R version 2.6.0 (2007-10-03)
Copyright (C) 2007 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(0.73,1.79,0.74,1.95,0.75,2.26,0.74,2.04,0.76,2.16,0.76,2.75,0.78,2.79,0.79,2.88,0.89,3.36,0.88,2.97,0.88,3.1,0.84,2.49,0.76,2.2,0.77,2.25,0.76,2.09,0.77,2.79,0.78,3.14,0.79,2.93,0.78,2.65,0.76,2.67,0.78,2.26,0.76,2.35,0.74,2.13,0.73,2.18,0.72,2.9,0.71,2.63,0.73,2.67,0.75,1.81,0.75,1.33,0.72,0.88,0.72,1.28,0.72,1.26,0.74,1.26,0.78,1.29,0.74,1.1,0.74,1.37,0.75,1.21,0.78,1.74,0.81,1.76,0.75,1.48,0.7,1.04,0.71,1.62,0.71,1.49,0.73,1.79,0.74,1.8,0.74,1.58,0.75,1.86,0.74,1.74,0.74,1.59,0.73,1.26,0.76,1.13,0.8,1.92,0.83,2.61,0.81,2.26,0.83,2.41,0.88,2.26,0.89,2.03,0.93,2.86,0.91,2.55,0.9,2.27,0.86,2.26,0.88,2.57,0.93,3.07,0.98,2.76,0.97,2.51,1.03,2.87,1.06,3.14,1.06,3.11,1.08,3.16,1.09,2.47,1.04,2.57,1,2.89),dim=c(2,72),dimnames=list(c('dsl','inf
'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('dsl','inf
'),1:72))
> 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 = 'Do not include Seasonal 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
dsl inf\r t
1 0.73 1.79 1
2 0.74 1.95 2
3 0.75 2.26 3
4 0.74 2.04 4
5 0.76 2.16 5
6 0.76 2.75 6
7 0.78 2.79 7
8 0.79 2.88 8
9 0.89 3.36 9
10 0.88 2.97 10
11 0.88 3.10 11
12 0.84 2.49 12
13 0.76 2.20 13
14 0.77 2.25 14
15 0.76 2.09 15
16 0.77 2.79 16
17 0.78 3.14 17
18 0.79 2.93 18
19 0.78 2.65 19
20 0.76 2.67 20
21 0.78 2.26 21
22 0.76 2.35 22
23 0.74 2.13 23
24 0.73 2.18 24
25 0.72 2.90 25
26 0.71 2.63 26
27 0.73 2.67 27
28 0.75 1.81 28
29 0.75 1.33 29
30 0.72 0.88 30
31 0.72 1.28 31
32 0.72 1.26 32
33 0.74 1.26 33
34 0.78 1.29 34
35 0.74 1.10 35
36 0.74 1.37 36
37 0.75 1.21 37
38 0.78 1.74 38
39 0.81 1.76 39
40 0.75 1.48 40
41 0.70 1.04 41
42 0.71 1.62 42
43 0.71 1.49 43
44 0.73 1.79 44
45 0.74 1.80 45
46 0.74 1.58 46
47 0.75 1.86 47
48 0.74 1.74 48
49 0.74 1.59 49
50 0.73 1.26 50
51 0.76 1.13 51
52 0.80 1.92 52
53 0.83 2.61 53
54 0.81 2.26 54
55 0.83 2.41 55
56 0.88 2.26 56
57 0.89 2.03 57
58 0.93 2.86 58
59 0.91 2.55 59
60 0.90 2.27 60
61 0.86 2.26 61
62 0.88 2.57 62
63 0.93 3.07 63
64 0.98 2.76 64
65 0.97 2.51 65
66 1.03 2.87 66
67 1.06 3.14 67
68 1.06 3.11 68
69 1.08 3.16 69
70 1.09 2.47 70
71 1.04 2.57 71
72 1.00 2.89 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `inf\r` t
0.490196 0.096524 0.003019
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.125589 -0.041948 0.004685 0.038679 0.150067
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4901962 0.0248861 19.698 < 2e-16 ***
`inf\r` 0.0965244 0.0098826 9.767 1.22e-14 ***
t 0.0030189 0.0003008 10.035 4.03e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.05304 on 69 degrees of freedom
Multiple R-Squared: 0.7431, Adjusted R-squared: 0.7356
F-statistic: 99.79 on 2 and 69 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1za8k1199639951.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/2jegc1199639951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3uyq71199639951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4i7xw1199639951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5guge1199639951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 72
Frequency = 1
1 2 3 4 5
0.0640061801 0.0555433907 0.0326019386 0.0408184280 0.0462166154
6 7 8 9 10
-0.0137516738 -0.0006315330 -0.0023376132 0.0483117836 0.0729374242
11 12 13 14 15
0.0573703673 0.0732313799 0.0182045786 0.0203594752 0.0227844996
16 17 18 19 20
-0.0378014756 -0.0646039044 -0.0373526591 -0.0233447045 -0.0482940754
21 22 23 24 25
0.0082620535 -0.0234440266 -0.0252275371 -0.0430726406 -0.1255891041
26 27 28 29 30
-0.1125463937 -0.0994262529 0.0005658641 0.0438787023 0.0542958079
31 32 33 34 35
0.0126671582 0.0115787641 0.0285598816 0.0626452665 0.0379660235
36 37 38 39 40
0.0088855481 0.0313105725 0.0071337484 0.0321843775 -0.0038076679
41 42 43 44 45
-0.0143558065 -0.0633588515 -0.0538295596 -0.0658057676 -0.0597898942
46 47 48 49 50
-0.0415734048 -0.0616191243 -0.0630550767 -0.0515952965 -0.0327611210
51 52 53 54 55
0.0067681709 -0.0325050019 -0.0721257329 -0.0613610691 -0.0588586143
56 57 58 59 60
0.0026011659 0.0317828996 -0.0113512500 -0.0044475629 0.0095603917
61 62 63 64 65
-0.0324932466 -0.0454346987 -0.0467157903 0.0301878968 0.0413001189
66 67 68 69 70
0.0635324458 0.0644519705 0.0643288205 0.0764837171 0.1500666831
71 72
0.0873953588 0.0134886625
> postscript(file="/var/www/html/rcomp/tmp/6ohzk1199639951.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0640061801 NA
1 0.0555433907 0.0640061801
2 0.0326019386 0.0555433907
3 0.0408184280 0.0326019386
4 0.0462166154 0.0408184280
5 -0.0137516738 0.0462166154
6 -0.0006315330 -0.0137516738
7 -0.0023376132 -0.0006315330
8 0.0483117836 -0.0023376132
9 0.0729374242 0.0483117836
10 0.0573703673 0.0729374242
11 0.0732313799 0.0573703673
12 0.0182045786 0.0732313799
13 0.0203594752 0.0182045786
14 0.0227844996 0.0203594752
15 -0.0378014756 0.0227844996
16 -0.0646039044 -0.0378014756
17 -0.0373526591 -0.0646039044
18 -0.0233447045 -0.0373526591
19 -0.0482940754 -0.0233447045
20 0.0082620535 -0.0482940754
21 -0.0234440266 0.0082620535
22 -0.0252275371 -0.0234440266
23 -0.0430726406 -0.0252275371
24 -0.1255891041 -0.0430726406
25 -0.1125463937 -0.1255891041
26 -0.0994262529 -0.1125463937
27 0.0005658641 -0.0994262529
28 0.0438787023 0.0005658641
29 0.0542958079 0.0438787023
30 0.0126671582 0.0542958079
31 0.0115787641 0.0126671582
32 0.0285598816 0.0115787641
33 0.0626452665 0.0285598816
34 0.0379660235 0.0626452665
35 0.0088855481 0.0379660235
36 0.0313105725 0.0088855481
37 0.0071337484 0.0313105725
38 0.0321843775 0.0071337484
39 -0.0038076679 0.0321843775
40 -0.0143558065 -0.0038076679
41 -0.0633588515 -0.0143558065
42 -0.0538295596 -0.0633588515
43 -0.0658057676 -0.0538295596
44 -0.0597898942 -0.0658057676
45 -0.0415734048 -0.0597898942
46 -0.0616191243 -0.0415734048
47 -0.0630550767 -0.0616191243
48 -0.0515952965 -0.0630550767
49 -0.0327611210 -0.0515952965
50 0.0067681709 -0.0327611210
51 -0.0325050019 0.0067681709
52 -0.0721257329 -0.0325050019
53 -0.0613610691 -0.0721257329
54 -0.0588586143 -0.0613610691
55 0.0026011659 -0.0588586143
56 0.0317828996 0.0026011659
57 -0.0113512500 0.0317828996
58 -0.0044475629 -0.0113512500
59 0.0095603917 -0.0044475629
60 -0.0324932466 0.0095603917
61 -0.0454346987 -0.0324932466
62 -0.0467157903 -0.0454346987
63 0.0301878968 -0.0467157903
64 0.0413001189 0.0301878968
65 0.0635324458 0.0413001189
66 0.0644519705 0.0635324458
67 0.0643288205 0.0644519705
68 0.0764837171 0.0643288205
69 0.1500666831 0.0764837171
70 0.0873953588 0.1500666831
71 0.0134886625 0.0873953588
72 NA 0.0134886625
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0555433907 0.0640061801
[2,] 0.0326019386 0.0555433907
[3,] 0.0408184280 0.0326019386
[4,] 0.0462166154 0.0408184280
[5,] -0.0137516738 0.0462166154
[6,] -0.0006315330 -0.0137516738
[7,] -0.0023376132 -0.0006315330
[8,] 0.0483117836 -0.0023376132
[9,] 0.0729374242 0.0483117836
[10,] 0.0573703673 0.0729374242
[11,] 0.0732313799 0.0573703673
[12,] 0.0182045786 0.0732313799
[13,] 0.0203594752 0.0182045786
[14,] 0.0227844996 0.0203594752
[15,] -0.0378014756 0.0227844996
[16,] -0.0646039044 -0.0378014756
[17,] -0.0373526591 -0.0646039044
[18,] -0.0233447045 -0.0373526591
[19,] -0.0482940754 -0.0233447045
[20,] 0.0082620535 -0.0482940754
[21,] -0.0234440266 0.0082620535
[22,] -0.0252275371 -0.0234440266
[23,] -0.0430726406 -0.0252275371
[24,] -0.1255891041 -0.0430726406
[25,] -0.1125463937 -0.1255891041
[26,] -0.0994262529 -0.1125463937
[27,] 0.0005658641 -0.0994262529
[28,] 0.0438787023 0.0005658641
[29,] 0.0542958079 0.0438787023
[30,] 0.0126671582 0.0542958079
[31,] 0.0115787641 0.0126671582
[32,] 0.0285598816 0.0115787641
[33,] 0.0626452665 0.0285598816
[34,] 0.0379660235 0.0626452665
[35,] 0.0088855481 0.0379660235
[36,] 0.0313105725 0.0088855481
[37,] 0.0071337484 0.0313105725
[38,] 0.0321843775 0.0071337484
[39,] -0.0038076679 0.0321843775
[40,] -0.0143558065 -0.0038076679
[41,] -0.0633588515 -0.0143558065
[42,] -0.0538295596 -0.0633588515
[43,] -0.0658057676 -0.0538295596
[44,] -0.0597898942 -0.0658057676
[45,] -0.0415734048 -0.0597898942
[46,] -0.0616191243 -0.0415734048
[47,] -0.0630550767 -0.0616191243
[48,] -0.0515952965 -0.0630550767
[49,] -0.0327611210 -0.0515952965
[50,] 0.0067681709 -0.0327611210
[51,] -0.0325050019 0.0067681709
[52,] -0.0721257329 -0.0325050019
[53,] -0.0613610691 -0.0721257329
[54,] -0.0588586143 -0.0613610691
[55,] 0.0026011659 -0.0588586143
[56,] 0.0317828996 0.0026011659
[57,] -0.0113512500 0.0317828996
[58,] -0.0044475629 -0.0113512500
[59,] 0.0095603917 -0.0044475629
[60,] -0.0324932466 0.0095603917
[61,] -0.0454346987 -0.0324932466
[62,] -0.0467157903 -0.0454346987
[63,] 0.0301878968 -0.0467157903
[64,] 0.0413001189 0.0301878968
[65,] 0.0635324458 0.0413001189
[66,] 0.0644519705 0.0635324458
[67,] 0.0643288205 0.0644519705
[68,] 0.0764837171 0.0643288205
[69,] 0.1500666831 0.0764837171
[70,] 0.0873953588 0.1500666831
[71,] 0.0134886625 0.0873953588
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0555433907 0.0640061801
2 0.0326019386 0.0555433907
3 0.0408184280 0.0326019386
4 0.0462166154 0.0408184280
5 -0.0137516738 0.0462166154
6 -0.0006315330 -0.0137516738
7 -0.0023376132 -0.0006315330
8 0.0483117836 -0.0023376132
9 0.0729374242 0.0483117836
10 0.0573703673 0.0729374242
11 0.0732313799 0.0573703673
12 0.0182045786 0.0732313799
13 0.0203594752 0.0182045786
14 0.0227844996 0.0203594752
15 -0.0378014756 0.0227844996
16 -0.0646039044 -0.0378014756
17 -0.0373526591 -0.0646039044
18 -0.0233447045 -0.0373526591
19 -0.0482940754 -0.0233447045
20 0.0082620535 -0.0482940754
21 -0.0234440266 0.0082620535
22 -0.0252275371 -0.0234440266
23 -0.0430726406 -0.0252275371
24 -0.1255891041 -0.0430726406
25 -0.1125463937 -0.1255891041
26 -0.0994262529 -0.1125463937
27 0.0005658641 -0.0994262529
28 0.0438787023 0.0005658641
29 0.0542958079 0.0438787023
30 0.0126671582 0.0542958079
31 0.0115787641 0.0126671582
32 0.0285598816 0.0115787641
33 0.0626452665 0.0285598816
34 0.0379660235 0.0626452665
35 0.0088855481 0.0379660235
36 0.0313105725 0.0088855481
37 0.0071337484 0.0313105725
38 0.0321843775 0.0071337484
39 -0.0038076679 0.0321843775
40 -0.0143558065 -0.0038076679
41 -0.0633588515 -0.0143558065
42 -0.0538295596 -0.0633588515
43 -0.0658057676 -0.0538295596
44 -0.0597898942 -0.0658057676
45 -0.0415734048 -0.0597898942
46 -0.0616191243 -0.0415734048
47 -0.0630550767 -0.0616191243
48 -0.0515952965 -0.0630550767
49 -0.0327611210 -0.0515952965
50 0.0067681709 -0.0327611210
51 -0.0325050019 0.0067681709
52 -0.0721257329 -0.0325050019
53 -0.0613610691 -0.0721257329
54 -0.0588586143 -0.0613610691
55 0.0026011659 -0.0588586143
56 0.0317828996 0.0026011659
57 -0.0113512500 0.0317828996
58 -0.0044475629 -0.0113512500
59 0.0095603917 -0.0044475629
60 -0.0324932466 0.0095603917
61 -0.0454346987 -0.0324932466
62 -0.0467157903 -0.0454346987
63 0.0301878968 -0.0467157903
64 0.0413001189 0.0301878968
65 0.0635324458 0.0413001189
66 0.0644519705 0.0635324458
67 0.0643288205 0.0644519705
68 0.0764837171 0.0643288205
69 0.1500666831 0.0764837171
70 0.0873953588 0.1500666831
71 0.0134886625 0.0873953588
> 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/7jgyr1199639951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/884a31199639951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9e94y1199639951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10jpvo1199639951.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/11s8e21199639951.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/12d1ij1199639952.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/13b0gy1199639952.tab")
>
> system("convert tmp/1za8k1199639951.ps tmp/1za8k1199639951.png")
> system("convert tmp/2jegc1199639951.ps tmp/2jegc1199639951.png")
> system("convert tmp/3uyq71199639951.ps tmp/3uyq71199639951.png")
> system("convert tmp/4i7xw1199639951.ps tmp/4i7xw1199639951.png")
> system("convert tmp/5guge1199639951.ps tmp/5guge1199639951.png")
> system("convert tmp/6ohzk1199639951.ps tmp/6ohzk1199639951.png")
> system("convert tmp/7jgyr1199639951.ps tmp/7jgyr1199639951.png")
> system("convert tmp/884a31199639951.ps tmp/884a31199639951.png")
> system("convert tmp/9e94y1199639951.ps tmp/9e94y1199639951.png")
>
>
> proc.time()
user system elapsed
2.251 1.452 2.691