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(10413,0,10709,0,10662,0,10570,0,10297,0,10635,0,10872,0,10296,0,10383,0,10431,0,10574,0,10653,0,10805,0,10872,0,10625,0,10407,0,10463,0,10556,0,10646,0,10702,0,11353,1,11346,1,11451,1,11964,1,12574,1,13031,1,13812,1,14544,1,14931,1,14886,1,16005,1,17064,1,15168,1,16050,1,15839,1,15137,1,14954,1,15648,1,15305,1,15579,1,16348,1,15928,1,16171,1,15937,1,15713,1,15594,1,15683,1,16438,1,17032,1,17696,1,17745,1,19394,1,20148,1,20108,1,18584,1,18441,1,18391,1,19178,1,18079,1,18483,1,19644,1),dim=c(2,61),dimnames=list(c('Goudkoers','DrasticChange'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Goudkoers','DrasticChange'),1:61))
> 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
Goudkoers DrasticChange M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 10413 0 1 0 0 0 0 0 0 0 0 0 0 1
2 10709 0 0 1 0 0 0 0 0 0 0 0 0 2
3 10662 0 0 0 1 0 0 0 0 0 0 0 0 3
4 10570 0 0 0 0 1 0 0 0 0 0 0 0 4
5 10297 0 0 0 0 0 1 0 0 0 0 0 0 5
6 10635 0 0 0 0 0 0 1 0 0 0 0 0 6
7 10872 0 0 0 0 0 0 0 1 0 0 0 0 7
8 10296 0 0 0 0 0 0 0 0 1 0 0 0 8
9 10383 0 0 0 0 0 0 0 0 0 1 0 0 9
10 10431 0 0 0 0 0 0 0 0 0 0 1 0 10
11 10574 0 0 0 0 0 0 0 0 0 0 0 1 11
12 10653 0 0 0 0 0 0 0 0 0 0 0 0 12
13 10805 0 1 0 0 0 0 0 0 0 0 0 0 13
14 10872 0 0 1 0 0 0 0 0 0 0 0 0 14
15 10625 0 0 0 1 0 0 0 0 0 0 0 0 15
16 10407 0 0 0 0 1 0 0 0 0 0 0 0 16
17 10463 0 0 0 0 0 1 0 0 0 0 0 0 17
18 10556 0 0 0 0 0 0 1 0 0 0 0 0 18
19 10646 0 0 0 0 0 0 0 1 0 0 0 0 19
20 10702 0 0 0 0 0 0 0 0 1 0 0 0 20
21 11353 1 0 0 0 0 0 0 0 0 1 0 0 21
22 11346 1 0 0 0 0 0 0 0 0 0 1 0 22
23 11451 1 0 0 0 0 0 0 0 0 0 0 1 23
24 11964 1 0 0 0 0 0 0 0 0 0 0 0 24
25 12574 1 1 0 0 0 0 0 0 0 0 0 0 25
26 13031 1 0 1 0 0 0 0 0 0 0 0 0 26
27 13812 1 0 0 1 0 0 0 0 0 0 0 0 27
28 14544 1 0 0 0 1 0 0 0 0 0 0 0 28
29 14931 1 0 0 0 0 1 0 0 0 0 0 0 29
30 14886 1 0 0 0 0 0 1 0 0 0 0 0 30
31 16005 1 0 0 0 0 0 0 1 0 0 0 0 31
32 17064 1 0 0 0 0 0 0 0 1 0 0 0 32
33 15168 1 0 0 0 0 0 0 0 0 1 0 0 33
34 16050 1 0 0 0 0 0 0 0 0 0 1 0 34
35 15839 1 0 0 0 0 0 0 0 0 0 0 1 35
36 15137 1 0 0 0 0 0 0 0 0 0 0 0 36
37 14954 1 1 0 0 0 0 0 0 0 0 0 0 37
38 15648 1 0 1 0 0 0 0 0 0 0 0 0 38
39 15305 1 0 0 1 0 0 0 0 0 0 0 0 39
40 15579 1 0 0 0 1 0 0 0 0 0 0 0 40
41 16348 1 0 0 0 0 1 0 0 0 0 0 0 41
42 15928 1 0 0 0 0 0 1 0 0 0 0 0 42
43 16171 1 0 0 0 0 0 0 1 0 0 0 0 43
44 15937 1 0 0 0 0 0 0 0 1 0 0 0 44
45 15713 1 0 0 0 0 0 0 0 0 1 0 0 45
46 15594 1 0 0 0 0 0 0 0 0 0 1 0 46
47 15683 1 0 0 0 0 0 0 0 0 0 0 1 47
48 16438 1 0 0 0 0 0 0 0 0 0 0 0 48
49 17032 1 1 0 0 0 0 0 0 0 0 0 0 49
50 17696 1 0 1 0 0 0 0 0 0 0 0 0 50
51 17745 1 0 0 1 0 0 0 0 0 0 0 0 51
52 19394 1 0 0 0 1 0 0 0 0 0 0 0 52
53 20148 1 0 0 0 0 1 0 0 0 0 0 0 53
54 20108 1 0 0 0 0 0 1 0 0 0 0 0 54
55 18584 1 0 0 0 0 0 0 1 0 0 0 0 55
56 18441 1 0 0 0 0 0 0 0 1 0 0 0 56
57 18391 1 0 0 0 0 0 0 0 0 1 0 0 57
58 19178 1 0 0 0 0 0 0 0 0 0 1 0 58
59 18079 1 0 0 0 0 0 0 0 0 0 0 1 59
60 18483 1 0 0 0 0 0 0 0 0 0 0 0 60
61 19644 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) DrasticChange M1 M2 M3
8213.36 772.59 597.17 795.04 675.21
M4 M5 M6 M7 M8
985.78 1165.94 992.71 867.28 741.25
M9 M10 M11 t
141.90 301.66 -51.37 158.43
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1609.656 -697.906 -8.894 601.850 2266.963
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8213.36 532.59 15.422 < 2e-16 ***
DrasticChange 772.59 488.43 1.582 0.1204
M1 597.17 621.11 0.961 0.3412
M2 795.04 651.83 1.220 0.2287
M3 675.21 650.96 1.037 0.3049
M4 985.78 650.35 1.516 0.1363
M5 1165.94 649.99 1.794 0.0793 .
M6 992.71 649.89 1.528 0.1333
M7 867.28 650.05 1.334 0.1886
M8 741.25 650.47 1.140 0.2602
M9 141.90 648.56 0.219 0.8278
M10 301.66 647.91 0.466 0.6437
M11 -51.37 647.52 -0.079 0.9371
t 158.43 12.96 12.222 3.37e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1024 on 47 degrees of freedom
Multiple R-squared: 0.9206, Adjusted R-squared: 0.8987
F-statistic: 41.95 on 13 and 47 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/freestat/rcomp/tmp/1z9wo1227121379.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/23jp61227121379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/332ge1227121379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4xalr1227121379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5r1le1227121379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
1444.03125 1383.73125 1298.13125 737.13125 125.53125 478.33125
7 8 9 10 11 12
682.33125 73.93125 601.85000 331.65000 669.25000 538.45000
13 14 15 16 17 18
-65.15625 -354.45625 -640.05625 -1327.05625 -1609.65625 -1501.85625
19 20 21 22 23 24
-1444.85625 -1421.25625 -1101.93125 -1427.13125 -1127.53125 -824.33125
25 26 27 28 29 30
-969.93750 -869.23750 -126.83750 136.16250 184.56250 154.36250
31 32 33 34 35 36
1240.36250 2266.96250 811.88125 1375.68125 1359.28125 447.48125
37 38 39 40 41 42
-491.12500 -153.42500 -535.02500 -730.02500 -299.62500 -704.82500
43 44 45 46 47 48
-494.82500 -761.22500 -544.30625 -981.50625 -697.90625 -152.70625
49 50 51 52 53 54
-314.31250 -6.61250 3.78750 1183.78750 1599.18750 1573.98750
55 56 57 58 59 60
16.98750 -158.41250 232.50625 701.30625 -203.09375 -8.89375
61
396.50000
> postscript(file="/var/www/html/freestat/rcomp/tmp/6ewqc1227121379.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 1444.03125 NA
1 1383.73125 1444.03125
2 1298.13125 1383.73125
3 737.13125 1298.13125
4 125.53125 737.13125
5 478.33125 125.53125
6 682.33125 478.33125
7 73.93125 682.33125
8 601.85000 73.93125
9 331.65000 601.85000
10 669.25000 331.65000
11 538.45000 669.25000
12 -65.15625 538.45000
13 -354.45625 -65.15625
14 -640.05625 -354.45625
15 -1327.05625 -640.05625
16 -1609.65625 -1327.05625
17 -1501.85625 -1609.65625
18 -1444.85625 -1501.85625
19 -1421.25625 -1444.85625
20 -1101.93125 -1421.25625
21 -1427.13125 -1101.93125
22 -1127.53125 -1427.13125
23 -824.33125 -1127.53125
24 -969.93750 -824.33125
25 -869.23750 -969.93750
26 -126.83750 -869.23750
27 136.16250 -126.83750
28 184.56250 136.16250
29 154.36250 184.56250
30 1240.36250 154.36250
31 2266.96250 1240.36250
32 811.88125 2266.96250
33 1375.68125 811.88125
34 1359.28125 1375.68125
35 447.48125 1359.28125
36 -491.12500 447.48125
37 -153.42500 -491.12500
38 -535.02500 -153.42500
39 -730.02500 -535.02500
40 -299.62500 -730.02500
41 -704.82500 -299.62500
42 -494.82500 -704.82500
43 -761.22500 -494.82500
44 -544.30625 -761.22500
45 -981.50625 -544.30625
46 -697.90625 -981.50625
47 -152.70625 -697.90625
48 -314.31250 -152.70625
49 -6.61250 -314.31250
50 3.78750 -6.61250
51 1183.78750 3.78750
52 1599.18750 1183.78750
53 1573.98750 1599.18750
54 16.98750 1573.98750
55 -158.41250 16.98750
56 232.50625 -158.41250
57 701.30625 232.50625
58 -203.09375 701.30625
59 -8.89375 -203.09375
60 396.50000 -8.89375
61 NA 396.50000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1383.73125 1444.03125
[2,] 1298.13125 1383.73125
[3,] 737.13125 1298.13125
[4,] 125.53125 737.13125
[5,] 478.33125 125.53125
[6,] 682.33125 478.33125
[7,] 73.93125 682.33125
[8,] 601.85000 73.93125
[9,] 331.65000 601.85000
[10,] 669.25000 331.65000
[11,] 538.45000 669.25000
[12,] -65.15625 538.45000
[13,] -354.45625 -65.15625
[14,] -640.05625 -354.45625
[15,] -1327.05625 -640.05625
[16,] -1609.65625 -1327.05625
[17,] -1501.85625 -1609.65625
[18,] -1444.85625 -1501.85625
[19,] -1421.25625 -1444.85625
[20,] -1101.93125 -1421.25625
[21,] -1427.13125 -1101.93125
[22,] -1127.53125 -1427.13125
[23,] -824.33125 -1127.53125
[24,] -969.93750 -824.33125
[25,] -869.23750 -969.93750
[26,] -126.83750 -869.23750
[27,] 136.16250 -126.83750
[28,] 184.56250 136.16250
[29,] 154.36250 184.56250
[30,] 1240.36250 154.36250
[31,] 2266.96250 1240.36250
[32,] 811.88125 2266.96250
[33,] 1375.68125 811.88125
[34,] 1359.28125 1375.68125
[35,] 447.48125 1359.28125
[36,] -491.12500 447.48125
[37,] -153.42500 -491.12500
[38,] -535.02500 -153.42500
[39,] -730.02500 -535.02500
[40,] -299.62500 -730.02500
[41,] -704.82500 -299.62500
[42,] -494.82500 -704.82500
[43,] -761.22500 -494.82500
[44,] -544.30625 -761.22500
[45,] -981.50625 -544.30625
[46,] -697.90625 -981.50625
[47,] -152.70625 -697.90625
[48,] -314.31250 -152.70625
[49,] -6.61250 -314.31250
[50,] 3.78750 -6.61250
[51,] 1183.78750 3.78750
[52,] 1599.18750 1183.78750
[53,] 1573.98750 1599.18750
[54,] 16.98750 1573.98750
[55,] -158.41250 16.98750
[56,] 232.50625 -158.41250
[57,] 701.30625 232.50625
[58,] -203.09375 701.30625
[59,] -8.89375 -203.09375
[60,] 396.50000 -8.89375
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1383.73125 1444.03125
2 1298.13125 1383.73125
3 737.13125 1298.13125
4 125.53125 737.13125
5 478.33125 125.53125
6 682.33125 478.33125
7 73.93125 682.33125
8 601.85000 73.93125
9 331.65000 601.85000
10 669.25000 331.65000
11 538.45000 669.25000
12 -65.15625 538.45000
13 -354.45625 -65.15625
14 -640.05625 -354.45625
15 -1327.05625 -640.05625
16 -1609.65625 -1327.05625
17 -1501.85625 -1609.65625
18 -1444.85625 -1501.85625
19 -1421.25625 -1444.85625
20 -1101.93125 -1421.25625
21 -1427.13125 -1101.93125
22 -1127.53125 -1427.13125
23 -824.33125 -1127.53125
24 -969.93750 -824.33125
25 -869.23750 -969.93750
26 -126.83750 -869.23750
27 136.16250 -126.83750
28 184.56250 136.16250
29 154.36250 184.56250
30 1240.36250 154.36250
31 2266.96250 1240.36250
32 811.88125 2266.96250
33 1375.68125 811.88125
34 1359.28125 1375.68125
35 447.48125 1359.28125
36 -491.12500 447.48125
37 -153.42500 -491.12500
38 -535.02500 -153.42500
39 -730.02500 -535.02500
40 -299.62500 -730.02500
41 -704.82500 -299.62500
42 -494.82500 -704.82500
43 -761.22500 -494.82500
44 -544.30625 -761.22500
45 -981.50625 -544.30625
46 -697.90625 -981.50625
47 -152.70625 -697.90625
48 -314.31250 -152.70625
49 -6.61250 -314.31250
50 3.78750 -6.61250
51 1183.78750 3.78750
52 1599.18750 1183.78750
53 1573.98750 1599.18750
54 16.98750 1573.98750
55 -158.41250 16.98750
56 232.50625 -158.41250
57 701.30625 232.50625
58 -203.09375 701.30625
59 -8.89375 -203.09375
60 396.50000 -8.89375
> 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/7er1j1227121379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8i4891227121379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9yn2a1227121379.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10wnfx1227121379.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/11otz71227121379.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/12zzfo1227121379.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/13eate1227121379.tab")
>
> system("convert tmp/1z9wo1227121379.ps tmp/1z9wo1227121379.png")
> system("convert tmp/23jp61227121379.ps tmp/23jp61227121379.png")
> system("convert tmp/332ge1227121379.ps tmp/332ge1227121379.png")
> system("convert tmp/4xalr1227121379.ps tmp/4xalr1227121379.png")
> system("convert tmp/5r1le1227121379.ps tmp/5r1le1227121379.png")
> system("convert tmp/6ewqc1227121379.ps tmp/6ewqc1227121379.png")
> system("convert tmp/7er1j1227121379.ps tmp/7er1j1227121379.png")
> system("convert tmp/8i4891227121379.ps tmp/8i4891227121379.png")
> system("convert tmp/9yn2a1227121379.ps tmp/9yn2a1227121379.png")
>
>
> proc.time()
user system elapsed
2.940 2.203 3.351