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(11703.7,0,16283.6,0,16726.5,0,14968.9,0,14861,0,14583.3,0,15305.8,0,17903.9,0,16379.4,0,15420.3,0,17870.5,0,15912.8,0,13866.5,0,17823.2,0,17872,0,17420.4,0,16704.4,0,15991.2,0,16583.6,0,19123.5,0,17838.7,0,17209.4,0,18586.5,0,16258.1,0,15141.6,0,19202.1,0,17746.5,0,19090.1,0,18040.3,0,17515.5,0,17751.8,0,21072.4,0,17170,0,19439.5,0,19795.4,0,17574.9,0,16165.4,0,19464.6,0,19932.1,0,19961.2,0,17343.4,0,18924.2,0,18574.1,0,21350.6,0,18594.6,0,19823.1,0,20844.4,0,19640.2,0,17735.4,0,19813.6,0,22160,0,20664.3,1,17877.4,1,21211.2,1,21423.1,1,21688.7,1,23243.2,1,21490.2,1,22925.8,1,23184.8,1,18562.2,1),dim=c(2,61),dimnames=list(c('Uitvoer','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Uitvoer','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
Uitvoer x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 11703.7 0 1 0 0 0 0 0 0 0 0 0 0 1
2 16283.6 0 0 1 0 0 0 0 0 0 0 0 0 2
3 16726.5 0 0 0 1 0 0 0 0 0 0 0 0 3
4 14968.9 0 0 0 0 1 0 0 0 0 0 0 0 4
5 14861.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 14583.3 0 0 0 0 0 0 1 0 0 0 0 0 6
7 15305.8 0 0 0 0 0 0 0 1 0 0 0 0 7
8 17903.9 0 0 0 0 0 0 0 0 1 0 0 0 8
9 16379.4 0 0 0 0 0 0 0 0 0 1 0 0 9
10 15420.3 0 0 0 0 0 0 0 0 0 0 1 0 10
11 17870.5 0 0 0 0 0 0 0 0 0 0 0 1 11
12 15912.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 13866.5 0 1 0 0 0 0 0 0 0 0 0 0 13
14 17823.2 0 0 1 0 0 0 0 0 0 0 0 0 14
15 17872.0 0 0 0 1 0 0 0 0 0 0 0 0 15
16 17420.4 0 0 0 0 1 0 0 0 0 0 0 0 16
17 16704.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 15991.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 16583.6 0 0 0 0 0 0 0 1 0 0 0 0 19
20 19123.5 0 0 0 0 0 0 0 0 1 0 0 0 20
21 17838.7 0 0 0 0 0 0 0 0 0 1 0 0 21
22 17209.4 0 0 0 0 0 0 0 0 0 0 1 0 22
23 18586.5 0 0 0 0 0 0 0 0 0 0 0 1 23
24 16258.1 0 0 0 0 0 0 0 0 0 0 0 0 24
25 15141.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 19202.1 0 0 1 0 0 0 0 0 0 0 0 0 26
27 17746.5 0 0 0 1 0 0 0 0 0 0 0 0 27
28 19090.1 0 0 0 0 1 0 0 0 0 0 0 0 28
29 18040.3 0 0 0 0 0 1 0 0 0 0 0 0 29
30 17515.5 0 0 0 0 0 0 1 0 0 0 0 0 30
31 17751.8 0 0 0 0 0 0 0 1 0 0 0 0 31
32 21072.4 0 0 0 0 0 0 0 0 1 0 0 0 32
33 17170.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 19439.5 0 0 0 0 0 0 0 0 0 0 1 0 34
35 19795.4 0 0 0 0 0 0 0 0 0 0 0 1 35
36 17574.9 0 0 0 0 0 0 0 0 0 0 0 0 36
37 16165.4 0 1 0 0 0 0 0 0 0 0 0 0 37
38 19464.6 0 0 1 0 0 0 0 0 0 0 0 0 38
39 19932.1 0 0 0 1 0 0 0 0 0 0 0 0 39
40 19961.2 0 0 0 0 1 0 0 0 0 0 0 0 40
41 17343.4 0 0 0 0 0 1 0 0 0 0 0 0 41
42 18924.2 0 0 0 0 0 0 1 0 0 0 0 0 42
43 18574.1 0 0 0 0 0 0 0 1 0 0 0 0 43
44 21350.6 0 0 0 0 0 0 0 0 1 0 0 0 44
45 18594.6 0 0 0 0 0 0 0 0 0 1 0 0 45
46 19823.1 0 0 0 0 0 0 0 0 0 0 1 0 46
47 20844.4 0 0 0 0 0 0 0 0 0 0 0 1 47
48 19640.2 0 0 0 0 0 0 0 0 0 0 0 0 48
49 17735.4 0 1 0 0 0 0 0 0 0 0 0 0 49
50 19813.6 0 0 1 0 0 0 0 0 0 0 0 0 50
51 22160.0 0 0 0 1 0 0 0 0 0 0 0 0 51
52 20664.3 1 0 0 0 1 0 0 0 0 0 0 0 52
53 17877.4 1 0 0 0 0 1 0 0 0 0 0 0 53
54 21211.2 1 0 0 0 0 0 1 0 0 0 0 0 54
55 21423.1 1 0 0 0 0 0 0 1 0 0 0 0 55
56 21688.7 1 0 0 0 0 0 0 0 1 0 0 0 56
57 23243.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 21490.2 1 0 0 0 0 0 0 0 0 0 1 0 58
59 22925.8 1 0 0 0 0 0 0 0 0 0 0 1 59
60 23184.8 1 0 0 0 0 0 0 0 0 0 0 0 60
61 18562.2 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
14807.23 657.57 -2466.52 1127.95 1398.63 701.36
M5 M6 M7 M8 M9 M10
-853.64 -273.18 -89.89 2110.93 428.97 360.97
M11 t
1589.68 99.32
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1997.57 -413.87 62.49 410.83 1760.97
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14807.226 441.892 33.509 < 2e-16 ***
x 657.574 378.701 1.736 0.089046 .
M1 -2466.521 500.198 -4.931 1.06e-05 ***
M2 1127.947 525.435 2.147 0.037010 *
M3 1398.630 525.032 2.664 0.010548 *
M4 701.358 525.141 1.336 0.188125
M5 -853.640 524.244 -1.628 0.110142
M6 -273.177 523.465 -0.522 0.604217
M7 -89.894 522.805 -0.172 0.864219
M8 2110.929 522.265 4.042 0.000195 ***
M9 428.972 521.844 0.822 0.415211
M10 360.974 521.544 0.692 0.492263
M11 1589.677 521.363 3.049 0.003763 **
t 99.317 7.922 12.538 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 824.3 on 47 degrees of freedom
Multiple R-squared: 0.9069, Adjusted R-squared: 0.8812
F-statistic: 35.23 on 13 and 47 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/freestat/rcomp/tmp/191511229259293.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/freestat/rcomp/tmp/2f20v1229259293.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3zh891229259293.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/41dy31229259293.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/583c51229259293.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6
-736.321413 149.792958 222.692958 -936.952174 410.827826 -546.652174
7 8 9 10 11 12
-106.752174 191.207826 249.347826 -741.072174 381.107826 -86.232174
13 14 15 16 17 18
234.672108 497.586479 176.386479 322.741347 1062.421347 -330.558653
19 20 21 22 23 24
-20.758653 219.001347 516.841347 -143.778653 -94.698653 -932.738653
25 26 27 28 29 30
317.965629 684.680000 -1140.920000 800.634868 1206.514868 1.934868
31 32 33 34 35 36
-44.365132 976.094868 -1343.665132 894.514868 -77.605132 -807.745132
37 38 39 40 41 42
149.959150 -244.626479 -147.126479 479.928389 -682.191611 218.828389
43 44 45 46 47 48
-413.871611 62.488389 -1110.871611 86.308389 -220.411611 65.748389
49 50 51 52 53 54
528.152671 -1087.432958 888.967042 -666.352428 -1997.572428 656.447572
55 56 57 58 59 60
585.747572 -1448.792428 1688.347572 -95.972428 11.607572 1760.967572
61
-494.428146
> postscript(file="/var/www/html/freestat/rcomp/tmp/6mkgv1229259293.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 -736.321413 NA
1 149.792958 -736.321413
2 222.692958 149.792958
3 -936.952174 222.692958
4 410.827826 -936.952174
5 -546.652174 410.827826
6 -106.752174 -546.652174
7 191.207826 -106.752174
8 249.347826 191.207826
9 -741.072174 249.347826
10 381.107826 -741.072174
11 -86.232174 381.107826
12 234.672108 -86.232174
13 497.586479 234.672108
14 176.386479 497.586479
15 322.741347 176.386479
16 1062.421347 322.741347
17 -330.558653 1062.421347
18 -20.758653 -330.558653
19 219.001347 -20.758653
20 516.841347 219.001347
21 -143.778653 516.841347
22 -94.698653 -143.778653
23 -932.738653 -94.698653
24 317.965629 -932.738653
25 684.680000 317.965629
26 -1140.920000 684.680000
27 800.634868 -1140.920000
28 1206.514868 800.634868
29 1.934868 1206.514868
30 -44.365132 1.934868
31 976.094868 -44.365132
32 -1343.665132 976.094868
33 894.514868 -1343.665132
34 -77.605132 894.514868
35 -807.745132 -77.605132
36 149.959150 -807.745132
37 -244.626479 149.959150
38 -147.126479 -244.626479
39 479.928389 -147.126479
40 -682.191611 479.928389
41 218.828389 -682.191611
42 -413.871611 218.828389
43 62.488389 -413.871611
44 -1110.871611 62.488389
45 86.308389 -1110.871611
46 -220.411611 86.308389
47 65.748389 -220.411611
48 528.152671 65.748389
49 -1087.432958 528.152671
50 888.967042 -1087.432958
51 -666.352428 888.967042
52 -1997.572428 -666.352428
53 656.447572 -1997.572428
54 585.747572 656.447572
55 -1448.792428 585.747572
56 1688.347572 -1448.792428
57 -95.972428 1688.347572
58 11.607572 -95.972428
59 1760.967572 11.607572
60 -494.428146 1760.967572
61 NA -494.428146
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 149.792958 -736.321413
[2,] 222.692958 149.792958
[3,] -936.952174 222.692958
[4,] 410.827826 -936.952174
[5,] -546.652174 410.827826
[6,] -106.752174 -546.652174
[7,] 191.207826 -106.752174
[8,] 249.347826 191.207826
[9,] -741.072174 249.347826
[10,] 381.107826 -741.072174
[11,] -86.232174 381.107826
[12,] 234.672108 -86.232174
[13,] 497.586479 234.672108
[14,] 176.386479 497.586479
[15,] 322.741347 176.386479
[16,] 1062.421347 322.741347
[17,] -330.558653 1062.421347
[18,] -20.758653 -330.558653
[19,] 219.001347 -20.758653
[20,] 516.841347 219.001347
[21,] -143.778653 516.841347
[22,] -94.698653 -143.778653
[23,] -932.738653 -94.698653
[24,] 317.965629 -932.738653
[25,] 684.680000 317.965629
[26,] -1140.920000 684.680000
[27,] 800.634868 -1140.920000
[28,] 1206.514868 800.634868
[29,] 1.934868 1206.514868
[30,] -44.365132 1.934868
[31,] 976.094868 -44.365132
[32,] -1343.665132 976.094868
[33,] 894.514868 -1343.665132
[34,] -77.605132 894.514868
[35,] -807.745132 -77.605132
[36,] 149.959150 -807.745132
[37,] -244.626479 149.959150
[38,] -147.126479 -244.626479
[39,] 479.928389 -147.126479
[40,] -682.191611 479.928389
[41,] 218.828389 -682.191611
[42,] -413.871611 218.828389
[43,] 62.488389 -413.871611
[44,] -1110.871611 62.488389
[45,] 86.308389 -1110.871611
[46,] -220.411611 86.308389
[47,] 65.748389 -220.411611
[48,] 528.152671 65.748389
[49,] -1087.432958 528.152671
[50,] 888.967042 -1087.432958
[51,] -666.352428 888.967042
[52,] -1997.572428 -666.352428
[53,] 656.447572 -1997.572428
[54,] 585.747572 656.447572
[55,] -1448.792428 585.747572
[56,] 1688.347572 -1448.792428
[57,] -95.972428 1688.347572
[58,] 11.607572 -95.972428
[59,] 1760.967572 11.607572
[60,] -494.428146 1760.967572
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 149.792958 -736.321413
2 222.692958 149.792958
3 -936.952174 222.692958
4 410.827826 -936.952174
5 -546.652174 410.827826
6 -106.752174 -546.652174
7 191.207826 -106.752174
8 249.347826 191.207826
9 -741.072174 249.347826
10 381.107826 -741.072174
11 -86.232174 381.107826
12 234.672108 -86.232174
13 497.586479 234.672108
14 176.386479 497.586479
15 322.741347 176.386479
16 1062.421347 322.741347
17 -330.558653 1062.421347
18 -20.758653 -330.558653
19 219.001347 -20.758653
20 516.841347 219.001347
21 -143.778653 516.841347
22 -94.698653 -143.778653
23 -932.738653 -94.698653
24 317.965629 -932.738653
25 684.680000 317.965629
26 -1140.920000 684.680000
27 800.634868 -1140.920000
28 1206.514868 800.634868
29 1.934868 1206.514868
30 -44.365132 1.934868
31 976.094868 -44.365132
32 -1343.665132 976.094868
33 894.514868 -1343.665132
34 -77.605132 894.514868
35 -807.745132 -77.605132
36 149.959150 -807.745132
37 -244.626479 149.959150
38 -147.126479 -244.626479
39 479.928389 -147.126479
40 -682.191611 479.928389
41 218.828389 -682.191611
42 -413.871611 218.828389
43 62.488389 -413.871611
44 -1110.871611 62.488389
45 86.308389 -1110.871611
46 -220.411611 86.308389
47 65.748389 -220.411611
48 528.152671 65.748389
49 -1087.432958 528.152671
50 888.967042 -1087.432958
51 -666.352428 888.967042
52 -1997.572428 -666.352428
53 656.447572 -1997.572428
54 585.747572 656.447572
55 -1448.792428 585.747572
56 1688.347572 -1448.792428
57 -95.972428 1688.347572
58 11.607572 -95.972428
59 1760.967572 11.607572
60 -494.428146 1760.967572
> 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/freestat/rcomp/tmp/7gmld1229259293.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8lqjc1229259293.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9iibx1229259293.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/10gv071229259293.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/freestat/rcomp/tmp/118jzc1229259293.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/freestat/rcomp/tmp/12ou5i1229259293.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/freestat/rcomp/tmp/13pdsb1229259293.tab")
>
> system("convert tmp/191511229259293.ps tmp/191511229259293.png")
> system("convert tmp/2f20v1229259293.ps tmp/2f20v1229259293.png")
> system("convert tmp/3zh891229259293.ps tmp/3zh891229259293.png")
> system("convert tmp/41dy31229259293.ps tmp/41dy31229259293.png")
> system("convert tmp/583c51229259293.ps tmp/583c51229259293.png")
> system("convert tmp/6mkgv1229259293.ps tmp/6mkgv1229259293.png")
> system("convert tmp/7gmld1229259293.ps tmp/7gmld1229259293.png")
> system("convert tmp/8lqjc1229259293.ps tmp/8lqjc1229259293.png")
> system("convert tmp/9iibx1229259293.ps tmp/9iibx1229259293.png")
>
>
> proc.time()
user system elapsed
2.941 2.205 3.364