R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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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
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> x <- array(list(3.926,0,3.517,0,4.142,0,4.353,0,5.029,0,4.755,0,3.862,0,4.406,0,4.567,0,4.863,0,4.121,0,3.626,0,3.804,0,3.491,0,4.151,0,4.254,0,4.717,0,4.866,0,4.001,0,3.758,0,4.78,0,5.016,0,4.296,0,4.467,0,3.891,1,3.872,1,3.867,1,3.973,1,4.64,1,4.538,1,3.836,1,3.77,1,4.374,1,4.497,1,3.945,1,3.862,1,3.608,1,3.301,1,3.882,1,3.605,1,4.305,1,4.216,1,3.971,1,3.988,1,4.317,1,4.484,1,4.247,1,3.52,1,3.687,1,3.405,1,3.99,1,4.047,1,4.549,1,4.559,1,3.926,1,4.206,1,4.517,1,4.387,1,3.219,1,3.129,1),dim=c(2,60),dimnames=list(c('Ongevallen','Superboete'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Ongevallen','Superboete'),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 = '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
Ongevallen Superboete M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3.926 0 1 0 0 0 0 0 0 0 0 0 0
2 3.517 0 0 1 0 0 0 0 0 0 0 0 0
3 4.142 0 0 0 1 0 0 0 0 0 0 0 0
4 4.353 0 0 0 0 1 0 0 0 0 0 0 0
5 5.029 0 0 0 0 0 1 0 0 0 0 0 0
6 4.755 0 0 0 0 0 0 1 0 0 0 0 0
7 3.862 0 0 0 0 0 0 0 1 0 0 0 0
8 4.406 0 0 0 0 0 0 0 0 1 0 0 0
9 4.567 0 0 0 0 0 0 0 0 0 1 0 0
10 4.863 0 0 0 0 0 0 0 0 0 0 1 0
11 4.121 0 0 0 0 0 0 0 0 0 0 0 1
12 3.626 0 0 0 0 0 0 0 0 0 0 0 0
13 3.804 0 1 0 0 0 0 0 0 0 0 0 0
14 3.491 0 0 1 0 0 0 0 0 0 0 0 0
15 4.151 0 0 0 1 0 0 0 0 0 0 0 0
16 4.254 0 0 0 0 1 0 0 0 0 0 0 0
17 4.717 0 0 0 0 0 1 0 0 0 0 0 0
18 4.866 0 0 0 0 0 0 1 0 0 0 0 0
19 4.001 0 0 0 0 0 0 0 1 0 0 0 0
20 3.758 0 0 0 0 0 0 0 0 1 0 0 0
21 4.780 0 0 0 0 0 0 0 0 0 1 0 0
22 5.016 0 0 0 0 0 0 0 0 0 0 1 0
23 4.296 0 0 0 0 0 0 0 0 0 0 0 1
24 4.467 0 0 0 0 0 0 0 0 0 0 0 0
25 3.891 1 1 0 0 0 0 0 0 0 0 0 0
26 3.872 1 0 1 0 0 0 0 0 0 0 0 0
27 3.867 1 0 0 1 0 0 0 0 0 0 0 0
28 3.973 1 0 0 0 1 0 0 0 0 0 0 0
29 4.640 1 0 0 0 0 1 0 0 0 0 0 0
30 4.538 1 0 0 0 0 0 1 0 0 0 0 0
31 3.836 1 0 0 0 0 0 0 1 0 0 0 0
32 3.770 1 0 0 0 0 0 0 0 1 0 0 0
33 4.374 1 0 0 0 0 0 0 0 0 1 0 0
34 4.497 1 0 0 0 0 0 0 0 0 0 1 0
35 3.945 1 0 0 0 0 0 0 0 0 0 0 1
36 3.862 1 0 0 0 0 0 0 0 0 0 0 0
37 3.608 1 1 0 0 0 0 0 0 0 0 0 0
38 3.301 1 0 1 0 0 0 0 0 0 0 0 0
39 3.882 1 0 0 1 0 0 0 0 0 0 0 0
40 3.605 1 0 0 0 1 0 0 0 0 0 0 0
41 4.305 1 0 0 0 0 1 0 0 0 0 0 0
42 4.216 1 0 0 0 0 0 1 0 0 0 0 0
43 3.971 1 0 0 0 0 0 0 1 0 0 0 0
44 3.988 1 0 0 0 0 0 0 0 1 0 0 0
45 4.317 1 0 0 0 0 0 0 0 0 1 0 0
46 4.484 1 0 0 0 0 0 0 0 0 0 1 0
47 4.247 1 0 0 0 0 0 0 0 0 0 0 1
48 3.520 1 0 0 0 0 0 0 0 0 0 0 0
49 3.687 1 1 0 0 0 0 0 0 0 0 0 0
50 3.405 1 0 1 0 0 0 0 0 0 0 0 0
51 3.990 1 0 0 1 0 0 0 0 0 0 0 0
52 4.047 1 0 0 0 1 0 0 0 0 0 0 0
53 4.549 1 0 0 0 0 1 0 0 0 0 0 0
54 4.559 1 0 0 0 0 0 1 0 0 0 0 0
55 3.926 1 0 0 0 0 0 0 1 0 0 0 0
56 4.206 1 0 0 0 0 0 0 0 1 0 0 0
57 4.517 1 0 0 0 0 0 0 0 0 1 0 0
58 4.387 1 0 0 0 0 0 0 0 0 0 1 0
59 3.219 1 0 0 0 0 0 0 0 0 0 0 1
60 3.129 1 0 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) Superboete M1 M2 M3 M4
3.8878 -0.2784 0.0624 -0.2036 0.2856 0.3256
M5 M6 M7 M8 M9 M10
0.9272 0.8660 0.1984 0.3048 0.7902 0.9286
M11
0.2448
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6352444 -0.0997361 0.0001611 0.1120083 0.5791667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.88783 0.11139 34.904 < 2e-16 ***
Superboete -0.27839 0.06188 -4.499 4.48e-05 ***
M1 0.06240 0.14851 0.420 0.6763
M2 -0.20360 0.14851 -1.371 0.1769
M3 0.28560 0.14851 1.923 0.0605 .
M4 0.32560 0.14851 2.192 0.0333 *
M5 0.92720 0.14851 6.243 1.15e-07 ***
M6 0.86600 0.14851 5.831 4.84e-07 ***
M7 0.19840 0.14851 1.336 0.1880
M8 0.30480 0.14851 2.052 0.0457 *
M9 0.79020 0.14851 5.321 2.82e-06 ***
M10 0.92860 0.14851 6.253 1.11e-07 ***
M11 0.24480 0.14851 1.648 0.1060
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2348 on 47 degrees of freedom
Multiple R-Squared: 0.7839, Adjusted R-squared: 0.7287
F-statistic: 14.21 on 12 and 47 DF, p-value: 7.839e-12
> postscript(file="/var/www/html/rcomp/tmp/1m4mt1195650795.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/26kyq1195650795.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3w0hm1195650795.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4av4e1195650795.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5i8tt1195650795.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5
-0.0242333333 -0.1672333333 -0.0314333333 0.1395666667 0.2139666667
6 7 8 9 10
0.0011666667 -0.2242333333 0.2133666667 -0.1110333333 0.0465666667
11 12 13 14 15
-0.0116333333 -0.2618333333 -0.1462333333 -0.1932333333 -0.0224333333
16 17 18 19 20
0.0405666667 -0.0980333333 0.1121666667 -0.0852333333 -0.4346333333
21 22 23 24 25
0.1019666667 0.1995666667 0.1633666667 0.5791666667 0.2191555556
26 27 28 29 30
0.4661555556 -0.0280444444 0.0379555556 0.1033555556 0.0625555556
31 32 33 34 35
0.0281555556 -0.1442444444 -0.0256444444 -0.0410444444 0.0907555556
36 37 38 39 40
0.2525555556 -0.0638444444 -0.1048444444 -0.0130444444 -0.3300444444
41 42 43 44 45
-0.2316444444 -0.2594444444 0.1631555556 0.0737555556 -0.0826444444
46 47 48 49 50
-0.0540444444 0.3927555556 -0.0894444444 0.0151555556 -0.0008444444
51 52 53 54 55
0.0949555556 0.1119555556 0.0123555556 0.0835555556 0.1181555556
56 57 58 59 60
0.2917555556 0.1173555556 -0.1510444444 -0.6352444444 -0.4804444444
> postscript(file="/var/www/html/rcomp/tmp/65bd41195650795.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 -0.0242333333 NA
1 -0.1672333333 -0.0242333333
2 -0.0314333333 -0.1672333333
3 0.1395666667 -0.0314333333
4 0.2139666667 0.1395666667
5 0.0011666667 0.2139666667
6 -0.2242333333 0.0011666667
7 0.2133666667 -0.2242333333
8 -0.1110333333 0.2133666667
9 0.0465666667 -0.1110333333
10 -0.0116333333 0.0465666667
11 -0.2618333333 -0.0116333333
12 -0.1462333333 -0.2618333333
13 -0.1932333333 -0.1462333333
14 -0.0224333333 -0.1932333333
15 0.0405666667 -0.0224333333
16 -0.0980333333 0.0405666667
17 0.1121666667 -0.0980333333
18 -0.0852333333 0.1121666667
19 -0.4346333333 -0.0852333333
20 0.1019666667 -0.4346333333
21 0.1995666667 0.1019666667
22 0.1633666667 0.1995666667
23 0.5791666667 0.1633666667
24 0.2191555556 0.5791666667
25 0.4661555556 0.2191555556
26 -0.0280444444 0.4661555556
27 0.0379555556 -0.0280444444
28 0.1033555556 0.0379555556
29 0.0625555556 0.1033555556
30 0.0281555556 0.0625555556
31 -0.1442444444 0.0281555556
32 -0.0256444444 -0.1442444444
33 -0.0410444444 -0.0256444444
34 0.0907555556 -0.0410444444
35 0.2525555556 0.0907555556
36 -0.0638444444 0.2525555556
37 -0.1048444444 -0.0638444444
38 -0.0130444444 -0.1048444444
39 -0.3300444444 -0.0130444444
40 -0.2316444444 -0.3300444444
41 -0.2594444444 -0.2316444444
42 0.1631555556 -0.2594444444
43 0.0737555556 0.1631555556
44 -0.0826444444 0.0737555556
45 -0.0540444444 -0.0826444444
46 0.3927555556 -0.0540444444
47 -0.0894444444 0.3927555556
48 0.0151555556 -0.0894444444
49 -0.0008444444 0.0151555556
50 0.0949555556 -0.0008444444
51 0.1119555556 0.0949555556
52 0.0123555556 0.1119555556
53 0.0835555556 0.0123555556
54 0.1181555556 0.0835555556
55 0.2917555556 0.1181555556
56 0.1173555556 0.2917555556
57 -0.1510444444 0.1173555556
58 -0.6352444444 -0.1510444444
59 -0.4804444444 -0.6352444444
60 NA -0.4804444444
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1672333333 -0.0242333333
[2,] -0.0314333333 -0.1672333333
[3,] 0.1395666667 -0.0314333333
[4,] 0.2139666667 0.1395666667
[5,] 0.0011666667 0.2139666667
[6,] -0.2242333333 0.0011666667
[7,] 0.2133666667 -0.2242333333
[8,] -0.1110333333 0.2133666667
[9,] 0.0465666667 -0.1110333333
[10,] -0.0116333333 0.0465666667
[11,] -0.2618333333 -0.0116333333
[12,] -0.1462333333 -0.2618333333
[13,] -0.1932333333 -0.1462333333
[14,] -0.0224333333 -0.1932333333
[15,] 0.0405666667 -0.0224333333
[16,] -0.0980333333 0.0405666667
[17,] 0.1121666667 -0.0980333333
[18,] -0.0852333333 0.1121666667
[19,] -0.4346333333 -0.0852333333
[20,] 0.1019666667 -0.4346333333
[21,] 0.1995666667 0.1019666667
[22,] 0.1633666667 0.1995666667
[23,] 0.5791666667 0.1633666667
[24,] 0.2191555556 0.5791666667
[25,] 0.4661555556 0.2191555556
[26,] -0.0280444444 0.4661555556
[27,] 0.0379555556 -0.0280444444
[28,] 0.1033555556 0.0379555556
[29,] 0.0625555556 0.1033555556
[30,] 0.0281555556 0.0625555556
[31,] -0.1442444444 0.0281555556
[32,] -0.0256444444 -0.1442444444
[33,] -0.0410444444 -0.0256444444
[34,] 0.0907555556 -0.0410444444
[35,] 0.2525555556 0.0907555556
[36,] -0.0638444444 0.2525555556
[37,] -0.1048444444 -0.0638444444
[38,] -0.0130444444 -0.1048444444
[39,] -0.3300444444 -0.0130444444
[40,] -0.2316444444 -0.3300444444
[41,] -0.2594444444 -0.2316444444
[42,] 0.1631555556 -0.2594444444
[43,] 0.0737555556 0.1631555556
[44,] -0.0826444444 0.0737555556
[45,] -0.0540444444 -0.0826444444
[46,] 0.3927555556 -0.0540444444
[47,] -0.0894444444 0.3927555556
[48,] 0.0151555556 -0.0894444444
[49,] -0.0008444444 0.0151555556
[50,] 0.0949555556 -0.0008444444
[51,] 0.1119555556 0.0949555556
[52,] 0.0123555556 0.1119555556
[53,] 0.0835555556 0.0123555556
[54,] 0.1181555556 0.0835555556
[55,] 0.2917555556 0.1181555556
[56,] 0.1173555556 0.2917555556
[57,] -0.1510444444 0.1173555556
[58,] -0.6352444444 -0.1510444444
[59,] -0.4804444444 -0.6352444444
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1672333333 -0.0242333333
2 -0.0314333333 -0.1672333333
3 0.1395666667 -0.0314333333
4 0.2139666667 0.1395666667
5 0.0011666667 0.2139666667
6 -0.2242333333 0.0011666667
7 0.2133666667 -0.2242333333
8 -0.1110333333 0.2133666667
9 0.0465666667 -0.1110333333
10 -0.0116333333 0.0465666667
11 -0.2618333333 -0.0116333333
12 -0.1462333333 -0.2618333333
13 -0.1932333333 -0.1462333333
14 -0.0224333333 -0.1932333333
15 0.0405666667 -0.0224333333
16 -0.0980333333 0.0405666667
17 0.1121666667 -0.0980333333
18 -0.0852333333 0.1121666667
19 -0.4346333333 -0.0852333333
20 0.1019666667 -0.4346333333
21 0.1995666667 0.1019666667
22 0.1633666667 0.1995666667
23 0.5791666667 0.1633666667
24 0.2191555556 0.5791666667
25 0.4661555556 0.2191555556
26 -0.0280444444 0.4661555556
27 0.0379555556 -0.0280444444
28 0.1033555556 0.0379555556
29 0.0625555556 0.1033555556
30 0.0281555556 0.0625555556
31 -0.1442444444 0.0281555556
32 -0.0256444444 -0.1442444444
33 -0.0410444444 -0.0256444444
34 0.0907555556 -0.0410444444
35 0.2525555556 0.0907555556
36 -0.0638444444 0.2525555556
37 -0.1048444444 -0.0638444444
38 -0.0130444444 -0.1048444444
39 -0.3300444444 -0.0130444444
40 -0.2316444444 -0.3300444444
41 -0.2594444444 -0.2316444444
42 0.1631555556 -0.2594444444
43 0.0737555556 0.1631555556
44 -0.0826444444 0.0737555556
45 -0.0540444444 -0.0826444444
46 0.3927555556 -0.0540444444
47 -0.0894444444 0.3927555556
48 0.0151555556 -0.0894444444
49 -0.0008444444 0.0151555556
50 0.0949555556 -0.0008444444
51 0.1119555556 0.0949555556
52 0.0123555556 0.1119555556
53 0.0835555556 0.0123555556
54 0.1181555556 0.0835555556
55 0.2917555556 0.1181555556
56 0.1173555556 0.2917555556
57 -0.1510444444 0.1173555556
58 -0.6352444444 -0.1510444444
59 -0.4804444444 -0.6352444444
> 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/7bh741195650796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jxzq1195650796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fop51195650796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10f7mo1195650796.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/11or2l1195650796.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/12i1881195650797.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/13r20s1195650797.tab")
>
> system("convert tmp/1m4mt1195650795.ps tmp/1m4mt1195650795.png")
> system("convert tmp/26kyq1195650795.ps tmp/26kyq1195650795.png")
> system("convert tmp/3w0hm1195650795.ps tmp/3w0hm1195650795.png")
> system("convert tmp/4av4e1195650795.ps tmp/4av4e1195650795.png")
> system("convert tmp/5i8tt1195650795.ps tmp/5i8tt1195650795.png")
> system("convert tmp/65bd41195650795.ps tmp/65bd41195650795.png")
> system("convert tmp/7bh741195650796.ps tmp/7bh741195650796.png")
> system("convert tmp/8jxzq1195650796.ps tmp/8jxzq1195650796.png")
> system("convert tmp/9fop51195650796.ps tmp/9fop51195650796.png")
>
>
> proc.time()
user system elapsed
2.328 1.496 2.904