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(7.8,0,7.6,0,7.5,0,7.6,0,7.5,0,7.3,0,7.6,0,7.5,0,7.6,0,7.9,0,7.9,0,8.1,0,8.2,0,8.0,0,7.5,0,6.8,0,6.5,0,6.6,0,7.6,0,8.0,0,8.0,0,7.7,0,7.5,0,7.6,0,7.7,0,7.9,0,7.8,0,7.5,0,7.5,0,7.1,0,7.5,0,7.5,0,7.6,0,7.7,1,7.9,1,8.1,1,8.2,1,8.2,1,8.1,1,7.9,1,7.3,1,6.9,1,6.6,1,6.7,1,6.9,1,7.0,1,7.1,1,7.2,1,7.1,1,6.9,1,7.0,1,6.8,1,6.4,1,6.7,1,6.7,1,6.4,1,6.3,1,6.2,1,6.5,1,6.8,1,6.8,1,6.5,1,6.3,1,5.9,1,5.9,1,6.4,1,6.4,1),dim=c(2,67),dimnames=list(c('y','x'),1:67))
> y <- array(NA,dim=c(2,67),dimnames=list(c('y','x'),1:67))
> 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 = '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
1 7.8 0 1 0 0 0 0 0 0 0 0 0 0
2 7.6 0 0 1 0 0 0 0 0 0 0 0 0
3 7.5 0 0 0 1 0 0 0 0 0 0 0 0
4 7.6 0 0 0 0 1 0 0 0 0 0 0 0
5 7.5 0 0 0 0 0 1 0 0 0 0 0 0
6 7.3 0 0 0 0 0 0 1 0 0 0 0 0
7 7.6 0 0 0 0 0 0 0 1 0 0 0 0
8 7.5 0 0 0 0 0 0 0 0 1 0 0 0
9 7.6 0 0 0 0 0 0 0 0 0 1 0 0
10 7.9 0 0 0 0 0 0 0 0 0 0 1 0
11 7.9 0 0 0 0 0 0 0 0 0 0 0 1
12 8.1 0 0 0 0 0 0 0 0 0 0 0 0
13 8.2 0 1 0 0 0 0 0 0 0 0 0 0
14 8.0 0 0 1 0 0 0 0 0 0 0 0 0
15 7.5 0 0 0 1 0 0 0 0 0 0 0 0
16 6.8 0 0 0 0 1 0 0 0 0 0 0 0
17 6.5 0 0 0 0 0 1 0 0 0 0 0 0
18 6.6 0 0 0 0 0 0 1 0 0 0 0 0
19 7.6 0 0 0 0 0 0 0 1 0 0 0 0
20 8.0 0 0 0 0 0 0 0 0 1 0 0 0
21 8.0 0 0 0 0 0 0 0 0 0 1 0 0
22 7.7 0 0 0 0 0 0 0 0 0 0 1 0
23 7.5 0 0 0 0 0 0 0 0 0 0 0 1
24 7.6 0 0 0 0 0 0 0 0 0 0 0 0
25 7.7 0 1 0 0 0 0 0 0 0 0 0 0
26 7.9 0 0 1 0 0 0 0 0 0 0 0 0
27 7.8 0 0 0 1 0 0 0 0 0 0 0 0
28 7.5 0 0 0 0 1 0 0 0 0 0 0 0
29 7.5 0 0 0 0 0 1 0 0 0 0 0 0
30 7.1 0 0 0 0 0 0 1 0 0 0 0 0
31 7.5 0 0 0 0 0 0 0 1 0 0 0 0
32 7.5 0 0 0 0 0 0 0 0 1 0 0 0
33 7.6 0 0 0 0 0 0 0 0 0 1 0 0
34 7.7 1 0 0 0 0 0 0 0 0 0 1 0
35 7.9 1 0 0 0 0 0 0 0 0 0 0 1
36 8.1 1 0 0 0 0 0 0 0 0 0 0 0
37 8.2 1 1 0 0 0 0 0 0 0 0 0 0
38 8.2 1 0 1 0 0 0 0 0 0 0 0 0
39 8.1 1 0 0 1 0 0 0 0 0 0 0 0
40 7.9 1 0 0 0 1 0 0 0 0 0 0 0
41 7.3 1 0 0 0 0 1 0 0 0 0 0 0
42 6.9 1 0 0 0 0 0 1 0 0 0 0 0
43 6.6 1 0 0 0 0 0 0 1 0 0 0 0
44 6.7 1 0 0 0 0 0 0 0 1 0 0 0
45 6.9 1 0 0 0 0 0 0 0 0 1 0 0
46 7.0 1 0 0 0 0 0 0 0 0 0 1 0
47 7.1 1 0 0 0 0 0 0 0 0 0 0 1
48 7.2 1 0 0 0 0 0 0 0 0 0 0 0
49 7.1 1 1 0 0 0 0 0 0 0 0 0 0
50 6.9 1 0 1 0 0 0 0 0 0 0 0 0
51 7.0 1 0 0 1 0 0 0 0 0 0 0 0
52 6.8 1 0 0 0 1 0 0 0 0 0 0 0
53 6.4 1 0 0 0 0 1 0 0 0 0 0 0
54 6.7 1 0 0 0 0 0 1 0 0 0 0 0
55 6.7 1 0 0 0 0 0 0 1 0 0 0 0
56 6.4 1 0 0 0 0 0 0 0 1 0 0 0
57 6.3 1 0 0 0 0 0 0 0 0 1 0 0
58 6.2 1 0 0 0 0 0 0 0 0 0 1 0
59 6.5 1 0 0 0 0 0 0 0 0 0 0 1
60 6.8 1 0 0 0 0 0 0 0 0 0 0 0
61 6.8 1 1 0 0 0 0 0 0 0 0 0 0
62 6.5 1 0 1 0 0 0 0 0 0 0 0 0
63 6.3 1 0 0 1 0 0 0 0 0 0 0 0
64 5.9 1 0 0 0 1 0 0 0 0 0 0 0
65 5.9 1 0 0 0 0 1 0 0 0 0 0 0
66 6.4 1 0 0 0 0 0 1 0 0 0 0 0
67 6.4 1 0 0 0 0 0 0 1 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
7.958909 -0.664848 0.006848 -0.109818 -0.259818 -0.543152
M5 M6 M7 M8 M9 M10
-0.776485 -0.793152 -0.559818 -0.472970 -0.412970 -0.260000
M11
-0.180000
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.850909 -0.272333 0.001091 0.200000 1.149091
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.958909 0.239188 33.275 < 2e-16 ***
x -0.664848 0.125026 -5.318 2.06e-06 ***
M1 0.006848 0.307777 0.022 0.9823
M2 -0.109818 0.307777 -0.357 0.7226
M3 -0.259818 0.307777 -0.844 0.4023
M4 -0.543152 0.307777 -1.765 0.0833 .
M5 -0.776485 0.307777 -2.523 0.0146 *
M6 -0.793152 0.307777 -2.577 0.0127 *
M7 -0.559818 0.307777 -1.819 0.0745 .
M8 -0.472970 0.322169 -1.468 0.1479
M9 -0.412970 0.322169 -1.282 0.2054
M10 -0.260000 0.321197 -0.809 0.4218
M11 -0.180000 0.321197 -0.560 0.5775
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5079 on 54 degrees of freedom
Multiple R-squared: 0.4553, Adjusted R-squared: 0.3342
F-statistic: 3.761 on 12 and 54 DF, p-value: 0.000372
> postscript(file="/var/www/html/rcomp/tmp/1g1b41228170319.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/292qa1228170319.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/31nup1228170319.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4t9se1228170319.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5sltr1228170319.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 67
Frequency = 1
1 2 3 4 5 6
-0.165757576 -0.249090909 -0.199090909 0.184242424 0.317575758 0.134242424
7 8 9 10 11 12
0.200909091 0.014060606 0.054060606 0.201090909 0.121090909 0.141090909
13 14 15 16 17 18
0.234242424 0.150909091 -0.199090909 -0.615757576 -0.682424242 -0.565757576
19 20 21 22 23 24
0.200909091 0.514060606 0.454060606 0.001090909 -0.278909091 -0.358909091
25 26 27 28 29 30
-0.265757576 0.050909091 0.100909091 0.084242424 0.317575758 -0.065757576
31 32 33 34 35 36
0.100909091 0.014060606 0.054060606 0.665939394 0.785939394 0.805939394
37 38 39 40 41 42
0.899090909 1.015757576 1.065757576 1.149090909 0.782424242 0.399090909
43 44 45 46 47 48
-0.134242424 -0.121090909 0.018909091 -0.034060606 -0.014060606 -0.094060606
49 50 51 52 53 54
-0.200909091 -0.284242424 -0.034242424 0.049090909 -0.117575758 0.199090909
55 56 57 58 59 60
-0.034242424 -0.421090909 -0.581090909 -0.834060606 -0.614060606 -0.494060606
61 62 63 64 65 66
-0.500909091 -0.684242424 -0.734242424 -0.850909091 -0.617575758 -0.100909091
67
-0.334242424
> postscript(file="/var/www/html/rcomp/tmp/6udr71228170319.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.165757576 NA
1 -0.249090909 -0.165757576
2 -0.199090909 -0.249090909
3 0.184242424 -0.199090909
4 0.317575758 0.184242424
5 0.134242424 0.317575758
6 0.200909091 0.134242424
7 0.014060606 0.200909091
8 0.054060606 0.014060606
9 0.201090909 0.054060606
10 0.121090909 0.201090909
11 0.141090909 0.121090909
12 0.234242424 0.141090909
13 0.150909091 0.234242424
14 -0.199090909 0.150909091
15 -0.615757576 -0.199090909
16 -0.682424242 -0.615757576
17 -0.565757576 -0.682424242
18 0.200909091 -0.565757576
19 0.514060606 0.200909091
20 0.454060606 0.514060606
21 0.001090909 0.454060606
22 -0.278909091 0.001090909
23 -0.358909091 -0.278909091
24 -0.265757576 -0.358909091
25 0.050909091 -0.265757576
26 0.100909091 0.050909091
27 0.084242424 0.100909091
28 0.317575758 0.084242424
29 -0.065757576 0.317575758
30 0.100909091 -0.065757576
31 0.014060606 0.100909091
32 0.054060606 0.014060606
33 0.665939394 0.054060606
34 0.785939394 0.665939394
35 0.805939394 0.785939394
36 0.899090909 0.805939394
37 1.015757576 0.899090909
38 1.065757576 1.015757576
39 1.149090909 1.065757576
40 0.782424242 1.149090909
41 0.399090909 0.782424242
42 -0.134242424 0.399090909
43 -0.121090909 -0.134242424
44 0.018909091 -0.121090909
45 -0.034060606 0.018909091
46 -0.014060606 -0.034060606
47 -0.094060606 -0.014060606
48 -0.200909091 -0.094060606
49 -0.284242424 -0.200909091
50 -0.034242424 -0.284242424
51 0.049090909 -0.034242424
52 -0.117575758 0.049090909
53 0.199090909 -0.117575758
54 -0.034242424 0.199090909
55 -0.421090909 -0.034242424
56 -0.581090909 -0.421090909
57 -0.834060606 -0.581090909
58 -0.614060606 -0.834060606
59 -0.494060606 -0.614060606
60 -0.500909091 -0.494060606
61 -0.684242424 -0.500909091
62 -0.734242424 -0.684242424
63 -0.850909091 -0.734242424
64 -0.617575758 -0.850909091
65 -0.100909091 -0.617575758
66 -0.334242424 -0.100909091
67 NA -0.334242424
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.249090909 -0.165757576
[2,] -0.199090909 -0.249090909
[3,] 0.184242424 -0.199090909
[4,] 0.317575758 0.184242424
[5,] 0.134242424 0.317575758
[6,] 0.200909091 0.134242424
[7,] 0.014060606 0.200909091
[8,] 0.054060606 0.014060606
[9,] 0.201090909 0.054060606
[10,] 0.121090909 0.201090909
[11,] 0.141090909 0.121090909
[12,] 0.234242424 0.141090909
[13,] 0.150909091 0.234242424
[14,] -0.199090909 0.150909091
[15,] -0.615757576 -0.199090909
[16,] -0.682424242 -0.615757576
[17,] -0.565757576 -0.682424242
[18,] 0.200909091 -0.565757576
[19,] 0.514060606 0.200909091
[20,] 0.454060606 0.514060606
[21,] 0.001090909 0.454060606
[22,] -0.278909091 0.001090909
[23,] -0.358909091 -0.278909091
[24,] -0.265757576 -0.358909091
[25,] 0.050909091 -0.265757576
[26,] 0.100909091 0.050909091
[27,] 0.084242424 0.100909091
[28,] 0.317575758 0.084242424
[29,] -0.065757576 0.317575758
[30,] 0.100909091 -0.065757576
[31,] 0.014060606 0.100909091
[32,] 0.054060606 0.014060606
[33,] 0.665939394 0.054060606
[34,] 0.785939394 0.665939394
[35,] 0.805939394 0.785939394
[36,] 0.899090909 0.805939394
[37,] 1.015757576 0.899090909
[38,] 1.065757576 1.015757576
[39,] 1.149090909 1.065757576
[40,] 0.782424242 1.149090909
[41,] 0.399090909 0.782424242
[42,] -0.134242424 0.399090909
[43,] -0.121090909 -0.134242424
[44,] 0.018909091 -0.121090909
[45,] -0.034060606 0.018909091
[46,] -0.014060606 -0.034060606
[47,] -0.094060606 -0.014060606
[48,] -0.200909091 -0.094060606
[49,] -0.284242424 -0.200909091
[50,] -0.034242424 -0.284242424
[51,] 0.049090909 -0.034242424
[52,] -0.117575758 0.049090909
[53,] 0.199090909 -0.117575758
[54,] -0.034242424 0.199090909
[55,] -0.421090909 -0.034242424
[56,] -0.581090909 -0.421090909
[57,] -0.834060606 -0.581090909
[58,] -0.614060606 -0.834060606
[59,] -0.494060606 -0.614060606
[60,] -0.500909091 -0.494060606
[61,] -0.684242424 -0.500909091
[62,] -0.734242424 -0.684242424
[63,] -0.850909091 -0.734242424
[64,] -0.617575758 -0.850909091
[65,] -0.100909091 -0.617575758
[66,] -0.334242424 -0.100909091
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.249090909 -0.165757576
2 -0.199090909 -0.249090909
3 0.184242424 -0.199090909
4 0.317575758 0.184242424
5 0.134242424 0.317575758
6 0.200909091 0.134242424
7 0.014060606 0.200909091
8 0.054060606 0.014060606
9 0.201090909 0.054060606
10 0.121090909 0.201090909
11 0.141090909 0.121090909
12 0.234242424 0.141090909
13 0.150909091 0.234242424
14 -0.199090909 0.150909091
15 -0.615757576 -0.199090909
16 -0.682424242 -0.615757576
17 -0.565757576 -0.682424242
18 0.200909091 -0.565757576
19 0.514060606 0.200909091
20 0.454060606 0.514060606
21 0.001090909 0.454060606
22 -0.278909091 0.001090909
23 -0.358909091 -0.278909091
24 -0.265757576 -0.358909091
25 0.050909091 -0.265757576
26 0.100909091 0.050909091
27 0.084242424 0.100909091
28 0.317575758 0.084242424
29 -0.065757576 0.317575758
30 0.100909091 -0.065757576
31 0.014060606 0.100909091
32 0.054060606 0.014060606
33 0.665939394 0.054060606
34 0.785939394 0.665939394
35 0.805939394 0.785939394
36 0.899090909 0.805939394
37 1.015757576 0.899090909
38 1.065757576 1.015757576
39 1.149090909 1.065757576
40 0.782424242 1.149090909
41 0.399090909 0.782424242
42 -0.134242424 0.399090909
43 -0.121090909 -0.134242424
44 0.018909091 -0.121090909
45 -0.034060606 0.018909091
46 -0.014060606 -0.034060606
47 -0.094060606 -0.014060606
48 -0.200909091 -0.094060606
49 -0.284242424 -0.200909091
50 -0.034242424 -0.284242424
51 0.049090909 -0.034242424
52 -0.117575758 0.049090909
53 0.199090909 -0.117575758
54 -0.034242424 0.199090909
55 -0.421090909 -0.034242424
56 -0.581090909 -0.421090909
57 -0.834060606 -0.581090909
58 -0.614060606 -0.834060606
59 -0.494060606 -0.614060606
60 -0.500909091 -0.494060606
61 -0.684242424 -0.500909091
62 -0.734242424 -0.684242424
63 -0.850909091 -0.734242424
64 -0.617575758 -0.850909091
65 -0.100909091 -0.617575758
66 -0.334242424 -0.100909091
> 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/73f421228170319.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8t0jr1228170319.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99jfp1228170319.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10dd671228170319.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/11cw671228170319.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/12obwl1228170319.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/13uvx01228170319.tab")
>
> system("convert tmp/1g1b41228170319.ps tmp/1g1b41228170319.png")
> system("convert tmp/292qa1228170319.ps tmp/292qa1228170319.png")
> system("convert tmp/31nup1228170319.ps tmp/31nup1228170319.png")
> system("convert tmp/4t9se1228170319.ps tmp/4t9se1228170319.png")
> system("convert tmp/5sltr1228170319.ps tmp/5sltr1228170319.png")
> system("convert tmp/6udr71228170319.ps tmp/6udr71228170319.png")
> system("convert tmp/73f421228170319.ps tmp/73f421228170319.png")
> system("convert tmp/8t0jr1228170319.ps tmp/8t0jr1228170319.png")
> system("convert tmp/99jfp1228170319.ps tmp/99jfp1228170319.png")
>
>
> proc.time()
user system elapsed
1.936 1.405 2.284