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(56983,0,57942,0,34857,0,39421,0,45612,0,65410,0,50125,0,46879,0,53875,0,49652,0,54167,0,61558,0,56874,0,51966,0,45897,0,46832,0,47852,0,58236,0,54216,0,52687,0,47659,0,50089,0,51247,0,48658,0,47233,0,46988,0,51784,0,53620,0,51479,0,50007,0,52634,0,49566,0,48522,0,53864,0,51477,0,56214,0,60032,0,57862,0,55684,0,75894,1,80564,1,84562,1,87546,1,83654,1,89745,1,79565,1,78498,1,79468,1,82479,1,84675,1,85479,1,83547,1,89654,1,84523,1,87469,1,87985,1,88423,1,90475,1,86542,1,87963,1),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 56983 0 1 0 0 0 0 0 0 0 0 0 0 1
2 57942 0 0 1 0 0 0 0 0 0 0 0 0 2
3 34857 0 0 0 1 0 0 0 0 0 0 0 0 3
4 39421 0 0 0 0 1 0 0 0 0 0 0 0 4
5 45612 0 0 0 0 0 1 0 0 0 0 0 0 5
6 65410 0 0 0 0 0 0 1 0 0 0 0 0 6
7 50125 0 0 0 0 0 0 0 1 0 0 0 0 7
8 46879 0 0 0 0 0 0 0 0 1 0 0 0 8
9 53875 0 0 0 0 0 0 0 0 0 1 0 0 9
10 49652 0 0 0 0 0 0 0 0 0 0 1 0 10
11 54167 0 0 0 0 0 0 0 0 0 0 0 1 11
12 61558 0 0 0 0 0 0 0 0 0 0 0 0 12
13 56874 0 1 0 0 0 0 0 0 0 0 0 0 13
14 51966 0 0 1 0 0 0 0 0 0 0 0 0 14
15 45897 0 0 0 1 0 0 0 0 0 0 0 0 15
16 46832 0 0 0 0 1 0 0 0 0 0 0 0 16
17 47852 0 0 0 0 0 1 0 0 0 0 0 0 17
18 58236 0 0 0 0 0 0 1 0 0 0 0 0 18
19 54216 0 0 0 0 0 0 0 1 0 0 0 0 19
20 52687 0 0 0 0 0 0 0 0 1 0 0 0 20
21 47659 0 0 0 0 0 0 0 0 0 1 0 0 21
22 50089 0 0 0 0 0 0 0 0 0 0 1 0 22
23 51247 0 0 0 0 0 0 0 0 0 0 0 1 23
24 48658 0 0 0 0 0 0 0 0 0 0 0 0 24
25 47233 0 1 0 0 0 0 0 0 0 0 0 0 25
26 46988 0 0 1 0 0 0 0 0 0 0 0 0 26
27 51784 0 0 0 1 0 0 0 0 0 0 0 0 27
28 53620 0 0 0 0 1 0 0 0 0 0 0 0 28
29 51479 0 0 0 0 0 1 0 0 0 0 0 0 29
30 50007 0 0 0 0 0 0 1 0 0 0 0 0 30
31 52634 0 0 0 0 0 0 0 1 0 0 0 0 31
32 49566 0 0 0 0 0 0 0 0 1 0 0 0 32
33 48522 0 0 0 0 0 0 0 0 0 1 0 0 33
34 53864 0 0 0 0 0 0 0 0 0 0 1 0 34
35 51477 0 0 0 0 0 0 0 0 0 0 0 1 35
36 56214 0 0 0 0 0 0 0 0 0 0 0 0 36
37 60032 0 1 0 0 0 0 0 0 0 0 0 0 37
38 57862 0 0 1 0 0 0 0 0 0 0 0 0 38
39 55684 0 0 0 1 0 0 0 0 0 0 0 0 39
40 75894 1 0 0 0 1 0 0 0 0 0 0 0 40
41 80564 1 0 0 0 0 1 0 0 0 0 0 0 41
42 84562 1 0 0 0 0 0 1 0 0 0 0 0 42
43 87546 1 0 0 0 0 0 0 1 0 0 0 0 43
44 83654 1 0 0 0 0 0 0 0 1 0 0 0 44
45 89745 1 0 0 0 0 0 0 0 0 1 0 0 45
46 79565 1 0 0 0 0 0 0 0 0 0 1 0 46
47 78498 1 0 0 0 0 0 0 0 0 0 0 1 47
48 79468 1 0 0 0 0 0 0 0 0 0 0 0 48
49 82479 1 1 0 0 0 0 0 0 0 0 0 0 49
50 84675 1 0 1 0 0 0 0 0 0 0 0 0 50
51 85479 1 0 0 1 0 0 0 0 0 0 0 0 51
52 83547 1 0 0 0 1 0 0 0 0 0 0 0 52
53 89654 1 0 0 0 0 1 0 0 0 0 0 0 53
54 84523 1 0 0 0 0 0 1 0 0 0 0 0 54
55 87469 1 0 0 0 0 0 0 1 0 0 0 0 55
56 87985 1 0 0 0 0 0 0 0 1 0 0 0 56
57 88423 1 0 0 0 0 0 0 0 0 1 0 0 57
58 90475 1 0 0 0 0 0 0 0 0 0 1 0 58
59 86542 1 0 0 0 0 0 0 0 0 0 0 1 59
60 87963 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
50587.8 29350.1 1176.0 219.0 -5050.9 -5921.8
M5 M6 M7 M8 M9 M10
-2875.8 2516.1 243.1 -2124.2 -757.0 -1796.3
M11 t
-2262.5 123.5
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11050.3 -2901.2 188.8 2977.7 11565.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50587.81 2826.59 17.897 < 2e-16 ***
x 29350.08 2464.90 11.907 1.19e-15 ***
M1 1176.01 3196.39 0.368 0.7146
M2 218.96 3189.85 0.069 0.9456
M3 -5050.89 3184.76 -1.586 0.1196
M4 -5921.76 3212.79 -1.843 0.0718 .
M5 -2875.82 3201.93 -0.898 0.3738
M6 2516.13 3192.50 0.788 0.4347
M7 243.07 3184.49 0.076 0.9395
M8 -2124.18 3177.93 -0.668 0.5072
M9 -757.04 3172.81 -0.239 0.8125
M10 -1796.29 3169.15 -0.567 0.5736
M11 -2262.55 3166.95 -0.714 0.4786
t 123.45 68.13 1.812 0.0765 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5006 on 46 degrees of freedom
Multiple R-squared: 0.93, Adjusted R-squared: 0.9102
F-statistic: 47 on 13 and 46 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/freestat/rcomp/tmp/1e9kh1227551861.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/freestat/rcomp/tmp/2wv1u1227551861.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3jiaf1227551861.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4ogrl1227551861.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5s22i1227551861.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
5095.72242 6888.32242 -11050.27758 -5738.86182 -2717.26182 11565.33818
7 8 9 10 11 12
-1570.06182 -2572.26182 2933.13818 -374.06182 4483.73818 9488.73818
13 14 15 16 17 18
3505.26909 -569.13091 -1491.73091 190.68485 -1958.71515 2909.88485
19 20 21 22 23 24
1039.48485 1754.28485 -4764.31515 -1418.51515 82.28485 -4892.71515
25 26 27 28 29 30
-7617.18424 -7028.58424 2913.81576 5497.23152 186.83152 -6800.56848
31 32 33 34 35 36
-2023.96848 -2848.16848 -5382.76848 875.03152 -1169.16848 1181.83152
37 38 39 40 41 42
3700.36242 2363.96242 5332.36242 -3060.30061 -1559.70061 -3077.10061
43 44 45 46 47 48
2056.49939 408.29939 5008.69939 -4255.50061 -4979.70061 -6395.70061
49 50 51 52 53 54
-4684.16970 -1654.56970 4295.83030 3111.24606 6048.84606 -4597.55394
55 56 57 58 59 60
498.04606 3257.84606 2205.24606 5173.04606 1582.84606 617.84606
> postscript(file="/var/www/html/freestat/rcomp/tmp/6lj681227551861.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 5095.72242 NA
1 6888.32242 5095.72242
2 -11050.27758 6888.32242
3 -5738.86182 -11050.27758
4 -2717.26182 -5738.86182
5 11565.33818 -2717.26182
6 -1570.06182 11565.33818
7 -2572.26182 -1570.06182
8 2933.13818 -2572.26182
9 -374.06182 2933.13818
10 4483.73818 -374.06182
11 9488.73818 4483.73818
12 3505.26909 9488.73818
13 -569.13091 3505.26909
14 -1491.73091 -569.13091
15 190.68485 -1491.73091
16 -1958.71515 190.68485
17 2909.88485 -1958.71515
18 1039.48485 2909.88485
19 1754.28485 1039.48485
20 -4764.31515 1754.28485
21 -1418.51515 -4764.31515
22 82.28485 -1418.51515
23 -4892.71515 82.28485
24 -7617.18424 -4892.71515
25 -7028.58424 -7617.18424
26 2913.81576 -7028.58424
27 5497.23152 2913.81576
28 186.83152 5497.23152
29 -6800.56848 186.83152
30 -2023.96848 -6800.56848
31 -2848.16848 -2023.96848
32 -5382.76848 -2848.16848
33 875.03152 -5382.76848
34 -1169.16848 875.03152
35 1181.83152 -1169.16848
36 3700.36242 1181.83152
37 2363.96242 3700.36242
38 5332.36242 2363.96242
39 -3060.30061 5332.36242
40 -1559.70061 -3060.30061
41 -3077.10061 -1559.70061
42 2056.49939 -3077.10061
43 408.29939 2056.49939
44 5008.69939 408.29939
45 -4255.50061 5008.69939
46 -4979.70061 -4255.50061
47 -6395.70061 -4979.70061
48 -4684.16970 -6395.70061
49 -1654.56970 -4684.16970
50 4295.83030 -1654.56970
51 3111.24606 4295.83030
52 6048.84606 3111.24606
53 -4597.55394 6048.84606
54 498.04606 -4597.55394
55 3257.84606 498.04606
56 2205.24606 3257.84606
57 5173.04606 2205.24606
58 1582.84606 5173.04606
59 617.84606 1582.84606
60 NA 617.84606
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6888.32242 5095.72242
[2,] -11050.27758 6888.32242
[3,] -5738.86182 -11050.27758
[4,] -2717.26182 -5738.86182
[5,] 11565.33818 -2717.26182
[6,] -1570.06182 11565.33818
[7,] -2572.26182 -1570.06182
[8,] 2933.13818 -2572.26182
[9,] -374.06182 2933.13818
[10,] 4483.73818 -374.06182
[11,] 9488.73818 4483.73818
[12,] 3505.26909 9488.73818
[13,] -569.13091 3505.26909
[14,] -1491.73091 -569.13091
[15,] 190.68485 -1491.73091
[16,] -1958.71515 190.68485
[17,] 2909.88485 -1958.71515
[18,] 1039.48485 2909.88485
[19,] 1754.28485 1039.48485
[20,] -4764.31515 1754.28485
[21,] -1418.51515 -4764.31515
[22,] 82.28485 -1418.51515
[23,] -4892.71515 82.28485
[24,] -7617.18424 -4892.71515
[25,] -7028.58424 -7617.18424
[26,] 2913.81576 -7028.58424
[27,] 5497.23152 2913.81576
[28,] 186.83152 5497.23152
[29,] -6800.56848 186.83152
[30,] -2023.96848 -6800.56848
[31,] -2848.16848 -2023.96848
[32,] -5382.76848 -2848.16848
[33,] 875.03152 -5382.76848
[34,] -1169.16848 875.03152
[35,] 1181.83152 -1169.16848
[36,] 3700.36242 1181.83152
[37,] 2363.96242 3700.36242
[38,] 5332.36242 2363.96242
[39,] -3060.30061 5332.36242
[40,] -1559.70061 -3060.30061
[41,] -3077.10061 -1559.70061
[42,] 2056.49939 -3077.10061
[43,] 408.29939 2056.49939
[44,] 5008.69939 408.29939
[45,] -4255.50061 5008.69939
[46,] -4979.70061 -4255.50061
[47,] -6395.70061 -4979.70061
[48,] -4684.16970 -6395.70061
[49,] -1654.56970 -4684.16970
[50,] 4295.83030 -1654.56970
[51,] 3111.24606 4295.83030
[52,] 6048.84606 3111.24606
[53,] -4597.55394 6048.84606
[54,] 498.04606 -4597.55394
[55,] 3257.84606 498.04606
[56,] 2205.24606 3257.84606
[57,] 5173.04606 2205.24606
[58,] 1582.84606 5173.04606
[59,] 617.84606 1582.84606
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6888.32242 5095.72242
2 -11050.27758 6888.32242
3 -5738.86182 -11050.27758
4 -2717.26182 -5738.86182
5 11565.33818 -2717.26182
6 -1570.06182 11565.33818
7 -2572.26182 -1570.06182
8 2933.13818 -2572.26182
9 -374.06182 2933.13818
10 4483.73818 -374.06182
11 9488.73818 4483.73818
12 3505.26909 9488.73818
13 -569.13091 3505.26909
14 -1491.73091 -569.13091
15 190.68485 -1491.73091
16 -1958.71515 190.68485
17 2909.88485 -1958.71515
18 1039.48485 2909.88485
19 1754.28485 1039.48485
20 -4764.31515 1754.28485
21 -1418.51515 -4764.31515
22 82.28485 -1418.51515
23 -4892.71515 82.28485
24 -7617.18424 -4892.71515
25 -7028.58424 -7617.18424
26 2913.81576 -7028.58424
27 5497.23152 2913.81576
28 186.83152 5497.23152
29 -6800.56848 186.83152
30 -2023.96848 -6800.56848
31 -2848.16848 -2023.96848
32 -5382.76848 -2848.16848
33 875.03152 -5382.76848
34 -1169.16848 875.03152
35 1181.83152 -1169.16848
36 3700.36242 1181.83152
37 2363.96242 3700.36242
38 5332.36242 2363.96242
39 -3060.30061 5332.36242
40 -1559.70061 -3060.30061
41 -3077.10061 -1559.70061
42 2056.49939 -3077.10061
43 408.29939 2056.49939
44 5008.69939 408.29939
45 -4255.50061 5008.69939
46 -4979.70061 -4255.50061
47 -6395.70061 -4979.70061
48 -4684.16970 -6395.70061
49 -1654.56970 -4684.16970
50 4295.83030 -1654.56970
51 3111.24606 4295.83030
52 6048.84606 3111.24606
53 -4597.55394 6048.84606
54 498.04606 -4597.55394
55 3257.84606 498.04606
56 2205.24606 3257.84606
57 5173.04606 2205.24606
58 1582.84606 5173.04606
59 617.84606 1582.84606
> 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/freestat/rcomp/tmp/7mc3z1227551862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8iklm1227551862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9ws6j1227551862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/1026ga1227551862.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/freestat/rcomp/tmp/11rpkn1227551862.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/freestat/rcomp/tmp/12rmyn1227551862.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/freestat/rcomp/tmp/13lwhs1227551862.tab")
>
> system("convert tmp/1e9kh1227551861.ps tmp/1e9kh1227551861.png")
> system("convert tmp/2wv1u1227551861.ps tmp/2wv1u1227551861.png")
> system("convert tmp/3jiaf1227551861.ps tmp/3jiaf1227551861.png")
> system("convert tmp/4ogrl1227551861.ps tmp/4ogrl1227551861.png")
> system("convert tmp/5s22i1227551861.ps tmp/5s22i1227551861.png")
> system("convert tmp/6lj681227551861.ps tmp/6lj681227551861.png")
> system("convert tmp/7mc3z1227551862.ps tmp/7mc3z1227551862.png")
> system("convert tmp/8iklm1227551862.ps tmp/8iklm1227551862.png")
> system("convert tmp/9ws6j1227551862.ps tmp/9ws6j1227551862.png")
>
>
> proc.time()
user system elapsed
2.943 2.223 3.607