R version 2.7.0 (2008-04-22)
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(8638.7,0,11063.7,0,11855.7,0,10684.5,0,11337.4,0,10478,0,11123.9,0,12909.3,0,11339.9,0,10462.2,0,12733.5,0,10519.2,0,10414.9,0,12476.8,0,12384.6,0,12266.7,0,12919.9,0,11497.3,0,12142,0,13919.4,0,12656.8,0,12034.1,0,13199.7,0,10881.3,0,11301.2,0,13643.9,0,12517,0,13981.1,0,14275.7,0,13435,0,13565.7,0,16216.3,0,12970,0,14079.9,0,14235,0,12213.4,0,12581,0,14130.4,0,14210.8,0,14378.5,0,13142.8,0,13714.7,1,13621.9,1,15379.8,1,13306.3,1,14391.2,1,14909.9,1,14025.4,1,12951.2,1,14344.3,1,16213.3,1,15544.5,1,14750.6,1,17292.7,1,17568.5,1,17930.8,1,18644.7,1,16694.8,1,17242.8,1,16979.9,1),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 8638.7 0 1 0 0 0 0 0 0 0 0 0 0 1
2 11063.7 0 0 1 0 0 0 0 0 0 0 0 0 2
3 11855.7 0 0 0 1 0 0 0 0 0 0 0 0 3
4 10684.5 0 0 0 0 1 0 0 0 0 0 0 0 4
5 11337.4 0 0 0 0 0 1 0 0 0 0 0 0 5
6 10478.0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 11123.9 0 0 0 0 0 0 0 1 0 0 0 0 7
8 12909.3 0 0 0 0 0 0 0 0 1 0 0 0 8
9 11339.9 0 0 0 0 0 0 0 0 0 1 0 0 9
10 10462.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 12733.5 0 0 0 0 0 0 0 0 0 0 0 1 11
12 10519.2 0 0 0 0 0 0 0 0 0 0 0 0 12
13 10414.9 0 1 0 0 0 0 0 0 0 0 0 0 13
14 12476.8 0 0 1 0 0 0 0 0 0 0 0 0 14
15 12384.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 12266.7 0 0 0 0 1 0 0 0 0 0 0 0 16
17 12919.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 11497.3 0 0 0 0 0 0 1 0 0 0 0 0 18
19 12142.0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 13919.4 0 0 0 0 0 0 0 0 1 0 0 0 20
21 12656.8 0 0 0 0 0 0 0 0 0 1 0 0 21
22 12034.1 0 0 0 0 0 0 0 0 0 0 1 0 22
23 13199.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 10881.3 0 0 0 0 0 0 0 0 0 0 0 0 24
25 11301.2 0 1 0 0 0 0 0 0 0 0 0 0 25
26 13643.9 0 0 1 0 0 0 0 0 0 0 0 0 26
27 12517.0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 13981.1 0 0 0 0 1 0 0 0 0 0 0 0 28
29 14275.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 13435.0 0 0 0 0 0 0 1 0 0 0 0 0 30
31 13565.7 0 0 0 0 0 0 0 1 0 0 0 0 31
32 16216.3 0 0 0 0 0 0 0 0 1 0 0 0 32
33 12970.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 14079.9 0 0 0 0 0 0 0 0 0 0 1 0 34
35 14235.0 0 0 0 0 0 0 0 0 0 0 0 1 35
36 12213.4 0 0 0 0 0 0 0 0 0 0 0 0 36
37 12581.0 0 1 0 0 0 0 0 0 0 0 0 0 37
38 14130.4 0 0 1 0 0 0 0 0 0 0 0 0 38
39 14210.8 0 0 0 1 0 0 0 0 0 0 0 0 39
40 14378.5 0 0 0 0 1 0 0 0 0 0 0 0 40
41 13142.8 0 0 0 0 0 1 0 0 0 0 0 0 41
42 13714.7 1 0 0 0 0 0 1 0 0 0 0 0 42
43 13621.9 1 0 0 0 0 0 0 1 0 0 0 0 43
44 15379.8 1 0 0 0 0 0 0 0 1 0 0 0 44
45 13306.3 1 0 0 0 0 0 0 0 0 1 0 0 45
46 14391.2 1 0 0 0 0 0 0 0 0 0 1 0 46
47 14909.9 1 0 0 0 0 0 0 0 0 0 0 1 47
48 14025.4 1 0 0 0 0 0 0 0 0 0 0 0 48
49 12951.2 1 1 0 0 0 0 0 0 0 0 0 0 49
50 14344.3 1 0 1 0 0 0 0 0 0 0 0 0 50
51 16213.3 1 0 0 1 0 0 0 0 0 0 0 0 51
52 15544.5 1 0 0 0 1 0 0 0 0 0 0 0 52
53 14750.6 1 0 0 0 0 1 0 0 0 0 0 0 53
54 17292.7 1 0 0 0 0 0 1 0 0 0 0 0 54
55 17568.5 1 0 0 0 0 0 0 1 0 0 0 0 55
56 17930.8 1 0 0 0 0 0 0 0 1 0 0 0 56
57 18644.7 1 0 0 0 0 0 0 0 0 1 0 0 57
58 16694.8 1 0 0 0 0 0 0 0 0 0 1 0 58
59 17242.8 1 0 0 0 0 0 0 0 0 0 0 1 59
60 16979.9 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
9196.9 -137.0 -618.3 1231.1 1430.5 1260.2
M5 M6 M7 M8 M9 M10
1069.4 990.0 1205.8 2767.5 1174.8 818.7
M11 t
1645.4 105.0
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1655.62 -530.22 -29.52 474.18 2422.19
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9196.88 487.87 18.851 < 2e-16 ***
x -137.01 420.15 -0.326 0.74583
M1 -618.30 561.93 -1.100 0.27692
M2 1231.07 560.91 2.195 0.03327 *
M3 1430.48 560.12 2.554 0.01403 *
M4 1260.21 559.55 2.252 0.02913 *
M5 1069.38 559.21 1.912 0.06208 .
M6 989.99 560.94 1.765 0.08422 .
M7 1205.81 559.70 2.154 0.03648 *
M8 2767.48 558.68 4.954 1.03e-05 ***
M9 1174.85 557.88 2.106 0.04070 *
M10 818.70 557.32 1.469 0.14864
M11 1645.39 556.97 2.954 0.00493 **
t 105.05 11.25 9.334 3.48e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 880.5 on 46 degrees of freedom
Multiple R-squared: 0.8661, Adjusted R-squared: 0.8283
F-statistic: 22.89 on 13 and 46 DF, p-value: 8.51e-16
> postscript(file="/var/www/html/rcomp/tmp/1zszz1227524900.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/2mgbf1227524900.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3jdo81227524900.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4r2dr1227524900.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yf6s1227524900.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-44.92330 425.65670 913.19670 -192.78330 545.89670 -339.16543
7 8 9 10 11 12
-14.12543 104.55457 22.73457 -603.86543 735.69457 61.73457
13 14 15 16 17 18
470.68729 578.16729 181.50729 128.82729 867.80729 -580.45484
19 20 21 22 23 24
-256.61484 -145.93484 79.04516 -292.55484 -58.69484 -836.75484
25 26 27 28 29 30
96.39787 484.67787 -946.68213 582.63787 963.01787 96.65575
31 32 33 34 35 36
-93.50425 890.37575 -868.34425 492.65575 -283.98425 -765.24425
37 38 39 40 41 42
115.60846 -289.41154 -513.47154 -280.55154 -1430.47154 -747.22304
43 44 45 46 47 48
-1160.88304 -1069.70304 -1655.62304 -319.62304 -732.66304 -76.82304
49 50 51 52 53 54
-637.77032 -1199.09032 365.44968 -238.13032 -946.25032 1570.18755
55 56 57 58 59 60
1525.12755 220.70755 2422.18755 723.38755 339.64755 1617.08755
> postscript(file="/var/www/html/rcomp/tmp/68tsn1227524900.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 -44.92330 NA
1 425.65670 -44.92330
2 913.19670 425.65670
3 -192.78330 913.19670
4 545.89670 -192.78330
5 -339.16543 545.89670
6 -14.12543 -339.16543
7 104.55457 -14.12543
8 22.73457 104.55457
9 -603.86543 22.73457
10 735.69457 -603.86543
11 61.73457 735.69457
12 470.68729 61.73457
13 578.16729 470.68729
14 181.50729 578.16729
15 128.82729 181.50729
16 867.80729 128.82729
17 -580.45484 867.80729
18 -256.61484 -580.45484
19 -145.93484 -256.61484
20 79.04516 -145.93484
21 -292.55484 79.04516
22 -58.69484 -292.55484
23 -836.75484 -58.69484
24 96.39787 -836.75484
25 484.67787 96.39787
26 -946.68213 484.67787
27 582.63787 -946.68213
28 963.01787 582.63787
29 96.65575 963.01787
30 -93.50425 96.65575
31 890.37575 -93.50425
32 -868.34425 890.37575
33 492.65575 -868.34425
34 -283.98425 492.65575
35 -765.24425 -283.98425
36 115.60846 -765.24425
37 -289.41154 115.60846
38 -513.47154 -289.41154
39 -280.55154 -513.47154
40 -1430.47154 -280.55154
41 -747.22304 -1430.47154
42 -1160.88304 -747.22304
43 -1069.70304 -1160.88304
44 -1655.62304 -1069.70304
45 -319.62304 -1655.62304
46 -732.66304 -319.62304
47 -76.82304 -732.66304
48 -637.77032 -76.82304
49 -1199.09032 -637.77032
50 365.44968 -1199.09032
51 -238.13032 365.44968
52 -946.25032 -238.13032
53 1570.18755 -946.25032
54 1525.12755 1570.18755
55 220.70755 1525.12755
56 2422.18755 220.70755
57 723.38755 2422.18755
58 339.64755 723.38755
59 1617.08755 339.64755
60 NA 1617.08755
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 425.65670 -44.92330
[2,] 913.19670 425.65670
[3,] -192.78330 913.19670
[4,] 545.89670 -192.78330
[5,] -339.16543 545.89670
[6,] -14.12543 -339.16543
[7,] 104.55457 -14.12543
[8,] 22.73457 104.55457
[9,] -603.86543 22.73457
[10,] 735.69457 -603.86543
[11,] 61.73457 735.69457
[12,] 470.68729 61.73457
[13,] 578.16729 470.68729
[14,] 181.50729 578.16729
[15,] 128.82729 181.50729
[16,] 867.80729 128.82729
[17,] -580.45484 867.80729
[18,] -256.61484 -580.45484
[19,] -145.93484 -256.61484
[20,] 79.04516 -145.93484
[21,] -292.55484 79.04516
[22,] -58.69484 -292.55484
[23,] -836.75484 -58.69484
[24,] 96.39787 -836.75484
[25,] 484.67787 96.39787
[26,] -946.68213 484.67787
[27,] 582.63787 -946.68213
[28,] 963.01787 582.63787
[29,] 96.65575 963.01787
[30,] -93.50425 96.65575
[31,] 890.37575 -93.50425
[32,] -868.34425 890.37575
[33,] 492.65575 -868.34425
[34,] -283.98425 492.65575
[35,] -765.24425 -283.98425
[36,] 115.60846 -765.24425
[37,] -289.41154 115.60846
[38,] -513.47154 -289.41154
[39,] -280.55154 -513.47154
[40,] -1430.47154 -280.55154
[41,] -747.22304 -1430.47154
[42,] -1160.88304 -747.22304
[43,] -1069.70304 -1160.88304
[44,] -1655.62304 -1069.70304
[45,] -319.62304 -1655.62304
[46,] -732.66304 -319.62304
[47,] -76.82304 -732.66304
[48,] -637.77032 -76.82304
[49,] -1199.09032 -637.77032
[50,] 365.44968 -1199.09032
[51,] -238.13032 365.44968
[52,] -946.25032 -238.13032
[53,] 1570.18755 -946.25032
[54,] 1525.12755 1570.18755
[55,] 220.70755 1525.12755
[56,] 2422.18755 220.70755
[57,] 723.38755 2422.18755
[58,] 339.64755 723.38755
[59,] 1617.08755 339.64755
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 425.65670 -44.92330
2 913.19670 425.65670
3 -192.78330 913.19670
4 545.89670 -192.78330
5 -339.16543 545.89670
6 -14.12543 -339.16543
7 104.55457 -14.12543
8 22.73457 104.55457
9 -603.86543 22.73457
10 735.69457 -603.86543
11 61.73457 735.69457
12 470.68729 61.73457
13 578.16729 470.68729
14 181.50729 578.16729
15 128.82729 181.50729
16 867.80729 128.82729
17 -580.45484 867.80729
18 -256.61484 -580.45484
19 -145.93484 -256.61484
20 79.04516 -145.93484
21 -292.55484 79.04516
22 -58.69484 -292.55484
23 -836.75484 -58.69484
24 96.39787 -836.75484
25 484.67787 96.39787
26 -946.68213 484.67787
27 582.63787 -946.68213
28 963.01787 582.63787
29 96.65575 963.01787
30 -93.50425 96.65575
31 890.37575 -93.50425
32 -868.34425 890.37575
33 492.65575 -868.34425
34 -283.98425 492.65575
35 -765.24425 -283.98425
36 115.60846 -765.24425
37 -289.41154 115.60846
38 -513.47154 -289.41154
39 -280.55154 -513.47154
40 -1430.47154 -280.55154
41 -747.22304 -1430.47154
42 -1160.88304 -747.22304
43 -1069.70304 -1160.88304
44 -1655.62304 -1069.70304
45 -319.62304 -1655.62304
46 -732.66304 -319.62304
47 -76.82304 -732.66304
48 -637.77032 -76.82304
49 -1199.09032 -637.77032
50 365.44968 -1199.09032
51 -238.13032 365.44968
52 -946.25032 -238.13032
53 1570.18755 -946.25032
54 1525.12755 1570.18755
55 220.70755 1525.12755
56 2422.18755 220.70755
57 723.38755 2422.18755
58 339.64755 723.38755
59 1617.08755 339.64755
> 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/74rya1227524900.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87cha1227524900.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ghbv1227524900.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10spqq1227524900.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/11xsl71227524900.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/121yw01227524900.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/13ye8j1227524901.tab")
>
> system("convert tmp/1zszz1227524900.ps tmp/1zszz1227524900.png")
> system("convert tmp/2mgbf1227524900.ps tmp/2mgbf1227524900.png")
> system("convert tmp/3jdo81227524900.ps tmp/3jdo81227524900.png")
> system("convert tmp/4r2dr1227524900.ps tmp/4r2dr1227524900.png")
> system("convert tmp/5yf6s1227524900.ps tmp/5yf6s1227524900.png")
> system("convert tmp/68tsn1227524900.ps tmp/68tsn1227524900.png")
> system("convert tmp/74rya1227524900.ps tmp/74rya1227524900.png")
> system("convert tmp/87cha1227524900.ps tmp/87cha1227524900.png")
> system("convert tmp/9ghbv1227524900.ps tmp/9ghbv1227524900.png")
>
>
> proc.time()
user system elapsed
3.984 2.455 4.348