R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9,676,8,642,9,402,9,610,9,294,9,448,10,319,9,548,9,801,9,596,8,923,9,746,9,829,9,125,9,782,9,441,9,162,9,915,10,444,10,209,9,985,9,842,9,429,10,132,9,849,9,172,10,313,9,819,9,955,10,048,10,082,10,541,10,208,10,233,9,439,9,963,10,158,9,225,10,474,9,757,10,490,10,281,10,444,10,640,10,695,10,786,9,832,9,747,10,411,9,511,10,402,9,701,10,540,10,112,10,915,11,183,10,384,10,834,9,886,10,216),dim=c(2,60),dimnames=list(c('y',''),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('y',''),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 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9 676 1 0 0 0 0 0 0 0 0 0 0 1
2 8 642 0 1 0 0 0 0 0 0 0 0 0 2
3 9 402 0 0 1 0 0 0 0 0 0 0 0 3
4 9 610 0 0 0 1 0 0 0 0 0 0 0 4
5 9 294 0 0 0 0 1 0 0 0 0 0 0 5
6 9 448 0 0 0 0 0 1 0 0 0 0 0 6
7 10 319 0 0 0 0 0 0 1 0 0 0 0 7
8 9 548 0 0 0 0 0 0 0 1 0 0 0 8
9 9 801 0 0 0 0 0 0 0 0 1 0 0 9
10 9 596 0 0 0 0 0 0 0 0 0 1 0 10
11 8 923 0 0 0 0 0 0 0 0 0 0 1 11
12 9 746 0 0 0 0 0 0 0 0 0 0 0 12
13 9 829 1 0 0 0 0 0 0 0 0 0 0 13
14 9 125 0 1 0 0 0 0 0 0 0 0 0 14
15 9 782 0 0 1 0 0 0 0 0 0 0 0 15
16 9 441 0 0 0 1 0 0 0 0 0 0 0 16
17 9 162 0 0 0 0 1 0 0 0 0 0 0 17
18 9 915 0 0 0 0 0 1 0 0 0 0 0 18
19 10 444 0 0 0 0 0 0 1 0 0 0 0 19
20 10 209 0 0 0 0 0 0 0 1 0 0 0 20
21 9 985 0 0 0 0 0 0 0 0 1 0 0 21
22 9 842 0 0 0 0 0 0 0 0 0 1 0 22
23 9 429 0 0 0 0 0 0 0 0 0 0 1 23
24 10 132 0 0 0 0 0 0 0 0 0 0 0 24
25 9 849 1 0 0 0 0 0 0 0 0 0 0 25
26 9 172 0 1 0 0 0 0 0 0 0 0 0 26
27 10 313 0 0 1 0 0 0 0 0 0 0 0 27
28 9 819 0 0 0 1 0 0 0 0 0 0 0 28
29 9 955 0 0 0 0 1 0 0 0 0 0 0 29
30 10 48 0 0 0 0 0 1 0 0 0 0 0 30
31 10 82 0 0 0 0 0 0 1 0 0 0 0 31
32 10 541 0 0 0 0 0 0 0 1 0 0 0 32
33 10 208 0 0 0 0 0 0 0 0 1 0 0 33
34 10 233 0 0 0 0 0 0 0 0 0 1 0 34
35 9 439 0 0 0 0 0 0 0 0 0 0 1 35
36 9 963 0 0 0 0 0 0 0 0 0 0 0 36
37 10 158 1 0 0 0 0 0 0 0 0 0 0 37
38 9 225 0 1 0 0 0 0 0 0 0 0 0 38
39 10 474 0 0 1 0 0 0 0 0 0 0 0 39
40 9 757 0 0 0 1 0 0 0 0 0 0 0 40
41 10 490 0 0 0 0 1 0 0 0 0 0 0 41
42 10 281 0 0 0 0 0 1 0 0 0 0 0 42
43 10 444 0 0 0 0 0 0 1 0 0 0 0 43
44 10 640 0 0 0 0 0 0 0 1 0 0 0 44
45 10 695 0 0 0 0 0 0 0 0 1 0 0 45
46 10 786 0 0 0 0 0 0 0 0 0 1 0 46
47 9 832 0 0 0 0 0 0 0 0 0 0 1 47
48 9 747 0 0 0 0 0 0 0 0 0 0 0 48
49 10 411 1 0 0 0 0 0 0 0 0 0 0 49
50 9 511 0 1 0 0 0 0 0 0 0 0 0 50
51 10 402 0 0 1 0 0 0 0 0 0 0 0 51
52 9 701 0 0 0 1 0 0 0 0 0 0 0 52
53 10 540 0 0 0 0 1 0 0 0 0 0 0 53
54 10 112 0 0 0 0 0 1 0 0 0 0 0 54
55 10 915 0 0 0 0 0 0 1 0 0 0 0 55
56 11 183 0 0 0 0 0 0 0 1 0 0 0 56
57 10 384 0 0 0 0 0 0 0 0 1 0 0 57
58 10 834 0 0 0 0 0 0 0 0 0 1 0 58
59 9 886 0 0 0 0 0 0 0 0 0 0 1 59
60 10 216 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) V2 M1 M2 M3 M4
9.2957905 -0.0009506 0.2173575 -0.6376164 0.2773850 -0.1587525
M5 M6 M7 M8 M9 M10
0.0549072 0.1160972 0.5744425 0.5409595 0.3042516 0.3279949
M11 t
-0.4482617 0.0177030
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.4976 -0.1313 0.0005 0.1431 0.4048
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.2957905 0.1479681 62.823 < 2e-16 ***
V2 -0.0009506 0.0001275 -7.457 1.89e-09 ***
M1 0.2173575 0.1540626 1.411 0.165019
M2 -0.6376164 0.1567594 -4.067 0.000184 ***
M3 0.2773850 0.1541045 1.800 0.078424 .
M4 -0.1587525 0.1539170 -1.031 0.307738
M5 0.0549072 0.1536063 0.357 0.722387
M6 0.1160972 0.1553702 0.747 0.458728
M7 0.5744425 0.1538346 3.734 0.000518 ***
M8 0.5409595 0.1539540 3.514 0.001004 **
M9 0.3042516 0.1529593 1.989 0.052656 .
M10 0.3279949 0.1532498 2.140 0.037669 *
M11 -0.4482617 0.1537731 -2.915 0.005477 **
t 0.0177030 0.0018423 9.609 1.42e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2415 on 46 degrees of freedom
Multiple R-squared: 0.8706, Adjusted R-squared: 0.8341
F-statistic: 23.81 on 13 and 46 DF, p-value: 3.965e-16
> postscript(file="/var/wessaorg/rcomp/tmp/12w5s1322210915.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ax4b1322210915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3dvoa1322210915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xn0f1322210915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5lxwb1322210915.ps",horizontal=F,onefile=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
0.111757607 -0.083292058 -0.244141535 0.372018742 -0.159735045 -0.092234932
7 8 9 10 11 12
0.309088723 -0.457442806 0.002065037 -0.234255248 -0.164853926 0.200924311
13 14 15 16 17 18
0.044763922 0.212809227 -0.095347993 -0.001069599 -0.497651019 0.139261204
19 20 21 22 23 24
0.215478111 0.007866083 -0.035459908 -0.212842712 0.153111263 0.404816957
25 26 27 28 29 30
-0.148660164 0.045051463 0.246382309 0.145822734 0.043742191 0.102650905
31 32 33 34 35 36
-0.341076903 0.111030608 0.013484201 -0.004197043 -0.049818869 -0.017666861
37 38 39 40 41 42
-0.017964066 -0.117002674 0.186993460 -0.125550923 0.389274911 0.111705582
43 44 45 46 47 48
-0.209394242 -0.007295721 0.263992427 0.309051081 0.111332532 -0.435433615
49 50 51 52 53 54
0.010102701 -0.057565958 -0.093886242 -0.391220954 0.224368961 -0.261382759
55 56 57 58 59 60
0.025904311 0.345841835 -0.244081757 0.142243921 -0.049771001 -0.152640793
> postscript(file="/var/wessaorg/rcomp/tmp/63fyp1322210915.ps",horizontal=F,onefile=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 0.111757607 NA
1 -0.083292058 0.111757607
2 -0.244141535 -0.083292058
3 0.372018742 -0.244141535
4 -0.159735045 0.372018742
5 -0.092234932 -0.159735045
6 0.309088723 -0.092234932
7 -0.457442806 0.309088723
8 0.002065037 -0.457442806
9 -0.234255248 0.002065037
10 -0.164853926 -0.234255248
11 0.200924311 -0.164853926
12 0.044763922 0.200924311
13 0.212809227 0.044763922
14 -0.095347993 0.212809227
15 -0.001069599 -0.095347993
16 -0.497651019 -0.001069599
17 0.139261204 -0.497651019
18 0.215478111 0.139261204
19 0.007866083 0.215478111
20 -0.035459908 0.007866083
21 -0.212842712 -0.035459908
22 0.153111263 -0.212842712
23 0.404816957 0.153111263
24 -0.148660164 0.404816957
25 0.045051463 -0.148660164
26 0.246382309 0.045051463
27 0.145822734 0.246382309
28 0.043742191 0.145822734
29 0.102650905 0.043742191
30 -0.341076903 0.102650905
31 0.111030608 -0.341076903
32 0.013484201 0.111030608
33 -0.004197043 0.013484201
34 -0.049818869 -0.004197043
35 -0.017666861 -0.049818869
36 -0.017964066 -0.017666861
37 -0.117002674 -0.017964066
38 0.186993460 -0.117002674
39 -0.125550923 0.186993460
40 0.389274911 -0.125550923
41 0.111705582 0.389274911
42 -0.209394242 0.111705582
43 -0.007295721 -0.209394242
44 0.263992427 -0.007295721
45 0.309051081 0.263992427
46 0.111332532 0.309051081
47 -0.435433615 0.111332532
48 0.010102701 -0.435433615
49 -0.057565958 0.010102701
50 -0.093886242 -0.057565958
51 -0.391220954 -0.093886242
52 0.224368961 -0.391220954
53 -0.261382759 0.224368961
54 0.025904311 -0.261382759
55 0.345841835 0.025904311
56 -0.244081757 0.345841835
57 0.142243921 -0.244081757
58 -0.049771001 0.142243921
59 -0.152640793 -0.049771001
60 NA -0.152640793
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.083292058 0.111757607
[2,] -0.244141535 -0.083292058
[3,] 0.372018742 -0.244141535
[4,] -0.159735045 0.372018742
[5,] -0.092234932 -0.159735045
[6,] 0.309088723 -0.092234932
[7,] -0.457442806 0.309088723
[8,] 0.002065037 -0.457442806
[9,] -0.234255248 0.002065037
[10,] -0.164853926 -0.234255248
[11,] 0.200924311 -0.164853926
[12,] 0.044763922 0.200924311
[13,] 0.212809227 0.044763922
[14,] -0.095347993 0.212809227
[15,] -0.001069599 -0.095347993
[16,] -0.497651019 -0.001069599
[17,] 0.139261204 -0.497651019
[18,] 0.215478111 0.139261204
[19,] 0.007866083 0.215478111
[20,] -0.035459908 0.007866083
[21,] -0.212842712 -0.035459908
[22,] 0.153111263 -0.212842712
[23,] 0.404816957 0.153111263
[24,] -0.148660164 0.404816957
[25,] 0.045051463 -0.148660164
[26,] 0.246382309 0.045051463
[27,] 0.145822734 0.246382309
[28,] 0.043742191 0.145822734
[29,] 0.102650905 0.043742191
[30,] -0.341076903 0.102650905
[31,] 0.111030608 -0.341076903
[32,] 0.013484201 0.111030608
[33,] -0.004197043 0.013484201
[34,] -0.049818869 -0.004197043
[35,] -0.017666861 -0.049818869
[36,] -0.017964066 -0.017666861
[37,] -0.117002674 -0.017964066
[38,] 0.186993460 -0.117002674
[39,] -0.125550923 0.186993460
[40,] 0.389274911 -0.125550923
[41,] 0.111705582 0.389274911
[42,] -0.209394242 0.111705582
[43,] -0.007295721 -0.209394242
[44,] 0.263992427 -0.007295721
[45,] 0.309051081 0.263992427
[46,] 0.111332532 0.309051081
[47,] -0.435433615 0.111332532
[48,] 0.010102701 -0.435433615
[49,] -0.057565958 0.010102701
[50,] -0.093886242 -0.057565958
[51,] -0.391220954 -0.093886242
[52,] 0.224368961 -0.391220954
[53,] -0.261382759 0.224368961
[54,] 0.025904311 -0.261382759
[55,] 0.345841835 0.025904311
[56,] -0.244081757 0.345841835
[57,] 0.142243921 -0.244081757
[58,] -0.049771001 0.142243921
[59,] -0.152640793 -0.049771001
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.083292058 0.111757607
2 -0.244141535 -0.083292058
3 0.372018742 -0.244141535
4 -0.159735045 0.372018742
5 -0.092234932 -0.159735045
6 0.309088723 -0.092234932
7 -0.457442806 0.309088723
8 0.002065037 -0.457442806
9 -0.234255248 0.002065037
10 -0.164853926 -0.234255248
11 0.200924311 -0.164853926
12 0.044763922 0.200924311
13 0.212809227 0.044763922
14 -0.095347993 0.212809227
15 -0.001069599 -0.095347993
16 -0.497651019 -0.001069599
17 0.139261204 -0.497651019
18 0.215478111 0.139261204
19 0.007866083 0.215478111
20 -0.035459908 0.007866083
21 -0.212842712 -0.035459908
22 0.153111263 -0.212842712
23 0.404816957 0.153111263
24 -0.148660164 0.404816957
25 0.045051463 -0.148660164
26 0.246382309 0.045051463
27 0.145822734 0.246382309
28 0.043742191 0.145822734
29 0.102650905 0.043742191
30 -0.341076903 0.102650905
31 0.111030608 -0.341076903
32 0.013484201 0.111030608
33 -0.004197043 0.013484201
34 -0.049818869 -0.004197043
35 -0.017666861 -0.049818869
36 -0.017964066 -0.017666861
37 -0.117002674 -0.017964066
38 0.186993460 -0.117002674
39 -0.125550923 0.186993460
40 0.389274911 -0.125550923
41 0.111705582 0.389274911
42 -0.209394242 0.111705582
43 -0.007295721 -0.209394242
44 0.263992427 -0.007295721
45 0.309051081 0.263992427
46 0.111332532 0.309051081
47 -0.435433615 0.111332532
48 0.010102701 -0.435433615
49 -0.057565958 0.010102701
50 -0.093886242 -0.057565958
51 -0.391220954 -0.093886242
52 0.224368961 -0.391220954
53 -0.261382759 0.224368961
54 0.025904311 -0.261382759
55 0.345841835 0.025904311
56 -0.244081757 0.345841835
57 0.142243921 -0.244081757
58 -0.049771001 0.142243921
59 -0.152640793 -0.049771001
> 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/wessaorg/rcomp/tmp/7tz4c1322210915.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/88ahb1322210915.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/96m3g1322210915.ps",horizontal=F,onefile=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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/10k7rx1322210915.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/113g801322210915.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1239go1322210915.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/136c0a1322210915.tab")
>
> try(system("convert tmp/12w5s1322210915.ps tmp/12w5s1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ax4b1322210915.ps tmp/2ax4b1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dvoa1322210915.ps tmp/3dvoa1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xn0f1322210915.ps tmp/4xn0f1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lxwb1322210915.ps tmp/5lxwb1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/63fyp1322210915.ps tmp/63fyp1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tz4c1322210915.ps tmp/7tz4c1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/88ahb1322210915.ps tmp/88ahb1322210915.png",intern=TRUE))
character(0)
> try(system("convert tmp/96m3g1322210915.ps tmp/96m3g1322210915.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.566 0.606 3.183