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.
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(87.0,0,96.3,0,107.1,0,115.2,0,106.1,0,89.5,0,91.3,0,97.6,0,100.7,0,104.6,0,94.7,0,101.8,0,102.5,0,105.3,0,110.3,1,109.8,1,117.3,1,118.8,1,131.3,1,125.9,1,133.1,1,147.0,1,145.8,1,164.4,1,149.8,1,137.7,1,151.7,1,156.8,1,180.0,1,180.4,1,170.4,1,191.6,1,199.5,1,218.2,1,217.5,1,205.0,1,194.0,1,199.3,1,219.3,1,211.1,1,215.2,1,240.2,1,242.2,1,240.7,1,255.4,1,253.0,1,218.2,1,203.7,1,205.6,1,215.6,1,188.5,1,202.9,1,214.0,1,230.3,1,230.0,1,241.0,1,259.6,1,247.8,1,270.3,1,289.7,1),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 87.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 96.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 107.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 115.2 0 0 0 0 1 0 0 0 0 0 0 0 4
5 106.1 0 0 0 0 0 1 0 0 0 0 0 0 5
6 89.5 0 0 0 0 0 0 1 0 0 0 0 0 6
7 91.3 0 0 0 0 0 0 0 1 0 0 0 0 7
8 97.6 0 0 0 0 0 0 0 0 1 0 0 0 8
9 100.7 0 0 0 0 0 0 0 0 0 1 0 0 9
10 104.6 0 0 0 0 0 0 0 0 0 0 1 0 10
11 94.7 0 0 0 0 0 0 0 0 0 0 0 1 11
12 101.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 102.5 0 1 0 0 0 0 0 0 0 0 0 0 13
14 105.3 0 0 1 0 0 0 0 0 0 0 0 0 14
15 110.3 1 0 0 1 0 0 0 0 0 0 0 0 15
16 109.8 1 0 0 0 1 0 0 0 0 0 0 0 16
17 117.3 1 0 0 0 0 1 0 0 0 0 0 0 17
18 118.8 1 0 0 0 0 0 1 0 0 0 0 0 18
19 131.3 1 0 0 0 0 0 0 1 0 0 0 0 19
20 125.9 1 0 0 0 0 0 0 0 1 0 0 0 20
21 133.1 1 0 0 0 0 0 0 0 0 1 0 0 21
22 147.0 1 0 0 0 0 0 0 0 0 0 1 0 22
23 145.8 1 0 0 0 0 0 0 0 0 0 0 1 23
24 164.4 1 0 0 0 0 0 0 0 0 0 0 0 24
25 149.8 1 1 0 0 0 0 0 0 0 0 0 0 25
26 137.7 1 0 1 0 0 0 0 0 0 0 0 0 26
27 151.7 1 0 0 1 0 0 0 0 0 0 0 0 27
28 156.8 1 0 0 0 1 0 0 0 0 0 0 0 28
29 180.0 1 0 0 0 0 1 0 0 0 0 0 0 29
30 180.4 1 0 0 0 0 0 1 0 0 0 0 0 30
31 170.4 1 0 0 0 0 0 0 1 0 0 0 0 31
32 191.6 1 0 0 0 0 0 0 0 1 0 0 0 32
33 199.5 1 0 0 0 0 0 0 0 0 1 0 0 33
34 218.2 1 0 0 0 0 0 0 0 0 0 1 0 34
35 217.5 1 0 0 0 0 0 0 0 0 0 0 1 35
36 205.0 1 0 0 0 0 0 0 0 0 0 0 0 36
37 194.0 1 1 0 0 0 0 0 0 0 0 0 0 37
38 199.3 1 0 1 0 0 0 0 0 0 0 0 0 38
39 219.3 1 0 0 1 0 0 0 0 0 0 0 0 39
40 211.1 1 0 0 0 1 0 0 0 0 0 0 0 40
41 215.2 1 0 0 0 0 1 0 0 0 0 0 0 41
42 240.2 1 0 0 0 0 0 1 0 0 0 0 0 42
43 242.2 1 0 0 0 0 0 0 1 0 0 0 0 43
44 240.7 1 0 0 0 0 0 0 0 1 0 0 0 44
45 255.4 1 0 0 0 0 0 0 0 0 1 0 0 45
46 253.0 1 0 0 0 0 0 0 0 0 0 1 0 46
47 218.2 1 0 0 0 0 0 0 0 0 0 0 1 47
48 203.7 1 0 0 0 0 0 0 0 0 0 0 0 48
49 205.6 1 1 0 0 0 0 0 0 0 0 0 0 49
50 215.6 1 0 1 0 0 0 0 0 0 0 0 0 50
51 188.5 1 0 0 1 0 0 0 0 0 0 0 0 51
52 202.9 1 0 0 0 1 0 0 0 0 0 0 0 52
53 214.0 1 0 0 0 0 1 0 0 0 0 0 0 53
54 230.3 1 0 0 0 0 0 1 0 0 0 0 0 54
55 230.0 1 0 0 0 0 0 0 1 0 0 0 0 55
56 241.0 1 0 0 0 0 0 0 0 1 0 0 0 56
57 259.6 1 0 0 0 0 0 0 0 0 1 0 0 57
58 247.8 1 0 0 0 0 0 0 0 0 0 1 0 58
59 270.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 289.7 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
82.657 3.817 -11.618 -11.536 -10.738 -9.936
M5 M6 M7 M8 M9 M10
-5.554 -3.212 -4.990 -1.648 5.674 7.156
M11 t
-0.642 2.978
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-39.116 -14.399 -5.605 12.783 32.660
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 82.6575 10.6788 7.740 7.16e-10 ***
X 3.8168 9.2481 0.413 0.682
M1 -11.6183 12.8813 -0.902 0.372
M2 -11.5363 12.8633 -0.897 0.374
M3 -10.7377 12.9322 -0.830 0.411
M4 -9.9358 12.8983 -0.770 0.445
M5 -5.5538 12.8684 -0.432 0.668
M6 -3.2118 12.8423 -0.250 0.804
M7 -4.9898 12.8203 -0.389 0.699
M8 -1.6479 12.8022 -0.129 0.898
M9 5.6741 12.7881 0.444 0.659
M10 7.1561 12.7780 0.560 0.578
M11 -0.6420 12.7720 -0.050 0.960
t 2.9780 0.2269 13.126 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.19 on 46 degrees of freedom
Multiple R-squared: 0.9037, Adjusted R-squared: 0.8765
F-statistic: 33.2 on 13 and 46 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1a5xl1229456771.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/2jebu1229456771.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3zobt1229456771.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44xc21229456771.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yhks1229456771.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
12.9827972 19.2227972 26.2461538 30.5661538 14.1061538 -7.8138462
7 8 9 10 11 12
-7.2138462 -7.2338462 -14.4338462 -14.9938462 -20.0738462 -16.5938462
13 14 15 16 17 18
-7.2535664 -7.5135664 -10.1069930 -14.3869930 -14.2469930 -18.0669930
19 20 21 22 23 24
-6.7669930 -18.4869930 -21.5869930 -12.1469930 -8.5269930 6.4530070
25 26 27 28 29 30
0.4932867 -14.6667133 -4.4433566 -3.1233566 12.7166434 7.7966434
31 32 33 34 35 36
-3.4033566 11.4766434 9.0766434 23.3166434 27.4366434 11.3166434
37 38 39 40 41 42
8.9569231 11.1969231 27.4202797 15.4402797 12.1802797 31.8602797
43 44 45 46 47 48
32.6602797 24.8402797 29.2402797 22.3802797 -7.5997203 -25.7197203
49 50 51 52 53 54
-15.1794406 -8.2394406 -39.1160839 -28.4960839 -24.7560839 -13.7760839
55 56 57 58 59 60
-15.2760839 -10.5960839 -2.2960839 -18.5560839 8.7639161 24.5439161
> postscript(file="/var/www/html/rcomp/tmp/6p92j1229456771.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 12.9827972 NA
1 19.2227972 12.9827972
2 26.2461538 19.2227972
3 30.5661538 26.2461538
4 14.1061538 30.5661538
5 -7.8138462 14.1061538
6 -7.2138462 -7.8138462
7 -7.2338462 -7.2138462
8 -14.4338462 -7.2338462
9 -14.9938462 -14.4338462
10 -20.0738462 -14.9938462
11 -16.5938462 -20.0738462
12 -7.2535664 -16.5938462
13 -7.5135664 -7.2535664
14 -10.1069930 -7.5135664
15 -14.3869930 -10.1069930
16 -14.2469930 -14.3869930
17 -18.0669930 -14.2469930
18 -6.7669930 -18.0669930
19 -18.4869930 -6.7669930
20 -21.5869930 -18.4869930
21 -12.1469930 -21.5869930
22 -8.5269930 -12.1469930
23 6.4530070 -8.5269930
24 0.4932867 6.4530070
25 -14.6667133 0.4932867
26 -4.4433566 -14.6667133
27 -3.1233566 -4.4433566
28 12.7166434 -3.1233566
29 7.7966434 12.7166434
30 -3.4033566 7.7966434
31 11.4766434 -3.4033566
32 9.0766434 11.4766434
33 23.3166434 9.0766434
34 27.4366434 23.3166434
35 11.3166434 27.4366434
36 8.9569231 11.3166434
37 11.1969231 8.9569231
38 27.4202797 11.1969231
39 15.4402797 27.4202797
40 12.1802797 15.4402797
41 31.8602797 12.1802797
42 32.6602797 31.8602797
43 24.8402797 32.6602797
44 29.2402797 24.8402797
45 22.3802797 29.2402797
46 -7.5997203 22.3802797
47 -25.7197203 -7.5997203
48 -15.1794406 -25.7197203
49 -8.2394406 -15.1794406
50 -39.1160839 -8.2394406
51 -28.4960839 -39.1160839
52 -24.7560839 -28.4960839
53 -13.7760839 -24.7560839
54 -15.2760839 -13.7760839
55 -10.5960839 -15.2760839
56 -2.2960839 -10.5960839
57 -18.5560839 -2.2960839
58 8.7639161 -18.5560839
59 24.5439161 8.7639161
60 NA 24.5439161
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 19.2227972 12.9827972
[2,] 26.2461538 19.2227972
[3,] 30.5661538 26.2461538
[4,] 14.1061538 30.5661538
[5,] -7.8138462 14.1061538
[6,] -7.2138462 -7.8138462
[7,] -7.2338462 -7.2138462
[8,] -14.4338462 -7.2338462
[9,] -14.9938462 -14.4338462
[10,] -20.0738462 -14.9938462
[11,] -16.5938462 -20.0738462
[12,] -7.2535664 -16.5938462
[13,] -7.5135664 -7.2535664
[14,] -10.1069930 -7.5135664
[15,] -14.3869930 -10.1069930
[16,] -14.2469930 -14.3869930
[17,] -18.0669930 -14.2469930
[18,] -6.7669930 -18.0669930
[19,] -18.4869930 -6.7669930
[20,] -21.5869930 -18.4869930
[21,] -12.1469930 -21.5869930
[22,] -8.5269930 -12.1469930
[23,] 6.4530070 -8.5269930
[24,] 0.4932867 6.4530070
[25,] -14.6667133 0.4932867
[26,] -4.4433566 -14.6667133
[27,] -3.1233566 -4.4433566
[28,] 12.7166434 -3.1233566
[29,] 7.7966434 12.7166434
[30,] -3.4033566 7.7966434
[31,] 11.4766434 -3.4033566
[32,] 9.0766434 11.4766434
[33,] 23.3166434 9.0766434
[34,] 27.4366434 23.3166434
[35,] 11.3166434 27.4366434
[36,] 8.9569231 11.3166434
[37,] 11.1969231 8.9569231
[38,] 27.4202797 11.1969231
[39,] 15.4402797 27.4202797
[40,] 12.1802797 15.4402797
[41,] 31.8602797 12.1802797
[42,] 32.6602797 31.8602797
[43,] 24.8402797 32.6602797
[44,] 29.2402797 24.8402797
[45,] 22.3802797 29.2402797
[46,] -7.5997203 22.3802797
[47,] -25.7197203 -7.5997203
[48,] -15.1794406 -25.7197203
[49,] -8.2394406 -15.1794406
[50,] -39.1160839 -8.2394406
[51,] -28.4960839 -39.1160839
[52,] -24.7560839 -28.4960839
[53,] -13.7760839 -24.7560839
[54,] -15.2760839 -13.7760839
[55,] -10.5960839 -15.2760839
[56,] -2.2960839 -10.5960839
[57,] -18.5560839 -2.2960839
[58,] 8.7639161 -18.5560839
[59,] 24.5439161 8.7639161
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 19.2227972 12.9827972
2 26.2461538 19.2227972
3 30.5661538 26.2461538
4 14.1061538 30.5661538
5 -7.8138462 14.1061538
6 -7.2138462 -7.8138462
7 -7.2338462 -7.2138462
8 -14.4338462 -7.2338462
9 -14.9938462 -14.4338462
10 -20.0738462 -14.9938462
11 -16.5938462 -20.0738462
12 -7.2535664 -16.5938462
13 -7.5135664 -7.2535664
14 -10.1069930 -7.5135664
15 -14.3869930 -10.1069930
16 -14.2469930 -14.3869930
17 -18.0669930 -14.2469930
18 -6.7669930 -18.0669930
19 -18.4869930 -6.7669930
20 -21.5869930 -18.4869930
21 -12.1469930 -21.5869930
22 -8.5269930 -12.1469930
23 6.4530070 -8.5269930
24 0.4932867 6.4530070
25 -14.6667133 0.4932867
26 -4.4433566 -14.6667133
27 -3.1233566 -4.4433566
28 12.7166434 -3.1233566
29 7.7966434 12.7166434
30 -3.4033566 7.7966434
31 11.4766434 -3.4033566
32 9.0766434 11.4766434
33 23.3166434 9.0766434
34 27.4366434 23.3166434
35 11.3166434 27.4366434
36 8.9569231 11.3166434
37 11.1969231 8.9569231
38 27.4202797 11.1969231
39 15.4402797 27.4202797
40 12.1802797 15.4402797
41 31.8602797 12.1802797
42 32.6602797 31.8602797
43 24.8402797 32.6602797
44 29.2402797 24.8402797
45 22.3802797 29.2402797
46 -7.5997203 22.3802797
47 -25.7197203 -7.5997203
48 -15.1794406 -25.7197203
49 -8.2394406 -15.1794406
50 -39.1160839 -8.2394406
51 -28.4960839 -39.1160839
52 -24.7560839 -28.4960839
53 -13.7760839 -24.7560839
54 -15.2760839 -13.7760839
55 -10.5960839 -15.2760839
56 -2.2960839 -10.5960839
57 -18.5560839 -2.2960839
58 8.7639161 -18.5560839
59 24.5439161 8.7639161
> 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/7k38x1229456771.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cgfa1229456771.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/911q31229456771.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10da9h1229456772.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/1113t11229456772.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/12t6d21229456772.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/13np1h1229456772.tab")
>
> system("convert tmp/1a5xl1229456771.ps tmp/1a5xl1229456771.png")
> system("convert tmp/2jebu1229456771.ps tmp/2jebu1229456771.png")
> system("convert tmp/3zobt1229456771.ps tmp/3zobt1229456771.png")
> system("convert tmp/44xc21229456771.ps tmp/44xc21229456771.png")
> system("convert tmp/5yhks1229456771.ps tmp/5yhks1229456771.png")
> system("convert tmp/6p92j1229456771.ps tmp/6p92j1229456771.png")
> system("convert tmp/7k38x1229456771.ps tmp/7k38x1229456771.png")
> system("convert tmp/8cgfa1229456771.ps tmp/8cgfa1229456771.png")
> system("convert tmp/911q31229456771.ps tmp/911q31229456771.png")
>
>
> proc.time()
user system elapsed
1.951 1.446 2.538