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(493.000,0,481.000,0,462.000,0,457.000,0,442.000,0,439.000,0,488.000,0,521.000,0,501.000,0,485.000,0,464.000,0,460.000,0,467.000,0,460.000,0,448.000,0,443.000,0,436.000,0,431.000,0,484.000,0,510.000,0,513.000,0,503.000,0,471.000,0,471.000,0,476.000,0,475.000,0,470.000,0,461.000,0,455.000,0,456.000,0,517.000,1,525.000,1,523.000,1,519.000,1,509.000,1,512.000,1,519.000,1,517.000,1,510.000,1,509.000,1,501.000,1,507.000,1,569.000,1,580.000,1,578.000,1,565.000,1,547.000,1,555.000,1,562.000,1,561.000,1,555.000,1,544.000,1,537.000,1,543.000,1,594.000,1,611.000,1,613.000,1,611.000,1,594.000,1,595.000,1),dim=c(2,60),dimnames=list(c('y','x'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('y','x'),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 = '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
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 493 0 1 0 0 0 0 0 0 0 0 0 0 1
2 481 0 0 1 0 0 0 0 0 0 0 0 0 2
3 462 0 0 0 1 0 0 0 0 0 0 0 0 3
4 457 0 0 0 0 1 0 0 0 0 0 0 0 4
5 442 0 0 0 0 0 1 0 0 0 0 0 0 5
6 439 0 0 0 0 0 0 1 0 0 0 0 0 6
7 488 0 0 0 0 0 0 0 1 0 0 0 0 7
8 521 0 0 0 0 0 0 0 0 1 0 0 0 8
9 501 0 0 0 0 0 0 0 0 0 1 0 0 9
10 485 0 0 0 0 0 0 0 0 0 0 1 0 10
11 464 0 0 0 0 0 0 0 0 0 0 0 1 11
12 460 0 0 0 0 0 0 0 0 0 0 0 0 12
13 467 0 1 0 0 0 0 0 0 0 0 0 0 13
14 460 0 0 1 0 0 0 0 0 0 0 0 0 14
15 448 0 0 0 1 0 0 0 0 0 0 0 0 15
16 443 0 0 0 0 1 0 0 0 0 0 0 0 16
17 436 0 0 0 0 0 1 0 0 0 0 0 0 17
18 431 0 0 0 0 0 0 1 0 0 0 0 0 18
19 484 0 0 0 0 0 0 0 1 0 0 0 0 19
20 510 0 0 0 0 0 0 0 0 1 0 0 0 20
21 513 0 0 0 0 0 0 0 0 0 1 0 0 21
22 503 0 0 0 0 0 0 0 0 0 0 1 0 22
23 471 0 0 0 0 0 0 0 0 0 0 0 1 23
24 471 0 0 0 0 0 0 0 0 0 0 0 0 24
25 476 0 1 0 0 0 0 0 0 0 0 0 0 25
26 475 0 0 1 0 0 0 0 0 0 0 0 0 26
27 470 0 0 0 1 0 0 0 0 0 0 0 0 27
28 461 0 0 0 0 1 0 0 0 0 0 0 0 28
29 455 0 0 0 0 0 1 0 0 0 0 0 0 29
30 456 0 0 0 0 0 0 1 0 0 0 0 0 30
31 517 1 0 0 0 0 0 0 1 0 0 0 0 31
32 525 1 0 0 0 0 0 0 0 1 0 0 0 32
33 523 1 0 0 0 0 0 0 0 0 1 0 0 33
34 519 1 0 0 0 0 0 0 0 0 0 1 0 34
35 509 1 0 0 0 0 0 0 0 0 0 0 1 35
36 512 1 0 0 0 0 0 0 0 0 0 0 0 36
37 519 1 1 0 0 0 0 0 0 0 0 0 0 37
38 517 1 0 1 0 0 0 0 0 0 0 0 0 38
39 510 1 0 0 1 0 0 0 0 0 0 0 0 39
40 509 1 0 0 0 1 0 0 0 0 0 0 0 40
41 501 1 0 0 0 0 1 0 0 0 0 0 0 41
42 507 1 0 0 0 0 0 1 0 0 0 0 0 42
43 569 1 0 0 0 0 0 0 1 0 0 0 0 43
44 580 1 0 0 0 0 0 0 0 1 0 0 0 44
45 578 1 0 0 0 0 0 0 0 0 1 0 0 45
46 565 1 0 0 0 0 0 0 0 0 0 1 0 46
47 547 1 0 0 0 0 0 0 0 0 0 0 1 47
48 555 1 0 0 0 0 0 0 0 0 0 0 0 48
49 562 1 1 0 0 0 0 0 0 0 0 0 0 49
50 561 1 0 1 0 0 0 0 0 0 0 0 0 50
51 555 1 0 0 1 0 0 0 0 0 0 0 0 51
52 544 1 0 0 0 1 0 0 0 0 0 0 0 52
53 537 1 0 0 0 0 1 0 0 0 0 0 0 53
54 543 1 0 0 0 0 0 1 0 0 0 0 0 54
55 594 1 0 0 0 0 0 0 1 0 0 0 0 55
56 611 1 0 0 0 0 0 0 0 1 0 0 0 56
57 613 1 0 0 0 0 0 0 0 0 1 0 0 57
58 611 1 0 0 0 0 0 0 0 0 0 1 0 58
59 594 1 0 0 0 0 0 0 0 0 0 0 1 59
60 595 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
441.7667 16.7222 8.5556 2.1000 -9.5556 -17.6111
M5 M6 M7 M8 M9 M10
-28.0667 -28.9222 21.0778 38.2222 32.5667 21.7111
M11 t
0.2556 1.8556
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-31.0889 -12.7056 -0.5278 10.6417 40.8222
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 441.7667 9.9833 44.251 < 2e-16 ***
x 16.7222 9.6064 1.741 0.08842 .
M1 8.5556 11.6505 0.734 0.46646
M2 2.1000 11.6207 0.181 0.85739
M3 -9.5556 11.5975 -0.824 0.41423
M4 -17.6111 11.5809 -1.521 0.13518
M5 -28.0667 11.5710 -2.426 0.01926 *
M6 -28.9222 11.5677 -2.500 0.01604 *
M7 21.0778 11.6108 1.815 0.07599 .
M8 38.2222 11.5809 3.300 0.00187 **
M9 32.5667 11.5577 2.818 0.00710 **
M10 21.7111 11.5410 1.881 0.06628 .
M11 0.2556 11.5310 0.022 0.98241
t 1.8556 0.2773 6.691 2.65e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18.23 on 46 degrees of freedom
Multiple R-squared: 0.8947, Adjusted R-squared: 0.865
F-statistic: 30.07 on 13 and 46 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/100uo1227103773.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/22e911227103773.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/301k91227103773.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4esdk1227103773.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5frn91227103773.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6
40.8222222 33.4222222 24.2222222 25.4222222 19.0222222 15.0222222
7 8 9 10 11 12
12.1666667 26.1666667 9.9666667 2.9666667 1.5666667 -4.0333333
13 14 15 16 17 18
-7.4444444 -9.8444444 -12.0444444 -10.8444444 -9.2444444 -15.2444444
19 20 21 22 23 24
-14.1000000 -7.1000000 -0.3000000 -1.3000000 -13.7000000 -15.3000000
25 26 27 28 29 30
-20.7111111 -17.1111111 -12.3111111 -15.1111111 -12.5111111 -12.5111111
31 32 33 34 35 36
-20.0888889 -31.0888889 -29.2888889 -24.2888889 -14.6888889 -13.2888889
37 38 39 40 41 42
-16.7000000 -14.1000000 -11.3000000 -6.1000000 -5.5000000 -0.5000000
43 44 45 46 47 48
9.6444444 1.6444444 3.4444444 -0.5555556 1.0444444 7.4444444
49 50 51 52 53 54
4.0333333 7.6333333 11.4333333 6.6333333 8.2333333 13.2333333
55 56 57 58 59 60
12.3777778 10.3777778 16.1777778 23.1777778 25.7777778 25.1777778
> postscript(file="/var/www/html/rcomp/tmp/6ybdl1227103773.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 40.8222222 NA
1 33.4222222 40.8222222
2 24.2222222 33.4222222
3 25.4222222 24.2222222
4 19.0222222 25.4222222
5 15.0222222 19.0222222
6 12.1666667 15.0222222
7 26.1666667 12.1666667
8 9.9666667 26.1666667
9 2.9666667 9.9666667
10 1.5666667 2.9666667
11 -4.0333333 1.5666667
12 -7.4444444 -4.0333333
13 -9.8444444 -7.4444444
14 -12.0444444 -9.8444444
15 -10.8444444 -12.0444444
16 -9.2444444 -10.8444444
17 -15.2444444 -9.2444444
18 -14.1000000 -15.2444444
19 -7.1000000 -14.1000000
20 -0.3000000 -7.1000000
21 -1.3000000 -0.3000000
22 -13.7000000 -1.3000000
23 -15.3000000 -13.7000000
24 -20.7111111 -15.3000000
25 -17.1111111 -20.7111111
26 -12.3111111 -17.1111111
27 -15.1111111 -12.3111111
28 -12.5111111 -15.1111111
29 -12.5111111 -12.5111111
30 -20.0888889 -12.5111111
31 -31.0888889 -20.0888889
32 -29.2888889 -31.0888889
33 -24.2888889 -29.2888889
34 -14.6888889 -24.2888889
35 -13.2888889 -14.6888889
36 -16.7000000 -13.2888889
37 -14.1000000 -16.7000000
38 -11.3000000 -14.1000000
39 -6.1000000 -11.3000000
40 -5.5000000 -6.1000000
41 -0.5000000 -5.5000000
42 9.6444444 -0.5000000
43 1.6444444 9.6444444
44 3.4444444 1.6444444
45 -0.5555556 3.4444444
46 1.0444444 -0.5555556
47 7.4444444 1.0444444
48 4.0333333 7.4444444
49 7.6333333 4.0333333
50 11.4333333 7.6333333
51 6.6333333 11.4333333
52 8.2333333 6.6333333
53 13.2333333 8.2333333
54 12.3777778 13.2333333
55 10.3777778 12.3777778
56 16.1777778 10.3777778
57 23.1777778 16.1777778
58 25.7777778 23.1777778
59 25.1777778 25.7777778
60 NA 25.1777778
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 33.4222222 40.8222222
[2,] 24.2222222 33.4222222
[3,] 25.4222222 24.2222222
[4,] 19.0222222 25.4222222
[5,] 15.0222222 19.0222222
[6,] 12.1666667 15.0222222
[7,] 26.1666667 12.1666667
[8,] 9.9666667 26.1666667
[9,] 2.9666667 9.9666667
[10,] 1.5666667 2.9666667
[11,] -4.0333333 1.5666667
[12,] -7.4444444 -4.0333333
[13,] -9.8444444 -7.4444444
[14,] -12.0444444 -9.8444444
[15,] -10.8444444 -12.0444444
[16,] -9.2444444 -10.8444444
[17,] -15.2444444 -9.2444444
[18,] -14.1000000 -15.2444444
[19,] -7.1000000 -14.1000000
[20,] -0.3000000 -7.1000000
[21,] -1.3000000 -0.3000000
[22,] -13.7000000 -1.3000000
[23,] -15.3000000 -13.7000000
[24,] -20.7111111 -15.3000000
[25,] -17.1111111 -20.7111111
[26,] -12.3111111 -17.1111111
[27,] -15.1111111 -12.3111111
[28,] -12.5111111 -15.1111111
[29,] -12.5111111 -12.5111111
[30,] -20.0888889 -12.5111111
[31,] -31.0888889 -20.0888889
[32,] -29.2888889 -31.0888889
[33,] -24.2888889 -29.2888889
[34,] -14.6888889 -24.2888889
[35,] -13.2888889 -14.6888889
[36,] -16.7000000 -13.2888889
[37,] -14.1000000 -16.7000000
[38,] -11.3000000 -14.1000000
[39,] -6.1000000 -11.3000000
[40,] -5.5000000 -6.1000000
[41,] -0.5000000 -5.5000000
[42,] 9.6444444 -0.5000000
[43,] 1.6444444 9.6444444
[44,] 3.4444444 1.6444444
[45,] -0.5555556 3.4444444
[46,] 1.0444444 -0.5555556
[47,] 7.4444444 1.0444444
[48,] 4.0333333 7.4444444
[49,] 7.6333333 4.0333333
[50,] 11.4333333 7.6333333
[51,] 6.6333333 11.4333333
[52,] 8.2333333 6.6333333
[53,] 13.2333333 8.2333333
[54,] 12.3777778 13.2333333
[55,] 10.3777778 12.3777778
[56,] 16.1777778 10.3777778
[57,] 23.1777778 16.1777778
[58,] 25.7777778 23.1777778
[59,] 25.1777778 25.7777778
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 33.4222222 40.8222222
2 24.2222222 33.4222222
3 25.4222222 24.2222222
4 19.0222222 25.4222222
5 15.0222222 19.0222222
6 12.1666667 15.0222222
7 26.1666667 12.1666667
8 9.9666667 26.1666667
9 2.9666667 9.9666667
10 1.5666667 2.9666667
11 -4.0333333 1.5666667
12 -7.4444444 -4.0333333
13 -9.8444444 -7.4444444
14 -12.0444444 -9.8444444
15 -10.8444444 -12.0444444
16 -9.2444444 -10.8444444
17 -15.2444444 -9.2444444
18 -14.1000000 -15.2444444
19 -7.1000000 -14.1000000
20 -0.3000000 -7.1000000
21 -1.3000000 -0.3000000
22 -13.7000000 -1.3000000
23 -15.3000000 -13.7000000
24 -20.7111111 -15.3000000
25 -17.1111111 -20.7111111
26 -12.3111111 -17.1111111
27 -15.1111111 -12.3111111
28 -12.5111111 -15.1111111
29 -12.5111111 -12.5111111
30 -20.0888889 -12.5111111
31 -31.0888889 -20.0888889
32 -29.2888889 -31.0888889
33 -24.2888889 -29.2888889
34 -14.6888889 -24.2888889
35 -13.2888889 -14.6888889
36 -16.7000000 -13.2888889
37 -14.1000000 -16.7000000
38 -11.3000000 -14.1000000
39 -6.1000000 -11.3000000
40 -5.5000000 -6.1000000
41 -0.5000000 -5.5000000
42 9.6444444 -0.5000000
43 1.6444444 9.6444444
44 3.4444444 1.6444444
45 -0.5555556 3.4444444
46 1.0444444 -0.5555556
47 7.4444444 1.0444444
48 4.0333333 7.4444444
49 7.6333333 4.0333333
50 11.4333333 7.6333333
51 6.6333333 11.4333333
52 8.2333333 6.6333333
53 13.2333333 8.2333333
54 12.3777778 13.2333333
55 10.3777778 12.3777778
56 16.1777778 10.3777778
57 23.1777778 16.1777778
58 25.7777778 23.1777778
59 25.1777778 25.7777778
> 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/7h8rx1227103773.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z42s1227103773.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93t3m1227103773.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1085sp1227103773.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/1131zc1227103773.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/1223j31227103773.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/13ela61227103773.tab")
>
> system("convert tmp/100uo1227103773.ps tmp/100uo1227103773.png")
> system("convert tmp/22e911227103773.ps tmp/22e911227103773.png")
> system("convert tmp/301k91227103773.ps tmp/301k91227103773.png")
> system("convert tmp/4esdk1227103773.ps tmp/4esdk1227103773.png")
> system("convert tmp/5frn91227103773.ps tmp/5frn91227103773.png")
> system("convert tmp/6ybdl1227103773.ps tmp/6ybdl1227103773.png")
> system("convert tmp/7h8rx1227103773.ps tmp/7h8rx1227103773.png")
> system("convert tmp/8z42s1227103773.ps tmp/8z42s1227103773.png")
> system("convert tmp/93t3m1227103773.ps tmp/93t3m1227103773.png")
>
>
> proc.time()
user system elapsed
1.922 1.423 2.294