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(101.2,0,100.1,0,99,0,99.8,0,101,0,96.6,0,103.1,0,105.2,0,100,0,103.2,0,99.7,0,99.1,0,105.1,0,101.7,0,104.9,0,104.3,0,101.8,0,105.9,0,103.8,0,101.3,0,100.7,0,101.2,0,102.9,0,106.2,0,104.7,0,103.9,0,101.5,0,103.2,0,104.7,0,102.2,0,101.5,0,102.6,0,105.2,0,99.4,0,103.5,0,100.9,0,101.7,0,104.1,0,105.3,0,103.7,0,106.7,1,106.4,1,106,1,107,1,108.6,1,108.1,1,107.5,1,110,1,107.6,1,110,1,110,1,108.7,1,109.1,1,109.9,1,109.8,1,111.1,1,109.9,1,112.8,1,114.6,1,92.5,1),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
1 101.2 0 1 0 0 0 0 0 0 0 0 0 0 1
2 100.1 0 0 1 0 0 0 0 0 0 0 0 0 2
3 99.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 99.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 101.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 96.6 0 0 0 0 0 0 1 0 0 0 0 0 6
7 103.1 0 0 0 0 0 0 0 1 0 0 0 0 7
8 105.2 0 0 0 0 0 0 0 0 1 0 0 0 8
9 100.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 103.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 99.7 0 0 0 0 0 0 0 0 0 0 0 1 11
12 99.1 0 0 0 0 0 0 0 0 0 0 0 0 12
13 105.1 0 1 0 0 0 0 0 0 0 0 0 0 13
14 101.7 0 0 1 0 0 0 0 0 0 0 0 0 14
15 104.9 0 0 0 1 0 0 0 0 0 0 0 0 15
16 104.3 0 0 0 0 1 0 0 0 0 0 0 0 16
17 101.8 0 0 0 0 0 1 0 0 0 0 0 0 17
18 105.9 0 0 0 0 0 0 1 0 0 0 0 0 18
19 103.8 0 0 0 0 0 0 0 1 0 0 0 0 19
20 101.3 0 0 0 0 0 0 0 0 1 0 0 0 20
21 100.7 0 0 0 0 0 0 0 0 0 1 0 0 21
22 101.2 0 0 0 0 0 0 0 0 0 0 1 0 22
23 102.9 0 0 0 0 0 0 0 0 0 0 0 1 23
24 106.2 0 0 0 0 0 0 0 0 0 0 0 0 24
25 104.7 0 1 0 0 0 0 0 0 0 0 0 0 25
26 103.9 0 0 1 0 0 0 0 0 0 0 0 0 26
27 101.5 0 0 0 1 0 0 0 0 0 0 0 0 27
28 103.2 0 0 0 0 1 0 0 0 0 0 0 0 28
29 104.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 102.2 0 0 0 0 0 0 1 0 0 0 0 0 30
31 101.5 0 0 0 0 0 0 0 1 0 0 0 0 31
32 102.6 0 0 0 0 0 0 0 0 1 0 0 0 32
33 105.2 0 0 0 0 0 0 0 0 0 1 0 0 33
34 99.4 0 0 0 0 0 0 0 0 0 0 1 0 34
35 103.5 0 0 0 0 0 0 0 0 0 0 0 1 35
36 100.9 0 0 0 0 0 0 0 0 0 0 0 0 36
37 101.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 104.1 0 0 1 0 0 0 0 0 0 0 0 0 38
39 105.3 0 0 0 1 0 0 0 0 0 0 0 0 39
40 103.7 0 0 0 0 1 0 0 0 0 0 0 0 40
41 106.7 1 0 0 0 0 1 0 0 0 0 0 0 41
42 106.4 1 0 0 0 0 0 1 0 0 0 0 0 42
43 106.0 1 0 0 0 0 0 0 1 0 0 0 0 43
44 107.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 108.6 1 0 0 0 0 0 0 0 0 1 0 0 45
46 108.1 1 0 0 0 0 0 0 0 0 0 1 0 46
47 107.5 1 0 0 0 0 0 0 0 0 0 0 1 47
48 110.0 1 0 0 0 0 0 0 0 0 0 0 0 48
49 107.6 1 1 0 0 0 0 0 0 0 0 0 0 49
50 110.0 1 0 1 0 0 0 0 0 0 0 0 0 50
51 110.0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 108.7 1 0 0 0 1 0 0 0 0 0 0 0 52
53 109.1 1 0 0 0 0 1 0 0 0 0 0 0 53
54 109.9 1 0 0 0 0 0 1 0 0 0 0 0 54
55 109.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 111.1 1 0 0 0 0 0 0 0 1 0 0 0 56
57 109.9 1 0 0 0 0 0 0 0 0 1 0 0 57
58 112.8 1 0 0 0 0 0 0 0 0 0 1 0 58
59 114.6 1 0 0 0 0 0 0 0 0 0 0 1 59
60 92.5 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
97.44250 3.71250 3.92188 3.74375 3.84563 3.56750
M5 M6 M7 M8 M9 M10
3.46687 2.92875 3.49062 4.01250 3.37437 3.35625
M11 t
3.97812 0.07812
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.342 -1.327 -0.070 1.418 6.882
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.44250 1.74921 55.707 <2e-16 ***
x 3.71250 1.51486 2.451 0.0181 *
M1 3.92188 1.99738 1.964 0.0556 .
M2 3.74375 1.99355 1.878 0.0667 .
M3 3.84563 1.99056 1.932 0.0595 .
M4 3.56750 1.98843 1.794 0.0794 .
M5 3.46687 1.99993 1.733 0.0897 .
M6 2.92875 1.99440 1.468 0.1488
M7 3.49062 1.98971 1.754 0.0860 .
M8 4.01250 1.98586 2.021 0.0492 *
M9 3.37437 1.98286 1.702 0.0955 .
M10 3.35625 1.98072 1.694 0.0969 .
M11 3.97812 1.97943 2.010 0.0503 .
t 0.07812 0.04123 1.895 0.0644 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.129 on 46 degrees of freedom
Multiple R-squared: 0.5543, Adjusted R-squared: 0.4283
F-statistic: 4.4 on 13 and 46 DF, p-value: 8.805e-05
> postscript(file="/var/www/html/rcomp/tmp/1jkl71227200188.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/2wut11227200188.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ze9j1227200188.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kszc1227200188.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x40a1227200188.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 7 8
-0.2425 -1.2425 -2.5225 -1.5225 -0.3000 -4.2400 1.6200 3.1200
9 10 11 12 13 14 15 16
-1.5200 1.6200 -2.5800 0.7200 2.7200 -0.5800 2.4400 2.0400
17 18 19 20 21 22 23 24
-0.4375 4.1225 1.3825 -1.7175 -1.7575 -1.3175 -0.3175 6.8825
25 26 27 28 29 30 31 32
1.3825 0.6825 -1.8975 0.0025 1.5250 -0.5150 -1.8550 -1.3550
33 34 35 36 37 38 39 40
1.8050 -4.0550 -0.6550 0.6450 -2.5550 -0.0550 0.9650 -0.4350
41 42 43 44 45 46 47 48
-1.1250 -0.9650 -2.0050 -1.6050 0.5550 -0.0050 -1.3050 5.0950
49 50 51 52 53 54 55 56
-1.3050 1.1950 1.0150 -0.0850 0.3375 1.5975 0.8575 1.5575
57 58 59 60
0.9175 3.7575 4.8575 -13.3425
> postscript(file="/var/www/html/rcomp/tmp/6xnks1227200188.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 -0.2425 NA
1 -1.2425 -0.2425
2 -2.5225 -1.2425
3 -1.5225 -2.5225
4 -0.3000 -1.5225
5 -4.2400 -0.3000
6 1.6200 -4.2400
7 3.1200 1.6200
8 -1.5200 3.1200
9 1.6200 -1.5200
10 -2.5800 1.6200
11 0.7200 -2.5800
12 2.7200 0.7200
13 -0.5800 2.7200
14 2.4400 -0.5800
15 2.0400 2.4400
16 -0.4375 2.0400
17 4.1225 -0.4375
18 1.3825 4.1225
19 -1.7175 1.3825
20 -1.7575 -1.7175
21 -1.3175 -1.7575
22 -0.3175 -1.3175
23 6.8825 -0.3175
24 1.3825 6.8825
25 0.6825 1.3825
26 -1.8975 0.6825
27 0.0025 -1.8975
28 1.5250 0.0025
29 -0.5150 1.5250
30 -1.8550 -0.5150
31 -1.3550 -1.8550
32 1.8050 -1.3550
33 -4.0550 1.8050
34 -0.6550 -4.0550
35 0.6450 -0.6550
36 -2.5550 0.6450
37 -0.0550 -2.5550
38 0.9650 -0.0550
39 -0.4350 0.9650
40 -1.1250 -0.4350
41 -0.9650 -1.1250
42 -2.0050 -0.9650
43 -1.6050 -2.0050
44 0.5550 -1.6050
45 -0.0050 0.5550
46 -1.3050 -0.0050
47 5.0950 -1.3050
48 -1.3050 5.0950
49 1.1950 -1.3050
50 1.0150 1.1950
51 -0.0850 1.0150
52 0.3375 -0.0850
53 1.5975 0.3375
54 0.8575 1.5975
55 1.5575 0.8575
56 0.9175 1.5575
57 3.7575 0.9175
58 4.8575 3.7575
59 -13.3425 4.8575
60 NA -13.3425
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.2425 -0.2425
[2,] -2.5225 -1.2425
[3,] -1.5225 -2.5225
[4,] -0.3000 -1.5225
[5,] -4.2400 -0.3000
[6,] 1.6200 -4.2400
[7,] 3.1200 1.6200
[8,] -1.5200 3.1200
[9,] 1.6200 -1.5200
[10,] -2.5800 1.6200
[11,] 0.7200 -2.5800
[12,] 2.7200 0.7200
[13,] -0.5800 2.7200
[14,] 2.4400 -0.5800
[15,] 2.0400 2.4400
[16,] -0.4375 2.0400
[17,] 4.1225 -0.4375
[18,] 1.3825 4.1225
[19,] -1.7175 1.3825
[20,] -1.7575 -1.7175
[21,] -1.3175 -1.7575
[22,] -0.3175 -1.3175
[23,] 6.8825 -0.3175
[24,] 1.3825 6.8825
[25,] 0.6825 1.3825
[26,] -1.8975 0.6825
[27,] 0.0025 -1.8975
[28,] 1.5250 0.0025
[29,] -0.5150 1.5250
[30,] -1.8550 -0.5150
[31,] -1.3550 -1.8550
[32,] 1.8050 -1.3550
[33,] -4.0550 1.8050
[34,] -0.6550 -4.0550
[35,] 0.6450 -0.6550
[36,] -2.5550 0.6450
[37,] -0.0550 -2.5550
[38,] 0.9650 -0.0550
[39,] -0.4350 0.9650
[40,] -1.1250 -0.4350
[41,] -0.9650 -1.1250
[42,] -2.0050 -0.9650
[43,] -1.6050 -2.0050
[44,] 0.5550 -1.6050
[45,] -0.0050 0.5550
[46,] -1.3050 -0.0050
[47,] 5.0950 -1.3050
[48,] -1.3050 5.0950
[49,] 1.1950 -1.3050
[50,] 1.0150 1.1950
[51,] -0.0850 1.0150
[52,] 0.3375 -0.0850
[53,] 1.5975 0.3375
[54,] 0.8575 1.5975
[55,] 1.5575 0.8575
[56,] 0.9175 1.5575
[57,] 3.7575 0.9175
[58,] 4.8575 3.7575
[59,] -13.3425 4.8575
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.2425 -0.2425
2 -2.5225 -1.2425
3 -1.5225 -2.5225
4 -0.3000 -1.5225
5 -4.2400 -0.3000
6 1.6200 -4.2400
7 3.1200 1.6200
8 -1.5200 3.1200
9 1.6200 -1.5200
10 -2.5800 1.6200
11 0.7200 -2.5800
12 2.7200 0.7200
13 -0.5800 2.7200
14 2.4400 -0.5800
15 2.0400 2.4400
16 -0.4375 2.0400
17 4.1225 -0.4375
18 1.3825 4.1225
19 -1.7175 1.3825
20 -1.7575 -1.7175
21 -1.3175 -1.7575
22 -0.3175 -1.3175
23 6.8825 -0.3175
24 1.3825 6.8825
25 0.6825 1.3825
26 -1.8975 0.6825
27 0.0025 -1.8975
28 1.5250 0.0025
29 -0.5150 1.5250
30 -1.8550 -0.5150
31 -1.3550 -1.8550
32 1.8050 -1.3550
33 -4.0550 1.8050
34 -0.6550 -4.0550
35 0.6450 -0.6550
36 -2.5550 0.6450
37 -0.0550 -2.5550
38 0.9650 -0.0550
39 -0.4350 0.9650
40 -1.1250 -0.4350
41 -0.9650 -1.1250
42 -2.0050 -0.9650
43 -1.6050 -2.0050
44 0.5550 -1.6050
45 -0.0050 0.5550
46 -1.3050 -0.0050
47 5.0950 -1.3050
48 -1.3050 5.0950
49 1.1950 -1.3050
50 1.0150 1.1950
51 -0.0850 1.0150
52 0.3375 -0.0850
53 1.5975 0.3375
54 0.8575 1.5975
55 1.5575 0.8575
56 0.9175 1.5575
57 3.7575 0.9175
58 4.8575 3.7575
59 -13.3425 4.8575
> 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/79aui1227200188.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k8u61227200188.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m3u51227200188.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10vnaq1227200188.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/11iofi1227200188.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/12674z1227200188.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/13sff91227200188.tab")
>
> system("convert tmp/1jkl71227200188.ps tmp/1jkl71227200188.png")
> system("convert tmp/2wut11227200188.ps tmp/2wut11227200188.png")
> system("convert tmp/3ze9j1227200188.ps tmp/3ze9j1227200188.png")
> system("convert tmp/4kszc1227200188.ps tmp/4kszc1227200188.png")
> system("convert tmp/5x40a1227200188.ps tmp/5x40a1227200188.png")
> system("convert tmp/6xnks1227200188.ps tmp/6xnks1227200188.png")
> system("convert tmp/79aui1227200188.ps tmp/79aui1227200188.png")
> system("convert tmp/8k8u61227200188.ps tmp/8k8u61227200188.png")
> system("convert tmp/9m3u51227200188.ps tmp/9m3u51227200188.png")
>
>
> proc.time()
user system elapsed
1.924 1.400 2.306