R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(20.7246301,0,21.44580352,0,22.09413114,0,21.53321848,0,23.3470789,0,23.5656163,0,26.42117166,0,25.21193138,0,26.43574082,0,29.33500366,0,29.40056488,0,33.05013946,0,28.38072368,0,26.0059506,0,29.31314992,0,30.36212944,0,35.74543406,0,36.15337054,0,34.20838768,0,37.90895432,0,38.70297354,0,42.11944156,0,42.16314904,0,39.79566054,0,37.36261082,0,38.3533137,0,42.60022384,0,41.24529196,0,42.15586446,0,46.94183352,0,47.42990038,0,47.0583868,0,50.18347162,0,50.12519498,0,43.22669772,0,40.04333626,0,40.37114236,0,42.2141411,0,36.99838182,0,39.74466848,0,42.68035422,0,46.2935059,0,46.97097184,0,48.72655562,0,52.36884562,1,50.05234918,1,54.03701444,1,57.78128856,1,64.71620872,1,63.4122689,1,64.3592643,1,66.02743312,1,72.13919574,1,76.60464328,1,86.97060062,1,93.48301514,1,95.58825876,1,81.88596378,1,70.5511573,1,50.38015528,1,36.24807008,0),dim=c(2,61),dimnames=list(c('Olie','Dumivariabele'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Olie','Dumivariabele'),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 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> 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
Olie Dumivariabele M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 20.72463 0 1 0 0 0 0 0 0 0 0 0 0
2 21.44580 0 0 1 0 0 0 0 0 0 0 0 0
3 22.09413 0 0 0 1 0 0 0 0 0 0 0 0
4 21.53322 0 0 0 0 1 0 0 0 0 0 0 0
5 23.34708 0 0 0 0 0 1 0 0 0 0 0 0
6 23.56562 0 0 0 0 0 0 1 0 0 0 0 0
7 26.42117 0 0 0 0 0 0 0 1 0 0 0 0
8 25.21193 0 0 0 0 0 0 0 0 1 0 0 0
9 26.43574 0 0 0 0 0 0 0 0 0 1 0 0
10 29.33500 0 0 0 0 0 0 0 0 0 0 1 0
11 29.40056 0 0 0 0 0 0 0 0 0 0 0 1
12 33.05014 0 0 0 0 0 0 0 0 0 0 0 0
13 28.38072 0 1 0 0 0 0 0 0 0 0 0 0
14 26.00595 0 0 1 0 0 0 0 0 0 0 0 0
15 29.31315 0 0 0 1 0 0 0 0 0 0 0 0
16 30.36213 0 0 0 0 1 0 0 0 0 0 0 0
17 35.74543 0 0 0 0 0 1 0 0 0 0 0 0
18 36.15337 0 0 0 0 0 0 1 0 0 0 0 0
19 34.20839 0 0 0 0 0 0 0 1 0 0 0 0
20 37.90895 0 0 0 0 0 0 0 0 1 0 0 0
21 38.70297 0 0 0 0 0 0 0 0 0 1 0 0
22 42.11944 0 0 0 0 0 0 0 0 0 0 1 0
23 42.16315 0 0 0 0 0 0 0 0 0 0 0 1
24 39.79566 0 0 0 0 0 0 0 0 0 0 0 0
25 37.36261 0 1 0 0 0 0 0 0 0 0 0 0
26 38.35331 0 0 1 0 0 0 0 0 0 0 0 0
27 42.60022 0 0 0 1 0 0 0 0 0 0 0 0
28 41.24529 0 0 0 0 1 0 0 0 0 0 0 0
29 42.15586 0 0 0 0 0 1 0 0 0 0 0 0
30 46.94183 0 0 0 0 0 0 1 0 0 0 0 0
31 47.42990 0 0 0 0 0 0 0 1 0 0 0 0
32 47.05839 0 0 0 0 0 0 0 0 1 0 0 0
33 50.18347 0 0 0 0 0 0 0 0 0 1 0 0
34 50.12519 0 0 0 0 0 0 0 0 0 0 1 0
35 43.22670 0 0 0 0 0 0 0 0 0 0 0 1
36 40.04334 0 0 0 0 0 0 0 0 0 0 0 0
37 40.37114 0 1 0 0 0 0 0 0 0 0 0 0
38 42.21414 0 0 1 0 0 0 0 0 0 0 0 0
39 36.99838 0 0 0 1 0 0 0 0 0 0 0 0
40 39.74467 0 0 0 0 1 0 0 0 0 0 0 0
41 42.68035 0 0 0 0 0 1 0 0 0 0 0 0
42 46.29351 0 0 0 0 0 0 1 0 0 0 0 0
43 46.97097 0 0 0 0 0 0 0 1 0 0 0 0
44 48.72656 0 0 0 0 0 0 0 0 1 0 0 0
45 52.36885 1 0 0 0 0 0 0 0 0 1 0 0
46 50.05235 1 0 0 0 0 0 0 0 0 0 1 0
47 54.03701 1 0 0 0 0 0 0 0 0 0 0 1
48 57.78129 1 0 0 0 0 0 0 0 0 0 0 0
49 64.71621 1 1 0 0 0 0 0 0 0 0 0 0
50 63.41227 1 0 1 0 0 0 0 0 0 0 0 0
51 64.35926 1 0 0 1 0 0 0 0 0 0 0 0
52 66.02743 1 0 0 0 1 0 0 0 0 0 0 0
53 72.13920 1 0 0 0 0 1 0 0 0 0 0 0
54 76.60464 1 0 0 0 0 0 1 0 0 0 0 0
55 86.97060 1 0 0 0 0 0 0 1 0 0 0 0
56 93.48302 1 0 0 0 0 0 0 0 1 0 0 0
57 95.58826 1 0 0 0 0 0 0 0 0 1 0 0
58 81.88596 1 0 0 0 0 0 0 0 0 0 1 0
59 70.55116 1 0 0 0 0 0 0 0 0 0 0 1
60 50.38016 1 0 0 0 0 0 0 0 0 0 0 0
61 36.24807 0 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dumivariabele M1 M2 M3
31.1648 32.6132 1.3669 0.5988 1.3855
M4 M5 M6 M7 M8
2.0951 5.5261 8.2243 10.7127 12.7903
M9 M10 M11
8.4457 6.4935 3.6656
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-20.219 -6.046 2.835 6.904 23.364
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.1648 5.0119 6.218 1.16e-07 ***
Dumivariabele 32.6132 3.2307 10.095 1.86e-13 ***
M1 1.3669 6.5999 0.207 0.8368
M2 0.5988 6.8786 0.087 0.9310
M3 1.3855 6.8786 0.201 0.8412
M4 2.0951 6.8786 0.305 0.7620
M5 5.5261 6.8786 0.803 0.4257
M6 8.2243 6.8786 1.196 0.2377
M7 10.7127 6.8786 1.557 0.1259
M8 12.7903 6.8786 1.859 0.0691 .
M9 8.4457 6.8482 1.233 0.2235
M10 6.4935 6.8482 0.948 0.3478
M11 3.6656 6.8482 0.535 0.5949
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.83 on 48 degrees of freedom
Multiple R-squared: 0.7059, Adjusted R-squared: 0.6324
F-statistic: 9.6 on 12 and 48 DF, p-value: 3.979e-09
> postscript(file="/var/www/html/rcomp/tmp/1bqir1229874562.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/26bit1229874562.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3n7fn1229874562.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45l9l1229874562.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xn3e1229874562.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> 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
-11.8070721 -10.3178575 -10.4562645 -11.7266953 -13.3438720 -15.8235431
7 8 9 10 11 12
-15.4564002 -18.7432027 -13.1748482 -8.3233179 -5.4298827 1.8852925
13 14 15 16 17 18
-4.1509785 -5.7577104 -3.2372457 -2.8977843 -0.9455169 -3.2357888
19 20 21 22 23 24
-7.6691842 -6.0461798 -0.9076154 4.4611200 7.3327015 8.6308136
25 26 27 28 29 30
4.8309086 6.5896527 10.0498282 7.9853782 5.4649135 7.5526742
31 32 33 34 35 36
5.5523285 3.1032527 10.5728826 12.4668734 8.3962501 8.8784893
37 38 39 40 41 42
7.8394402 10.4504801 4.4479862 6.4847547 5.9894033 6.9043465
43 44 45 46 47 48
5.0934000 4.7714215 -19.8549161 -20.2191451 -13.4066059 -5.9967311
49 50 51 52 53 54
-0.4286662 -0.9645649 -0.8043041 0.1543466 2.8350721 4.6023112
55 56 57 58 59 60
12.4798560 16.9147083 23.3644970 11.6144695 3.1075370 -13.3978644
61
3.7163679
> postscript(file="/var/www/html/rcomp/tmp/6ed7v1229874562.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 -11.8070721 NA
1 -10.3178575 -11.8070721
2 -10.4562645 -10.3178575
3 -11.7266953 -10.4562645
4 -13.3438720 -11.7266953
5 -15.8235431 -13.3438720
6 -15.4564002 -15.8235431
7 -18.7432027 -15.4564002
8 -13.1748482 -18.7432027
9 -8.3233179 -13.1748482
10 -5.4298827 -8.3233179
11 1.8852925 -5.4298827
12 -4.1509785 1.8852925
13 -5.7577104 -4.1509785
14 -3.2372457 -5.7577104
15 -2.8977843 -3.2372457
16 -0.9455169 -2.8977843
17 -3.2357888 -0.9455169
18 -7.6691842 -3.2357888
19 -6.0461798 -7.6691842
20 -0.9076154 -6.0461798
21 4.4611200 -0.9076154
22 7.3327015 4.4611200
23 8.6308136 7.3327015
24 4.8309086 8.6308136
25 6.5896527 4.8309086
26 10.0498282 6.5896527
27 7.9853782 10.0498282
28 5.4649135 7.9853782
29 7.5526742 5.4649135
30 5.5523285 7.5526742
31 3.1032527 5.5523285
32 10.5728826 3.1032527
33 12.4668734 10.5728826
34 8.3962501 12.4668734
35 8.8784893 8.3962501
36 7.8394402 8.8784893
37 10.4504801 7.8394402
38 4.4479862 10.4504801
39 6.4847547 4.4479862
40 5.9894033 6.4847547
41 6.9043465 5.9894033
42 5.0934000 6.9043465
43 4.7714215 5.0934000
44 -19.8549161 4.7714215
45 -20.2191451 -19.8549161
46 -13.4066059 -20.2191451
47 -5.9967311 -13.4066059
48 -0.4286662 -5.9967311
49 -0.9645649 -0.4286662
50 -0.8043041 -0.9645649
51 0.1543466 -0.8043041
52 2.8350721 0.1543466
53 4.6023112 2.8350721
54 12.4798560 4.6023112
55 16.9147083 12.4798560
56 23.3644970 16.9147083
57 11.6144695 23.3644970
58 3.1075370 11.6144695
59 -13.3978644 3.1075370
60 3.7163679 -13.3978644
61 NA 3.7163679
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -10.3178575 -11.8070721
[2,] -10.4562645 -10.3178575
[3,] -11.7266953 -10.4562645
[4,] -13.3438720 -11.7266953
[5,] -15.8235431 -13.3438720
[6,] -15.4564002 -15.8235431
[7,] -18.7432027 -15.4564002
[8,] -13.1748482 -18.7432027
[9,] -8.3233179 -13.1748482
[10,] -5.4298827 -8.3233179
[11,] 1.8852925 -5.4298827
[12,] -4.1509785 1.8852925
[13,] -5.7577104 -4.1509785
[14,] -3.2372457 -5.7577104
[15,] -2.8977843 -3.2372457
[16,] -0.9455169 -2.8977843
[17,] -3.2357888 -0.9455169
[18,] -7.6691842 -3.2357888
[19,] -6.0461798 -7.6691842
[20,] -0.9076154 -6.0461798
[21,] 4.4611200 -0.9076154
[22,] 7.3327015 4.4611200
[23,] 8.6308136 7.3327015
[24,] 4.8309086 8.6308136
[25,] 6.5896527 4.8309086
[26,] 10.0498282 6.5896527
[27,] 7.9853782 10.0498282
[28,] 5.4649135 7.9853782
[29,] 7.5526742 5.4649135
[30,] 5.5523285 7.5526742
[31,] 3.1032527 5.5523285
[32,] 10.5728826 3.1032527
[33,] 12.4668734 10.5728826
[34,] 8.3962501 12.4668734
[35,] 8.8784893 8.3962501
[36,] 7.8394402 8.8784893
[37,] 10.4504801 7.8394402
[38,] 4.4479862 10.4504801
[39,] 6.4847547 4.4479862
[40,] 5.9894033 6.4847547
[41,] 6.9043465 5.9894033
[42,] 5.0934000 6.9043465
[43,] 4.7714215 5.0934000
[44,] -19.8549161 4.7714215
[45,] -20.2191451 -19.8549161
[46,] -13.4066059 -20.2191451
[47,] -5.9967311 -13.4066059
[48,] -0.4286662 -5.9967311
[49,] -0.9645649 -0.4286662
[50,] -0.8043041 -0.9645649
[51,] 0.1543466 -0.8043041
[52,] 2.8350721 0.1543466
[53,] 4.6023112 2.8350721
[54,] 12.4798560 4.6023112
[55,] 16.9147083 12.4798560
[56,] 23.3644970 16.9147083
[57,] 11.6144695 23.3644970
[58,] 3.1075370 11.6144695
[59,] -13.3978644 3.1075370
[60,] 3.7163679 -13.3978644
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -10.3178575 -11.8070721
2 -10.4562645 -10.3178575
3 -11.7266953 -10.4562645
4 -13.3438720 -11.7266953
5 -15.8235431 -13.3438720
6 -15.4564002 -15.8235431
7 -18.7432027 -15.4564002
8 -13.1748482 -18.7432027
9 -8.3233179 -13.1748482
10 -5.4298827 -8.3233179
11 1.8852925 -5.4298827
12 -4.1509785 1.8852925
13 -5.7577104 -4.1509785
14 -3.2372457 -5.7577104
15 -2.8977843 -3.2372457
16 -0.9455169 -2.8977843
17 -3.2357888 -0.9455169
18 -7.6691842 -3.2357888
19 -6.0461798 -7.6691842
20 -0.9076154 -6.0461798
21 4.4611200 -0.9076154
22 7.3327015 4.4611200
23 8.6308136 7.3327015
24 4.8309086 8.6308136
25 6.5896527 4.8309086
26 10.0498282 6.5896527
27 7.9853782 10.0498282
28 5.4649135 7.9853782
29 7.5526742 5.4649135
30 5.5523285 7.5526742
31 3.1032527 5.5523285
32 10.5728826 3.1032527
33 12.4668734 10.5728826
34 8.3962501 12.4668734
35 8.8784893 8.3962501
36 7.8394402 8.8784893
37 10.4504801 7.8394402
38 4.4479862 10.4504801
39 6.4847547 4.4479862
40 5.9894033 6.4847547
41 6.9043465 5.9894033
42 5.0934000 6.9043465
43 4.7714215 5.0934000
44 -19.8549161 4.7714215
45 -20.2191451 -19.8549161
46 -13.4066059 -20.2191451
47 -5.9967311 -13.4066059
48 -0.4286662 -5.9967311
49 -0.9645649 -0.4286662
50 -0.8043041 -0.9645649
51 0.1543466 -0.8043041
52 2.8350721 0.1543466
53 4.6023112 2.8350721
54 12.4798560 4.6023112
55 16.9147083 12.4798560
56 23.3644970 16.9147083
57 11.6144695 23.3644970
58 3.1075370 11.6144695
59 -13.3978644 3.1075370
60 3.7163679 -13.3978644
> 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/7ep5g1229874562.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/832kv1229874562.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9f0uc1229874562.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10vngf1229874562.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/11csp51229874562.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/12k2re1229874562.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/13rnhr1229874562.tab")
>
> system("convert tmp/1bqir1229874562.ps tmp/1bqir1229874562.png")
> system("convert tmp/26bit1229874562.ps tmp/26bit1229874562.png")
> system("convert tmp/3n7fn1229874562.ps tmp/3n7fn1229874562.png")
> system("convert tmp/45l9l1229874562.ps tmp/45l9l1229874562.png")
> system("convert tmp/5xn3e1229874562.ps tmp/5xn3e1229874562.png")
> system("convert tmp/6ed7v1229874562.ps tmp/6ed7v1229874562.png")
> system("convert tmp/7ep5g1229874562.ps tmp/7ep5g1229874562.png")
> system("convert tmp/832kv1229874562.ps tmp/832kv1229874562.png")
> system("convert tmp/9f0uc1229874562.ps tmp/9f0uc1229874562.png")
>
>
> proc.time()
user system elapsed
1.923 1.444 2.496