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(476,2.9,475,2.6,470,2.7,461,1.8,455,1.3,456,0.9,517,1.3,525,1.3,523,1.3,519,1.3,509,1.1,512,1.4,519,1.2,517,1.7,510,1.8,509,1.5,501,1,507,1.6,569,1.5,580,1.8,578,1.8,565,1.6,547,1.9,555,1.7,562,1.6,561,1.3,555,1.1,544,1.9,537,2.6,543,2.3,594,2.4,611,2.2,613,2,611,2.9,594,2.6,595,2.3,591,2.3,589,2.6,584,3.1,573,2.8,567,2.5,569,2.9,621,3.1,629,3.1,628,3.2,612,2.5,595,2.6,597,2.9,593,2.6,590,2.4,580,1.7,574,2,573,2.2,573,1.9,620,1.6,626,1.6,620,1.2,588,1.2,566,1.5,557,1.6),dim=c(2,60),dimnames=list(c('Werkloosheid*1000','Inflatie'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Werkloosheid*1000','Inflatie'),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
Werkloosheid*1000 Inflatie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 476 2.9 1 0 0 0 0 0 0 0 0 0 0 1
2 475 2.6 0 1 0 0 0 0 0 0 0 0 0 2
3 470 2.7 0 0 1 0 0 0 0 0 0 0 0 3
4 461 1.8 0 0 0 1 0 0 0 0 0 0 0 4
5 455 1.3 0 0 0 0 1 0 0 0 0 0 0 5
6 456 0.9 0 0 0 0 0 1 0 0 0 0 0 6
7 517 1.3 0 0 0 0 0 0 1 0 0 0 0 7
8 525 1.3 0 0 0 0 0 0 0 1 0 0 0 8
9 523 1.3 0 0 0 0 0 0 0 0 1 0 0 9
10 519 1.3 0 0 0 0 0 0 0 0 0 1 0 10
11 509 1.1 0 0 0 0 0 0 0 0 0 0 1 11
12 512 1.4 0 0 0 0 0 0 0 0 0 0 0 12
13 519 1.2 1 0 0 0 0 0 0 0 0 0 0 13
14 517 1.7 0 1 0 0 0 0 0 0 0 0 0 14
15 510 1.8 0 0 1 0 0 0 0 0 0 0 0 15
16 509 1.5 0 0 0 1 0 0 0 0 0 0 0 16
17 501 1.0 0 0 0 0 1 0 0 0 0 0 0 17
18 507 1.6 0 0 0 0 0 1 0 0 0 0 0 18
19 569 1.5 0 0 0 0 0 0 1 0 0 0 0 19
20 580 1.8 0 0 0 0 0 0 0 1 0 0 0 20
21 578 1.8 0 0 0 0 0 0 0 0 1 0 0 21
22 565 1.6 0 0 0 0 0 0 0 0 0 1 0 22
23 547 1.9 0 0 0 0 0 0 0 0 0 0 1 23
24 555 1.7 0 0 0 0 0 0 0 0 0 0 0 24
25 562 1.6 1 0 0 0 0 0 0 0 0 0 0 25
26 561 1.3 0 1 0 0 0 0 0 0 0 0 0 26
27 555 1.1 0 0 1 0 0 0 0 0 0 0 0 27
28 544 1.9 0 0 0 1 0 0 0 0 0 0 0 28
29 537 2.6 0 0 0 0 1 0 0 0 0 0 0 29
30 543 2.3 0 0 0 0 0 1 0 0 0 0 0 30
31 594 2.4 0 0 0 0 0 0 1 0 0 0 0 31
32 611 2.2 0 0 0 0 0 0 0 1 0 0 0 32
33 613 2.0 0 0 0 0 0 0 0 0 1 0 0 33
34 611 2.9 0 0 0 0 0 0 0 0 0 1 0 34
35 594 2.6 0 0 0 0 0 0 0 0 0 0 1 35
36 595 2.3 0 0 0 0 0 0 0 0 0 0 0 36
37 591 2.3 1 0 0 0 0 0 0 0 0 0 0 37
38 589 2.6 0 1 0 0 0 0 0 0 0 0 0 38
39 584 3.1 0 0 1 0 0 0 0 0 0 0 0 39
40 573 2.8 0 0 0 1 0 0 0 0 0 0 0 40
41 567 2.5 0 0 0 0 1 0 0 0 0 0 0 41
42 569 2.9 0 0 0 0 0 1 0 0 0 0 0 42
43 621 3.1 0 0 0 0 0 0 1 0 0 0 0 43
44 629 3.1 0 0 0 0 0 0 0 1 0 0 0 44
45 628 3.2 0 0 0 0 0 0 0 0 1 0 0 45
46 612 2.5 0 0 0 0 0 0 0 0 0 1 0 46
47 595 2.6 0 0 0 0 0 0 0 0 0 0 1 47
48 597 2.9 0 0 0 0 0 0 0 0 0 0 0 48
49 593 2.6 1 0 0 0 0 0 0 0 0 0 0 49
50 590 2.4 0 1 0 0 0 0 0 0 0 0 0 50
51 580 1.7 0 0 1 0 0 0 0 0 0 0 0 51
52 574 2.0 0 0 0 1 0 0 0 0 0 0 0 52
53 573 2.2 0 0 0 0 1 0 0 0 0 0 0 53
54 573 1.9 0 0 0 0 0 1 0 0 0 0 0 54
55 620 1.6 0 0 0 0 0 0 1 0 0 0 0 55
56 626 1.6 0 0 0 0 0 0 0 1 0 0 0 56
57 620 1.2 0 0 0 0 0 0 0 0 1 0 0 57
58 588 1.2 0 0 0 0 0 0 0 0 0 1 0 58
59 566 1.5 0 0 0 0 0 0 0 0 0 0 1 59
60 557 1.6 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) Inflatie M1 M2 M3 M4
465.4071 14.4669 4.1033 0.3825 -7.5596 -15.9230
M5 M6 M7 M8 M9 M10
-22.2865 -21.2073 30.6039 38.3938 36.1197 20.7989
M11 t
1.4995 1.9208
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-46.8015 -6.9730 0.9164 11.0050 29.3776
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 465.4071 11.5749 40.208 < 2e-16 ***
Inflatie 14.4669 4.0561 3.567 0.000857 ***
M1 4.1033 11.9154 0.344 0.732138
M2 0.3825 11.8932 0.032 0.974482
M3 -7.5596 11.8600 -0.637 0.527024
M4 -15.9230 11.8246 -1.347 0.184706
M5 -22.2865 11.8026 -1.888 0.065306 .
M6 -21.2073 11.7912 -1.799 0.078652 .
M7 30.6039 11.7844 2.597 0.012587 *
M8 38.3938 11.7775 3.260 0.002101 **
M9 36.1197 11.7695 3.069 0.003596 **
M10 20.7989 11.7661 1.768 0.083743 .
M11 1.4995 11.7619 0.127 0.899112
t 1.9208 0.1500 12.806 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 18.6 on 46 degrees of freedom
Multiple R-Squared: 0.8775, Adjusted R-squared: 0.8429
F-statistic: 25.35 on 13 and 46 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/17n8e1195321995.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/2gl731195321995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/39eop1195321995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4s2n71195321995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/51c841195321995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-37.3852788 -32.2452010 -32.6705706 -22.2076913 -16.5315824 -12.7448120
7 8 9 10 11 12
-11.2635668 -12.9742283 -14.6209209 -5.2209209 5.0511413 3.2897406
13 14 15 16 17 18
7.1590337 -0.2744293 -2.6997990 7.0829247 10.7590337 5.0788781
19 20 21 22 23 24
14.7935862 11.7428469 10.0961543 13.3895395 8.4281388 18.9002010
25 26 27 28 29 30
21.3228015 26.4628793 29.3775875 13.2466926 0.5624903 7.9025681
31 32 33 34 35 36
3.7238911 13.9066148 19.1533074 17.5330740 22.2518289 27.1705837
37 38 39 40 41 42
17.1464916 12.6064138 6.3942738 6.1769975 8.9597212 2.1729508
43 44 45 46 47 48
-2.4524188 -4.1630803 -6.2564655 1.2703827 0.2023671 -2.5590337
49 50 51 52 53 54
-8.2430480 -6.5496628 -0.4014916 -4.2989235 -3.7496628 -2.4095850
55 56 57 58 59 60
-4.8014916 -8.5121531 -8.3720753 -26.9720753 -35.9334761 -46.8014916
> postscript(file="/var/www/html/rcomp/tmp/6xtur1195321995.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 -37.3852788 NA
1 -32.2452010 -37.3852788
2 -32.6705706 -32.2452010
3 -22.2076913 -32.6705706
4 -16.5315824 -22.2076913
5 -12.7448120 -16.5315824
6 -11.2635668 -12.7448120
7 -12.9742283 -11.2635668
8 -14.6209209 -12.9742283
9 -5.2209209 -14.6209209
10 5.0511413 -5.2209209
11 3.2897406 5.0511413
12 7.1590337 3.2897406
13 -0.2744293 7.1590337
14 -2.6997990 -0.2744293
15 7.0829247 -2.6997990
16 10.7590337 7.0829247
17 5.0788781 10.7590337
18 14.7935862 5.0788781
19 11.7428469 14.7935862
20 10.0961543 11.7428469
21 13.3895395 10.0961543
22 8.4281388 13.3895395
23 18.9002010 8.4281388
24 21.3228015 18.9002010
25 26.4628793 21.3228015
26 29.3775875 26.4628793
27 13.2466926 29.3775875
28 0.5624903 13.2466926
29 7.9025681 0.5624903
30 3.7238911 7.9025681
31 13.9066148 3.7238911
32 19.1533074 13.9066148
33 17.5330740 19.1533074
34 22.2518289 17.5330740
35 27.1705837 22.2518289
36 17.1464916 27.1705837
37 12.6064138 17.1464916
38 6.3942738 12.6064138
39 6.1769975 6.3942738
40 8.9597212 6.1769975
41 2.1729508 8.9597212
42 -2.4524188 2.1729508
43 -4.1630803 -2.4524188
44 -6.2564655 -4.1630803
45 1.2703827 -6.2564655
46 0.2023671 1.2703827
47 -2.5590337 0.2023671
48 -8.2430480 -2.5590337
49 -6.5496628 -8.2430480
50 -0.4014916 -6.5496628
51 -4.2989235 -0.4014916
52 -3.7496628 -4.2989235
53 -2.4095850 -3.7496628
54 -4.8014916 -2.4095850
55 -8.5121531 -4.8014916
56 -8.3720753 -8.5121531
57 -26.9720753 -8.3720753
58 -35.9334761 -26.9720753
59 -46.8014916 -35.9334761
60 NA -46.8014916
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -32.2452010 -37.3852788
[2,] -32.6705706 -32.2452010
[3,] -22.2076913 -32.6705706
[4,] -16.5315824 -22.2076913
[5,] -12.7448120 -16.5315824
[6,] -11.2635668 -12.7448120
[7,] -12.9742283 -11.2635668
[8,] -14.6209209 -12.9742283
[9,] -5.2209209 -14.6209209
[10,] 5.0511413 -5.2209209
[11,] 3.2897406 5.0511413
[12,] 7.1590337 3.2897406
[13,] -0.2744293 7.1590337
[14,] -2.6997990 -0.2744293
[15,] 7.0829247 -2.6997990
[16,] 10.7590337 7.0829247
[17,] 5.0788781 10.7590337
[18,] 14.7935862 5.0788781
[19,] 11.7428469 14.7935862
[20,] 10.0961543 11.7428469
[21,] 13.3895395 10.0961543
[22,] 8.4281388 13.3895395
[23,] 18.9002010 8.4281388
[24,] 21.3228015 18.9002010
[25,] 26.4628793 21.3228015
[26,] 29.3775875 26.4628793
[27,] 13.2466926 29.3775875
[28,] 0.5624903 13.2466926
[29,] 7.9025681 0.5624903
[30,] 3.7238911 7.9025681
[31,] 13.9066148 3.7238911
[32,] 19.1533074 13.9066148
[33,] 17.5330740 19.1533074
[34,] 22.2518289 17.5330740
[35,] 27.1705837 22.2518289
[36,] 17.1464916 27.1705837
[37,] 12.6064138 17.1464916
[38,] 6.3942738 12.6064138
[39,] 6.1769975 6.3942738
[40,] 8.9597212 6.1769975
[41,] 2.1729508 8.9597212
[42,] -2.4524188 2.1729508
[43,] -4.1630803 -2.4524188
[44,] -6.2564655 -4.1630803
[45,] 1.2703827 -6.2564655
[46,] 0.2023671 1.2703827
[47,] -2.5590337 0.2023671
[48,] -8.2430480 -2.5590337
[49,] -6.5496628 -8.2430480
[50,] -0.4014916 -6.5496628
[51,] -4.2989235 -0.4014916
[52,] -3.7496628 -4.2989235
[53,] -2.4095850 -3.7496628
[54,] -4.8014916 -2.4095850
[55,] -8.5121531 -4.8014916
[56,] -8.3720753 -8.5121531
[57,] -26.9720753 -8.3720753
[58,] -35.9334761 -26.9720753
[59,] -46.8014916 -35.9334761
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -32.2452010 -37.3852788
2 -32.6705706 -32.2452010
3 -22.2076913 -32.6705706
4 -16.5315824 -22.2076913
5 -12.7448120 -16.5315824
6 -11.2635668 -12.7448120
7 -12.9742283 -11.2635668
8 -14.6209209 -12.9742283
9 -5.2209209 -14.6209209
10 5.0511413 -5.2209209
11 3.2897406 5.0511413
12 7.1590337 3.2897406
13 -0.2744293 7.1590337
14 -2.6997990 -0.2744293
15 7.0829247 -2.6997990
16 10.7590337 7.0829247
17 5.0788781 10.7590337
18 14.7935862 5.0788781
19 11.7428469 14.7935862
20 10.0961543 11.7428469
21 13.3895395 10.0961543
22 8.4281388 13.3895395
23 18.9002010 8.4281388
24 21.3228015 18.9002010
25 26.4628793 21.3228015
26 29.3775875 26.4628793
27 13.2466926 29.3775875
28 0.5624903 13.2466926
29 7.9025681 0.5624903
30 3.7238911 7.9025681
31 13.9066148 3.7238911
32 19.1533074 13.9066148
33 17.5330740 19.1533074
34 22.2518289 17.5330740
35 27.1705837 22.2518289
36 17.1464916 27.1705837
37 12.6064138 17.1464916
38 6.3942738 12.6064138
39 6.1769975 6.3942738
40 8.9597212 6.1769975
41 2.1729508 8.9597212
42 -2.4524188 2.1729508
43 -4.1630803 -2.4524188
44 -6.2564655 -4.1630803
45 1.2703827 -6.2564655
46 0.2023671 1.2703827
47 -2.5590337 0.2023671
48 -8.2430480 -2.5590337
49 -6.5496628 -8.2430480
50 -0.4014916 -6.5496628
51 -4.2989235 -0.4014916
52 -3.7496628 -4.2989235
53 -2.4095850 -3.7496628
54 -4.8014916 -2.4095850
55 -8.5121531 -4.8014916
56 -8.3720753 -8.5121531
57 -26.9720753 -8.3720753
58 -35.9334761 -26.9720753
59 -46.8014916 -35.9334761
> 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/72m0f1195321995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kql11195321995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jegv1195321995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10bocn1195321995.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/11yj5l1195321996.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/12beeo1195321996.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/13bwzy1195321996.tab")
>
> system("convert tmp/17n8e1195321995.ps tmp/17n8e1195321995.png")
> system("convert tmp/2gl731195321995.ps tmp/2gl731195321995.png")
> system("convert tmp/39eop1195321995.ps tmp/39eop1195321995.png")
> system("convert tmp/4s2n71195321995.ps tmp/4s2n71195321995.png")
> system("convert tmp/51c841195321995.ps tmp/51c841195321995.png")
> system("convert tmp/6xtur1195321995.ps tmp/6xtur1195321995.png")
> system("convert tmp/72m0f1195321995.ps tmp/72m0f1195321995.png")
> system("convert tmp/8kql11195321995.ps tmp/8kql11195321995.png")
> system("convert tmp/9jegv1195321995.ps tmp/9jegv1195321995.png")
>
>
> proc.time()
user system elapsed
4.158 2.470 4.489