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.
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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(8.00,96.80,8.10,114.10,7.70,110.30,7.50,103.90,7.60,101.60,7.80,94.60,7.80,95.90,7.80,104.70,7.50,102.80,7.50,98.10,7.10,113.90,7.50,80.90,7.50,95.70,7.60,113.20,7.70,105.90,7.70,108.80,7.90,102.30,8.10,99.00,8.20,100.70,8.20,115.50,8.20,100.70,7.90,109.90,7.30,114.60,6.90,85.40,6.60,100.50,6.70,114.80,6.90,116.50,7.00,112.90,7.10,102.00,7.20,106.00,7.10,105.30,6.90,118.80,7.00,106.10,6.80,109.30,6.40,117.20,6.70,92.50,6.60,104.20,6.40,112.50,6.30,122.40,6.20,113.30,6.50,100.00,6.80,110.70,6.80,112.80,6.40,109.80,6.10,117.30,5.80,109.10,6.10,115.90,7.20,96.00,7.30,99.80,6.90,116.80,6.10,115.70,5.80,99.40,6.20,94.30,7.10,91.00,7.70,93.20,7.90,103.10,7.70,94.10,7.40,91.80,7.50,102.70,8.00,82.60),dim=c(2,60),dimnames=list(c('Wman','Ecogr'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Wman','Ecogr'),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
Wman Ecogr t
1 8.0 96.8 1
2 8.1 114.1 2
3 7.7 110.3 3
4 7.5 103.9 4
5 7.6 101.6 5
6 7.8 94.6 6
7 7.8 95.9 7
8 7.8 104.7 8
9 7.5 102.8 9
10 7.5 98.1 10
11 7.1 113.9 11
12 7.5 80.9 12
13 7.5 95.7 13
14 7.6 113.2 14
15 7.7 105.9 15
16 7.7 108.8 16
17 7.9 102.3 17
18 8.1 99.0 18
19 8.2 100.7 19
20 8.2 115.5 20
21 8.2 100.7 21
22 7.9 109.9 22
23 7.3 114.6 23
24 6.9 85.4 24
25 6.6 100.5 25
26 6.7 114.8 26
27 6.9 116.5 27
28 7.0 112.9 28
29 7.1 102.0 29
30 7.2 106.0 30
31 7.1 105.3 31
32 6.9 118.8 32
33 7.0 106.1 33
34 6.8 109.3 34
35 6.4 117.2 35
36 6.7 92.5 36
37 6.6 104.2 37
38 6.4 112.5 38
39 6.3 122.4 39
40 6.2 113.3 40
41 6.5 100.0 41
42 6.8 110.7 42
43 6.8 112.8 43
44 6.4 109.8 44
45 6.1 117.3 45
46 5.8 109.1 46
47 6.1 115.9 47
48 7.2 96.0 48
49 7.3 99.8 49
50 6.9 116.8 50
51 6.1 115.7 51
52 5.8 99.4 52
53 6.2 94.3 53
54 7.1 91.0 54
55 7.7 93.2 55
56 7.9 103.1 56
57 7.7 94.1 57
58 7.4 91.8 58
59 7.5 102.7 59
60 8.0 82.6 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Ecogr t
10.41641 -0.02538 -0.01929
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.09058 -0.37020 -0.04225 0.35573 1.18047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.416406 0.777349 13.400 < 2e-16 ***
Ecogr -0.025382 0.007259 -3.497 0.00092 ***
t -0.019285 0.003934 -4.902 8.25e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5273 on 57 degrees of freedom
Multiple R-squared: 0.3791, Adjusted R-squared: 0.3573
F-statistic: 17.4 on 2 and 57 DF, p-value: 1.263e-06
> 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,] 0.0903249972 0.1806499943 0.9096750
[2,] 0.0454168658 0.0908337315 0.9545831
[3,] 0.0204064333 0.0408128666 0.9795936
[4,] 0.0078568123 0.0157136247 0.9921432
[5,] 0.0025398686 0.0050797373 0.9974601
[6,] 0.0016879962 0.0033759924 0.9983120
[7,] 0.0006049416 0.0012098833 0.9993951
[8,] 0.0002689798 0.0005379597 0.9997310
[9,] 0.0003468753 0.0006937506 0.9996531
[10,] 0.0003267489 0.0006534977 0.9996733
[11,] 0.0002250758 0.0004501516 0.9997749
[12,] 0.0003139500 0.0006279001 0.9996860
[13,] 0.0007255272 0.0014510544 0.9992745
[14,] 0.0014775801 0.0029551601 0.9985224
[15,] 0.0033197959 0.0066395919 0.9966802
[16,] 0.0047218066 0.0094436132 0.9952782
[17,] 0.0056259850 0.0112519699 0.9943740
[18,] 0.0142707688 0.0285415376 0.9857292
[19,] 0.0462346647 0.0924693295 0.9537653
[20,] 0.1416253245 0.2832506490 0.8583747
[21,] 0.2020393733 0.4040787466 0.7979606
[22,] 0.1988653894 0.3977307788 0.8011346
[23,] 0.1760251848 0.3520503695 0.8239748
[24,] 0.1358983586 0.2717967172 0.8641016
[25,] 0.1164526668 0.2329053336 0.8835473
[26,] 0.0975141129 0.1950282258 0.9024859
[27,] 0.1062514939 0.2125029877 0.8937485
[28,] 0.0967984003 0.1935968006 0.9032016
[29,] 0.0896265432 0.1792530863 0.9103735
[30,] 0.0936439053 0.1872878106 0.9063561
[31,] 0.0675378509 0.1350757017 0.9324621
[32,] 0.0509974360 0.1019948720 0.9490026
[33,] 0.0411233287 0.0822466575 0.9588767
[34,] 0.0358562702 0.0717125404 0.9641437
[35,] 0.0272169197 0.0544338393 0.9727831
[36,] 0.0168528911 0.0337057821 0.9831471
[37,] 0.0178642549 0.0357285098 0.9821357
[38,] 0.0251530290 0.0503060579 0.9748470
[39,] 0.0182293099 0.0364586198 0.9817707
[40,] 0.0121163587 0.0242327174 0.9878836
[41,] 0.0122858683 0.0245717366 0.9877141
[42,] 0.0071944461 0.0143888921 0.9928056
[43,] 0.0160594809 0.0321189619 0.9839405
[44,] 0.1033071567 0.2066143134 0.8966928
[45,] 0.1942795393 0.3885590786 0.8057205
[46,] 0.1262877629 0.2525755257 0.8737122
[47,] 0.2230439846 0.4460879693 0.7769560
[48,] 0.6064760152 0.7870479697 0.3935240
[49,] 0.7219081430 0.5561837140 0.2780919
> postscript(file="/var/www/html/rcomp/tmp/1lees1259169834.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2l1xl1259169834.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3c8131259169834.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u0kh1259169834.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tqwa1259169834.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
0.059866874 0.618262656 0.141096129 -0.202063858 -0.141157236 -0.099546483
7 8 9 10 11 12
-0.047264300 0.195383632 -0.133556906 -0.233567323 -0.213244691 -0.631568537
13 14 15 16 17 18
-0.236628005 0.326844197 0.260840319 0.353733862 0.408035665 0.543560187
19 20 21 22 23 24
0.705995210 1.100935742 0.744566115 0.697366887 0.235948209 -0.885923657
25 26 27 28 29 30
-0.783368495 -0.301119013 -0.038683991 -0.010774098 -0.168153535 0.052660317
31 32 33 34 35 36
-0.045821700 0.116122102 -0.086945115 -0.186436943 -0.366632900 -0.674285317
37 38 39 40 41 42
-0.458029295 -0.428072413 -0.257504171 -0.569195828 -0.587492304 0.003381618
43 44 45 46 47 48
0.075969480 -0.380891367 -0.471240165 -0.960087932 -0.468204200 0.145977464
49 50 51 52 53 54
0.361714896 0.412496048 -0.396138810 -1.090581586 -0.800744844 0.034779679
55 56 57 58 59 60
0.709905752 1.180473994 0.771320547 0.432227169 0.828177511 0.837282755
> postscript(file="/var/www/html/rcomp/tmp/6eygs1259169834.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 0.059866874 NA
1 0.618262656 0.059866874
2 0.141096129 0.618262656
3 -0.202063858 0.141096129
4 -0.141157236 -0.202063858
5 -0.099546483 -0.141157236
6 -0.047264300 -0.099546483
7 0.195383632 -0.047264300
8 -0.133556906 0.195383632
9 -0.233567323 -0.133556906
10 -0.213244691 -0.233567323
11 -0.631568537 -0.213244691
12 -0.236628005 -0.631568537
13 0.326844197 -0.236628005
14 0.260840319 0.326844197
15 0.353733862 0.260840319
16 0.408035665 0.353733862
17 0.543560187 0.408035665
18 0.705995210 0.543560187
19 1.100935742 0.705995210
20 0.744566115 1.100935742
21 0.697366887 0.744566115
22 0.235948209 0.697366887
23 -0.885923657 0.235948209
24 -0.783368495 -0.885923657
25 -0.301119013 -0.783368495
26 -0.038683991 -0.301119013
27 -0.010774098 -0.038683991
28 -0.168153535 -0.010774098
29 0.052660317 -0.168153535
30 -0.045821700 0.052660317
31 0.116122102 -0.045821700
32 -0.086945115 0.116122102
33 -0.186436943 -0.086945115
34 -0.366632900 -0.186436943
35 -0.674285317 -0.366632900
36 -0.458029295 -0.674285317
37 -0.428072413 -0.458029295
38 -0.257504171 -0.428072413
39 -0.569195828 -0.257504171
40 -0.587492304 -0.569195828
41 0.003381618 -0.587492304
42 0.075969480 0.003381618
43 -0.380891367 0.075969480
44 -0.471240165 -0.380891367
45 -0.960087932 -0.471240165
46 -0.468204200 -0.960087932
47 0.145977464 -0.468204200
48 0.361714896 0.145977464
49 0.412496048 0.361714896
50 -0.396138810 0.412496048
51 -1.090581586 -0.396138810
52 -0.800744844 -1.090581586
53 0.034779679 -0.800744844
54 0.709905752 0.034779679
55 1.180473994 0.709905752
56 0.771320547 1.180473994
57 0.432227169 0.771320547
58 0.828177511 0.432227169
59 0.837282755 0.828177511
60 NA 0.837282755
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.618262656 0.059866874
[2,] 0.141096129 0.618262656
[3,] -0.202063858 0.141096129
[4,] -0.141157236 -0.202063858
[5,] -0.099546483 -0.141157236
[6,] -0.047264300 -0.099546483
[7,] 0.195383632 -0.047264300
[8,] -0.133556906 0.195383632
[9,] -0.233567323 -0.133556906
[10,] -0.213244691 -0.233567323
[11,] -0.631568537 -0.213244691
[12,] -0.236628005 -0.631568537
[13,] 0.326844197 -0.236628005
[14,] 0.260840319 0.326844197
[15,] 0.353733862 0.260840319
[16,] 0.408035665 0.353733862
[17,] 0.543560187 0.408035665
[18,] 0.705995210 0.543560187
[19,] 1.100935742 0.705995210
[20,] 0.744566115 1.100935742
[21,] 0.697366887 0.744566115
[22,] 0.235948209 0.697366887
[23,] -0.885923657 0.235948209
[24,] -0.783368495 -0.885923657
[25,] -0.301119013 -0.783368495
[26,] -0.038683991 -0.301119013
[27,] -0.010774098 -0.038683991
[28,] -0.168153535 -0.010774098
[29,] 0.052660317 -0.168153535
[30,] -0.045821700 0.052660317
[31,] 0.116122102 -0.045821700
[32,] -0.086945115 0.116122102
[33,] -0.186436943 -0.086945115
[34,] -0.366632900 -0.186436943
[35,] -0.674285317 -0.366632900
[36,] -0.458029295 -0.674285317
[37,] -0.428072413 -0.458029295
[38,] -0.257504171 -0.428072413
[39,] -0.569195828 -0.257504171
[40,] -0.587492304 -0.569195828
[41,] 0.003381618 -0.587492304
[42,] 0.075969480 0.003381618
[43,] -0.380891367 0.075969480
[44,] -0.471240165 -0.380891367
[45,] -0.960087932 -0.471240165
[46,] -0.468204200 -0.960087932
[47,] 0.145977464 -0.468204200
[48,] 0.361714896 0.145977464
[49,] 0.412496048 0.361714896
[50,] -0.396138810 0.412496048
[51,] -1.090581586 -0.396138810
[52,] -0.800744844 -1.090581586
[53,] 0.034779679 -0.800744844
[54,] 0.709905752 0.034779679
[55,] 1.180473994 0.709905752
[56,] 0.771320547 1.180473994
[57,] 0.432227169 0.771320547
[58,] 0.828177511 0.432227169
[59,] 0.837282755 0.828177511
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.618262656 0.059866874
2 0.141096129 0.618262656
3 -0.202063858 0.141096129
4 -0.141157236 -0.202063858
5 -0.099546483 -0.141157236
6 -0.047264300 -0.099546483
7 0.195383632 -0.047264300
8 -0.133556906 0.195383632
9 -0.233567323 -0.133556906
10 -0.213244691 -0.233567323
11 -0.631568537 -0.213244691
12 -0.236628005 -0.631568537
13 0.326844197 -0.236628005
14 0.260840319 0.326844197
15 0.353733862 0.260840319
16 0.408035665 0.353733862
17 0.543560187 0.408035665
18 0.705995210 0.543560187
19 1.100935742 0.705995210
20 0.744566115 1.100935742
21 0.697366887 0.744566115
22 0.235948209 0.697366887
23 -0.885923657 0.235948209
24 -0.783368495 -0.885923657
25 -0.301119013 -0.783368495
26 -0.038683991 -0.301119013
27 -0.010774098 -0.038683991
28 -0.168153535 -0.010774098
29 0.052660317 -0.168153535
30 -0.045821700 0.052660317
31 0.116122102 -0.045821700
32 -0.086945115 0.116122102
33 -0.186436943 -0.086945115
34 -0.366632900 -0.186436943
35 -0.674285317 -0.366632900
36 -0.458029295 -0.674285317
37 -0.428072413 -0.458029295
38 -0.257504171 -0.428072413
39 -0.569195828 -0.257504171
40 -0.587492304 -0.569195828
41 0.003381618 -0.587492304
42 0.075969480 0.003381618
43 -0.380891367 0.075969480
44 -0.471240165 -0.380891367
45 -0.960087932 -0.471240165
46 -0.468204200 -0.960087932
47 0.145977464 -0.468204200
48 0.361714896 0.145977464
49 0.412496048 0.361714896
50 -0.396138810 0.412496048
51 -1.090581586 -0.396138810
52 -0.800744844 -1.090581586
53 0.034779679 -0.800744844
54 0.709905752 0.034779679
55 1.180473994 0.709905752
56 0.771320547 1.180473994
57 0.432227169 0.771320547
58 0.828177511 0.432227169
59 0.837282755 0.828177511
> 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/7c7961259169834.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8d9vx1259169834.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9oghb1259169834.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108ctw1259169834.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118nij1259169834.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/1283o71259169834.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/13a2c91259169834.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/14yzbh1259169834.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/15zqoq1259169835.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/16k8n81259169835.tab")
+ }
> system("convert tmp/1lees1259169834.ps tmp/1lees1259169834.png")
> system("convert tmp/2l1xl1259169834.ps tmp/2l1xl1259169834.png")
> system("convert tmp/3c8131259169834.ps tmp/3c8131259169834.png")
> system("convert tmp/4u0kh1259169834.ps tmp/4u0kh1259169834.png")
> system("convert tmp/5tqwa1259169834.ps tmp/5tqwa1259169834.png")
> system("convert tmp/6eygs1259169834.ps tmp/6eygs1259169834.png")
> system("convert tmp/7c7961259169834.ps tmp/7c7961259169834.png")
> system("convert tmp/8d9vx1259169834.ps tmp/8d9vx1259169834.png")
> system("convert tmp/9oghb1259169834.ps tmp/9oghb1259169834.png")
> system("convert tmp/108ctw1259169834.ps tmp/108ctw1259169834.png")
>
>
> proc.time()
user system elapsed
2.388 1.622 2.960