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(7.8,0,7.6,0,7.5,0,7.6,0,7.5,0,7.3,0,7.6,0,7.5,0,7.6,0,7.9,0,7.9,0,8.1,0,8.2,0,8.0,0,7.5,0,6.8,0,6.5,0,6.6,0,7.6,0,8.0,0,8.0,0,7.7,0,7.5,0,7.6,0,7.7,0,7.9,0,7.8,0,7.5,0,7.5,0,7.1,0,7.5,0,7.5,1,7.6,1,7.7,1,7.7,1,7.9,1,8.1,1,8.2,1,8.2,1,8.1,1,7.9,1,7.3,1,6.9,1,6.6,1,6.7,1,6.9,1,7.0,1,7.1,1,7.2,1,7.1,1,6.9,1,7.0,1,6.8,1,6.4,1,6.7,1,6.7,1,6.4,1,6.3,1,6.2,1,6.5,1,6.8,1,6.8,1,6.5,1,6.3,1,5.9,1,5.9,1,6.4,1,6.4,1),dim=c(2,68),dimnames=list(c('y','x'),1:68))
> y <- array(NA,dim=c(2,68),dimnames=list(c('y','x'),1:68))
> 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 = 'Do not include Seasonal 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 t
1 7.8 0 1
2 7.6 0 2
3 7.5 0 3
4 7.6 0 4
5 7.5 0 5
6 7.3 0 6
7 7.6 0 7
8 7.5 0 8
9 7.6 0 9
10 7.9 0 10
11 7.9 0 11
12 8.1 0 12
13 8.2 0 13
14 8.0 0 14
15 7.5 0 15
16 6.8 0 16
17 6.5 0 17
18 6.6 0 18
19 7.6 0 19
20 8.0 0 20
21 8.0 0 21
22 7.7 0 22
23 7.5 0 23
24 7.6 0 24
25 7.7 0 25
26 7.9 0 26
27 7.8 0 27
28 7.5 0 28
29 7.5 0 29
30 7.1 0 30
31 7.5 0 31
32 7.5 1 32
33 7.6 1 33
34 7.7 1 34
35 7.7 1 35
36 7.9 1 36
37 8.1 1 37
38 8.2 1 38
39 8.2 1 39
40 8.1 1 40
41 7.9 1 41
42 7.3 1 42
43 6.9 1 43
44 6.6 1 44
45 6.7 1 45
46 6.9 1 46
47 7.0 1 47
48 7.1 1 48
49 7.2 1 49
50 7.1 1 50
51 6.9 1 51
52 7.0 1 52
53 6.8 1 53
54 6.4 1 54
55 6.7 1 55
56 6.7 1 56
57 6.4 1 57
58 6.3 1 58
59 6.2 1 59
60 6.5 1 60
61 6.8 1 61
62 6.8 1 62
63 6.5 1 63
64 6.3 1 64
65 5.9 1 65
66 5.9 1 66
67 6.4 1 67
68 6.4 1 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x t
8.11680 0.55796 -0.03371
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.043708 -0.330656 0.006081 0.325417 0.839984
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.116804 0.115568 70.234 < 2e-16 ***
x 0.557963 0.209622 2.662 0.00978 **
t -0.033712 0.005319 -6.338 2.52e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4353 on 65 degrees of freedom
Multiple R-squared: 0.5219, Adjusted R-squared: 0.5072
F-statistic: 35.48 on 2 and 65 DF, p-value: 3.829e-11
> postscript(file="/var/www/html/rcomp/tmp/17wnn1227546259.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/270x71227546259.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3u8761227546259.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4h7sx1227546259.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59btp1227546259.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 68
Frequency = 1
1 2 3 4 5 6
-0.283092690 -0.449381134 -0.515669579 -0.381958023 -0.448246467 -0.614534912
7 8 9 10 11 12
-0.280823356 -0.347111800 -0.213400245 0.120311311 0.154022867 0.387734422
13 14 15 16 17 18
0.521445978 0.355157534 -0.111130911 -0.777419355 -1.043707799 -0.909996243
19 20 21 22 23 24
0.123715312 0.557426868 0.591138424 0.324849979 0.158561535 0.292273091
25 26 27 28 29 30
0.425984646 0.659696202 0.593407758 0.327119313 0.360830869 -0.005457575
31 32 33 34 35 36
0.428253980 -0.095997192 0.037714364 0.171425920 0.205137475 0.438849031
37 38 39 40 41 42
0.672560587 0.806272143 0.839983698 0.773695254 0.607406810 0.041118365
43 44 45 46 47 48
-0.325170079 -0.591458523 -0.457746968 -0.224035412 -0.090323856 0.043387699
49 50 51 52 53 54
0.177099255 0.110810811 -0.055477634 0.078233922 -0.088054522 -0.454342966
55 56 57 58 59 60
-0.120631411 -0.086919855 -0.353208299 -0.419496744 -0.485785188 -0.152073632
61 62 63 64 65 66
0.181637923 0.215349479 -0.050938965 -0.217227410 -0.583515854 -0.549804298
67 68
-0.016092742 0.017618813
> postscript(file="/var/www/html/rcomp/tmp/6dvy51227546259.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.283092690 NA
1 -0.449381134 -0.283092690
2 -0.515669579 -0.449381134
3 -0.381958023 -0.515669579
4 -0.448246467 -0.381958023
5 -0.614534912 -0.448246467
6 -0.280823356 -0.614534912
7 -0.347111800 -0.280823356
8 -0.213400245 -0.347111800
9 0.120311311 -0.213400245
10 0.154022867 0.120311311
11 0.387734422 0.154022867
12 0.521445978 0.387734422
13 0.355157534 0.521445978
14 -0.111130911 0.355157534
15 -0.777419355 -0.111130911
16 -1.043707799 -0.777419355
17 -0.909996243 -1.043707799
18 0.123715312 -0.909996243
19 0.557426868 0.123715312
20 0.591138424 0.557426868
21 0.324849979 0.591138424
22 0.158561535 0.324849979
23 0.292273091 0.158561535
24 0.425984646 0.292273091
25 0.659696202 0.425984646
26 0.593407758 0.659696202
27 0.327119313 0.593407758
28 0.360830869 0.327119313
29 -0.005457575 0.360830869
30 0.428253980 -0.005457575
31 -0.095997192 0.428253980
32 0.037714364 -0.095997192
33 0.171425920 0.037714364
34 0.205137475 0.171425920
35 0.438849031 0.205137475
36 0.672560587 0.438849031
37 0.806272143 0.672560587
38 0.839983698 0.806272143
39 0.773695254 0.839983698
40 0.607406810 0.773695254
41 0.041118365 0.607406810
42 -0.325170079 0.041118365
43 -0.591458523 -0.325170079
44 -0.457746968 -0.591458523
45 -0.224035412 -0.457746968
46 -0.090323856 -0.224035412
47 0.043387699 -0.090323856
48 0.177099255 0.043387699
49 0.110810811 0.177099255
50 -0.055477634 0.110810811
51 0.078233922 -0.055477634
52 -0.088054522 0.078233922
53 -0.454342966 -0.088054522
54 -0.120631411 -0.454342966
55 -0.086919855 -0.120631411
56 -0.353208299 -0.086919855
57 -0.419496744 -0.353208299
58 -0.485785188 -0.419496744
59 -0.152073632 -0.485785188
60 0.181637923 -0.152073632
61 0.215349479 0.181637923
62 -0.050938965 0.215349479
63 -0.217227410 -0.050938965
64 -0.583515854 -0.217227410
65 -0.549804298 -0.583515854
66 -0.016092742 -0.549804298
67 0.017618813 -0.016092742
68 NA 0.017618813
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.449381134 -0.283092690
[2,] -0.515669579 -0.449381134
[3,] -0.381958023 -0.515669579
[4,] -0.448246467 -0.381958023
[5,] -0.614534912 -0.448246467
[6,] -0.280823356 -0.614534912
[7,] -0.347111800 -0.280823356
[8,] -0.213400245 -0.347111800
[9,] 0.120311311 -0.213400245
[10,] 0.154022867 0.120311311
[11,] 0.387734422 0.154022867
[12,] 0.521445978 0.387734422
[13,] 0.355157534 0.521445978
[14,] -0.111130911 0.355157534
[15,] -0.777419355 -0.111130911
[16,] -1.043707799 -0.777419355
[17,] -0.909996243 -1.043707799
[18,] 0.123715312 -0.909996243
[19,] 0.557426868 0.123715312
[20,] 0.591138424 0.557426868
[21,] 0.324849979 0.591138424
[22,] 0.158561535 0.324849979
[23,] 0.292273091 0.158561535
[24,] 0.425984646 0.292273091
[25,] 0.659696202 0.425984646
[26,] 0.593407758 0.659696202
[27,] 0.327119313 0.593407758
[28,] 0.360830869 0.327119313
[29,] -0.005457575 0.360830869
[30,] 0.428253980 -0.005457575
[31,] -0.095997192 0.428253980
[32,] 0.037714364 -0.095997192
[33,] 0.171425920 0.037714364
[34,] 0.205137475 0.171425920
[35,] 0.438849031 0.205137475
[36,] 0.672560587 0.438849031
[37,] 0.806272143 0.672560587
[38,] 0.839983698 0.806272143
[39,] 0.773695254 0.839983698
[40,] 0.607406810 0.773695254
[41,] 0.041118365 0.607406810
[42,] -0.325170079 0.041118365
[43,] -0.591458523 -0.325170079
[44,] -0.457746968 -0.591458523
[45,] -0.224035412 -0.457746968
[46,] -0.090323856 -0.224035412
[47,] 0.043387699 -0.090323856
[48,] 0.177099255 0.043387699
[49,] 0.110810811 0.177099255
[50,] -0.055477634 0.110810811
[51,] 0.078233922 -0.055477634
[52,] -0.088054522 0.078233922
[53,] -0.454342966 -0.088054522
[54,] -0.120631411 -0.454342966
[55,] -0.086919855 -0.120631411
[56,] -0.353208299 -0.086919855
[57,] -0.419496744 -0.353208299
[58,] -0.485785188 -0.419496744
[59,] -0.152073632 -0.485785188
[60,] 0.181637923 -0.152073632
[61,] 0.215349479 0.181637923
[62,] -0.050938965 0.215349479
[63,] -0.217227410 -0.050938965
[64,] -0.583515854 -0.217227410
[65,] -0.549804298 -0.583515854
[66,] -0.016092742 -0.549804298
[67,] 0.017618813 -0.016092742
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.449381134 -0.283092690
2 -0.515669579 -0.449381134
3 -0.381958023 -0.515669579
4 -0.448246467 -0.381958023
5 -0.614534912 -0.448246467
6 -0.280823356 -0.614534912
7 -0.347111800 -0.280823356
8 -0.213400245 -0.347111800
9 0.120311311 -0.213400245
10 0.154022867 0.120311311
11 0.387734422 0.154022867
12 0.521445978 0.387734422
13 0.355157534 0.521445978
14 -0.111130911 0.355157534
15 -0.777419355 -0.111130911
16 -1.043707799 -0.777419355
17 -0.909996243 -1.043707799
18 0.123715312 -0.909996243
19 0.557426868 0.123715312
20 0.591138424 0.557426868
21 0.324849979 0.591138424
22 0.158561535 0.324849979
23 0.292273091 0.158561535
24 0.425984646 0.292273091
25 0.659696202 0.425984646
26 0.593407758 0.659696202
27 0.327119313 0.593407758
28 0.360830869 0.327119313
29 -0.005457575 0.360830869
30 0.428253980 -0.005457575
31 -0.095997192 0.428253980
32 0.037714364 -0.095997192
33 0.171425920 0.037714364
34 0.205137475 0.171425920
35 0.438849031 0.205137475
36 0.672560587 0.438849031
37 0.806272143 0.672560587
38 0.839983698 0.806272143
39 0.773695254 0.839983698
40 0.607406810 0.773695254
41 0.041118365 0.607406810
42 -0.325170079 0.041118365
43 -0.591458523 -0.325170079
44 -0.457746968 -0.591458523
45 -0.224035412 -0.457746968
46 -0.090323856 -0.224035412
47 0.043387699 -0.090323856
48 0.177099255 0.043387699
49 0.110810811 0.177099255
50 -0.055477634 0.110810811
51 0.078233922 -0.055477634
52 -0.088054522 0.078233922
53 -0.454342966 -0.088054522
54 -0.120631411 -0.454342966
55 -0.086919855 -0.120631411
56 -0.353208299 -0.086919855
57 -0.419496744 -0.353208299
58 -0.485785188 -0.419496744
59 -0.152073632 -0.485785188
60 0.181637923 -0.152073632
61 0.215349479 0.181637923
62 -0.050938965 0.215349479
63 -0.217227410 -0.050938965
64 -0.583515854 -0.217227410
65 -0.549804298 -0.583515854
66 -0.016092742 -0.549804298
67 0.017618813 -0.016092742
> 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/7lofu1227546259.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k2o51227546259.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90ds21227546259.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10ub4e1227546259.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/11gr6d1227546259.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/12ob3z1227546260.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/13zobl1227546260.tab")
>
> system("convert tmp/17wnn1227546259.ps tmp/17wnn1227546259.png")
> system("convert tmp/270x71227546259.ps tmp/270x71227546259.png")
> system("convert tmp/3u8761227546259.ps tmp/3u8761227546259.png")
> system("convert tmp/4h7sx1227546259.ps tmp/4h7sx1227546259.png")
> system("convert tmp/59btp1227546259.ps tmp/59btp1227546259.png")
> system("convert tmp/6dvy51227546259.ps tmp/6dvy51227546259.png")
> system("convert tmp/7lofu1227546259.ps tmp/7lofu1227546259.png")
> system("convert tmp/8k2o51227546259.ps tmp/8k2o51227546259.png")
> system("convert tmp/90ds21227546259.ps tmp/90ds21227546259.png")
>
>
> proc.time()
user system elapsed
1.919 1.408 2.294