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 = '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 t
1 20.72463 0 1 0 0 0 0 0 0 0 0 0 0 1
2 21.44580 0 0 1 0 0 0 0 0 0 0 0 0 2
3 22.09413 0 0 0 1 0 0 0 0 0 0 0 0 3
4 21.53322 0 0 0 0 1 0 0 0 0 0 0 0 4
5 23.34708 0 0 0 0 0 1 0 0 0 0 0 0 5
6 23.56562 0 0 0 0 0 0 1 0 0 0 0 0 6
7 26.42117 0 0 0 0 0 0 0 1 0 0 0 0 7
8 25.21193 0 0 0 0 0 0 0 0 1 0 0 0 8
9 26.43574 0 0 0 0 0 0 0 0 0 1 0 0 9
10 29.33500 0 0 0 0 0 0 0 0 0 0 1 0 10
11 29.40056 0 0 0 0 0 0 0 0 0 0 0 1 11
12 33.05014 0 0 0 0 0 0 0 0 0 0 0 0 12
13 28.38072 0 1 0 0 0 0 0 0 0 0 0 0 13
14 26.00595 0 0 1 0 0 0 0 0 0 0 0 0 14
15 29.31315 0 0 0 1 0 0 0 0 0 0 0 0 15
16 30.36213 0 0 0 0 1 0 0 0 0 0 0 0 16
17 35.74543 0 0 0 0 0 1 0 0 0 0 0 0 17
18 36.15337 0 0 0 0 0 0 1 0 0 0 0 0 18
19 34.20839 0 0 0 0 0 0 0 1 0 0 0 0 19
20 37.90895 0 0 0 0 0 0 0 0 1 0 0 0 20
21 38.70297 0 0 0 0 0 0 0 0 0 1 0 0 21
22 42.11944 0 0 0 0 0 0 0 0 0 0 1 0 22
23 42.16315 0 0 0 0 0 0 0 0 0 0 0 1 23
24 39.79566 0 0 0 0 0 0 0 0 0 0 0 0 24
25 37.36261 0 1 0 0 0 0 0 0 0 0 0 0 25
26 38.35331 0 0 1 0 0 0 0 0 0 0 0 0 26
27 42.60022 0 0 0 1 0 0 0 0 0 0 0 0 27
28 41.24529 0 0 0 0 1 0 0 0 0 0 0 0 28
29 42.15586 0 0 0 0 0 1 0 0 0 0 0 0 29
30 46.94183 0 0 0 0 0 0 1 0 0 0 0 0 30
31 47.42990 0 0 0 0 0 0 0 1 0 0 0 0 31
32 47.05839 0 0 0 0 0 0 0 0 1 0 0 0 32
33 50.18347 0 0 0 0 0 0 0 0 0 1 0 0 33
34 50.12519 0 0 0 0 0 0 0 0 0 0 1 0 34
35 43.22670 0 0 0 0 0 0 0 0 0 0 0 1 35
36 40.04334 0 0 0 0 0 0 0 0 0 0 0 0 36
37 40.37114 0 1 0 0 0 0 0 0 0 0 0 0 37
38 42.21414 0 0 1 0 0 0 0 0 0 0 0 0 38
39 36.99838 0 0 0 1 0 0 0 0 0 0 0 0 39
40 39.74467 0 0 0 0 1 0 0 0 0 0 0 0 40
41 42.68035 0 0 0 0 0 1 0 0 0 0 0 0 41
42 46.29351 0 0 0 0 0 0 1 0 0 0 0 0 42
43 46.97097 0 0 0 0 0 0 0 1 0 0 0 0 43
44 48.72656 0 0 0 0 0 0 0 0 1 0 0 0 44
45 52.36885 1 0 0 0 0 0 0 0 0 1 0 0 45
46 50.05235 1 0 0 0 0 0 0 0 0 0 1 0 46
47 54.03701 1 0 0 0 0 0 0 0 0 0 0 1 47
48 57.78129 1 0 0 0 0 0 0 0 0 0 0 0 48
49 64.71621 1 1 0 0 0 0 0 0 0 0 0 0 49
50 63.41227 1 0 1 0 0 0 0 0 0 0 0 0 50
51 64.35926 1 0 0 1 0 0 0 0 0 0 0 0 51
52 66.02743 1 0 0 0 1 0 0 0 0 0 0 0 52
53 72.13920 1 0 0 0 0 1 0 0 0 0 0 0 53
54 76.60464 1 0 0 0 0 0 1 0 0 0 0 0 54
55 86.97060 1 0 0 0 0 0 0 1 0 0 0 0 55
56 93.48302 1 0 0 0 0 0 0 0 1 0 0 0 56
57 95.58826 1 0 0 0 0 0 0 0 0 1 0 0 57
58 81.88596 1 0 0 0 0 0 0 0 0 0 1 0 58
59 70.55116 1 0 0 0 0 0 0 0 0 0 0 1 59
60 50.38016 1 0 0 0 0 0 0 0 0 0 0 0 60
61 36.24807 0 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dumivariabele M1 M2 M3
17.6501 16.2406 0.3332 2.8976 3.1270
M4 M5 M6 M7 M8
3.2792 6.1529 8.2938 10.2249 11.7451
M9 M10 M11 t
10.1177 7.6081 4.2229 0.5573
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.0836 -3.2425 0.1762 3.3151 19.8122
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.65007 4.19797 4.204 0.000116 ***
Dumivariabele 16.24065 3.43257 4.731 2.07e-05 ***
M1 0.33324 4.82010 0.069 0.945176
M2 2.89758 5.03322 0.576 0.567570
M3 3.12699 5.02802 0.622 0.537005
M4 3.27918 5.02426 0.653 0.517149
M5 6.15289 5.02193 1.225 0.226603
M6 8.29377 5.02103 1.652 0.105240
M7 10.22486 5.02157 2.036 0.047389 *
M8 11.74509 5.02354 2.338 0.023694 *
M9 10.11772 5.00530 2.021 0.048954 *
M10 7.60813 5.00170 1.521 0.134932
M11 4.22293 4.99954 0.845 0.402578
t 0.55733 0.08491 6.564 3.75e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.904 on 47 degrees of freedom
Multiple R-squared: 0.8466, Adjusted R-squared: 0.8041
F-statistic: 19.95 on 13 and 47 DF, p-value: 7.532e-15
> postscript(file="/var/www/html/freestat/rcomp/tmp/13lpt1229872143.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/freestat/rcomp/tmp/2rxeh1229872143.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3o1gd1229872143.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4ovyq1229872143.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/56pso1229872143.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
2.1839947 -0.2165058 -0.3549128 -1.6253435 -3.2425203 -5.7221913
7 8 9 10 11 12
-5.3550485 -8.6418510 -6.3480014 -1.4964711 1.3969641 8.7121393
13 14 15 16 17 18
3.1521599 -2.3442870 0.1761776 0.5156391 2.4679065 0.1776346
19 20 21 22 23 24
-4.2557608 -2.6327564 -0.7686970 4.6000384 7.4716199 8.7697320
25 26 27 28 29 30
5.4461187 3.3151477 6.7753232 4.7108732 2.1904085 4.2781692
31 32 33 34 35 36
2.2778235 -0.1712523 4.0238727 5.9178635 1.8472402 2.3294794
37 38 39 40 41 42
1.7667219 0.4880467 -5.5144472 -3.4776786 -3.9730300 -3.0580868
43 44 45 46 47 48
-4.8690334 -5.1910118 -16.7193295 -17.0835585 -10.2710193 -2.8611445
49 50 51 52 53 54
3.1832120 -1.2424016 -1.0821409 -0.1234902 2.5572353 4.3244744
55 56 57 58 59 60
12.2020192 16.6368715 19.8121553 8.0621277 -0.4448048 -16.9502062
61
-15.7322071
> postscript(file="/var/www/html/freestat/rcomp/tmp/6w5t81229872143.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 2.1839947 NA
1 -0.2165058 2.1839947
2 -0.3549128 -0.2165058
3 -1.6253435 -0.3549128
4 -3.2425203 -1.6253435
5 -5.7221913 -3.2425203
6 -5.3550485 -5.7221913
7 -8.6418510 -5.3550485
8 -6.3480014 -8.6418510
9 -1.4964711 -6.3480014
10 1.3969641 -1.4964711
11 8.7121393 1.3969641
12 3.1521599 8.7121393
13 -2.3442870 3.1521599
14 0.1761776 -2.3442870
15 0.5156391 0.1761776
16 2.4679065 0.5156391
17 0.1776346 2.4679065
18 -4.2557608 0.1776346
19 -2.6327564 -4.2557608
20 -0.7686970 -2.6327564
21 4.6000384 -0.7686970
22 7.4716199 4.6000384
23 8.7697320 7.4716199
24 5.4461187 8.7697320
25 3.3151477 5.4461187
26 6.7753232 3.3151477
27 4.7108732 6.7753232
28 2.1904085 4.7108732
29 4.2781692 2.1904085
30 2.2778235 4.2781692
31 -0.1712523 2.2778235
32 4.0238727 -0.1712523
33 5.9178635 4.0238727
34 1.8472402 5.9178635
35 2.3294794 1.8472402
36 1.7667219 2.3294794
37 0.4880467 1.7667219
38 -5.5144472 0.4880467
39 -3.4776786 -5.5144472
40 -3.9730300 -3.4776786
41 -3.0580868 -3.9730300
42 -4.8690334 -3.0580868
43 -5.1910118 -4.8690334
44 -16.7193295 -5.1910118
45 -17.0835585 -16.7193295
46 -10.2710193 -17.0835585
47 -2.8611445 -10.2710193
48 3.1832120 -2.8611445
49 -1.2424016 3.1832120
50 -1.0821409 -1.2424016
51 -0.1234902 -1.0821409
52 2.5572353 -0.1234902
53 4.3244744 2.5572353
54 12.2020192 4.3244744
55 16.6368715 12.2020192
56 19.8121553 16.6368715
57 8.0621277 19.8121553
58 -0.4448048 8.0621277
59 -16.9502062 -0.4448048
60 -15.7322071 -16.9502062
61 NA -15.7322071
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.2165058 2.1839947
[2,] -0.3549128 -0.2165058
[3,] -1.6253435 -0.3549128
[4,] -3.2425203 -1.6253435
[5,] -5.7221913 -3.2425203
[6,] -5.3550485 -5.7221913
[7,] -8.6418510 -5.3550485
[8,] -6.3480014 -8.6418510
[9,] -1.4964711 -6.3480014
[10,] 1.3969641 -1.4964711
[11,] 8.7121393 1.3969641
[12,] 3.1521599 8.7121393
[13,] -2.3442870 3.1521599
[14,] 0.1761776 -2.3442870
[15,] 0.5156391 0.1761776
[16,] 2.4679065 0.5156391
[17,] 0.1776346 2.4679065
[18,] -4.2557608 0.1776346
[19,] -2.6327564 -4.2557608
[20,] -0.7686970 -2.6327564
[21,] 4.6000384 -0.7686970
[22,] 7.4716199 4.6000384
[23,] 8.7697320 7.4716199
[24,] 5.4461187 8.7697320
[25,] 3.3151477 5.4461187
[26,] 6.7753232 3.3151477
[27,] 4.7108732 6.7753232
[28,] 2.1904085 4.7108732
[29,] 4.2781692 2.1904085
[30,] 2.2778235 4.2781692
[31,] -0.1712523 2.2778235
[32,] 4.0238727 -0.1712523
[33,] 5.9178635 4.0238727
[34,] 1.8472402 5.9178635
[35,] 2.3294794 1.8472402
[36,] 1.7667219 2.3294794
[37,] 0.4880467 1.7667219
[38,] -5.5144472 0.4880467
[39,] -3.4776786 -5.5144472
[40,] -3.9730300 -3.4776786
[41,] -3.0580868 -3.9730300
[42,] -4.8690334 -3.0580868
[43,] -5.1910118 -4.8690334
[44,] -16.7193295 -5.1910118
[45,] -17.0835585 -16.7193295
[46,] -10.2710193 -17.0835585
[47,] -2.8611445 -10.2710193
[48,] 3.1832120 -2.8611445
[49,] -1.2424016 3.1832120
[50,] -1.0821409 -1.2424016
[51,] -0.1234902 -1.0821409
[52,] 2.5572353 -0.1234902
[53,] 4.3244744 2.5572353
[54,] 12.2020192 4.3244744
[55,] 16.6368715 12.2020192
[56,] 19.8121553 16.6368715
[57,] 8.0621277 19.8121553
[58,] -0.4448048 8.0621277
[59,] -16.9502062 -0.4448048
[60,] -15.7322071 -16.9502062
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.2165058 2.1839947
2 -0.3549128 -0.2165058
3 -1.6253435 -0.3549128
4 -3.2425203 -1.6253435
5 -5.7221913 -3.2425203
6 -5.3550485 -5.7221913
7 -8.6418510 -5.3550485
8 -6.3480014 -8.6418510
9 -1.4964711 -6.3480014
10 1.3969641 -1.4964711
11 8.7121393 1.3969641
12 3.1521599 8.7121393
13 -2.3442870 3.1521599
14 0.1761776 -2.3442870
15 0.5156391 0.1761776
16 2.4679065 0.5156391
17 0.1776346 2.4679065
18 -4.2557608 0.1776346
19 -2.6327564 -4.2557608
20 -0.7686970 -2.6327564
21 4.6000384 -0.7686970
22 7.4716199 4.6000384
23 8.7697320 7.4716199
24 5.4461187 8.7697320
25 3.3151477 5.4461187
26 6.7753232 3.3151477
27 4.7108732 6.7753232
28 2.1904085 4.7108732
29 4.2781692 2.1904085
30 2.2778235 4.2781692
31 -0.1712523 2.2778235
32 4.0238727 -0.1712523
33 5.9178635 4.0238727
34 1.8472402 5.9178635
35 2.3294794 1.8472402
36 1.7667219 2.3294794
37 0.4880467 1.7667219
38 -5.5144472 0.4880467
39 -3.4776786 -5.5144472
40 -3.9730300 -3.4776786
41 -3.0580868 -3.9730300
42 -4.8690334 -3.0580868
43 -5.1910118 -4.8690334
44 -16.7193295 -5.1910118
45 -17.0835585 -16.7193295
46 -10.2710193 -17.0835585
47 -2.8611445 -10.2710193
48 3.1832120 -2.8611445
49 -1.2424016 3.1832120
50 -1.0821409 -1.2424016
51 -0.1234902 -1.0821409
52 2.5572353 -0.1234902
53 4.3244744 2.5572353
54 12.2020192 4.3244744
55 16.6368715 12.2020192
56 19.8121553 16.6368715
57 8.0621277 19.8121553
58 -0.4448048 8.0621277
59 -16.9502062 -0.4448048
60 -15.7322071 -16.9502062
> 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/freestat/rcomp/tmp/7w6hx1229872143.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8uh941229872143.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/97h2e1229872143.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/10gxdn1229872143.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/freestat/rcomp/tmp/11jhra1229872143.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/freestat/rcomp/tmp/12m3e51229872143.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/freestat/rcomp/tmp/136xx21229872143.tab")
>
> system("convert tmp/13lpt1229872143.ps tmp/13lpt1229872143.png")
> system("convert tmp/2rxeh1229872143.ps tmp/2rxeh1229872143.png")
> system("convert tmp/3o1gd1229872143.ps tmp/3o1gd1229872143.png")
> system("convert tmp/4ovyq1229872143.ps tmp/4ovyq1229872143.png")
> system("convert tmp/56pso1229872143.ps tmp/56pso1229872143.png")
> system("convert tmp/6w5t81229872143.ps tmp/6w5t81229872143.png")
> system("convert tmp/7w6hx1229872143.ps tmp/7w6hx1229872143.png")
> system("convert tmp/8uh941229872143.ps tmp/8uh941229872143.png")
> system("convert tmp/97h2e1229872143.ps tmp/97h2e1229872143.png")
>
>
> proc.time()
user system elapsed
2.943 2.187 3.384