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(733.6,0,844.9,0,864.3,0,833.5,0,814.9,0,820.4,0,710.8,0,773.1,0,801.2,0,832.9,0,808.3,0,817.2,0,745.5,0,932.6,0,1057.0,0,879.9,0,1089.5,0,903.0,0,846.1,0,959.1,0,952.0,0,1092.5,0,1188.9,0,996.7,0,1034.3,0,898.2,0,1111.6,0,900.5,0,1049.2,0,1010.9,0,875.9,0,849.9,0,713.4,1,918.6,1,912.5,1,767.0,1,902.2,1,891.9,1,874.0,1,930.9,1,944.2,1,935.9,1,937.1,1,885.1,1,892.4,1,987.3,1,946.3,1,799.6,1,875.4,1,846.2,1,880.6,1,885.7,1,868.9,1,882.5,1,789.6,1,773.3,1,804.3,1,817.8,1,836.7,1,721.8,1),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 = '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
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 733.6 0 1 0 0 0 0 0 0 0 0 0 0
2 844.9 0 0 1 0 0 0 0 0 0 0 0 0
3 864.3 0 0 0 1 0 0 0 0 0 0 0 0
4 833.5 0 0 0 0 1 0 0 0 0 0 0 0
5 814.9 0 0 0 0 0 1 0 0 0 0 0 0
6 820.4 0 0 0 0 0 0 1 0 0 0 0 0
7 710.8 0 0 0 0 0 0 0 1 0 0 0 0
8 773.1 0 0 0 0 0 0 0 0 1 0 0 0
9 801.2 0 0 0 0 0 0 0 0 0 1 0 0
10 832.9 0 0 0 0 0 0 0 0 0 0 1 0
11 808.3 0 0 0 0 0 0 0 0 0 0 0 1
12 817.2 0 0 0 0 0 0 0 0 0 0 0 0
13 745.5 0 1 0 0 0 0 0 0 0 0 0 0
14 932.6 0 0 1 0 0 0 0 0 0 0 0 0
15 1057.0 0 0 0 1 0 0 0 0 0 0 0 0
16 879.9 0 0 0 0 1 0 0 0 0 0 0 0
17 1089.5 0 0 0 0 0 1 0 0 0 0 0 0
18 903.0 0 0 0 0 0 0 1 0 0 0 0 0
19 846.1 0 0 0 0 0 0 0 1 0 0 0 0
20 959.1 0 0 0 0 0 0 0 0 1 0 0 0
21 952.0 0 0 0 0 0 0 0 0 0 1 0 0
22 1092.5 0 0 0 0 0 0 0 0 0 0 1 0
23 1188.9 0 0 0 0 0 0 0 0 0 0 0 1
24 996.7 0 0 0 0 0 0 0 0 0 0 0 0
25 1034.3 0 1 0 0 0 0 0 0 0 0 0 0
26 898.2 0 0 1 0 0 0 0 0 0 0 0 0
27 1111.6 0 0 0 1 0 0 0 0 0 0 0 0
28 900.5 0 0 0 0 1 0 0 0 0 0 0 0
29 1049.2 0 0 0 0 0 1 0 0 0 0 0 0
30 1010.9 0 0 0 0 0 0 1 0 0 0 0 0
31 875.9 0 0 0 0 0 0 0 1 0 0 0 0
32 849.9 0 0 0 0 0 0 0 0 1 0 0 0
33 713.4 1 0 0 0 0 0 0 0 0 1 0 0
34 918.6 1 0 0 0 0 0 0 0 0 0 1 0
35 912.5 1 0 0 0 0 0 0 0 0 0 0 1
36 767.0 1 0 0 0 0 0 0 0 0 0 0 0
37 902.2 1 1 0 0 0 0 0 0 0 0 0 0
38 891.9 1 0 1 0 0 0 0 0 0 0 0 0
39 874.0 1 0 0 1 0 0 0 0 0 0 0 0
40 930.9 1 0 0 0 1 0 0 0 0 0 0 0
41 944.2 1 0 0 0 0 1 0 0 0 0 0 0
42 935.9 1 0 0 0 0 0 1 0 0 0 0 0
43 937.1 1 0 0 0 0 0 0 1 0 0 0 0
44 885.1 1 0 0 0 0 0 0 0 1 0 0 0
45 892.4 1 0 0 0 0 0 0 0 0 1 0 0
46 987.3 1 0 0 0 0 0 0 0 0 0 1 0
47 946.3 1 0 0 0 0 0 0 0 0 0 0 1
48 799.6 1 0 0 0 0 0 0 0 0 0 0 0
49 875.4 1 1 0 0 0 0 0 0 0 0 0 0
50 846.2 1 0 1 0 0 0 0 0 0 0 0 0
51 880.6 1 0 0 1 0 0 0 0 0 0 0 0
52 885.7 1 0 0 0 1 0 0 0 0 0 0 0
53 868.9 1 0 0 0 0 1 0 0 0 0 0 0
54 882.5 1 0 0 0 0 0 1 0 0 0 0 0
55 789.6 1 0 0 0 0 0 0 1 0 0 0 0
56 773.3 1 0 0 0 0 0 0 0 1 0 0 0
57 804.3 1 0 0 0 0 0 0 0 0 1 0 0
58 817.8 1 0 0 0 0 0 0 0 0 0 1 0
59 836.7 1 0 0 0 0 0 0 0 0 0 0 1
60 721.8 1 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) x M1 M2 M3 M4
845.465 -41.675 29.405 53.965 128.705 57.305
M5 M6 M7 M8 M9 M10
124.545 81.745 3.105 19.305 12.200 109.360
M11
118.080
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-155.245 -58.730 -3.612 69.205 225.355
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 845.465 46.473 18.192 <2e-16 ***
x -41.675 25.819 -1.614 0.1132
M1 29.405 62.179 0.473 0.6385
M2 53.965 62.179 0.868 0.3899
M3 128.705 62.179 2.070 0.0440 *
M4 57.305 62.179 0.922 0.3614
M5 124.545 62.179 2.003 0.0510 .
M6 81.745 62.179 1.315 0.1950
M7 3.105 62.179 0.050 0.9604
M8 19.305 62.179 0.310 0.7576
M9 12.200 61.965 0.197 0.8448
M10 109.360 61.965 1.765 0.0841 .
M11 118.080 61.965 1.906 0.0628 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 97.97 on 47 degrees of freedom
Multiple R-Squared: 0.263, Adjusted R-squared: 0.07478
F-statistic: 1.397 on 12 and 47 DF, p-value: 0.2009
> postscript(file="/var/www/html/rcomp/tmp/15lhm1197645014.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/25ovf1197645014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3e7s41197645014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ynar1197645014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/541721197645014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 7 8
-141.270 -54.530 -109.870 -69.270 -155.110 -106.810 -137.770 -91.670
9 10 11 12 13 14 15 16
-56.465 -121.925 -155.245 -28.265 -129.370 33.170 82.830 -22.870
17 18 19 20 21 22 23 24
119.490 -24.210 -2.470 94.330 94.335 137.675 225.355 151.235
25 26 27 28 29 30 31 32
159.430 -1.230 137.430 -2.270 79.190 83.690 27.330 -14.870
33 34 35 36 37 38 39 40
-102.590 5.450 -9.370 -36.790 69.005 34.145 -58.495 69.805
41 42 43 44 45 46 47 48
15.865 50.365 130.205 62.005 76.410 74.150 24.430 -4.190
49 50 51 52 53 54 55 56
42.205 -11.555 -51.895 24.605 -59.435 -3.035 -17.295 -49.795
57 58 59 60
-11.690 -95.350 -85.170 -81.990
> postscript(file="/var/www/html/rcomp/tmp/6k5k81197645014.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 -141.270 NA
1 -54.530 -141.270
2 -109.870 -54.530
3 -69.270 -109.870
4 -155.110 -69.270
5 -106.810 -155.110
6 -137.770 -106.810
7 -91.670 -137.770
8 -56.465 -91.670
9 -121.925 -56.465
10 -155.245 -121.925
11 -28.265 -155.245
12 -129.370 -28.265
13 33.170 -129.370
14 82.830 33.170
15 -22.870 82.830
16 119.490 -22.870
17 -24.210 119.490
18 -2.470 -24.210
19 94.330 -2.470
20 94.335 94.330
21 137.675 94.335
22 225.355 137.675
23 151.235 225.355
24 159.430 151.235
25 -1.230 159.430
26 137.430 -1.230
27 -2.270 137.430
28 79.190 -2.270
29 83.690 79.190
30 27.330 83.690
31 -14.870 27.330
32 -102.590 -14.870
33 5.450 -102.590
34 -9.370 5.450
35 -36.790 -9.370
36 69.005 -36.790
37 34.145 69.005
38 -58.495 34.145
39 69.805 -58.495
40 15.865 69.805
41 50.365 15.865
42 130.205 50.365
43 62.005 130.205
44 76.410 62.005
45 74.150 76.410
46 24.430 74.150
47 -4.190 24.430
48 42.205 -4.190
49 -11.555 42.205
50 -51.895 -11.555
51 24.605 -51.895
52 -59.435 24.605
53 -3.035 -59.435
54 -17.295 -3.035
55 -49.795 -17.295
56 -11.690 -49.795
57 -95.350 -11.690
58 -85.170 -95.350
59 -81.990 -85.170
60 NA -81.990
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -54.530 -141.270
[2,] -109.870 -54.530
[3,] -69.270 -109.870
[4,] -155.110 -69.270
[5,] -106.810 -155.110
[6,] -137.770 -106.810
[7,] -91.670 -137.770
[8,] -56.465 -91.670
[9,] -121.925 -56.465
[10,] -155.245 -121.925
[11,] -28.265 -155.245
[12,] -129.370 -28.265
[13,] 33.170 -129.370
[14,] 82.830 33.170
[15,] -22.870 82.830
[16,] 119.490 -22.870
[17,] -24.210 119.490
[18,] -2.470 -24.210
[19,] 94.330 -2.470
[20,] 94.335 94.330
[21,] 137.675 94.335
[22,] 225.355 137.675
[23,] 151.235 225.355
[24,] 159.430 151.235
[25,] -1.230 159.430
[26,] 137.430 -1.230
[27,] -2.270 137.430
[28,] 79.190 -2.270
[29,] 83.690 79.190
[30,] 27.330 83.690
[31,] -14.870 27.330
[32,] -102.590 -14.870
[33,] 5.450 -102.590
[34,] -9.370 5.450
[35,] -36.790 -9.370
[36,] 69.005 -36.790
[37,] 34.145 69.005
[38,] -58.495 34.145
[39,] 69.805 -58.495
[40,] 15.865 69.805
[41,] 50.365 15.865
[42,] 130.205 50.365
[43,] 62.005 130.205
[44,] 76.410 62.005
[45,] 74.150 76.410
[46,] 24.430 74.150
[47,] -4.190 24.430
[48,] 42.205 -4.190
[49,] -11.555 42.205
[50,] -51.895 -11.555
[51,] 24.605 -51.895
[52,] -59.435 24.605
[53,] -3.035 -59.435
[54,] -17.295 -3.035
[55,] -49.795 -17.295
[56,] -11.690 -49.795
[57,] -95.350 -11.690
[58,] -85.170 -95.350
[59,] -81.990 -85.170
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -54.530 -141.270
2 -109.870 -54.530
3 -69.270 -109.870
4 -155.110 -69.270
5 -106.810 -155.110
6 -137.770 -106.810
7 -91.670 -137.770
8 -56.465 -91.670
9 -121.925 -56.465
10 -155.245 -121.925
11 -28.265 -155.245
12 -129.370 -28.265
13 33.170 -129.370
14 82.830 33.170
15 -22.870 82.830
16 119.490 -22.870
17 -24.210 119.490
18 -2.470 -24.210
19 94.330 -2.470
20 94.335 94.330
21 137.675 94.335
22 225.355 137.675
23 151.235 225.355
24 159.430 151.235
25 -1.230 159.430
26 137.430 -1.230
27 -2.270 137.430
28 79.190 -2.270
29 83.690 79.190
30 27.330 83.690
31 -14.870 27.330
32 -102.590 -14.870
33 5.450 -102.590
34 -9.370 5.450
35 -36.790 -9.370
36 69.005 -36.790
37 34.145 69.005
38 -58.495 34.145
39 69.805 -58.495
40 15.865 69.805
41 50.365 15.865
42 130.205 50.365
43 62.005 130.205
44 76.410 62.005
45 74.150 76.410
46 24.430 74.150
47 -4.190 24.430
48 42.205 -4.190
49 -11.555 42.205
50 -51.895 -11.555
51 24.605 -51.895
52 -59.435 24.605
53 -3.035 -59.435
54 -17.295 -3.035
55 -49.795 -17.295
56 -11.690 -49.795
57 -95.350 -11.690
58 -85.170 -95.350
59 -81.990 -85.170
> 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/7gdax1197645014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zf0s1197645014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9pxn81197645014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/107otu1197645014.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/11xh3c1197645014.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/12no1t1197645014.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/1328zf1197645014.tab")
>
> system("convert tmp/15lhm1197645014.ps tmp/15lhm1197645014.png")
> system("convert tmp/25ovf1197645014.ps tmp/25ovf1197645014.png")
> system("convert tmp/3e7s41197645014.ps tmp/3e7s41197645014.png")
> system("convert tmp/4ynar1197645014.ps tmp/4ynar1197645014.png")
> system("convert tmp/541721197645014.ps tmp/541721197645014.png")
> system("convert tmp/6k5k81197645014.ps tmp/6k5k81197645014.png")
> system("convert tmp/7gdax1197645014.ps tmp/7gdax1197645014.png")
> system("convert tmp/8zf0s1197645014.ps tmp/8zf0s1197645014.png")
> system("convert tmp/9pxn81197645014.ps tmp/9pxn81197645014.png")
>
>
> proc.time()
user system elapsed
2.236 1.431 2.858