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(3804,0,3491,0,4151,0,4254,1,4717,1,4866,1,4001,1,3758,1,4780,1,5016,1,4296,0,4467,0,3891,0,3872,0,3867,0,3973,1,4640,1,4538,1,3836,1,3770,1,4374,1,4497,1,3945,0,3862,0,3608,0,3301,0,3882,0,3605,0,4305,1,4216,1,3971,1,3988,1,4317,1,4484,1,4247,0,3520,0,3686,0,3403,0,3990,0,4053,0,4548,1,4559,1,3922,1,4209,1,4517,1,4386,1,3221,0,3127,0,3777,0,3322,0,3899,0,4033,1,4463,1,4819,1,4246,1,4255,1,4760,1,4581,0,4309,0,4016,0,3601,0,3257,0,3823,0,3940,1,4534,1,4575,1,3953,1,4206,1,4649,1,4353,1,3835,0,3944,0),dim=c(2,72),dimnames=list(c('Ong','d'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Ong','d'),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 = 'No 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
Ong d M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3804 0 1 0 0 0 0 0 0 0 0 0 0
2 3491 0 0 1 0 0 0 0 0 0 0 0 0
3 4151 0 0 0 1 0 0 0 0 0 0 0 0
4 4254 1 0 0 0 1 0 0 0 0 0 0 0
5 4717 1 0 0 0 0 1 0 0 0 0 0 0
6 4866 1 0 0 0 0 0 1 0 0 0 0 0
7 4001 1 0 0 0 0 0 0 1 0 0 0 0
8 3758 1 0 0 0 0 0 0 0 1 0 0 0
9 4780 1 0 0 0 0 0 0 0 0 1 0 0
10 5016 1 0 0 0 0 0 0 0 0 0 1 0
11 4296 0 0 0 0 0 0 0 0 0 0 0 1
12 4467 0 0 0 0 0 0 0 0 0 0 0 0
13 3891 0 1 0 0 0 0 0 0 0 0 0 0
14 3872 0 0 1 0 0 0 0 0 0 0 0 0
15 3867 0 0 0 1 0 0 0 0 0 0 0 0
16 3973 1 0 0 0 1 0 0 0 0 0 0 0
17 4640 1 0 0 0 0 1 0 0 0 0 0 0
18 4538 1 0 0 0 0 0 1 0 0 0 0 0
19 3836 1 0 0 0 0 0 0 1 0 0 0 0
20 3770 1 0 0 0 0 0 0 0 1 0 0 0
21 4374 1 0 0 0 0 0 0 0 0 1 0 0
22 4497 1 0 0 0 0 0 0 0 0 0 1 0
23 3945 0 0 0 0 0 0 0 0 0 0 0 1
24 3862 0 0 0 0 0 0 0 0 0 0 0 0
25 3608 0 1 0 0 0 0 0 0 0 0 0 0
26 3301 0 0 1 0 0 0 0 0 0 0 0 0
27 3882 0 0 0 1 0 0 0 0 0 0 0 0
28 3605 0 0 0 0 1 0 0 0 0 0 0 0
29 4305 1 0 0 0 0 1 0 0 0 0 0 0
30 4216 1 0 0 0 0 0 1 0 0 0 0 0
31 3971 1 0 0 0 0 0 0 1 0 0 0 0
32 3988 1 0 0 0 0 0 0 0 1 0 0 0
33 4317 1 0 0 0 0 0 0 0 0 1 0 0
34 4484 1 0 0 0 0 0 0 0 0 0 1 0
35 4247 0 0 0 0 0 0 0 0 0 0 0 1
36 3520 0 0 0 0 0 0 0 0 0 0 0 0
37 3686 0 1 0 0 0 0 0 0 0 0 0 0
38 3403 0 0 1 0 0 0 0 0 0 0 0 0
39 3990 0 0 0 1 0 0 0 0 0 0 0 0
40 4053 0 0 0 0 1 0 0 0 0 0 0 0
41 4548 1 0 0 0 0 1 0 0 0 0 0 0
42 4559 1 0 0 0 0 0 1 0 0 0 0 0
43 3922 1 0 0 0 0 0 0 1 0 0 0 0
44 4209 1 0 0 0 0 0 0 0 1 0 0 0
45 4517 1 0 0 0 0 0 0 0 0 1 0 0
46 4386 1 0 0 0 0 0 0 0 0 0 1 0
47 3221 0 0 0 0 0 0 0 0 0 0 0 1
48 3127 0 0 0 0 0 0 0 0 0 0 0 0
49 3777 0 1 0 0 0 0 0 0 0 0 0 0
50 3322 0 0 1 0 0 0 0 0 0 0 0 0
51 3899 0 0 0 1 0 0 0 0 0 0 0 0
52 4033 1 0 0 0 1 0 0 0 0 0 0 0
53 4463 1 0 0 0 0 1 0 0 0 0 0 0
54 4819 1 0 0 0 0 0 1 0 0 0 0 0
55 4246 1 0 0 0 0 0 0 1 0 0 0 0
56 4255 1 0 0 0 0 0 0 0 1 0 0 0
57 4760 1 0 0 0 0 0 0 0 0 1 0 0
58 4581 0 0 0 0 0 0 0 0 0 0 1 0
59 4309 0 0 0 0 0 0 0 0 0 0 0 1
60 4016 0 0 0 0 0 0 0 0 0 0 0 0
61 3601 0 1 0 0 0 0 0 0 0 0 0 0
62 3257 0 0 1 0 0 0 0 0 0 0 0 0
63 3823 0 0 0 1 0 0 0 0 0 0 0 0
64 3940 1 0 0 0 1 0 0 0 0 0 0 0
65 4534 1 0 0 0 0 1 0 0 0 0 0 0
66 4575 1 0 0 0 0 0 1 0 0 0 0 0
67 3953 1 0 0 0 0 0 0 1 0 0 0 0
68 4206 1 0 0 0 0 0 0 0 1 0 0 0
69 4649 1 0 0 0 0 0 0 0 0 1 0 0
70 4353 1 0 0 0 0 0 0 0 0 0 1 0
71 3835 0 0 0 0 0 0 0 0 0 0 0 1
72 3944 0 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d M1 M2 M3 M4
3822.67 123.00 -94.83 -381.67 112.67 71.67
M5 M6 M7 M8 M9 M10
588.83 649.83 42.50 85.33 620.50 627.67
M11
152.83
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-754.50 -119.21 -32.83 166.12 644.33
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3822.67 102.51 37.292 < 2e-16 ***
d 123.00 170.58 0.721 0.47372
M1 -94.83 144.97 -0.654 0.51554
M2 -381.67 144.97 -2.633 0.01079 *
M3 112.67 144.97 0.777 0.44015
M4 71.67 184.25 0.389 0.69870
M5 588.83 223.86 2.630 0.01086 *
M6 649.83 223.86 2.903 0.00519 **
M7 42.50 223.86 0.190 0.85008
M8 85.33 223.86 0.381 0.70443
M9 620.50 223.86 2.772 0.00745 **
M10 627.67 203.03 3.091 0.00304 **
M11 152.83 144.97 1.054 0.29606
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 251.1 on 59 degrees of freedom
Multiple R-Squared: 0.7191, Adjusted R-squared: 0.6619
F-statistic: 12.58 on 12 and 59 DF, p-value: 3.344e-12
> postscript(file="/var/www/html/rcomp/tmp/12xpd1195654013.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/208wz1195654013.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/37g1h1195654013.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46bq51195654013.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57etg1195654013.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6 7
76.16667 50.00000 215.66667 236.66667 182.50000 270.50000 12.83333
8 9 10 11 12 13 14
-273.00000 213.83333 442.66667 320.50000 644.33333 163.16667 431.00000
15 16 17 18 19 20 21
-68.33333 -44.33333 105.50000 -57.50000 -152.16667 -261.00000 -192.16667
22 23 24 25 26 27 28
-76.33333 -30.50000 39.33333 -119.83333 -140.00000 -53.33333 -289.33333
29 30 31 32 33 34 35
-229.50000 -379.50000 -17.16667 -43.00000 -249.16667 -89.33333 271.50000
36 37 38 39 40 41 42
-302.66667 -41.83333 -38.00000 54.66667 158.66667 13.50000 -36.50000
43 44 45 46 47 48 49
-66.16667 178.00000 -49.16667 -187.33333 -754.50000 -695.66667 49.16667
50 51 52 53 54 55 56
-119.00000 -36.33333 15.66667 -71.50000 223.50000 257.83333 224.00000
57 58 59 60 61 62 63
193.83333 130.66667 333.50000 193.33333 -126.83333 -184.00000 -112.33333
64 65 66 67 68 69 70
-77.33333 -0.50000 -20.50000 -35.16667 175.00000 82.83333 -220.33333
71 72
-140.50000 121.33333
> postscript(file="/var/www/html/rcomp/tmp/6agml1195654013.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 76.16667 NA
1 50.00000 76.16667
2 215.66667 50.00000
3 236.66667 215.66667
4 182.50000 236.66667
5 270.50000 182.50000
6 12.83333 270.50000
7 -273.00000 12.83333
8 213.83333 -273.00000
9 442.66667 213.83333
10 320.50000 442.66667
11 644.33333 320.50000
12 163.16667 644.33333
13 431.00000 163.16667
14 -68.33333 431.00000
15 -44.33333 -68.33333
16 105.50000 -44.33333
17 -57.50000 105.50000
18 -152.16667 -57.50000
19 -261.00000 -152.16667
20 -192.16667 -261.00000
21 -76.33333 -192.16667
22 -30.50000 -76.33333
23 39.33333 -30.50000
24 -119.83333 39.33333
25 -140.00000 -119.83333
26 -53.33333 -140.00000
27 -289.33333 -53.33333
28 -229.50000 -289.33333
29 -379.50000 -229.50000
30 -17.16667 -379.50000
31 -43.00000 -17.16667
32 -249.16667 -43.00000
33 -89.33333 -249.16667
34 271.50000 -89.33333
35 -302.66667 271.50000
36 -41.83333 -302.66667
37 -38.00000 -41.83333
38 54.66667 -38.00000
39 158.66667 54.66667
40 13.50000 158.66667
41 -36.50000 13.50000
42 -66.16667 -36.50000
43 178.00000 -66.16667
44 -49.16667 178.00000
45 -187.33333 -49.16667
46 -754.50000 -187.33333
47 -695.66667 -754.50000
48 49.16667 -695.66667
49 -119.00000 49.16667
50 -36.33333 -119.00000
51 15.66667 -36.33333
52 -71.50000 15.66667
53 223.50000 -71.50000
54 257.83333 223.50000
55 224.00000 257.83333
56 193.83333 224.00000
57 130.66667 193.83333
58 333.50000 130.66667
59 193.33333 333.50000
60 -126.83333 193.33333
61 -184.00000 -126.83333
62 -112.33333 -184.00000
63 -77.33333 -112.33333
64 -0.50000 -77.33333
65 -20.50000 -0.50000
66 -35.16667 -20.50000
67 175.00000 -35.16667
68 82.83333 175.00000
69 -220.33333 82.83333
70 -140.50000 -220.33333
71 121.33333 -140.50000
72 NA 121.33333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 50.00000 76.16667
[2,] 215.66667 50.00000
[3,] 236.66667 215.66667
[4,] 182.50000 236.66667
[5,] 270.50000 182.50000
[6,] 12.83333 270.50000
[7,] -273.00000 12.83333
[8,] 213.83333 -273.00000
[9,] 442.66667 213.83333
[10,] 320.50000 442.66667
[11,] 644.33333 320.50000
[12,] 163.16667 644.33333
[13,] 431.00000 163.16667
[14,] -68.33333 431.00000
[15,] -44.33333 -68.33333
[16,] 105.50000 -44.33333
[17,] -57.50000 105.50000
[18,] -152.16667 -57.50000
[19,] -261.00000 -152.16667
[20,] -192.16667 -261.00000
[21,] -76.33333 -192.16667
[22,] -30.50000 -76.33333
[23,] 39.33333 -30.50000
[24,] -119.83333 39.33333
[25,] -140.00000 -119.83333
[26,] -53.33333 -140.00000
[27,] -289.33333 -53.33333
[28,] -229.50000 -289.33333
[29,] -379.50000 -229.50000
[30,] -17.16667 -379.50000
[31,] -43.00000 -17.16667
[32,] -249.16667 -43.00000
[33,] -89.33333 -249.16667
[34,] 271.50000 -89.33333
[35,] -302.66667 271.50000
[36,] -41.83333 -302.66667
[37,] -38.00000 -41.83333
[38,] 54.66667 -38.00000
[39,] 158.66667 54.66667
[40,] 13.50000 158.66667
[41,] -36.50000 13.50000
[42,] -66.16667 -36.50000
[43,] 178.00000 -66.16667
[44,] -49.16667 178.00000
[45,] -187.33333 -49.16667
[46,] -754.50000 -187.33333
[47,] -695.66667 -754.50000
[48,] 49.16667 -695.66667
[49,] -119.00000 49.16667
[50,] -36.33333 -119.00000
[51,] 15.66667 -36.33333
[52,] -71.50000 15.66667
[53,] 223.50000 -71.50000
[54,] 257.83333 223.50000
[55,] 224.00000 257.83333
[56,] 193.83333 224.00000
[57,] 130.66667 193.83333
[58,] 333.50000 130.66667
[59,] 193.33333 333.50000
[60,] -126.83333 193.33333
[61,] -184.00000 -126.83333
[62,] -112.33333 -184.00000
[63,] -77.33333 -112.33333
[64,] -0.50000 -77.33333
[65,] -20.50000 -0.50000
[66,] -35.16667 -20.50000
[67,] 175.00000 -35.16667
[68,] 82.83333 175.00000
[69,] -220.33333 82.83333
[70,] -140.50000 -220.33333
[71,] 121.33333 -140.50000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 50.00000 76.16667
2 215.66667 50.00000
3 236.66667 215.66667
4 182.50000 236.66667
5 270.50000 182.50000
6 12.83333 270.50000
7 -273.00000 12.83333
8 213.83333 -273.00000
9 442.66667 213.83333
10 320.50000 442.66667
11 644.33333 320.50000
12 163.16667 644.33333
13 431.00000 163.16667
14 -68.33333 431.00000
15 -44.33333 -68.33333
16 105.50000 -44.33333
17 -57.50000 105.50000
18 -152.16667 -57.50000
19 -261.00000 -152.16667
20 -192.16667 -261.00000
21 -76.33333 -192.16667
22 -30.50000 -76.33333
23 39.33333 -30.50000
24 -119.83333 39.33333
25 -140.00000 -119.83333
26 -53.33333 -140.00000
27 -289.33333 -53.33333
28 -229.50000 -289.33333
29 -379.50000 -229.50000
30 -17.16667 -379.50000
31 -43.00000 -17.16667
32 -249.16667 -43.00000
33 -89.33333 -249.16667
34 271.50000 -89.33333
35 -302.66667 271.50000
36 -41.83333 -302.66667
37 -38.00000 -41.83333
38 54.66667 -38.00000
39 158.66667 54.66667
40 13.50000 158.66667
41 -36.50000 13.50000
42 -66.16667 -36.50000
43 178.00000 -66.16667
44 -49.16667 178.00000
45 -187.33333 -49.16667
46 -754.50000 -187.33333
47 -695.66667 -754.50000
48 49.16667 -695.66667
49 -119.00000 49.16667
50 -36.33333 -119.00000
51 15.66667 -36.33333
52 -71.50000 15.66667
53 223.50000 -71.50000
54 257.83333 223.50000
55 224.00000 257.83333
56 193.83333 224.00000
57 130.66667 193.83333
58 333.50000 130.66667
59 193.33333 333.50000
60 -126.83333 193.33333
61 -184.00000 -126.83333
62 -112.33333 -184.00000
63 -77.33333 -112.33333
64 -0.50000 -77.33333
65 -20.50000 -0.50000
66 -35.16667 -20.50000
67 175.00000 -35.16667
68 82.83333 175.00000
69 -220.33333 82.83333
70 -140.50000 -220.33333
71 121.33333 -140.50000
> 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/7fykn1195654013.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8m1ku1195654013.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fhk91195654013.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/105gt21195654013.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/119r8k1195654013.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/12hwk41195654014.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/13j0nc1195654014.tab")
>
> system("convert tmp/12xpd1195654013.ps tmp/12xpd1195654013.png")
> system("convert tmp/208wz1195654013.ps tmp/208wz1195654013.png")
> system("convert tmp/37g1h1195654013.ps tmp/37g1h1195654013.png")
> system("convert tmp/46bq51195654013.ps tmp/46bq51195654013.png")
> system("convert tmp/57etg1195654013.ps tmp/57etg1195654013.png")
> system("convert tmp/6agml1195654013.ps tmp/6agml1195654013.png")
> system("convert tmp/7fykn1195654013.ps tmp/7fykn1195654013.png")
> system("convert tmp/8m1ku1195654013.ps tmp/8m1ku1195654013.png")
> system("convert tmp/9fhk91195654013.ps tmp/9fhk91195654013.png")
>
>
> proc.time()
user system elapsed
2.304 1.476 2.701