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(1178,0,2141,0,2238,0,2685,0,4341,0,5376,0,4478,0,6404,0,4617,0,3024,0,1897,0,2075,0,1351,0,2211,1,2453,1,3042,1,4765,1,4992,1,4601,1,6266,1,4812,1,3159,1,1916,1,2237,1,1595,1,2453,1,2226,1,3597,1,4706,1,4974,1,5756,1,5493,1,5004,1,3225,1,2006,1,2291,1,1588,1,2105,1,2191,1,3591,1,4668,1,4885,1,5822,1,5599,1,5340,1,3082,1,2010,1,2301,1,1514,1,1979,1,2480,1,3499,1,4676,1,5585,1,5610,1,5796,1,6199,1,3030,1,1930,1,2552,1),dim=c(2,60),dimnames=list(c('Huwelijken','x
'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Huwelijken','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
Huwelijken x\r\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1178 0 1 0 0 0 0 0 0 0 0 0 0 1
2 2141 0 0 1 0 0 0 0 0 0 0 0 0 2
3 2238 0 0 0 1 0 0 0 0 0 0 0 0 3
4 2685 0 0 0 0 1 0 0 0 0 0 0 0 4
5 4341 0 0 0 0 0 1 0 0 0 0 0 0 5
6 5376 0 0 0 0 0 0 1 0 0 0 0 0 6
7 4478 0 0 0 0 0 0 0 1 0 0 0 0 7
8 6404 0 0 0 0 0 0 0 0 1 0 0 0 8
9 4617 0 0 0 0 0 0 0 0 0 1 0 0 9
10 3024 0 0 0 0 0 0 0 0 0 0 1 0 10
11 1897 0 0 0 0 0 0 0 0 0 0 0 1 11
12 2075 0 0 0 0 0 0 0 0 0 0 0 0 12
13 1351 0 1 0 0 0 0 0 0 0 0 0 0 13
14 2211 1 0 1 0 0 0 0 0 0 0 0 0 14
15 2453 1 0 0 1 0 0 0 0 0 0 0 0 15
16 3042 1 0 0 0 1 0 0 0 0 0 0 0 16
17 4765 1 0 0 0 0 1 0 0 0 0 0 0 17
18 4992 1 0 0 0 0 0 1 0 0 0 0 0 18
19 4601 1 0 0 0 0 0 0 1 0 0 0 0 19
20 6266 1 0 0 0 0 0 0 0 1 0 0 0 20
21 4812 1 0 0 0 0 0 0 0 0 1 0 0 21
22 3159 1 0 0 0 0 0 0 0 0 0 1 0 22
23 1916 1 0 0 0 0 0 0 0 0 0 0 1 23
24 2237 1 0 0 0 0 0 0 0 0 0 0 0 24
25 1595 1 1 0 0 0 0 0 0 0 0 0 0 25
26 2453 1 0 1 0 0 0 0 0 0 0 0 0 26
27 2226 1 0 0 1 0 0 0 0 0 0 0 0 27
28 3597 1 0 0 0 1 0 0 0 0 0 0 0 28
29 4706 1 0 0 0 0 1 0 0 0 0 0 0 29
30 4974 1 0 0 0 0 0 1 0 0 0 0 0 30
31 5756 1 0 0 0 0 0 0 1 0 0 0 0 31
32 5493 1 0 0 0 0 0 0 0 1 0 0 0 32
33 5004 1 0 0 0 0 0 0 0 0 1 0 0 33
34 3225 1 0 0 0 0 0 0 0 0 0 1 0 34
35 2006 1 0 0 0 0 0 0 0 0 0 0 1 35
36 2291 1 0 0 0 0 0 0 0 0 0 0 0 36
37 1588 1 1 0 0 0 0 0 0 0 0 0 0 37
38 2105 1 0 1 0 0 0 0 0 0 0 0 0 38
39 2191 1 0 0 1 0 0 0 0 0 0 0 0 39
40 3591 1 0 0 0 1 0 0 0 0 0 0 0 40
41 4668 1 0 0 0 0 1 0 0 0 0 0 0 41
42 4885 1 0 0 0 0 0 1 0 0 0 0 0 42
43 5822 1 0 0 0 0 0 0 1 0 0 0 0 43
44 5599 1 0 0 0 0 0 0 0 1 0 0 0 44
45 5340 1 0 0 0 0 0 0 0 0 1 0 0 45
46 3082 1 0 0 0 0 0 0 0 0 0 1 0 46
47 2010 1 0 0 0 0 0 0 0 0 0 0 1 47
48 2301 1 0 0 0 0 0 0 0 0 0 0 0 48
49 1514 1 1 0 0 0 0 0 0 0 0 0 0 49
50 1979 1 0 1 0 0 0 0 0 0 0 0 0 50
51 2480 1 0 0 1 0 0 0 0 0 0 0 0 51
52 3499 1 0 0 0 1 0 0 0 0 0 0 0 52
53 4676 1 0 0 0 0 1 0 0 0 0 0 0 53
54 5585 1 0 0 0 0 0 1 0 0 0 0 0 54
55 5610 1 0 0 0 0 0 0 1 0 0 0 0 55
56 5796 1 0 0 0 0 0 0 0 1 0 0 0 56
57 6199 1 0 0 0 0 0 0 0 0 1 0 0 57
58 3030 1 0 0 0 0 0 0 0 0 0 1 0 58
59 1930 1 0 0 0 0 0 0 0 0 0 0 1 59
60 2552 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\r\r` M1 M2 M3 M4
2028.025 51.143 -767.858 -51.661 81.965 1040.991
M5 M6 M7 M8 M9 M10
2383.217 2908.243 2993.070 3645.096 2921.722 825.148
M11 t
-333.226 6.174
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-588.542 -177.757 6.828 130.579 846.198
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2028.025 169.093 11.994 9.24e-16 ***
`x\r\r` 51.143 145.622 0.351 0.727041
M1 -767.858 203.366 -3.776 0.000456 ***
M2 -51.661 204.626 -0.252 0.801806
M3 81.965 204.055 0.402 0.689779
M4 1040.991 203.543 5.114 5.98e-06 ***
M5 2383.217 203.091 11.735 1.98e-15 ***
M6 2908.243 202.698 14.348 < 2e-16 ***
M7 2993.070 202.364 14.791 < 2e-16 ***
M8 3645.096 202.091 18.037 < 2e-16 ***
M9 2921.722 201.879 14.473 < 2e-16 ***
M10 825.148 201.727 4.090 0.000172 ***
M11 -333.226 201.635 -1.653 0.105219
t 6.174 3.503 1.762 0.084645 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 318.8 on 46 degrees of freedom
Multiple R-Squared: 0.967, Adjusted R-squared: 0.9576
F-statistic: 103.6 on 13 and 46 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1w73q1195416674.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/2qb5e1195416674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/31svp1195416674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4aevf1195416674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5krda1195416674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-88.340522 152.288000 109.488000 -408.712000 -101.112000 402.688000
7 8 9 10 11 12
-586.312000 681.488000 -388.312000 109.088000 134.288000 -27.112000
13 14 15 16 17 18
10.572522 97.058435 199.258435 -176.941565 197.658435 -106.541565
19 20 21 22 23 24
-588.541565 418.258435 -318.541565 118.858435 28.058435 9.658435
25 26 27 28 29 30
129.342957 264.971478 -101.828522 303.971478 64.571478 -198.628522
31 32 33 34 35 36
492.371478 -428.828522 -200.628522 110.771478 43.971478 -10.428522
37 38 39 40 41 42
48.256000 -157.115478 -210.915478 223.884522 -47.515478 -361.715478
43 44 45 46 47 48
484.284522 -396.915478 61.284522 -106.315478 -26.115478 -74.515478
49 50 51 52 53 54
-99.830957 -357.202435 3.997565 57.797565 -113.602435 264.197565
55 56 57 58 59 60
198.197565 -274.002435 846.197565 -232.402435 -180.202435 102.397565
> postscript(file="/var/www/html/rcomp/tmp/6488c1195416674.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 -88.340522 NA
1 152.288000 -88.340522
2 109.488000 152.288000
3 -408.712000 109.488000
4 -101.112000 -408.712000
5 402.688000 -101.112000
6 -586.312000 402.688000
7 681.488000 -586.312000
8 -388.312000 681.488000
9 109.088000 -388.312000
10 134.288000 109.088000
11 -27.112000 134.288000
12 10.572522 -27.112000
13 97.058435 10.572522
14 199.258435 97.058435
15 -176.941565 199.258435
16 197.658435 -176.941565
17 -106.541565 197.658435
18 -588.541565 -106.541565
19 418.258435 -588.541565
20 -318.541565 418.258435
21 118.858435 -318.541565
22 28.058435 118.858435
23 9.658435 28.058435
24 129.342957 9.658435
25 264.971478 129.342957
26 -101.828522 264.971478
27 303.971478 -101.828522
28 64.571478 303.971478
29 -198.628522 64.571478
30 492.371478 -198.628522
31 -428.828522 492.371478
32 -200.628522 -428.828522
33 110.771478 -200.628522
34 43.971478 110.771478
35 -10.428522 43.971478
36 48.256000 -10.428522
37 -157.115478 48.256000
38 -210.915478 -157.115478
39 223.884522 -210.915478
40 -47.515478 223.884522
41 -361.715478 -47.515478
42 484.284522 -361.715478
43 -396.915478 484.284522
44 61.284522 -396.915478
45 -106.315478 61.284522
46 -26.115478 -106.315478
47 -74.515478 -26.115478
48 -99.830957 -74.515478
49 -357.202435 -99.830957
50 3.997565 -357.202435
51 57.797565 3.997565
52 -113.602435 57.797565
53 264.197565 -113.602435
54 198.197565 264.197565
55 -274.002435 198.197565
56 846.197565 -274.002435
57 -232.402435 846.197565
58 -180.202435 -232.402435
59 102.397565 -180.202435
60 NA 102.397565
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 152.288000 -88.340522
[2,] 109.488000 152.288000
[3,] -408.712000 109.488000
[4,] -101.112000 -408.712000
[5,] 402.688000 -101.112000
[6,] -586.312000 402.688000
[7,] 681.488000 -586.312000
[8,] -388.312000 681.488000
[9,] 109.088000 -388.312000
[10,] 134.288000 109.088000
[11,] -27.112000 134.288000
[12,] 10.572522 -27.112000
[13,] 97.058435 10.572522
[14,] 199.258435 97.058435
[15,] -176.941565 199.258435
[16,] 197.658435 -176.941565
[17,] -106.541565 197.658435
[18,] -588.541565 -106.541565
[19,] 418.258435 -588.541565
[20,] -318.541565 418.258435
[21,] 118.858435 -318.541565
[22,] 28.058435 118.858435
[23,] 9.658435 28.058435
[24,] 129.342957 9.658435
[25,] 264.971478 129.342957
[26,] -101.828522 264.971478
[27,] 303.971478 -101.828522
[28,] 64.571478 303.971478
[29,] -198.628522 64.571478
[30,] 492.371478 -198.628522
[31,] -428.828522 492.371478
[32,] -200.628522 -428.828522
[33,] 110.771478 -200.628522
[34,] 43.971478 110.771478
[35,] -10.428522 43.971478
[36,] 48.256000 -10.428522
[37,] -157.115478 48.256000
[38,] -210.915478 -157.115478
[39,] 223.884522 -210.915478
[40,] -47.515478 223.884522
[41,] -361.715478 -47.515478
[42,] 484.284522 -361.715478
[43,] -396.915478 484.284522
[44,] 61.284522 -396.915478
[45,] -106.315478 61.284522
[46,] -26.115478 -106.315478
[47,] -74.515478 -26.115478
[48,] -99.830957 -74.515478
[49,] -357.202435 -99.830957
[50,] 3.997565 -357.202435
[51,] 57.797565 3.997565
[52,] -113.602435 57.797565
[53,] 264.197565 -113.602435
[54,] 198.197565 264.197565
[55,] -274.002435 198.197565
[56,] 846.197565 -274.002435
[57,] -232.402435 846.197565
[58,] -180.202435 -232.402435
[59,] 102.397565 -180.202435
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 152.288000 -88.340522
2 109.488000 152.288000
3 -408.712000 109.488000
4 -101.112000 -408.712000
5 402.688000 -101.112000
6 -586.312000 402.688000
7 681.488000 -586.312000
8 -388.312000 681.488000
9 109.088000 -388.312000
10 134.288000 109.088000
11 -27.112000 134.288000
12 10.572522 -27.112000
13 97.058435 10.572522
14 199.258435 97.058435
15 -176.941565 199.258435
16 197.658435 -176.941565
17 -106.541565 197.658435
18 -588.541565 -106.541565
19 418.258435 -588.541565
20 -318.541565 418.258435
21 118.858435 -318.541565
22 28.058435 118.858435
23 9.658435 28.058435
24 129.342957 9.658435
25 264.971478 129.342957
26 -101.828522 264.971478
27 303.971478 -101.828522
28 64.571478 303.971478
29 -198.628522 64.571478
30 492.371478 -198.628522
31 -428.828522 492.371478
32 -200.628522 -428.828522
33 110.771478 -200.628522
34 43.971478 110.771478
35 -10.428522 43.971478
36 48.256000 -10.428522
37 -157.115478 48.256000
38 -210.915478 -157.115478
39 223.884522 -210.915478
40 -47.515478 223.884522
41 -361.715478 -47.515478
42 484.284522 -361.715478
43 -396.915478 484.284522
44 61.284522 -396.915478
45 -106.315478 61.284522
46 -26.115478 -106.315478
47 -74.515478 -26.115478
48 -99.830957 -74.515478
49 -357.202435 -99.830957
50 3.997565 -357.202435
51 57.797565 3.997565
52 -113.602435 57.797565
53 264.197565 -113.602435
54 198.197565 264.197565
55 -274.002435 198.197565
56 846.197565 -274.002435
57 -232.402435 846.197565
58 -180.202435 -232.402435
59 102.397565 -180.202435
> 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/7h9js1195416674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tbng1195416674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92bfk1195416675.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1088511195416675.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/11lwp41195416675.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/12sdkk1195416675.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/13qk7s1195416675.tab")
>
> system("convert tmp/1w73q1195416674.ps tmp/1w73q1195416674.png")
> system("convert tmp/2qb5e1195416674.ps tmp/2qb5e1195416674.png")
> system("convert tmp/31svp1195416674.ps tmp/31svp1195416674.png")
> system("convert tmp/4aevf1195416674.ps tmp/4aevf1195416674.png")
> system("convert tmp/5krda1195416674.ps tmp/5krda1195416674.png")
> system("convert tmp/6488c1195416674.ps tmp/6488c1195416674.png")
> system("convert tmp/7h9js1195416674.ps tmp/7h9js1195416674.png")
> system("convert tmp/8tbng1195416674.ps tmp/8tbng1195416674.png")
> system("convert tmp/92bfk1195416675.ps tmp/92bfk1195416675.png")
>
>
> proc.time()
user system elapsed
4.179 2.496 4.518