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 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(0.84,0,0.76,0,0.77,0,0.76,0,0.77,0,0.78,0,0.79,0,0.78,0,0.76,0,0.78,1,0.76,1,0.74,1,0.73,1,0.72,1,0.71,1,0.73,1,0.75,1,0.75,1,0.72,1,0.72,1,0.72,1,0.74,1,0.78,1,0.74,1,0.74,1,0.75,1,0.78,1,0.81,1,0.75,1,0.7,1,0.71,1,0.71,1,0.73,1,0.74,1,0.74,1,0.75,1,0.74,1,0.74,1,0.73,1,0.76,1,0.8,1,0.83,1,0.81,1,0.83,1,0.88,1,0.89,1,0.93,1,0.91,1,0.9,1,0.86,1,0.88,1,0.93,1,0.98,1,0.97,1,1.03,1,1.06,1,1.06,1,1.09,1,1.04,1,1,1,1.04,1),dim=c(2,61),dimnames=list(c('y','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('y','x'),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
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 0.84 0 1 0 0 0 0 0 0 0 0 0 0 1
2 0.76 0 0 1 0 0 0 0 0 0 0 0 0 2
3 0.77 0 0 0 1 0 0 0 0 0 0 0 0 3
4 0.76 0 0 0 0 1 0 0 0 0 0 0 0 4
5 0.77 0 0 0 0 0 1 0 0 0 0 0 0 5
6 0.78 0 0 0 0 0 0 1 0 0 0 0 0 6
7 0.79 0 0 0 0 0 0 0 1 0 0 0 0 7
8 0.78 0 0 0 0 0 0 0 0 1 0 0 0 8
9 0.76 0 0 0 0 0 0 0 0 0 1 0 0 9
10 0.78 1 0 0 0 0 0 0 0 0 0 1 0 10
11 0.76 1 0 0 0 0 0 0 0 0 0 0 1 11
12 0.74 1 0 0 0 0 0 0 0 0 0 0 0 12
13 0.73 1 1 0 0 0 0 0 0 0 0 0 0 13
14 0.72 1 0 1 0 0 0 0 0 0 0 0 0 14
15 0.71 1 0 0 1 0 0 0 0 0 0 0 0 15
16 0.73 1 0 0 0 1 0 0 0 0 0 0 0 16
17 0.75 1 0 0 0 0 1 0 0 0 0 0 0 17
18 0.75 1 0 0 0 0 0 1 0 0 0 0 0 18
19 0.72 1 0 0 0 0 0 0 1 0 0 0 0 19
20 0.72 1 0 0 0 0 0 0 0 1 0 0 0 20
21 0.72 1 0 0 0 0 0 0 0 0 1 0 0 21
22 0.74 1 0 0 0 0 0 0 0 0 0 1 0 22
23 0.78 1 0 0 0 0 0 0 0 0 0 0 1 23
24 0.74 1 0 0 0 0 0 0 0 0 0 0 0 24
25 0.74 1 1 0 0 0 0 0 0 0 0 0 0 25
26 0.75 1 0 1 0 0 0 0 0 0 0 0 0 26
27 0.78 1 0 0 1 0 0 0 0 0 0 0 0 27
28 0.81 1 0 0 0 1 0 0 0 0 0 0 0 28
29 0.75 1 0 0 0 0 1 0 0 0 0 0 0 29
30 0.70 1 0 0 0 0 0 1 0 0 0 0 0 30
31 0.71 1 0 0 0 0 0 0 1 0 0 0 0 31
32 0.71 1 0 0 0 0 0 0 0 1 0 0 0 32
33 0.73 1 0 0 0 0 0 0 0 0 1 0 0 33
34 0.74 1 0 0 0 0 0 0 0 0 0 1 0 34
35 0.74 1 0 0 0 0 0 0 0 0 0 0 1 35
36 0.75 1 0 0 0 0 0 0 0 0 0 0 0 36
37 0.74 1 1 0 0 0 0 0 0 0 0 0 0 37
38 0.74 1 0 1 0 0 0 0 0 0 0 0 0 38
39 0.73 1 0 0 1 0 0 0 0 0 0 0 0 39
40 0.76 1 0 0 0 1 0 0 0 0 0 0 0 40
41 0.80 1 0 0 0 0 1 0 0 0 0 0 0 41
42 0.83 1 0 0 0 0 0 1 0 0 0 0 0 42
43 0.81 1 0 0 0 0 0 0 1 0 0 0 0 43
44 0.83 1 0 0 0 0 0 0 0 1 0 0 0 44
45 0.88 1 0 0 0 0 0 0 0 0 1 0 0 45
46 0.89 1 0 0 0 0 0 0 0 0 0 1 0 46
47 0.93 1 0 0 0 0 0 0 0 0 0 0 1 47
48 0.91 1 0 0 0 0 0 0 0 0 0 0 0 48
49 0.90 1 1 0 0 0 0 0 0 0 0 0 0 49
50 0.86 1 0 1 0 0 0 0 0 0 0 0 0 50
51 0.88 1 0 0 1 0 0 0 0 0 0 0 0 51
52 0.93 1 0 0 0 1 0 0 0 0 0 0 0 52
53 0.98 1 0 0 0 0 1 0 0 0 0 0 0 53
54 0.97 1 0 0 0 0 0 1 0 0 0 0 0 54
55 1.03 1 0 0 0 0 0 0 1 0 0 0 0 55
56 1.06 1 0 0 0 0 0 0 0 1 0 0 0 56
57 1.06 1 0 0 0 0 0 0 0 0 1 0 0 57
58 1.09 1 0 0 0 0 0 0 0 0 0 1 0 58
59 1.04 1 0 0 0 0 0 0 0 0 0 0 1 59
60 1.00 1 0 0 0 0 0 0 0 0 0 0 0 60
61 1.04 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) x M1 M2 M3 M4
0.761614 -0.160983 0.008415 -0.031039 -0.029354 -0.011670
M5 M6 M7 M8 M9 M10
-0.005986 -0.016302 -0.016618 -0.014933 -0.011249 0.032632
M11 t
0.028316 0.006316
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.11000 -0.03359 0.00421 0.04399 0.12062
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7616138 0.0367157 20.744 < 2e-16 ***
x -0.1609828 0.0294926 -5.458 1.76e-06 ***
M1 0.0084152 0.0382887 0.220 0.827
M2 -0.0310385 0.0401212 -0.774 0.443
M3 -0.0293543 0.0400925 -0.732 0.468
M4 -0.0116701 0.0400723 -0.291 0.772
M5 -0.0059859 0.0400607 -0.149 0.882
M6 -0.0163017 0.0400576 -0.407 0.686
M7 -0.0166175 0.0400630 -0.415 0.680
M8 -0.0149333 0.0400770 -0.373 0.711
M9 -0.0112491 0.0400996 -0.281 0.780
M10 0.0326316 0.0398007 0.820 0.416
M11 0.0283158 0.0397877 0.712 0.480
t 0.0063158 0.0005854 10.789 2.61e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0629 on 47 degrees of freedom
Multiple R-squared: 0.7313, Adjusted R-squared: 0.657
F-statistic: 9.84 on 13 and 47 DF, p-value: 1.836e-09
> postscript(file="/var/www/html/rcomp/tmp/15m1u1227470864.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/2zbss1227470864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3btl21227470864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qrgx1227470864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5arct1227470864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.0636551724 0.0167931034 0.0187931034 -0.0152068966 -0.0172068966
6 7 8 9 10
-0.0032068966 0.0007931034 -0.0172068966 -0.0472068966 0.0835793103
11 12 13 14 15
0.0615793103 0.0635793103 0.0388482759 0.0619862069 0.0439862069
16 17 18 19 20
0.0399862069 0.0479862069 0.0519862069 0.0159862069 0.0079862069
21 22 23 24 25
-0.0020137931 -0.0322103448 0.0057896552 -0.0122103448 -0.0269413793
26 27 28 29 30
0.0161965517 0.0381965517 0.0441965517 -0.0278034483 -0.0738034483
31 32 33 34 35
-0.0698034483 -0.0778034483 -0.0678034483 -0.1080000000 -0.1100000000
36 37 38 39 40
-0.0780000000 -0.1027310345 -0.0695931034 -0.0875931034 -0.0815931034
41 42 43 44 45
-0.0535931034 -0.0195931034 -0.0455931034 -0.0335931034 0.0064068966
46 47 48 49 50
-0.0337896552 0.0042103448 0.0062103448 -0.0185206897 -0.0253827586
51 52 53 54 55
-0.0133827586 0.0126172414 0.0506172414 0.0446172414 0.0986172414
56 57 58 59 60
0.1206172414 0.1106172414 0.0904206897 0.0384206897 0.0204206897
61
0.0456896552
> postscript(file="/var/www/html/rcomp/tmp/6ucs61227470864.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 0.0636551724 NA
1 0.0167931034 0.0636551724
2 0.0187931034 0.0167931034
3 -0.0152068966 0.0187931034
4 -0.0172068966 -0.0152068966
5 -0.0032068966 -0.0172068966
6 0.0007931034 -0.0032068966
7 -0.0172068966 0.0007931034
8 -0.0472068966 -0.0172068966
9 0.0835793103 -0.0472068966
10 0.0615793103 0.0835793103
11 0.0635793103 0.0615793103
12 0.0388482759 0.0635793103
13 0.0619862069 0.0388482759
14 0.0439862069 0.0619862069
15 0.0399862069 0.0439862069
16 0.0479862069 0.0399862069
17 0.0519862069 0.0479862069
18 0.0159862069 0.0519862069
19 0.0079862069 0.0159862069
20 -0.0020137931 0.0079862069
21 -0.0322103448 -0.0020137931
22 0.0057896552 -0.0322103448
23 -0.0122103448 0.0057896552
24 -0.0269413793 -0.0122103448
25 0.0161965517 -0.0269413793
26 0.0381965517 0.0161965517
27 0.0441965517 0.0381965517
28 -0.0278034483 0.0441965517
29 -0.0738034483 -0.0278034483
30 -0.0698034483 -0.0738034483
31 -0.0778034483 -0.0698034483
32 -0.0678034483 -0.0778034483
33 -0.1080000000 -0.0678034483
34 -0.1100000000 -0.1080000000
35 -0.0780000000 -0.1100000000
36 -0.1027310345 -0.0780000000
37 -0.0695931034 -0.1027310345
38 -0.0875931034 -0.0695931034
39 -0.0815931034 -0.0875931034
40 -0.0535931034 -0.0815931034
41 -0.0195931034 -0.0535931034
42 -0.0455931034 -0.0195931034
43 -0.0335931034 -0.0455931034
44 0.0064068966 -0.0335931034
45 -0.0337896552 0.0064068966
46 0.0042103448 -0.0337896552
47 0.0062103448 0.0042103448
48 -0.0185206897 0.0062103448
49 -0.0253827586 -0.0185206897
50 -0.0133827586 -0.0253827586
51 0.0126172414 -0.0133827586
52 0.0506172414 0.0126172414
53 0.0446172414 0.0506172414
54 0.0986172414 0.0446172414
55 0.1206172414 0.0986172414
56 0.1106172414 0.1206172414
57 0.0904206897 0.1106172414
58 0.0384206897 0.0904206897
59 0.0204206897 0.0384206897
60 0.0456896552 0.0204206897
61 NA 0.0456896552
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0167931034 0.0636551724
[2,] 0.0187931034 0.0167931034
[3,] -0.0152068966 0.0187931034
[4,] -0.0172068966 -0.0152068966
[5,] -0.0032068966 -0.0172068966
[6,] 0.0007931034 -0.0032068966
[7,] -0.0172068966 0.0007931034
[8,] -0.0472068966 -0.0172068966
[9,] 0.0835793103 -0.0472068966
[10,] 0.0615793103 0.0835793103
[11,] 0.0635793103 0.0615793103
[12,] 0.0388482759 0.0635793103
[13,] 0.0619862069 0.0388482759
[14,] 0.0439862069 0.0619862069
[15,] 0.0399862069 0.0439862069
[16,] 0.0479862069 0.0399862069
[17,] 0.0519862069 0.0479862069
[18,] 0.0159862069 0.0519862069
[19,] 0.0079862069 0.0159862069
[20,] -0.0020137931 0.0079862069
[21,] -0.0322103448 -0.0020137931
[22,] 0.0057896552 -0.0322103448
[23,] -0.0122103448 0.0057896552
[24,] -0.0269413793 -0.0122103448
[25,] 0.0161965517 -0.0269413793
[26,] 0.0381965517 0.0161965517
[27,] 0.0441965517 0.0381965517
[28,] -0.0278034483 0.0441965517
[29,] -0.0738034483 -0.0278034483
[30,] -0.0698034483 -0.0738034483
[31,] -0.0778034483 -0.0698034483
[32,] -0.0678034483 -0.0778034483
[33,] -0.1080000000 -0.0678034483
[34,] -0.1100000000 -0.1080000000
[35,] -0.0780000000 -0.1100000000
[36,] -0.1027310345 -0.0780000000
[37,] -0.0695931034 -0.1027310345
[38,] -0.0875931034 -0.0695931034
[39,] -0.0815931034 -0.0875931034
[40,] -0.0535931034 -0.0815931034
[41,] -0.0195931034 -0.0535931034
[42,] -0.0455931034 -0.0195931034
[43,] -0.0335931034 -0.0455931034
[44,] 0.0064068966 -0.0335931034
[45,] -0.0337896552 0.0064068966
[46,] 0.0042103448 -0.0337896552
[47,] 0.0062103448 0.0042103448
[48,] -0.0185206897 0.0062103448
[49,] -0.0253827586 -0.0185206897
[50,] -0.0133827586 -0.0253827586
[51,] 0.0126172414 -0.0133827586
[52,] 0.0506172414 0.0126172414
[53,] 0.0446172414 0.0506172414
[54,] 0.0986172414 0.0446172414
[55,] 0.1206172414 0.0986172414
[56,] 0.1106172414 0.1206172414
[57,] 0.0904206897 0.1106172414
[58,] 0.0384206897 0.0904206897
[59,] 0.0204206897 0.0384206897
[60,] 0.0456896552 0.0204206897
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0167931034 0.0636551724
2 0.0187931034 0.0167931034
3 -0.0152068966 0.0187931034
4 -0.0172068966 -0.0152068966
5 -0.0032068966 -0.0172068966
6 0.0007931034 -0.0032068966
7 -0.0172068966 0.0007931034
8 -0.0472068966 -0.0172068966
9 0.0835793103 -0.0472068966
10 0.0615793103 0.0835793103
11 0.0635793103 0.0615793103
12 0.0388482759 0.0635793103
13 0.0619862069 0.0388482759
14 0.0439862069 0.0619862069
15 0.0399862069 0.0439862069
16 0.0479862069 0.0399862069
17 0.0519862069 0.0479862069
18 0.0159862069 0.0519862069
19 0.0079862069 0.0159862069
20 -0.0020137931 0.0079862069
21 -0.0322103448 -0.0020137931
22 0.0057896552 -0.0322103448
23 -0.0122103448 0.0057896552
24 -0.0269413793 -0.0122103448
25 0.0161965517 -0.0269413793
26 0.0381965517 0.0161965517
27 0.0441965517 0.0381965517
28 -0.0278034483 0.0441965517
29 -0.0738034483 -0.0278034483
30 -0.0698034483 -0.0738034483
31 -0.0778034483 -0.0698034483
32 -0.0678034483 -0.0778034483
33 -0.1080000000 -0.0678034483
34 -0.1100000000 -0.1080000000
35 -0.0780000000 -0.1100000000
36 -0.1027310345 -0.0780000000
37 -0.0695931034 -0.1027310345
38 -0.0875931034 -0.0695931034
39 -0.0815931034 -0.0875931034
40 -0.0535931034 -0.0815931034
41 -0.0195931034 -0.0535931034
42 -0.0455931034 -0.0195931034
43 -0.0335931034 -0.0455931034
44 0.0064068966 -0.0335931034
45 -0.0337896552 0.0064068966
46 0.0042103448 -0.0337896552
47 0.0062103448 0.0042103448
48 -0.0185206897 0.0062103448
49 -0.0253827586 -0.0185206897
50 -0.0133827586 -0.0253827586
51 0.0126172414 -0.0133827586
52 0.0506172414 0.0126172414
53 0.0446172414 0.0506172414
54 0.0986172414 0.0446172414
55 0.1206172414 0.0986172414
56 0.1106172414 0.1206172414
57 0.0904206897 0.1106172414
58 0.0384206897 0.0904206897
59 0.0204206897 0.0384206897
60 0.0456896552 0.0204206897
> 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/7ginh1227470864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/832gu1227470864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97bbb1227470864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10vdr71227470864.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/1148tn1227470864.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/12nh0o1227470864.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/13v9nc1227470864.tab")
>
> system("convert tmp/15m1u1227470864.ps tmp/15m1u1227470864.png")
> system("convert tmp/2zbss1227470864.ps tmp/2zbss1227470864.png")
> system("convert tmp/3btl21227470864.ps tmp/3btl21227470864.png")
> system("convert tmp/4qrgx1227470864.ps tmp/4qrgx1227470864.png")
> system("convert tmp/5arct1227470864.ps tmp/5arct1227470864.png")
> system("convert tmp/6ucs61227470864.ps tmp/6ucs61227470864.png")
> system("convert tmp/7ginh1227470864.ps tmp/7ginh1227470864.png")
> system("convert tmp/832gu1227470864.ps tmp/832gu1227470864.png")
> system("convert tmp/97bbb1227470864.ps tmp/97bbb1227470864.png")
>
>
> proc.time()
user system elapsed
1.956 1.421 2.302