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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
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(0,115.4,0,106.9,0,107.1,0,99.3,0,99.2,0,108.3,0,105.6,0,99.5,0,107.4,0,93.1,0,88.1,0,110.7,0,113.1,0,99.6,0,93.6,0,98.6,0,99.6,0,114.3,1,107.8,1,101.2,1,112.5,1,100.5,1,93.9,1,116.2,1,112,1,106.4,1,95.7,1,96,1,95.8,1,103,1,102.2,1,98.4,1,111.4,1,86.6,1,91.3,1,107.9,1,101.8,1,104.4,1,93.4,1,100.1,1,98.5,1,112.9,1,101.4,1,107.1,1,110.8,1,90.3,1,95.5,1,111.4,1,113,1,107.5,1,95.9,1,106.3,1,105.2,1,117.2,1,106.9,1,108.2,1,110,1,96.1,1,100.6),dim=c(2,59),dimnames=list(c('A','B
'),1:59))
> y <- array(NA,dim=c(2,59),dimnames=list(c('A','B
'),1:59))
> 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 = '2'
> #'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
B\r A M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 115.4 0 1 0 0 0 0 0 0 0 0 0 0 1
2 106.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 107.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 99.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 99.2 0 0 0 0 0 1 0 0 0 0 0 0 5
6 108.3 0 0 0 0 0 0 1 0 0 0 0 0 6
7 105.6 0 0 0 0 0 0 0 1 0 0 0 0 7
8 99.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 107.4 0 0 0 0 0 0 0 0 0 1 0 0 9
10 93.1 0 0 0 0 0 0 0 0 0 0 1 0 10
11 88.1 0 0 0 0 0 0 0 0 0 0 0 1 11
12 110.7 0 0 0 0 0 0 0 0 0 0 0 0 12
13 113.1 0 1 0 0 0 0 0 0 0 0 0 0 13
14 99.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 93.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 98.6 0 0 0 0 1 0 0 0 0 0 0 0 16
17 99.6 0 0 0 0 0 1 0 0 0 0 0 0 17
18 114.3 0 0 0 0 0 0 1 0 0 0 0 0 18
19 107.8 1 0 0 0 0 0 0 1 0 0 0 0 19
20 101.2 1 0 0 0 0 0 0 0 1 0 0 0 20
21 112.5 1 0 0 0 0 0 0 0 0 1 0 0 21
22 100.5 1 0 0 0 0 0 0 0 0 0 1 0 22
23 93.9 1 0 0 0 0 0 0 0 0 0 0 1 23
24 116.2 1 0 0 0 0 0 0 0 0 0 0 0 24
25 112.0 1 1 0 0 0 0 0 0 0 0 0 0 25
26 106.4 1 0 1 0 0 0 0 0 0 0 0 0 26
27 95.7 1 0 0 1 0 0 0 0 0 0 0 0 27
28 96.0 1 0 0 0 1 0 0 0 0 0 0 0 28
29 95.8 1 0 0 0 0 1 0 0 0 0 0 0 29
30 103.0 1 0 0 0 0 0 1 0 0 0 0 0 30
31 102.2 1 0 0 0 0 0 0 1 0 0 0 0 31
32 98.4 1 0 0 0 0 0 0 0 1 0 0 0 32
33 111.4 1 0 0 0 0 0 0 0 0 1 0 0 33
34 86.6 1 0 0 0 0 0 0 0 0 0 1 0 34
35 91.3 1 0 0 0 0 0 0 0 0 0 0 1 35
36 107.9 1 0 0 0 0 0 0 0 0 0 0 0 36
37 101.8 1 1 0 0 0 0 0 0 0 0 0 0 37
38 104.4 1 0 1 0 0 0 0 0 0 0 0 0 38
39 93.4 1 0 0 1 0 0 0 0 0 0 0 0 39
40 100.1 1 0 0 0 1 0 0 0 0 0 0 0 40
41 98.5 1 0 0 0 0 1 0 0 0 0 0 0 41
42 112.9 1 0 0 0 0 0 1 0 0 0 0 0 42
43 101.4 1 0 0 0 0 0 0 1 0 0 0 0 43
44 107.1 1 0 0 0 0 0 0 0 1 0 0 0 44
45 110.8 1 0 0 0 0 0 0 0 0 1 0 0 45
46 90.3 1 0 0 0 0 0 0 0 0 0 1 0 46
47 95.5 1 0 0 0 0 0 0 0 0 0 0 1 47
48 111.4 1 0 0 0 0 0 0 0 0 0 0 0 48
49 113.0 1 1 0 0 0 0 0 0 0 0 0 0 49
50 107.5 1 0 1 0 0 0 0 0 0 0 0 0 50
51 95.9 1 0 0 1 0 0 0 0 0 0 0 0 51
52 106.3 1 0 0 0 1 0 0 0 0 0 0 0 52
53 105.2 1 0 0 0 0 1 0 0 0 0 0 0 53
54 117.2 1 0 0 0 0 0 1 0 0 0 0 0 54
55 106.9 1 0 0 0 0 0 0 1 0 0 0 0 55
56 108.2 1 0 0 0 0 0 0 0 1 0 0 0 56
57 110.0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 96.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 100.6 1 0 0 0 0 0 0 0 0 0 0 1 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) A M1 M2 M3 M4
110.60858 -1.81895 -0.37857 -6.55542 -14.45228 -11.60913
M5 M6 M7 M8 M9 M10
-12.08599 -0.68284 -6.75591 -8.73276 -1.26962 -18.44647
M11 t
-17.96332 0.07685
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.4547 -2.4938 -0.1547 2.0192 10.7131
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 110.60858 2.39372 46.208 < 2e-16 ***
A -1.81895 2.06351 -0.881 0.382741
M1 -0.37857 2.90409 -0.130 0.896864
M2 -6.55542 2.90403 -2.257 0.028886 *
M3 -14.45228 2.90503 -4.975 1.00e-05 ***
M4 -11.60913 2.90708 -3.993 0.000238 ***
M5 -12.08599 2.91019 -4.153 0.000144 ***
M6 -0.68284 2.91435 -0.234 0.815812
M7 -6.75591 2.89867 -2.331 0.024310 *
M8 -8.73276 2.89869 -3.013 0.004240 **
M9 -1.26962 2.89977 -0.438 0.663601
M10 -18.44647 2.90191 -6.357 9.21e-08 ***
M11 -17.96332 2.90510 -6.183 1.67e-07 ***
t 0.07685 0.05543 1.386 0.172437
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.32 on 45 degrees of freedom
Multiple R-Squared: 0.7446, Adjusted R-squared: 0.6708
F-statistic: 10.09 on 13 and 45 DF, p-value: 1.948e-09
> postscript(file="/var/www/html/rcomp/tmp/1yl3w1195401892.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/20xxq1195401892.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/38eqa1195401892.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rgxe1195401892.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dg3e1195401892.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 59
Frequency = 1
1 2 3 4 5 6
5.09313492 2.69313492 10.71313492 -0.00686508 0.29313492 -2.08686508
7 8 9 10 11 12
1.20934524 -2.99065476 -2.63065476 0.16934524 -5.39065476 -0.83083333
13 14 15 16 17 18
1.87088294 -5.52911706 -3.70911706 -1.62911706 -0.22911706 2.99088294
19 20 21 22 23 24
4.30604167 -0.39395833 3.36604167 8.46604167 1.30604167 5.56586310
25 26 27 28 29 30
1.66757937 2.16757937 -0.71242063 -3.33242063 -3.13242063 -7.41242063
31 32 33 34 35 36
-2.21621032 -4.11621032 1.34378968 -6.35621032 -2.21621032 -3.65638889
37 38 39 40 41 42
-9.45467262 -0.75467262 -3.93467262 -0.15467262 -1.35467262 1.56532738
43 44 45 46 47 48
-3.93846230 3.66153770 -0.17846230 -3.57846230 1.06153770 -1.07864087
49 50 51 52 53 54
0.82307540 1.42307540 -2.35692460 5.12307540 4.42307540 4.94307540
55 56 57 58 59
0.63928571 3.83928571 -1.90071429 1.29928571 5.23928571
> postscript(file="/var/www/html/rcomp/tmp/6s7jn1195401892.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 5.09313492 NA
1 2.69313492 5.09313492
2 10.71313492 2.69313492
3 -0.00686508 10.71313492
4 0.29313492 -0.00686508
5 -2.08686508 0.29313492
6 1.20934524 -2.08686508
7 -2.99065476 1.20934524
8 -2.63065476 -2.99065476
9 0.16934524 -2.63065476
10 -5.39065476 0.16934524
11 -0.83083333 -5.39065476
12 1.87088294 -0.83083333
13 -5.52911706 1.87088294
14 -3.70911706 -5.52911706
15 -1.62911706 -3.70911706
16 -0.22911706 -1.62911706
17 2.99088294 -0.22911706
18 4.30604167 2.99088294
19 -0.39395833 4.30604167
20 3.36604167 -0.39395833
21 8.46604167 3.36604167
22 1.30604167 8.46604167
23 5.56586310 1.30604167
24 1.66757937 5.56586310
25 2.16757937 1.66757937
26 -0.71242063 2.16757937
27 -3.33242063 -0.71242063
28 -3.13242063 -3.33242063
29 -7.41242063 -3.13242063
30 -2.21621032 -7.41242063
31 -4.11621032 -2.21621032
32 1.34378968 -4.11621032
33 -6.35621032 1.34378968
34 -2.21621032 -6.35621032
35 -3.65638889 -2.21621032
36 -9.45467262 -3.65638889
37 -0.75467262 -9.45467262
38 -3.93467262 -0.75467262
39 -0.15467262 -3.93467262
40 -1.35467262 -0.15467262
41 1.56532738 -1.35467262
42 -3.93846230 1.56532738
43 3.66153770 -3.93846230
44 -0.17846230 3.66153770
45 -3.57846230 -0.17846230
46 1.06153770 -3.57846230
47 -1.07864087 1.06153770
48 0.82307540 -1.07864087
49 1.42307540 0.82307540
50 -2.35692460 1.42307540
51 5.12307540 -2.35692460
52 4.42307540 5.12307540
53 4.94307540 4.42307540
54 0.63928571 4.94307540
55 3.83928571 0.63928571
56 -1.90071429 3.83928571
57 1.29928571 -1.90071429
58 5.23928571 1.29928571
59 NA 5.23928571
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.69313492 5.09313492
[2,] 10.71313492 2.69313492
[3,] -0.00686508 10.71313492
[4,] 0.29313492 -0.00686508
[5,] -2.08686508 0.29313492
[6,] 1.20934524 -2.08686508
[7,] -2.99065476 1.20934524
[8,] -2.63065476 -2.99065476
[9,] 0.16934524 -2.63065476
[10,] -5.39065476 0.16934524
[11,] -0.83083333 -5.39065476
[12,] 1.87088294 -0.83083333
[13,] -5.52911706 1.87088294
[14,] -3.70911706 -5.52911706
[15,] -1.62911706 -3.70911706
[16,] -0.22911706 -1.62911706
[17,] 2.99088294 -0.22911706
[18,] 4.30604167 2.99088294
[19,] -0.39395833 4.30604167
[20,] 3.36604167 -0.39395833
[21,] 8.46604167 3.36604167
[22,] 1.30604167 8.46604167
[23,] 5.56586310 1.30604167
[24,] 1.66757937 5.56586310
[25,] 2.16757937 1.66757937
[26,] -0.71242063 2.16757937
[27,] -3.33242063 -0.71242063
[28,] -3.13242063 -3.33242063
[29,] -7.41242063 -3.13242063
[30,] -2.21621032 -7.41242063
[31,] -4.11621032 -2.21621032
[32,] 1.34378968 -4.11621032
[33,] -6.35621032 1.34378968
[34,] -2.21621032 -6.35621032
[35,] -3.65638889 -2.21621032
[36,] -9.45467262 -3.65638889
[37,] -0.75467262 -9.45467262
[38,] -3.93467262 -0.75467262
[39,] -0.15467262 -3.93467262
[40,] -1.35467262 -0.15467262
[41,] 1.56532738 -1.35467262
[42,] -3.93846230 1.56532738
[43,] 3.66153770 -3.93846230
[44,] -0.17846230 3.66153770
[45,] -3.57846230 -0.17846230
[46,] 1.06153770 -3.57846230
[47,] -1.07864087 1.06153770
[48,] 0.82307540 -1.07864087
[49,] 1.42307540 0.82307540
[50,] -2.35692460 1.42307540
[51,] 5.12307540 -2.35692460
[52,] 4.42307540 5.12307540
[53,] 4.94307540 4.42307540
[54,] 0.63928571 4.94307540
[55,] 3.83928571 0.63928571
[56,] -1.90071429 3.83928571
[57,] 1.29928571 -1.90071429
[58,] 5.23928571 1.29928571
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.69313492 5.09313492
2 10.71313492 2.69313492
3 -0.00686508 10.71313492
4 0.29313492 -0.00686508
5 -2.08686508 0.29313492
6 1.20934524 -2.08686508
7 -2.99065476 1.20934524
8 -2.63065476 -2.99065476
9 0.16934524 -2.63065476
10 -5.39065476 0.16934524
11 -0.83083333 -5.39065476
12 1.87088294 -0.83083333
13 -5.52911706 1.87088294
14 -3.70911706 -5.52911706
15 -1.62911706 -3.70911706
16 -0.22911706 -1.62911706
17 2.99088294 -0.22911706
18 4.30604167 2.99088294
19 -0.39395833 4.30604167
20 3.36604167 -0.39395833
21 8.46604167 3.36604167
22 1.30604167 8.46604167
23 5.56586310 1.30604167
24 1.66757937 5.56586310
25 2.16757937 1.66757937
26 -0.71242063 2.16757937
27 -3.33242063 -0.71242063
28 -3.13242063 -3.33242063
29 -7.41242063 -3.13242063
30 -2.21621032 -7.41242063
31 -4.11621032 -2.21621032
32 1.34378968 -4.11621032
33 -6.35621032 1.34378968
34 -2.21621032 -6.35621032
35 -3.65638889 -2.21621032
36 -9.45467262 -3.65638889
37 -0.75467262 -9.45467262
38 -3.93467262 -0.75467262
39 -0.15467262 -3.93467262
40 -1.35467262 -0.15467262
41 1.56532738 -1.35467262
42 -3.93846230 1.56532738
43 3.66153770 -3.93846230
44 -0.17846230 3.66153770
45 -3.57846230 -0.17846230
46 1.06153770 -3.57846230
47 -1.07864087 1.06153770
48 0.82307540 -1.07864087
49 1.42307540 0.82307540
50 -2.35692460 1.42307540
51 5.12307540 -2.35692460
52 4.42307540 5.12307540
53 4.94307540 4.42307540
54 0.63928571 4.94307540
55 3.83928571 0.63928571
56 -1.90071429 3.83928571
57 1.29928571 -1.90071429
58 5.23928571 1.29928571
> 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/7zxgg1195401892.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86f2r1195401892.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jd831195401893.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10uthq1195401893.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/1169im1195401893.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/12u1lj1195401893.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/139wiu1195401893.tab")
>
> system("convert tmp/1yl3w1195401892.ps tmp/1yl3w1195401892.png")
> system("convert tmp/20xxq1195401892.ps tmp/20xxq1195401892.png")
> system("convert tmp/38eqa1195401892.ps tmp/38eqa1195401892.png")
> system("convert tmp/4rgxe1195401892.ps tmp/4rgxe1195401892.png")
> system("convert tmp/5dg3e1195401892.ps tmp/5dg3e1195401892.png")
> system("convert tmp/6s7jn1195401892.ps tmp/6s7jn1195401892.png")
> system("convert tmp/7zxgg1195401892.ps tmp/7zxgg1195401892.png")
> system("convert tmp/86f2r1195401892.ps tmp/86f2r1195401892.png")
> system("convert tmp/9jd831195401893.ps tmp/9jd831195401893.png")
>
>
> proc.time()
user system elapsed
4.188 2.483 4.518