620
[46,] 291.423773 156.105804
[47,] 211.090046 291.423773
[48,] 629.408844 211.090046
[49,] -708.351603 629.408844
[50,] 174.865322 -708.351603
[51,] -160.869166 174.865322
[52,] -743.106527 -160.869166
[53,] -668.002254 -743.106527
[54,] -1277.818400 -668.002254
[55,] -400.571788 -1277.818400
[56,] -219.508741 -400.571788
[57,] -192.613403 -219.508741
[58,] -88.002628 -192.613403
[59,] 155.403064 -88.002628
[60,] 267.828380 155.403064
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -697.432151 119.361161
2 -53.764848 -697.432151
3 -122.824347 -53.764848
4 -437.846337 -122.824347
5 -89.930057 -437.846337
6 -464.738129 -89.930057
7 -326.120159 -464.738129
8 60.296562 -326.120159
9 82.717672 60.296562
10 75.003013 82.717672
11 39.076566 75.003013
12 493.192997 39.076566
13 -531.723925 493.192997
14 602.258646 -531.723925
15 135.393302 602.258646
16 -14.617591 135.393302
17 -120.680401 -14.617591
18 -247.040695 -120.680401
19 196.513146 -247.040695
20 316.623805 196.513146
21 4.614936 316.623805
22 495.376869 4.614936
23 44.277275 495.376869
24 463.499719 44.277275
25 -487.348684 463.499719
26 339.109498 -487.348684
27 17.998556 339.109498
28 80.511830 17.998556
29 135.520662 80.511830
30 -175.377513 135.520662
31 281.505175 -175.377513
32 686.987443 281.505175
33 -91.773646 686.987443
34 155.741201 -91.773646
35 462.984960 155.741201
36 744.634648 462.984960
37 171.071060 744.634648
38 255.357870 171.071060
39 170.661621 255.357870
40 -255.681193 170.661621
41 229.606392 -255.681193
42 -614.357176 229.606392
43 393.836927 -614.357176
44 50.242620 393.836927
45 156.105804 50.242620
46 291.423773 156.105804
47 211.090046 291.423773
48 629.408844 211.090046
49 -708.351603 629.408844
50 174.865322 -708.351603
51 -160.869166 174.865322
52 -743.106527 -160.869166
53 -668.002254 -743.106527
54 -1277.818400 -668.002254
55 -400.571788 -1277.818400
56 -219.508741 -400.571788
57 -192.613403 -219.508741
58 -88.002628 -192.613403
59 155.403064 -88.002628
60 267.828380 155.403064
> 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/77lkn1258736035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80iyj1258736035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9a52h1258736035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10km191258736035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11uome1258736035.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/122qro1258736035.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/13mh441258736035.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/14ysqw1258736035.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/15hcy11258736035.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/16duo41258736035.tab")
+ }
>
> system("convert tmp/19wze1258736035.ps tmp/19wze1258736035.png")
> system("convert tmp/2kwsz1258736035.ps tmp/2kwsz1258736035.png")
> system("convert tmp/39xtx1258736035.ps tmp/39xtx1258736035.png")
> system("convert tmp/4idl41258736035.ps tmp/4idl41258736035.png")
> system("convert tmp/5axo01258736035.ps tmp/5axo01258736035.png")
> system("convert tmp/6wi061258736035.ps tmp/6wi061258736035.png")
> system("convert tmp/77lkn1258736035.ps tmp/77lkn1258736035.png")
> system("convert tmp/80iyj1258736035.ps tmp/80iyj1258736035.png")
> system("convert tmp/9a52h1258736035.ps tmp/9a52h1258736035.png")
> system("convert tmp/10km191258736035.ps tmp/10km191258736035.png")
>
>
> proc.time()
user system elapsed
2.532 1.585 4.619