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.
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Type 'license()' or 'licence()' for distribution details.
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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
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Type 'q()' to quit R.
> x <- array(list(7.8,0,7.6,0,7.5,0,7.6,0,7.5,0,7.3,0,7.6,0,7.5,0,7.6,0,7.9,0,7.9,0,8.1,0,8.2,0,8.0,0,7.5,0,6.8,0,6.5,0,6.6,0,7.6,0,8.0,0,8.0,0,7.7,0,7.5,0,7.6,0,7.7,0,7.9,0,7.8,0,7.5,0,7.5,0,7.1,0,7.5,0,7.5,0,7.6,0,7.7,0,7.7,0,7.9,1,8.1,1,8.2,1,8.2,1,8.1,1,7.9,1,7.3,1,6.9,1,6.6,1,6.7,1,6.9,1,7.0,1,7.1,1,7.2,1,7.1,1,6.9,1,7.0,1,6.8,1,6.4,1,6.7,1,6.7,1,6.4,1,6.3,1,6.2,1,6.5,1,6.8,1,6.8,1,6.5,1,6.3,1,5.9,1,5.9,1,6.4,1,6.4,1),dim=c(2,68),dimnames=list(c('y','x'),1:68))
> y <- array(NA,dim=c(2,68),dimnames=list(c('y','x'),1:68))
> 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 0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.7 0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 1 0 0 0 0 0 0 0 0 0 0 0 36
37 8.1 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 8.2 1 0 0 1 0 0 0 0 0 0 0 0 39
40 8.1 1 0 0 0 1 0 0 0 0 0 0 0 40
41 7.9 1 0 0 0 0 1 0 0 0 0 0 0 41
42 7.3 1 0 0 0 0 0 1 0 0 0 0 0 42
43 6.9 1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.6 1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.7 1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 1 0 0 0 0 0 0 0 0 0 0 1 47
48 7.1 1 0 0 0 0 0 0 0 0 0 0 0 48
49 7.2 1 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 1 0 1 0 0 0 0 0 0 0 0 0 50
51 6.9 1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 6.8 1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 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 6.7 1 0 0 0 0 0 0 0 1 0 0 0 56
57 6.4 1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.3 1 0 0 0 0 0 0 0 0 0 1 0 58
59 6.2 1 0 0 0 0 0 0 0 0 0 0 1 59
60 6.5 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 6.8 1 0 1 0 0 0 0 0 0 0 0 0 62
63 6.5 1 0 0 1 0 0 0 0 0 0 0 0 63
64 6.3 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 5.9 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
68 6.4 1 0 0 0 0 0 0 0 1 0 0 0 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
8.21700 0.13500 0.08767 0.07817 -0.09800 -0.25750
M5 M6 M7 M8 M9 M10
-0.43367 -0.65983 -0.28600 -0.26217 -0.22450 -0.16067
M11 t
-0.17683 -0.02383
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.87817 -0.25475 -0.05517 0.23283 0.95883
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.217000 0.225413 36.453 < 2e-16 ***
x 0.135000 0.221036 0.611 0.5439
M1 0.087667 0.268051 0.327 0.7449
M2 0.078167 0.267924 0.292 0.7716
M3 -0.098000 0.267916 -0.366 0.7160
M4 -0.257500 0.268026 -0.961 0.3410
M5 -0.433667 0.268254 -1.617 0.1118
M6 -0.659833 0.268599 -2.457 0.0173 *
M7 -0.286000 0.269062 -1.063 0.2925
M8 -0.262167 0.269641 -0.972 0.3352
M9 -0.224500 0.281261 -0.798 0.4283
M10 -0.160667 0.281751 -0.570 0.5709
M11 -0.176833 0.282352 -0.626 0.5338
t -0.023833 0.005627 -4.235 8.94e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4421 on 54 degrees of freedom
Multiple R-squared: 0.5904, Adjusted R-squared: 0.4918
F-statistic: 5.988 on 13 and 54 DF, p-value: 1.041e-06
> postscript(file="/var/www/html/rcomp/tmp/1ppt71228161064.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/2wnnk1228161064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3biq11228161064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4reqp1228161064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xi1a1228161064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 68
Frequency = 1
1 2 3 4 5 6
-0.480833333 -0.647500000 -0.547500000 -0.264166667 -0.164166667 -0.114166667
7 8 9 10 11 12
-0.164166667 -0.264166667 -0.178000000 0.082000000 0.122000000 0.169000000
13 14 15 16 17 18
0.205166667 0.038500000 -0.261500000 -0.778166667 -0.878166667 -0.528166667
19 20 21 22 23 24
0.121833333 0.521833333 0.508000000 0.168000000 0.008000000 -0.045000000
25 26 27 28 29 30
-0.008833333 0.224500000 0.324500000 0.207833333 0.407833333 0.257833333
31 32 33 34 35 36
0.307833333 0.307833333 0.394000000 0.454000000 0.494000000 0.406000000
37 38 39 40 41 42
0.542166667 0.675500000 0.875500000 0.958833333 0.958833333 0.608833333
43 44 45 46 47 48
-0.141166667 -0.441166667 -0.355000000 -0.195000000 -0.055000000 -0.108000000
49 50 51 52 53 54
-0.071833333 -0.138500000 -0.138500000 0.144833333 0.144833333 -0.005166667
55 56 57 58 59 60
-0.055166667 -0.055166667 -0.369000000 -0.509000000 -0.569000000 -0.422000000
61 62 63 64 65 66
-0.185833333 -0.152500000 -0.252500000 -0.269166667 -0.469166667 -0.219166667
67 68
-0.069166667 -0.069166667
> postscript(file="/var/www/html/rcomp/tmp/6fpo61228161064.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.480833333 NA
1 -0.647500000 -0.480833333
2 -0.547500000 -0.647500000
3 -0.264166667 -0.547500000
4 -0.164166667 -0.264166667
5 -0.114166667 -0.164166667
6 -0.164166667 -0.114166667
7 -0.264166667 -0.164166667
8 -0.178000000 -0.264166667
9 0.082000000 -0.178000000
10 0.122000000 0.082000000
11 0.169000000 0.122000000
12 0.205166667 0.169000000
13 0.038500000 0.205166667
14 -0.261500000 0.038500000
15 -0.778166667 -0.261500000
16 -0.878166667 -0.778166667
17 -0.528166667 -0.878166667
18 0.121833333 -0.528166667
19 0.521833333 0.121833333
20 0.508000000 0.521833333
21 0.168000000 0.508000000
22 0.008000000 0.168000000
23 -0.045000000 0.008000000
24 -0.008833333 -0.045000000
25 0.224500000 -0.008833333
26 0.324500000 0.224500000
27 0.207833333 0.324500000
28 0.407833333 0.207833333
29 0.257833333 0.407833333
30 0.307833333 0.257833333
31 0.307833333 0.307833333
32 0.394000000 0.307833333
33 0.454000000 0.394000000
34 0.494000000 0.454000000
35 0.406000000 0.494000000
36 0.542166667 0.406000000
37 0.675500000 0.542166667
38 0.875500000 0.675500000
39 0.958833333 0.875500000
40 0.958833333 0.958833333
41 0.608833333 0.958833333
42 -0.141166667 0.608833333
43 -0.441166667 -0.141166667
44 -0.355000000 -0.441166667
45 -0.195000000 -0.355000000
46 -0.055000000 -0.195000000
47 -0.108000000 -0.055000000
48 -0.071833333 -0.108000000
49 -0.138500000 -0.071833333
50 -0.138500000 -0.138500000
51 0.144833333 -0.138500000
52 0.144833333 0.144833333
53 -0.005166667 0.144833333
54 -0.055166667 -0.005166667
55 -0.055166667 -0.055166667
56 -0.369000000 -0.055166667
57 -0.509000000 -0.369000000
58 -0.569000000 -0.509000000
59 -0.422000000 -0.569000000
60 -0.185833333 -0.422000000
61 -0.152500000 -0.185833333
62 -0.252500000 -0.152500000
63 -0.269166667 -0.252500000
64 -0.469166667 -0.269166667
65 -0.219166667 -0.469166667
66 -0.069166667 -0.219166667
67 -0.069166667 -0.069166667
68 NA -0.069166667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.647500000 -0.480833333
[2,] -0.547500000 -0.647500000
[3,] -0.264166667 -0.547500000
[4,] -0.164166667 -0.264166667
[5,] -0.114166667 -0.164166667
[6,] -0.164166667 -0.114166667
[7,] -0.264166667 -0.164166667
[8,] -0.178000000 -0.264166667
[9,] 0.082000000 -0.178000000
[10,] 0.122000000 0.082000000
[11,] 0.169000000 0.122000000
[12,] 0.205166667 0.169000000
[13,] 0.038500000 0.205166667
[14,] -0.261500000 0.038500000
[15,] -0.778166667 -0.261500000
[16,] -0.878166667 -0.778166667
[17,] -0.528166667 -0.878166667
[18,] 0.121833333 -0.528166667
[19,] 0.521833333 0.121833333
[20,] 0.508000000 0.521833333
[21,] 0.168000000 0.508000000
[22,] 0.008000000 0.168000000
[23,] -0.045000000 0.008000000
[24,] -0.008833333 -0.045000000
[25,] 0.224500000 -0.008833333
[26,] 0.324500000 0.224500000
[27,] 0.207833333 0.324500000
[28,] 0.407833333 0.207833333
[29,] 0.257833333 0.407833333
[30,] 0.307833333 0.257833333
[31,] 0.307833333 0.307833333
[32,] 0.394000000 0.307833333
[33,] 0.454000000 0.394000000
[34,] 0.494000000 0.454000000
[35,] 0.406000000 0.494000000
[36,] 0.542166667 0.406000000
[37,] 0.675500000 0.542166667
[38,] 0.875500000 0.675500000
[39,] 0.958833333 0.875500000
[40,] 0.958833333 0.958833333
[41,] 0.608833333 0.958833333
[42,] -0.141166667 0.608833333
[43,] -0.441166667 -0.141166667
[44,] -0.355000000 -0.441166667
[45,] -0.195000000 -0.355000000
[46,] -0.055000000 -0.195000000
[47,] -0.108000000 -0.055000000
[48,] -0.071833333 -0.108000000
[49,] -0.138500000 -0.071833333
[50,] -0.138500000 -0.138500000
[51,] 0.144833333 -0.138500000
[52,] 0.144833333 0.144833333
[53,] -0.005166667 0.144833333
[54,] -0.055166667 -0.005166667
[55,] -0.055166667 -0.055166667
[56,] -0.369000000 -0.055166667
[57,] -0.509000000 -0.369000000
[58,] -0.569000000 -0.509000000
[59,] -0.422000000 -0.569000000
[60,] -0.185833333 -0.422000000
[61,] -0.152500000 -0.185833333
[62,] -0.252500000 -0.152500000
[63,] -0.269166667 -0.252500000
[64,] -0.469166667 -0.269166667
[65,] -0.219166667 -0.469166667
[66,] -0.069166667 -0.219166667
[67,] -0.069166667 -0.069166667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.647500000 -0.480833333
2 -0.547500000 -0.647500000
3 -0.264166667 -0.547500000
4 -0.164166667 -0.264166667
5 -0.114166667 -0.164166667
6 -0.164166667 -0.114166667
7 -0.264166667 -0.164166667
8 -0.178000000 -0.264166667
9 0.082000000 -0.178000000
10 0.122000000 0.082000000
11 0.169000000 0.122000000
12 0.205166667 0.169000000
13 0.038500000 0.205166667
14 -0.261500000 0.038500000
15 -0.778166667 -0.261500000
16 -0.878166667 -0.778166667
17 -0.528166667 -0.878166667
18 0.121833333 -0.528166667
19 0.521833333 0.121833333
20 0.508000000 0.521833333
21 0.168000000 0.508000000
22 0.008000000 0.168000000
23 -0.045000000 0.008000000
24 -0.008833333 -0.045000000
25 0.224500000 -0.008833333
26 0.324500000 0.224500000
27 0.207833333 0.324500000
28 0.407833333 0.207833333
29 0.257833333 0.407833333
30 0.307833333 0.257833333
31 0.307833333 0.307833333
32 0.394000000 0.307833333
33 0.454000000 0.394000000
34 0.494000000 0.454000000
35 0.406000000 0.494000000
36 0.542166667 0.406000000
37 0.675500000 0.542166667
38 0.875500000 0.675500000
39 0.958833333 0.875500000
40 0.958833333 0.958833333
41 0.608833333 0.958833333
42 -0.141166667 0.608833333
43 -0.441166667 -0.141166667
44 -0.355000000 -0.441166667
45 -0.195000000 -0.355000000
46 -0.055000000 -0.195000000
47 -0.108000000 -0.055000000
48 -0.071833333 -0.108000000
49 -0.138500000 -0.071833333
50 -0.138500000 -0.138500000
51 0.144833333 -0.138500000
52 0.144833333 0.144833333
53 -0.005166667 0.144833333
54 -0.055166667 -0.005166667
55 -0.055166667 -0.055166667
56 -0.369000000 -0.055166667
57 -0.509000000 -0.369000000
58 -0.569000000 -0.509000000
59 -0.422000000 -0.569000000
60 -0.185833333 -0.422000000
61 -0.152500000 -0.185833333
62 -0.252500000 -0.152500000
63 -0.269166667 -0.252500000
64 -0.469166667 -0.269166667
65 -0.219166667 -0.469166667
66 -0.069166667 -0.219166667
67 -0.069166667 -0.069166667
> 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/780m11228161064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83b7m1228161064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kce61228161064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10xawy1228161064.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/11nv5p1228161064.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/124pvc1228161064.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/1391sa1228161064.tab")
>
> system("convert tmp/1ppt71228161064.ps tmp/1ppt71228161064.png")
> system("convert tmp/2wnnk1228161064.ps tmp/2wnnk1228161064.png")
> system("convert tmp/3biq11228161064.ps tmp/3biq11228161064.png")
> system("convert tmp/4reqp1228161064.ps tmp/4reqp1228161064.png")
> system("convert tmp/5xi1a1228161064.ps tmp/5xi1a1228161064.png")
> system("convert tmp/6fpo61228161064.ps tmp/6fpo61228161064.png")
> system("convert tmp/780m11228161064.ps tmp/780m11228161064.png")
> system("convert tmp/83b7m1228161064.ps tmp/83b7m1228161064.png")
> system("convert tmp/9kce61228161064.ps tmp/9kce61228161064.png")
>
>
> proc.time()
user system elapsed
1.953 1.397 2.911