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(7.8,0,7.6,0,7.5,0,7.6,0,7.5,0,7.3,0,7.6,0,7.5,0,7.6,0,7.9,0,7.9,0,8.1,0,8.2,0,8.0,0,7.5,0,6.8,0,6.5,0,6.6,0,7.6,0,8.0,0,8.0,0,7.7,0,7.5,0,7.6,0,7.7,0,7.9,0,7.8,0,7.5,0,7.5,0,7.1,0,7.5,0,7.5,0,7.6,0,7.7,1,7.9,1,8.1,1,8.2,1,8.2,1,8.1,1,7.9,1,7.3,1,6.9,1,6.6,1,6.7,1,6.9,1,7.0,1,7.1,1,7.2,1,7.1,1,6.9,1,7.0,1,6.8,1,6.4,1,6.7,1,6.7,1,6.4,1,6.3,1,6.2,1,6.5,1,6.8,1,6.8,1,6.5,1,6.3,1,5.9,1,5.9,1,6.4,1,6.4,1),dim=c(2,67),dimnames=list(c('y','x'),1:67))
> y <- array(NA,dim=c(2,67),dimnames=list(c('y','x'),1:67))
> 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 = '0'
> #'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 7.8 0 1 0 0 0 0 0 0 0 0 0 0 1
2 7.6 0 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 0 0 0 1 0 0 0 0 0 0 0 0 3
4 7.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 7.5 0 0 0 0 0 1 0 0 0 0 0 0 5
6 7.3 0 0 0 0 0 0 1 0 0 0 0 0 6
7 7.6 0 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 7.6 0 0 0 0 0 0 0 0 0 1 0 0 9
10 7.9 0 0 0 0 0 0 0 0 0 0 1 0 10
11 7.9 0 0 0 0 0 0 0 0 0 0 0 1 11
12 8.1 0 0 0 0 0 0 0 0 0 0 0 0 12
13 8.2 0 1 0 0 0 0 0 0 0 0 0 0 13
14 8.0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 0 0 0 1 0 0 0 0 0 0 0 0 15
16 6.8 0 0 0 0 1 0 0 0 0 0 0 0 16
17 6.5 0 0 0 0 0 1 0 0 0 0 0 0 17
18 6.6 0 0 0 0 0 0 1 0 0 0 0 0 18
19 7.6 0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 8.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 7.7 0 0 0 0 0 0 0 0 0 0 1 0 22
23 7.5 0 0 0 0 0 0 0 0 0 0 0 1 23
24 7.6 0 0 0 0 0 0 0 0 0 0 0 0 24
25 7.7 0 1 0 0 0 0 0 0 0 0 0 0 25
26 7.9 0 0 1 0 0 0 0 0 0 0 0 0 26
27 7.8 0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.5 0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.5 0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.1 0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.5 0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.5 0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.6 0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.7 1 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 1 0 0 0 0 0 0 0 0 0 0 0 36
37 8.2 1 1 0 0 0 0 0 0 0 0 0 0 37
38 8.2 1 0 1 0 0 0 0 0 0 0 0 0 38
39 8.1 1 0 0 1 0 0 0 0 0 0 0 0 39
40 7.9 1 0 0 0 1 0 0 0 0 0 0 0 40
41 7.3 1 0 0 0 0 1 0 0 0 0 0 0 41
42 6.9 1 0 0 0 0 0 1 0 0 0 0 0 42
43 6.6 1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.7 1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.9 1 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.1 1 0 0 0 0 0 0 0 0 0 0 1 47
48 7.2 1 0 0 0 0 0 0 0 0 0 0 0 48
49 7.1 1 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 6.8 1 0 0 0 1 0 0 0 0 0 0 0 52
53 6.4 1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.7 1 0 0 0 0 0 1 0 0 0 0 0 54
55 6.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 6.4 1 0 0 0 0 0 0 0 1 0 0 0 56
57 6.3 1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.2 1 0 0 0 0 0 0 0 0 0 1 0 58
59 6.5 1 0 0 0 0 0 0 0 0 0 0 1 59
60 6.8 1 0 0 0 0 0 0 0 0 0 0 0 60
61 6.8 1 1 0 0 0 0 0 0 0 0 0 0 61
62 6.5 1 0 1 0 0 0 0 0 0 0 0 0 62
63 6.3 1 0 0 1 0 0 0 0 0 0 0 0 63
64 5.9 1 0 0 0 1 0 0 0 0 0 0 0 64
65 5.9 1 0 0 0 0 1 0 0 0 0 0 0 65
66 6.4 1 0 0 0 0 0 1 0 0 0 0 0 66
67 6.4 1 0 0 0 0 0 0 1 0 0 0 0 67
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
8.38947 0.26206 -0.03750 -0.12676 -0.24935 -0.50528
M5 M6 M7 M8 M9 M10
-0.71120 -0.70046 -0.43972 -0.39722 -0.30981 -0.31482
M11 t
-0.20741 -0.02741
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.71232 -0.25656 -0.04123 0.27654 0.85009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.389474 0.216535 38.744 < 2e-16 ***
x 0.262061 0.211976 1.236 0.22181
M1 -0.037504 0.255961 -0.147 0.88407
M2 -0.126762 0.255831 -0.495 0.62230
M3 -0.249353 0.255817 -0.975 0.33412
M4 -0.505278 0.255920 -1.974 0.05356 .
M5 -0.711202 0.256139 -2.777 0.00758 **
M6 -0.700461 0.256475 -2.731 0.00855 **
M7 -0.439719 0.256926 -1.711 0.09284 .
M8 -0.397222 0.268196 -1.481 0.14450
M9 -0.309814 0.268559 -1.154 0.25383
M10 -0.314817 0.267186 -1.178 0.24395
M11 -0.207409 0.267019 -0.777 0.44076
t -0.027409 0.005463 -5.017 6.26e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4221 on 53 degrees of freedom
Multiple R-squared: 0.6307, Adjusted R-squared: 0.5401
F-statistic: 6.961 on 13 and 53 DF, p-value: 1.349e-07
> postscript(file="/var/www/html/rcomp/tmp/15zj01228170546.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/2lnqu1228170546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3irop1228170546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4whr21228170546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vocs1228170546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 67
Frequency = 1
1 2 3 4 5 6
-0.524561404 -0.607894737 -0.557894737 -0.174561404 -0.041228070 -0.224561404
7 8 9 10 11 12
-0.157894737 -0.272982456 -0.232982456 0.099429825 0.019429825 0.039429825
13 14 15 16 17 18
0.204342105 0.121008772 -0.228991228 -0.645657895 -0.712324561 -0.595657895
19 20 21 22 23 24
0.171008772 0.555921053 0.495921053 0.228333333 -0.051666667 -0.131666667
25 26 27 28 29 30
0.033245614 0.349912281 0.399912281 0.383245614 0.616578947 0.233245614
31 32 33 34 35 36
0.399912281 0.384824561 0.424824561 0.295175439 0.415175439 0.435175439
37 38 39 40 41 42
0.600087719 0.716754386 0.766754386 0.850087719 0.483421053 0.100087719
43 44 45 46 47 48
-0.433245614 -0.348333333 -0.208333333 -0.075921053 -0.055921053 -0.135921053
49 50 51 52 53 54
-0.171008772 -0.254342105 -0.004342105 0.078991228 -0.087675439 0.228991228
55 56 57 58 59 60
-0.004342105 -0.319429825 -0.479429825 -0.547017544 -0.327017544 -0.207017544
61 62 63 64 65 66
-0.142105263 -0.325438596 -0.375438596 -0.492105263 -0.258771930 0.257894737
67
0.024561404
> postscript(file="/var/www/html/rcomp/tmp/6txsp1228170546.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.524561404 NA
1 -0.607894737 -0.524561404
2 -0.557894737 -0.607894737
3 -0.174561404 -0.557894737
4 -0.041228070 -0.174561404
5 -0.224561404 -0.041228070
6 -0.157894737 -0.224561404
7 -0.272982456 -0.157894737
8 -0.232982456 -0.272982456
9 0.099429825 -0.232982456
10 0.019429825 0.099429825
11 0.039429825 0.019429825
12 0.204342105 0.039429825
13 0.121008772 0.204342105
14 -0.228991228 0.121008772
15 -0.645657895 -0.228991228
16 -0.712324561 -0.645657895
17 -0.595657895 -0.712324561
18 0.171008772 -0.595657895
19 0.555921053 0.171008772
20 0.495921053 0.555921053
21 0.228333333 0.495921053
22 -0.051666667 0.228333333
23 -0.131666667 -0.051666667
24 0.033245614 -0.131666667
25 0.349912281 0.033245614
26 0.399912281 0.349912281
27 0.383245614 0.399912281
28 0.616578947 0.383245614
29 0.233245614 0.616578947
30 0.399912281 0.233245614
31 0.384824561 0.399912281
32 0.424824561 0.384824561
33 0.295175439 0.424824561
34 0.415175439 0.295175439
35 0.435175439 0.415175439
36 0.600087719 0.435175439
37 0.716754386 0.600087719
38 0.766754386 0.716754386
39 0.850087719 0.766754386
40 0.483421053 0.850087719
41 0.100087719 0.483421053
42 -0.433245614 0.100087719
43 -0.348333333 -0.433245614
44 -0.208333333 -0.348333333
45 -0.075921053 -0.208333333
46 -0.055921053 -0.075921053
47 -0.135921053 -0.055921053
48 -0.171008772 -0.135921053
49 -0.254342105 -0.171008772
50 -0.004342105 -0.254342105
51 0.078991228 -0.004342105
52 -0.087675439 0.078991228
53 0.228991228 -0.087675439
54 -0.004342105 0.228991228
55 -0.319429825 -0.004342105
56 -0.479429825 -0.319429825
57 -0.547017544 -0.479429825
58 -0.327017544 -0.547017544
59 -0.207017544 -0.327017544
60 -0.142105263 -0.207017544
61 -0.325438596 -0.142105263
62 -0.375438596 -0.325438596
63 -0.492105263 -0.375438596
64 -0.258771930 -0.492105263
65 0.257894737 -0.258771930
66 0.024561404 0.257894737
67 NA 0.024561404
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.607894737 -0.524561404
[2,] -0.557894737 -0.607894737
[3,] -0.174561404 -0.557894737
[4,] -0.041228070 -0.174561404
[5,] -0.224561404 -0.041228070
[6,] -0.157894737 -0.224561404
[7,] -0.272982456 -0.157894737
[8,] -0.232982456 -0.272982456
[9,] 0.099429825 -0.232982456
[10,] 0.019429825 0.099429825
[11,] 0.039429825 0.019429825
[12,] 0.204342105 0.039429825
[13,] 0.121008772 0.204342105
[14,] -0.228991228 0.121008772
[15,] -0.645657895 -0.228991228
[16,] -0.712324561 -0.645657895
[17,] -0.595657895 -0.712324561
[18,] 0.171008772 -0.595657895
[19,] 0.555921053 0.171008772
[20,] 0.495921053 0.555921053
[21,] 0.228333333 0.495921053
[22,] -0.051666667 0.228333333
[23,] -0.131666667 -0.051666667
[24,] 0.033245614 -0.131666667
[25,] 0.349912281 0.033245614
[26,] 0.399912281 0.349912281
[27,] 0.383245614 0.399912281
[28,] 0.616578947 0.383245614
[29,] 0.233245614 0.616578947
[30,] 0.399912281 0.233245614
[31,] 0.384824561 0.399912281
[32,] 0.424824561 0.384824561
[33,] 0.295175439 0.424824561
[34,] 0.415175439 0.295175439
[35,] 0.435175439 0.415175439
[36,] 0.600087719 0.435175439
[37,] 0.716754386 0.600087719
[38,] 0.766754386 0.716754386
[39,] 0.850087719 0.766754386
[40,] 0.483421053 0.850087719
[41,] 0.100087719 0.483421053
[42,] -0.433245614 0.100087719
[43,] -0.348333333 -0.433245614
[44,] -0.208333333 -0.348333333
[45,] -0.075921053 -0.208333333
[46,] -0.055921053 -0.075921053
[47,] -0.135921053 -0.055921053
[48,] -0.171008772 -0.135921053
[49,] -0.254342105 -0.171008772
[50,] -0.004342105 -0.254342105
[51,] 0.078991228 -0.004342105
[52,] -0.087675439 0.078991228
[53,] 0.228991228 -0.087675439
[54,] -0.004342105 0.228991228
[55,] -0.319429825 -0.004342105
[56,] -0.479429825 -0.319429825
[57,] -0.547017544 -0.479429825
[58,] -0.327017544 -0.547017544
[59,] -0.207017544 -0.327017544
[60,] -0.142105263 -0.207017544
[61,] -0.325438596 -0.142105263
[62,] -0.375438596 -0.325438596
[63,] -0.492105263 -0.375438596
[64,] -0.258771930 -0.492105263
[65,] 0.257894737 -0.258771930
[66,] 0.024561404 0.257894737
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.607894737 -0.524561404
2 -0.557894737 -0.607894737
3 -0.174561404 -0.557894737
4 -0.041228070 -0.174561404
5 -0.224561404 -0.041228070
6 -0.157894737 -0.224561404
7 -0.272982456 -0.157894737
8 -0.232982456 -0.272982456
9 0.099429825 -0.232982456
10 0.019429825 0.099429825
11 0.039429825 0.019429825
12 0.204342105 0.039429825
13 0.121008772 0.204342105
14 -0.228991228 0.121008772
15 -0.645657895 -0.228991228
16 -0.712324561 -0.645657895
17 -0.595657895 -0.712324561
18 0.171008772 -0.595657895
19 0.555921053 0.171008772
20 0.495921053 0.555921053
21 0.228333333 0.495921053
22 -0.051666667 0.228333333
23 -0.131666667 -0.051666667
24 0.033245614 -0.131666667
25 0.349912281 0.033245614
26 0.399912281 0.349912281
27 0.383245614 0.399912281
28 0.616578947 0.383245614
29 0.233245614 0.616578947
30 0.399912281 0.233245614
31 0.384824561 0.399912281
32 0.424824561 0.384824561
33 0.295175439 0.424824561
34 0.415175439 0.295175439
35 0.435175439 0.415175439
36 0.600087719 0.435175439
37 0.716754386 0.600087719
38 0.766754386 0.716754386
39 0.850087719 0.766754386
40 0.483421053 0.850087719
41 0.100087719 0.483421053
42 -0.433245614 0.100087719
43 -0.348333333 -0.433245614
44 -0.208333333 -0.348333333
45 -0.075921053 -0.208333333
46 -0.055921053 -0.075921053
47 -0.135921053 -0.055921053
48 -0.171008772 -0.135921053
49 -0.254342105 -0.171008772
50 -0.004342105 -0.254342105
51 0.078991228 -0.004342105
52 -0.087675439 0.078991228
53 0.228991228 -0.087675439
54 -0.004342105 0.228991228
55 -0.319429825 -0.004342105
56 -0.479429825 -0.319429825
57 -0.547017544 -0.479429825
58 -0.327017544 -0.547017544
59 -0.207017544 -0.327017544
60 -0.142105263 -0.207017544
61 -0.325438596 -0.142105263
62 -0.375438596 -0.325438596
63 -0.492105263 -0.375438596
64 -0.258771930 -0.492105263
65 0.257894737 -0.258771930
66 0.024561404 0.257894737
> 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/7drjl1228170546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qvuj1228170546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90jy41228170546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10i1jp1228170546.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/11rl6d1228170546.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/12nz661228170546.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/130xm91228170546.tab")
>
> system("convert tmp/15zj01228170546.ps tmp/15zj01228170546.png")
> system("convert tmp/2lnqu1228170546.ps tmp/2lnqu1228170546.png")
> system("convert tmp/3irop1228170546.ps tmp/3irop1228170546.png")
> system("convert tmp/4whr21228170546.ps tmp/4whr21228170546.png")
> system("convert tmp/5vocs1228170546.ps tmp/5vocs1228170546.png")
> system("convert tmp/6txsp1228170546.ps tmp/6txsp1228170546.png")
> system("convert tmp/7drjl1228170546.ps tmp/7drjl1228170546.png")
> system("convert tmp/8qvuj1228170546.ps tmp/8qvuj1228170546.png")
> system("convert tmp/90jy41228170546.ps tmp/90jy41228170546.png")
>
>
> proc.time()
user system elapsed
3.973 2.436 4.325